data-prep.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data source - https://www.football-data.co.uk/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 886,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Div</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FTHG</th>\n",
       "      <th>FTAG</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HTHG</th>\n",
       "      <th>HTAG</th>\n",
       "      <th>HTR</th>\n",
       "      <th>Referee</th>\n",
       "      <th>HS</th>\n",
       "      <th>AS</th>\n",
       "      <th>HST</th>\n",
       "      <th>AST</th>\n",
       "      <th>HF</th>\n",
       "      <th>AF</th>\n",
       "      <th>HC</th>\n",
       "      <th>AC</th>\n",
       "      <th>HY</th>\n",
       "      <th>AY</th>\n",
       "      <th>HR</th>\n",
       "      <th>AR</th>\n",
       "      <th>B365H</th>\n",
       "      <th>B365D</th>\n",
       "      <th>B365A</th>\n",
       "      <th>BWH</th>\n",
       "      <th>BWD</th>\n",
       "      <th>BWA</th>\n",
       "      <th>IWH</th>\n",
       "      <th>IWD</th>\n",
       "      <th>IWA</th>\n",
       "      <th>PSH</th>\n",
       "      <th>PSD</th>\n",
       "      <th>PSA</th>\n",
       "      <th>WHH</th>\n",
       "      <th>WHD</th>\n",
       "      <th>WHA</th>\n",
       "      <th>VCH</th>\n",
       "      <th>VCD</th>\n",
       "      <th>VCA</th>\n",
       "      <th>MaxH</th>\n",
       "      <th>MaxD</th>\n",
       "      <th>MaxA</th>\n",
       "      <th>AvgH</th>\n",
       "      <th>AvgD</th>\n",
       "      <th>AvgA</th>\n",
       "      <th>B365&gt;2.5</th>\n",
       "      <th>B365&lt;2.5</th>\n",
       "      <th>P&gt;2.5</th>\n",
       "      <th>P&lt;2.5</th>\n",
       "      <th>Max&gt;2.5</th>\n",
       "      <th>Max&lt;2.5</th>\n",
       "      <th>Avg&gt;2.5</th>\n",
       "      <th>Avg&lt;2.5</th>\n",
       "      <th>AHh</th>\n",
       "      <th>B365AHH</th>\n",
       "      <th>B365AHA</th>\n",
       "      <th>PAHH</th>\n",
       "      <th>PAHA</th>\n",
       "      <th>MaxAHH</th>\n",
       "      <th>MaxAHA</th>\n",
       "      <th>AvgAHH</th>\n",
       "      <th>AvgAHA</th>\n",
       "      <th>B365CH</th>\n",
       "      <th>B365CD</th>\n",
       "      <th>B365CA</th>\n",
       "      <th>BWCH</th>\n",
       "      <th>BWCD</th>\n",
       "      <th>BWCA</th>\n",
       "      <th>IWCH</th>\n",
       "      <th>IWCD</th>\n",
       "      <th>IWCA</th>\n",
       "      <th>PSCH</th>\n",
       "      <th>PSCD</th>\n",
       "      <th>PSCA</th>\n",
       "      <th>WHCH</th>\n",
       "      <th>WHCD</th>\n",
       "      <th>WHCA</th>\n",
       "      <th>VCCH</th>\n",
       "      <th>VCCD</th>\n",
       "      <th>VCCA</th>\n",
       "      <th>MaxCH</th>\n",
       "      <th>MaxCD</th>\n",
       "      <th>MaxCA</th>\n",
       "      <th>AvgCH</th>\n",
       "      <th>AvgCD</th>\n",
       "      <th>AvgCA</th>\n",
       "      <th>B365C&gt;2.5</th>\n",
       "      <th>B365C&lt;2.5</th>\n",
       "      <th>PC&gt;2.5</th>\n",
       "      <th>PC&lt;2.5</th>\n",
       "      <th>MaxC&gt;2.5</th>\n",
       "      <th>MaxC&lt;2.5</th>\n",
       "      <th>AvgC&gt;2.5</th>\n",
       "      <th>AvgC&lt;2.5</th>\n",
       "      <th>AHCh</th>\n",
       "      <th>B365CAHH</th>\n",
       "      <th>B365CAHA</th>\n",
       "      <th>PCAHH</th>\n",
       "      <th>PCAHA</th>\n",
       "      <th>MaxCAHH</th>\n",
       "      <th>MaxCAHA</th>\n",
       "      <th>AvgCAHH</th>\n",
       "      <th>AvgCAHA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E0</td>\n",
       "      <td>11/08/2023</td>\n",
       "      <td>20:00</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>C Pawson</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>5.50</td>\n",
       "      <td>1.33</td>\n",
       "      <td>8.75</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.34</td>\n",
       "      <td>8.00</td>\n",
       "      <td>5.50</td>\n",
       "      <td>1.35</td>\n",
       "      <td>8.58</td>\n",
       "      <td>5.51</td>\n",
       "      <td>1.37</td>\n",
       "      <td>8.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>9.50</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.33</td>\n",
       "      <td>9.50</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.39</td>\n",
       "      <td>9.02</td>\n",
       "      <td>5.35</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.29</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.27</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.07</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.07</td>\n",
       "      <td>1.93</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2.03</td>\n",
       "      <td>9.00</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.33</td>\n",
       "      <td>8.75</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.33</td>\n",
       "      <td>8.50</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.62</td>\n",
       "      <td>5.81</td>\n",
       "      <td>1.33</td>\n",
       "      <td>7.50</td>\n",
       "      <td>4.60</td>\n",
       "      <td>1.29</td>\n",
       "      <td>10.50</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.30</td>\n",
       "      <td>10.50</td>\n",
       "      <td>5.81</td>\n",
       "      <td>1.36</td>\n",
       "      <td>9.27</td>\n",
       "      <td>5.45</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.45</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E0</td>\n",
       "      <td>12/08/2023</td>\n",
       "      <td>12:30</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>M Oliver</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.18</td>\n",
       "      <td>7.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>1.17</td>\n",
       "      <td>7.50</td>\n",
       "      <td>15.50</td>\n",
       "      <td>1.20</td>\n",
       "      <td>7.25</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1.18</td>\n",
       "      <td>7.86</td>\n",
       "      <td>15.67</td>\n",
       "      <td>1.12</td>\n",
       "      <td>6.50</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1.14</td>\n",
       "      <td>7.50</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1.21</td>\n",
       "      <td>8.50</td>\n",
       "      <td>17.50</td>\n",
       "      <td>1.18</td>\n",
       "      <td>7.64</td>\n",
       "      <td>15.67</td>\n",
       "      <td>1.44</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.93</td>\n",
       "      <td>1.45</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.85</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>1.88</td>\n",
       "      <td>2.02</td>\n",
       "      <td>1.88</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.91</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.87</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.18</td>\n",
       "      <td>7.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>1.18</td>\n",
       "      <td>7.00</td>\n",
       "      <td>14.50</td>\n",
       "      <td>1.20</td>\n",
       "      <td>7.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1.19</td>\n",
       "      <td>8.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>1.12</td>\n",
       "      <td>6.50</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1.22</td>\n",
       "      <td>7.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>1.22</td>\n",
       "      <td>8.40</td>\n",
       "      <td>19.00</td>\n",
       "      <td>1.19</td>\n",
       "      <td>7.43</td>\n",
       "      <td>15.98</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.63</td>\n",
       "      <td>1.49</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.79</td>\n",
       "      <td>1.49</td>\n",
       "      <td>2.63</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.93</td>\n",
       "      <td>1.97</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E0</td>\n",
       "      <td>12/08/2023</td>\n",
       "      <td>15:00</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>P Bankes</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.40</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2.65</td>\n",
       "      <td>3.40</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.71</td>\n",
       "      <td>2.62</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.63</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.69</td>\n",
       "      <td>3.44</td>\n",
       "      <td>2.64</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.63</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.65</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.75</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.63</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.88</td>\n",
       "      <td>3.67</td>\n",
       "      <td>2.70</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.53</td>\n",
       "      <td>2.59</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.18</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.23</td>\n",
       "      <td>1.74</td>\n",
       "      <td>2.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.02</td>\n",
       "      <td>1.91</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E0</td>\n",
       "      <td>12/08/2023</td>\n",
       "      <td>15:00</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>Luton</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>D Coote</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.33</td>\n",
       "      <td>5.50</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1.32</td>\n",
       "      <td>5.50</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1.35</td>\n",
       "      <td>5.25</td>\n",
       "      <td>8.50</td>\n",
       "      <td>1.33</td>\n",
       "      <td>5.65</td>\n",
       "      <td>9.61</td>\n",
       "      <td>1.25</td>\n",
       "      <td>4.60</td>\n",
       "      <td>8.50</td>\n",
       "      <td>1.29</td>\n",
       "      <td>5.25</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.36</td>\n",
       "      <td>6.00</td>\n",
       "      <td>10.50</td>\n",
       "      <td>1.33</td>\n",
       "      <td>5.52</td>\n",
       "      <td>9.61</td>\n",
       "      <td>1.62</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1.62</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.34</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.93</td>\n",
       "      <td>1.93</td>\n",
       "      <td>1.25</td>\n",
       "      <td>6.50</td>\n",
       "      <td>11.00</td>\n",
       "      <td>1.26</td>\n",
       "      <td>6.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>1.30</td>\n",
       "      <td>5.50</td>\n",
       "      <td>9.50</td>\n",
       "      <td>1.27</td>\n",
       "      <td>6.36</td>\n",
       "      <td>11.36</td>\n",
       "      <td>1.22</td>\n",
       "      <td>5.50</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.75</td>\n",
       "      <td>13.00</td>\n",
       "      <td>1.34</td>\n",
       "      <td>6.59</td>\n",
       "      <td>13.00</td>\n",
       "      <td>1.28</td>\n",
       "      <td>5.99</td>\n",
       "      <td>10.91</td>\n",
       "      <td>1.53</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.56</td>\n",
       "      <td>2.54</td>\n",
       "      <td>1.62</td>\n",
       "      <td>2.66</td>\n",
       "      <td>1.55</td>\n",
       "      <td>2.48</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.91</td>\n",
       "      <td>2.14</td>\n",
       "      <td>1.93</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E0</td>\n",
       "      <td>12/08/2023</td>\n",
       "      <td>15:00</td>\n",
       "      <td>Everton</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>S Attwell</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.27</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.35</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.30</td>\n",
       "      <td>3.57</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.24</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.89</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.89</td>\n",
       "      <td>2.04</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.86</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>1.93</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.97</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.93</td>\n",
       "      <td>2.30</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.35</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.10</td>\n",
       "      <td>2.35</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.15</td>\n",
       "      <td>2.39</td>\n",
       "      <td>3.32</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.38</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.42</td>\n",
       "      <td>3.42</td>\n",
       "      <td>3.56</td>\n",
       "      <td>2.32</td>\n",
       "      <td>3.28</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.23</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1.71</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.04</td>\n",
       "      <td>1.88</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>E0</td>\n",
       "      <td>13/03/2024</td>\n",
       "      <td>19:30</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Luton</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>H</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>S Allison</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>4.75</td>\n",
       "      <td>5.75</td>\n",
       "      <td>1.50</td>\n",
       "      <td>4.60</td>\n",
       "      <td>5.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.51</td>\n",
       "      <td>4.71</td>\n",
       "      <td>6.18</td>\n",
       "      <td>1.44</td>\n",
       "      <td>4.80</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1.45</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.55</td>\n",
       "      <td>4.98</td>\n",
       "      <td>6.40</td>\n",
       "      <td>1.50</td>\n",
       "      <td>4.77</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.44</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1.49</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1.47</td>\n",
       "      <td>2.71</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.53</td>\n",
       "      <td>4.75</td>\n",
       "      <td>5.50</td>\n",
       "      <td>1.51</td>\n",
       "      <td>4.80</td>\n",
       "      <td>5.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.53</td>\n",
       "      <td>4.89</td>\n",
       "      <td>5.69</td>\n",
       "      <td>1.53</td>\n",
       "      <td>4.80</td>\n",
       "      <td>5.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>4.80</td>\n",
       "      <td>5.50</td>\n",
       "      <td>1.57</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.52</td>\n",
       "      <td>4.87</td>\n",
       "      <td>5.65</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1.41</td>\n",
       "      <td>3.04</td>\n",
       "      <td>1.43</td>\n",
       "      <td>3.10</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.99</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.84</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.13</td>\n",
       "      <td>1.82</td>\n",
       "      <td>2.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>E0</td>\n",
       "      <td>16/03/2024</td>\n",
       "      <td>15:00</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Brentford</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>D Bond</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2.15</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.72</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.21</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.66</td>\n",
       "      <td>2.16</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.12</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.87</td>\n",
       "      <td>3.10</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.10</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.28</td>\n",
       "      <td>3.10</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.33</td>\n",
       "      <td>3.12</td>\n",
       "      <td>3.64</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.16</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.21</td>\n",
       "      <td>1.74</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.97</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>E0</td>\n",
       "      <td>16/03/2024</td>\n",
       "      <td>15:00</td>\n",
       "      <td>Luton</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>D England</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.83</td>\n",
       "      <td>3.69</td>\n",
       "      <td>2.44</td>\n",
       "      <td>2.75</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.38</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.81</td>\n",
       "      <td>3.63</td>\n",
       "      <td>2.42</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.15</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.79</td>\n",
       "      <td>2.13</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.63</td>\n",
       "      <td>3.40</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.67</td>\n",
       "      <td>3.59</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2.55</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.63</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.64</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.34</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>E0</td>\n",
       "      <td>16/03/2024</td>\n",
       "      <td>17:30</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>R Jones</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.80</td>\n",
       "      <td>1.95</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.42</td>\n",
       "      <td>4.09</td>\n",
       "      <td>2.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.95</td>\n",
       "      <td>3.54</td>\n",
       "      <td>4.16</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.93</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.72</td>\n",
       "      <td>1.49</td>\n",
       "      <td>2.62</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.89</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.89</td>\n",
       "      <td>2.02</td>\n",
       "      <td>1.93</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.45</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.51</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.13</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.94</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.44</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.49</td>\n",
       "      <td>2.72</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.84</td>\n",
       "      <td>1.47</td>\n",
       "      <td>2.72</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.04</td>\n",
       "      <td>1.88</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>E0</td>\n",
       "      <td>17/03/2024</td>\n",
       "      <td>14:00</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>J Gillett</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.63</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.65</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.66</td>\n",
       "      <td>3.82</td>\n",
       "      <td>2.52</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.75</td>\n",
       "      <td>3.92</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.65</td>\n",
       "      <td>3.73</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1.58</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.55</td>\n",
       "      <td>1.56</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.89</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.04</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.88</td>\n",
       "      <td>2.38</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.40</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.48</td>\n",
       "      <td>3.69</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.62</td>\n",
       "      <td>2.40</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.45</td>\n",
       "      <td>3.63</td>\n",
       "      <td>2.77</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.42</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.58</td>\n",
       "      <td>2.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.84</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2.12</td>\n",
       "      <td>1.82</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>283 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Div        Date   Time     HomeTeam       AwayTeam  FTHG  FTAG FTR  HTHG  \\\n",
       "0    E0  11/08/2023  20:00      Burnley       Man City     0     3   A     0   \n",
       "1    E0  12/08/2023  12:30      Arsenal  Nott'm Forest     2     1   H     2   \n",
       "2    E0  12/08/2023  15:00  Bournemouth       West Ham     1     1   D     0   \n",
       "3    E0  12/08/2023  15:00     Brighton          Luton     4     1   H     1   \n",
       "4    E0  12/08/2023  15:00      Everton         Fulham     0     1   A     0   \n",
       "..   ..         ...    ...          ...            ...   ...   ...  ..   ...   \n",
       "278  E0  13/03/2024  19:30  Bournemouth          Luton     4     3   H     0   \n",
       "279  E0  16/03/2024  15:00      Burnley      Brentford     2     1   H     1   \n",
       "280  E0  16/03/2024  15:00        Luton  Nott'm Forest     1     1   D     0   \n",
       "281  E0  16/03/2024  17:30       Fulham      Tottenham     3     0   H     1   \n",
       "282  E0  17/03/2024  14:00     West Ham    Aston Villa     1     1   D     1   \n",
       "\n",
       "     HTAG HTR    Referee  HS  AS  HST  AST  HF  AF  HC  AC  HY  AY  HR  AR  \\\n",
       "0       2   A   C Pawson   6  17    1    8  11   8   6   5   0   0   1   0   \n",
       "1       0   H   M Oliver  15   6    7    2  12  12   8   3   2   2   0   0   \n",
       "2       0   D   P Bankes  14  16    5    3   9  14  10   4   1   4   0   0   \n",
       "3       0   H    D Coote  27   9   12    3  11  12   6   7   2   2   0   0   \n",
       "4       0   D  S Attwell  19   9    9    2  12   6  10   4   0   2   0   0   \n",
       "..    ...  ..        ...  ..  ..  ...  ...  ..  ..  ..  ..  ..  ..  ..  ..   \n",
       "278     3   A  S Allison  24   8   10    4  16  11  11   3   1   2   0   0   \n",
       "279     0   H     D Bond  17   9    6    5  14  17   5   4   2   4   0   1   \n",
       "280     1   A  D England  10  16    4    6  10  17   6   7   1   2   0   0   \n",
       "281     0   H    R Jones  16  14    7    5   9  10   4   5   2   3   0   0   \n",
       "282     0   H  J Gillett  13  13    5    5  13  12   7  11   3   3   0   0   \n",
       "\n",
       "     B365H  B365D  B365A   BWH   BWD    BWA   IWH   IWD    IWA   PSH   PSD  \\\n",
       "0     8.00   5.50   1.33  8.75  5.25   1.34  8.00  5.50   1.35  8.58  5.51   \n",
       "1     1.18   7.00  15.00  1.17  7.50  15.50  1.20  7.25  14.00  1.18  7.86   \n",
       "2     2.70   3.40   2.55  2.65  3.40   2.55  2.70  3.45   2.60  2.70  3.47   \n",
       "3     1.33   5.50   9.00  1.32  5.50   9.00  1.35  5.25   8.50  1.33  5.65   \n",
       "4     2.20   3.40   3.30  2.20  3.40   3.25  2.25  3.40   3.25  2.27  3.45   \n",
       "..     ...    ...    ...   ...   ...    ...   ...   ...    ...   ...   ...   \n",
       "278   1.50   4.75   5.75  1.50  4.60   5.75   NaN   NaN    NaN  1.51  4.71   \n",
       "279   3.25   3.60   2.15  3.20  3.60   2.15   NaN   NaN    NaN  3.23  3.72   \n",
       "280   2.80   3.50   2.45  2.70  3.60   2.45   NaN   NaN    NaN  2.83  3.69   \n",
       "281   3.50   3.80   1.95  3.30  3.80   2.00   NaN   NaN    NaN  3.42  4.09   \n",
       "282   2.63   3.75   2.45  2.65  3.70   2.45   NaN   NaN    NaN  2.66  3.82   \n",
       "\n",
       "       PSA   WHH   WHD    WHA   VCH   VCD    VCA  MaxH  MaxD   MaxA  AvgH  \\\n",
       "0     1.37  8.00  5.00   1.25  9.50  5.25   1.33  9.50  5.68   1.39  9.02   \n",
       "1    15.67  1.12  6.50  12.00  1.14  7.50  17.00  1.21  8.50  17.50  1.18   \n",
       "2     2.71  2.62  3.20   2.30  2.63  3.30   2.63  2.80  3.62   2.75  2.69   \n",
       "3     9.61  1.25  4.60   8.50  1.29  5.25  10.00  1.36  6.00  10.50  1.33   \n",
       "4     3.35  2.10  3.20   3.00  2.20  3.30   3.25  2.30  3.57   3.45  2.24   \n",
       "..     ...   ...   ...    ...   ...   ...    ...   ...   ...    ...   ...   \n",
       "278   6.18  1.44  4.80   5.80  1.45  4.60   6.00  1.55  4.98   6.40  1.50   \n",
       "279   2.20  3.20  3.70   2.10  3.25  3.50   2.10  3.41  3.80   2.21  3.27   \n",
       "280   2.44  2.75  3.70   2.38  2.80  3.50   2.30  2.95  3.80   2.47  2.81   \n",
       "281   2.01  3.40  4.00   1.95  3.40  3.75   1.95  3.54  4.16   2.08  3.40   \n",
       "282   2.52  2.60  3.75   2.45  2.60  3.50   2.45  2.75  3.92   2.58  2.65   \n",
       "\n",
       "     AvgD   AvgA  B365>2.5  B365<2.5  P>2.5  P<2.5  Max>2.5  Max<2.5  Avg>2.5  \\\n",
       "0    5.35   1.35      1.67      2.20   1.68   2.29     1.71     2.40     1.65   \n",
       "1    7.64  15.67      1.44      2.75   1.42   2.93     1.45     2.98     1.42   \n",
       "2    3.44   2.64      1.90      2.00   1.90   1.99     1.95     2.03     1.88   \n",
       "3    5.52   9.61      1.62      2.30   1.62   2.40     1.65     2.45     1.61   \n",
       "4    3.43   3.30      2.01      1.89   2.00   1.89     2.04     1.92     1.97   \n",
       "..    ...    ...       ...       ...    ...    ...      ...      ...      ...   \n",
       "278  4.77   6.00      1.44      2.70   1.48   2.75     1.49     2.82     1.47   \n",
       "279  3.66   2.16      1.75      2.05   1.78   2.12     1.80     2.17     1.75   \n",
       "280  3.63   2.42      1.67      2.15   1.69   2.26     1.73     2.31     1.68   \n",
       "281  3.93   2.01      1.48      2.60   1.50   2.70     1.52     2.72     1.49   \n",
       "282  3.73   2.51      1.57      2.35   1.58   2.50     1.63     2.55     1.56   \n",
       "\n",
       "     Avg<2.5   AHh  B365AHH  B365AHA  PAHH  PAHA  MaxAHH  MaxAHA  AvgAHH  \\\n",
       "0       2.27  1.50     1.86     2.07  1.86  2.07    1.93    2.09    1.85   \n",
       "1       2.85 -2.00     1.88     2.02  1.88  2.01    1.91    2.06    1.87   \n",
       "2       1.94  0.00     1.95     1.95  1.95  1.95    1.98    1.99    1.94   \n",
       "3       2.34 -1.50     1.95     1.95  1.95  1.95    1.98    2.00    1.93   \n",
       "4       1.86 -0.25     1.93     1.97  1.95  1.95    1.97    2.00    1.92   \n",
       "..       ...   ...      ...      ...   ...   ...     ...     ...     ...   \n",
       "278     2.71 -1.25     2.05     1.85  2.06  1.85    2.09    1.90    2.03   \n",
       "279     2.10  0.25     1.99     1.91  1.98  1.92    2.00    1.94    1.96   \n",
       "280     2.22  0.25     1.81     2.09  1.79  2.13    1.81    2.17    1.77   \n",
       "281     2.62  0.50     1.89     2.01  1.89  2.02    1.93    2.05    1.86   \n",
       "282     2.44  0.00     2.01     1.89  2.01  1.90    2.04    1.91    1.99   \n",
       "\n",
       "     AvgAHA  B365CH  B365CD  B365CA  BWCH  BWCD   BWCA  IWCH  IWCD   IWCA  \\\n",
       "0      2.03    9.00    5.25    1.33  8.75  5.25   1.33  8.50  5.25   1.35   \n",
       "1      1.99    1.18    7.00   15.00  1.18  7.00  14.50  1.20  7.00  14.00   \n",
       "2      1.92    2.63    3.50    2.60  2.65  3.50   2.50  2.60  3.50   2.60   \n",
       "3      1.93    1.25    6.50   11.00  1.26  6.00  11.00  1.30  5.50   9.50   \n",
       "4      1.93    2.30    3.20    3.20  2.35  3.20   3.10  2.35  3.25   3.15   \n",
       "..      ...     ...     ...     ...   ...   ...    ...   ...   ...    ...   \n",
       "278    1.83    1.53    4.75    5.50  1.51  4.80   5.50   NaN   NaN    NaN   \n",
       "279    1.87    3.10    3.75    2.20  3.10  3.50   2.25   NaN   NaN    NaN   \n",
       "280    2.08    2.63    3.40    2.63  2.60  3.50   2.60   NaN   NaN    NaN   \n",
       "281    2.00    3.25    4.00    2.00  3.25  3.90   2.00   NaN   NaN    NaN   \n",
       "282    1.88    2.38    3.75    2.80  2.40  3.50   2.80   NaN   NaN    NaN   \n",
       "\n",
       "     PSCH  PSCD   PSCA  WHCH  WHCD   WHCA   VCCH  VCCD   VCCA  MaxCH  MaxCD  \\\n",
       "0    9.62  5.81   1.33  7.50  4.60   1.29  10.50  5.25   1.30  10.50   5.81   \n",
       "1    1.19  8.00  16.00  1.12  6.50  12.00   1.22  7.00  13.00   1.22   8.40   \n",
       "2    2.75  3.60   2.63  2.50  3.20   2.45   2.63  3.50   2.60   2.88   3.67   \n",
       "3    1.27  6.36  11.36  1.22  5.50   9.00   1.25  5.75  13.00   1.34   6.59   \n",
       "4    2.39  3.32   3.30  2.10  3.20   3.00   2.38  3.20   3.20   2.42   3.42   \n",
       "..    ...   ...    ...   ...   ...    ...    ...   ...    ...    ...    ...   \n",
       "278  1.53  4.89   5.69  1.53  4.80   5.50   1.50  4.80   5.50   1.57   5.00   \n",
       "279  3.18  3.65   2.28  3.10  3.70   2.20   3.13  3.60   2.20   3.27   3.80   \n",
       "280  2.67  3.59   2.67  2.60  3.75   2.55   2.55  3.60   2.63   2.70   3.75   \n",
       "281  3.45  4.00   2.05  3.25  4.00   2.00   3.40  3.90   2.00   3.51   4.10   \n",
       "282  2.48  3.69   2.83  2.50  3.75   2.62   2.40  3.70   2.75   2.50   3.80   \n",
       "\n",
       "     MaxCA  AvgCH  AvgCD  AvgCA  B365C>2.5  B365C<2.5  PC>2.5  PC<2.5  \\\n",
       "0     1.36   9.27   5.45   1.33       1.67       2.20    1.65    2.35   \n",
       "1    19.00   1.19   7.43  15.98       1.50       2.63    1.49    2.65   \n",
       "2     2.70   2.70   3.53   2.59       1.73       2.10    1.76    2.18   \n",
       "3    13.00   1.28   5.99  10.91       1.53       2.50    1.56    2.54   \n",
       "4     3.56   2.32   3.28   3.27       2.10       1.73    2.23    1.72   \n",
       "..     ...    ...    ...    ...        ...        ...     ...     ...   \n",
       "278   6.00   1.52   4.87   5.65       1.40       2.88    1.41    3.04   \n",
       "279   2.33   3.12   3.64   2.26       1.70       2.10    1.77    2.16   \n",
       "280   2.80   2.63   3.58   2.64       1.70       2.10    1.72    2.20   \n",
       "281   2.13   3.34   3.94   2.05       1.44       2.70    1.49    2.72   \n",
       "282   2.88   2.45   3.63   2.77       1.57       2.35    1.63    2.42   \n",
       "\n",
       "     MaxC>2.5  MaxC<2.5  AvgC>2.5  AvgC<2.5  AHCh  B365CAHH  B365CAHA  PCAHH  \\\n",
       "0        1.73      2.45      1.64      2.28  1.50      1.95      1.98   1.95   \n",
       "1        1.52      2.79      1.49      2.63 -2.00      1.95      1.98   1.93   \n",
       "2        1.83      2.23      1.74      2.12  0.00      2.02      1.91   2.01   \n",
       "3        1.62      2.66      1.55      2.48 -1.75      2.01      1.92   2.00   \n",
       "4        2.26      1.81      2.17      1.71 -0.25      2.06      1.87   2.04   \n",
       "..        ...       ...       ...       ...   ...       ...       ...    ...   \n",
       "278      1.43      3.10      1.40      2.99 -1.00      1.84      2.06   1.85   \n",
       "279      1.78      2.21      1.74      2.13  0.25      1.95      1.95   1.94   \n",
       "280      1.78      2.34      1.68      2.23  0.00      1.95      1.95   1.96   \n",
       "281      1.50      2.84      1.47      2.72  0.50      1.86      2.04   1.88   \n",
       "282      1.63      2.50      1.58      2.39  0.00      1.83      2.10   1.84   \n",
       "\n",
       "     PCAHA  MaxCAHH  MaxCAHA  AvgCAHH  AvgCAHA  \n",
       "0     1.97      NaN      NaN     1.92     1.95  \n",
       "1     1.97     2.01     2.09     1.95     1.92  \n",
       "2     1.92     2.06     1.96     1.96     1.91  \n",
       "3     1.91     2.14     1.93     2.00     1.86  \n",
       "4     1.88     2.08     1.99     1.98     1.88  \n",
       "..     ...      ...      ...      ...      ...  \n",
       "278   2.09     1.87     2.13     1.82     2.07  \n",
       "279   1.98     1.97     2.00     1.91     1.94  \n",
       "280   1.96     1.97     1.99     1.94     1.94  \n",
       "281   2.05     1.90     2.11     1.83     2.04  \n",
       "282   2.10     1.85     2.12     1.82     2.06  \n",
       "\n",
       "[283 rows x 106 columns]"
      ]
     },
     "execution_count": 886,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df_21 = pd.read_csv('data/2020-2021.csv')\n",
    "df_22 = pd.read_csv('data/2021-2022.csv')\n",
    "df_23 = pd.read_csv('data/2022-2023.csv')\n",
    "df_24 = pd.read_csv('data/2023-2024.csv')\n",
    "\n",
    "\n",
    "# Join the two seasons together\n",
    "\n",
    "df = pd.concat([df_24,\n",
    "                #df_23,\n",
    "                #df_22,\n",
    "                #df_21\n",
    "                     ], ignore_index=True)\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting only match related columns. \n",
    "\n",
    "- Match Statistics (where available)\n",
    "- Attendance = Crowd Attendance\n",
    "- Referee = Match Referee\n",
    "- HS = Home Team Shots\n",
    "- AS = Away Team Shots\n",
    "- HST = Home Team Shots on Target\n",
    "- AST = Away Team Shots on Target\n",
    "- HHW = Home Team Hit Woodwork\n",
    "- AHW = Away Team Hit Woodwork\n",
    "- HC = Home Team Corners\n",
    "- AC = Away Team Corners\n",
    "- HF = Home Team Fouls Committed\n",
    "- AF = Away Team Fouls Committed\n",
    "- HFKC = Home Team Free Kicks Conceded\n",
    "- AFKC = Away Team Free Kicks Conceded\n",
    "- HO = Home Team Offsides\n",
    "- AO = Away Team Offsides\n",
    "- HY = Home Team Yellow Cards\n",
    "- AY = Away Team Yellow Cards\n",
    "- HR = Home Team Red Cards\n",
    "- AR = Away Team Red Cards\n",
    "- HBP = Home Team Bookings Points (10 = yellow, 25 = red)\n",
    "- ABP = Away Team Bookings Points (10 = yellow, 25 = red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 887,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new rows that we will want to use later for future predictions.\n",
    "new_rows = {\n",
    "    'HomeTeam':['Brentford', 'Man City', 'Liverpool', 'Newcastle', 'Chelsea', 'Aston Villa'],\n",
    "    'AwayTeam':['Man United', 'Arsenal', 'Brighton','West Ham', 'Burnley', 'Wolves'],\n",
    "    'Year' : [2024]*6,\n",
    "    'Month' : [3]*6,\n",
    "    'Day' : [30, 31, 31, 30, 30, 30],\n",
    "    'Hour': [20, 16, 14, 12, 15, 25],\n",
    "    'Min':[00, 30, 00, 30, 00, 00]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 888,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Min</th>\n",
       "      <th>Div</th>\n",
       "      <th>FTHG</th>\n",
       "      <th>FTAG</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HTHG</th>\n",
       "      <th>HTAG</th>\n",
       "      <th>HTR</th>\n",
       "      <th>Referee</th>\n",
       "      <th>HS</th>\n",
       "      <th>AS</th>\n",
       "      <th>HST</th>\n",
       "      <th>AST</th>\n",
       "      <th>HF</th>\n",
       "      <th>AF</th>\n",
       "      <th>HC</th>\n",
       "      <th>AC</th>\n",
       "      <th>HY</th>\n",
       "      <th>AY</th>\n",
       "      <th>HR</th>\n",
       "      <th>AR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>2023</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>E0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>A</td>\n",
       "      <td>C Pawson</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>2023</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>E0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>M Oliver</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>2023</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>E0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>D</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>P Bankes</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Luton</td>\n",
       "      <td>2023</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>E0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>D Coote</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Everton</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>2023</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>E0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>S Attwell</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>Brentford</td>\n",
       "      <td>Man United</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        HomeTeam       AwayTeam  Year  Month  Day  Hour  Min  Div  FTHG  FTAG  \\\n",
       "0        Burnley       Man City  2023      8   11    20    0   E0   0.0   3.0   \n",
       "1        Arsenal  Nott'm Forest  2023      8   12    12   30   E0   2.0   1.0   \n",
       "2    Bournemouth       West Ham  2023      8   12    15    0   E0   1.0   1.0   \n",
       "3       Brighton          Luton  2023      8   12    15    0   E0   4.0   1.0   \n",
       "4        Everton         Fulham  2023      8   12    15    0   E0   0.0   1.0   \n",
       "..           ...            ...   ...    ...  ...   ...  ...  ...   ...   ...   \n",
       "284      Chelsea        Burnley  2024      3   30    15    0  NaN   NaN   NaN   \n",
       "285    Brentford     Man United  2024      3   30    20    0  NaN   NaN   NaN   \n",
       "286  Aston Villa         Wolves  2024      3   30    25    0  NaN   NaN   NaN   \n",
       "287    Liverpool       Brighton  2024      3   31    14    0  NaN   NaN   NaN   \n",
       "288     Man City        Arsenal  2024      3   31    16   30  NaN   NaN   NaN   \n",
       "\n",
       "     FTR  HTHG  HTAG  HTR    Referee    HS    AS   HST  AST    HF    AF    HC  \\\n",
       "0      A   0.0   2.0    A   C Pawson   6.0  17.0   1.0  8.0  11.0   8.0   6.0   \n",
       "1      H   2.0   0.0    H   M Oliver  15.0   6.0   7.0  2.0  12.0  12.0   8.0   \n",
       "2      D   0.0   0.0    D   P Bankes  14.0  16.0   5.0  3.0   9.0  14.0  10.0   \n",
       "3      H   1.0   0.0    H    D Coote  27.0   9.0  12.0  3.0  11.0  12.0   6.0   \n",
       "4      A   0.0   0.0    D  S Attwell  19.0   9.0   9.0  2.0  12.0   6.0  10.0   \n",
       "..   ...   ...   ...  ...        ...   ...   ...   ...  ...   ...   ...   ...   \n",
       "284  NaN   NaN   NaN  NaN        NaN   NaN   NaN   NaN  NaN   NaN   NaN   NaN   \n",
       "285  NaN   NaN   NaN  NaN        NaN   NaN   NaN   NaN  NaN   NaN   NaN   NaN   \n",
       "286  NaN   NaN   NaN  NaN        NaN   NaN   NaN   NaN  NaN   NaN   NaN   NaN   \n",
       "287  NaN   NaN   NaN  NaN        NaN   NaN   NaN   NaN  NaN   NaN   NaN   NaN   \n",
       "288  NaN   NaN   NaN  NaN        NaN   NaN   NaN   NaN  NaN   NaN   NaN   NaN   \n",
       "\n",
       "      AC   HY   AY   HR   AR  \n",
       "0    5.0  0.0  0.0  1.0  0.0  \n",
       "1    3.0  2.0  2.0  0.0  0.0  \n",
       "2    4.0  1.0  4.0  0.0  0.0  \n",
       "3    7.0  2.0  2.0  0.0  0.0  \n",
       "4    4.0  0.0  2.0  0.0  0.0  \n",
       "..   ...  ...  ...  ...  ...  \n",
       "284  NaN  NaN  NaN  NaN  NaN  \n",
       "285  NaN  NaN  NaN  NaN  NaN  \n",
       "286  NaN  NaN  NaN  NaN  NaN  \n",
       "287  NaN  NaN  NaN  NaN  NaN  \n",
       "288  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[289 rows x 27 columns]"
      ]
     },
     "execution_count": 888,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df.columns[:24]].copy()\n",
    "df.loc[:,'Day'] = (df['Date'].apply(lambda x:x.split('/')[0])).astype(int)\n",
    "df.loc[:,'Month'] = (df['Date'].apply(lambda x:x.split('/')[1])).astype(int)\n",
    "df.loc[:,'Year'] = (df['Date'].apply(lambda x:x.split('/')[2])).astype(int)\n",
    "df.loc[:,'Hour'] = (df['Time'].apply(lambda x:x.split(\":\")[0])).astype(int)\n",
    "df.loc[:,'Min'] = (df['Time'].apply(lambda x:x.split(\":\")[1])).astype(int)\n",
    "df = df.drop(columns = ['Date', 'Time'])\n",
    "df = pd.concat([pd.DataFrame(new_rows), df], ignore_index=True)\n",
    "df = df.sort_values(by= ['Year', 'Month', 'Day', 'Hour']).reset_index(drop = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 889,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Arsenal             14\n",
       "Aston Villa         15\n",
       "Bournemouth         14\n",
       "Brentford           15\n",
       "Brighton            14\n",
       "Burnley             15\n",
       "Chelsea             14\n",
       "Crystal Palace      14\n",
       "Everton             14\n",
       "Fulham              15\n",
       "Liverpool           15\n",
       "Luton               15\n",
       "Man City            15\n",
       "Man United          14\n",
       "Newcastle           15\n",
       "Nott'm Forest       14\n",
       "Sheffield United    14\n",
       "Tottenham           14\n",
       "West Ham            15\n",
       "Wolves              14\n",
       "Name: HomeTeam, dtype: int64"
      ]
     },
     "execution_count": 889,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HomeTeam'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 890,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Min</th>\n",
       "      <th>Div</th>\n",
       "      <th>FTHG</th>\n",
       "      <th>FTAG</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HTHG</th>\n",
       "      <th>HTAG</th>\n",
       "      <th>HTR</th>\n",
       "      <th>Referee</th>\n",
       "      <th>HS</th>\n",
       "      <th>AS</th>\n",
       "      <th>HST</th>\n",
       "      <th>AST</th>\n",
       "      <th>HF</th>\n",
       "      <th>AF</th>\n",
       "      <th>HC</th>\n",
       "      <th>AC</th>\n",
       "      <th>HY</th>\n",
       "      <th>AY</th>\n",
       "      <th>HR</th>\n",
       "      <th>AR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>2023</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>E0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>A</td>\n",
       "      <td>C Pawson</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>2023</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>E0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>M Oliver</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>2023</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>E0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>D</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>P Bankes</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Luton</td>\n",
       "      <td>2023</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>E0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>D Coote</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Everton</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>2023</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>E0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>S Attwell</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>Brentford</td>\n",
       "      <td>Man United</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        HomeTeam       AwayTeam  Year  Month  Day  Hour  Min  Div  FTHG  FTAG  \\\n",
       "0        Burnley       Man City  2023      8   11    20    0   E0   0.0   3.0   \n",
       "1        Arsenal  Nott'm Forest  2023      8   12    12   30   E0   2.0   1.0   \n",
       "2    Bournemouth       West Ham  2023      8   12    15    0   E0   1.0   1.0   \n",
       "3       Brighton          Luton  2023      8   12    15    0   E0   4.0   1.0   \n",
       "4        Everton         Fulham  2023      8   12    15    0   E0   0.0   1.0   \n",
       "..           ...            ...   ...    ...  ...   ...  ...  ...   ...   ...   \n",
       "284      Chelsea        Burnley  2024      3   30    15    0  NaN   NaN   NaN   \n",
       "285    Brentford     Man United  2024      3   30    20    0  NaN   NaN   NaN   \n",
       "286  Aston Villa         Wolves  2024      3   30    25    0  NaN   NaN   NaN   \n",
       "287    Liverpool       Brighton  2024      3   31    14    0  NaN   NaN   NaN   \n",
       "288     Man City        Arsenal  2024      3   31    16   30  NaN   NaN   NaN   \n",
       "\n",
       "     FTR  HTHG  HTAG  HTR    Referee    HS    AS   HST  AST    HF    AF    HC  \\\n",
       "0      A   0.0   2.0    A   C Pawson   6.0  17.0   1.0  8.0  11.0   8.0   6.0   \n",
       "1      H   2.0   0.0    H   M Oliver  15.0   6.0   7.0  2.0  12.0  12.0   8.0   \n",
       "2      D   0.0   0.0    D   P Bankes  14.0  16.0   5.0  3.0   9.0  14.0  10.0   \n",
       "3      H   1.0   0.0    H    D Coote  27.0   9.0  12.0  3.0  11.0  12.0   6.0   \n",
       "4      A   0.0   0.0    D  S Attwell  19.0   9.0   9.0  2.0  12.0   6.0  10.0   \n",
       "..   ...   ...   ...  ...        ...   ...   ...   ...  ...   ...   ...   ...   \n",
       "284  NaN   NaN   NaN  NaN        NaN   NaN   NaN   NaN  NaN   NaN   NaN   NaN   \n",
       "285  NaN   NaN   NaN  NaN        NaN   NaN   NaN   NaN  NaN   NaN   NaN   NaN   \n",
       "286  NaN   NaN   NaN  NaN        NaN   NaN   NaN   NaN  NaN   NaN   NaN   NaN   \n",
       "287  NaN   NaN   NaN  NaN        NaN   NaN   NaN   NaN  NaN   NaN   NaN   NaN   \n",
       "288  NaN   NaN   NaN  NaN        NaN   NaN   NaN   NaN  NaN   NaN   NaN   NaN   \n",
       "\n",
       "      AC   HY   AY   HR   AR  \n",
       "0    5.0  0.0  0.0  1.0  0.0  \n",
       "1    3.0  2.0  2.0  0.0  0.0  \n",
       "2    4.0  1.0  4.0  0.0  0.0  \n",
       "3    7.0  2.0  2.0  0.0  0.0  \n",
       "4    4.0  0.0  2.0  0.0  0.0  \n",
       "..   ...  ...  ...  ...  ...  \n",
       "284  NaN  NaN  NaN  NaN  NaN  \n",
       "285  NaN  NaN  NaN  NaN  NaN  \n",
       "286  NaN  NaN  NaN  NaN  NaN  \n",
       "287  NaN  NaN  NaN  NaN  NaN  \n",
       "288  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[289 rows x 27 columns]"
      ]
     },
     "execution_count": 890,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by= ['Year', 'Month', 'Day', 'Hour', 'Min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 891,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first part we need:\n",
    "# 1. A row for each match, from the pespective of both the home and away team.\n",
    "# 2. A column called team_playing\n",
    "# 3. A column for opponent\n",
    "\n",
    "df['team_playing'] = df['HomeTeam']\n",
    "df['opponent'] = df['AwayTeam']\n",
    "\n",
    "df2 = df.copy()\n",
    "df2['team_playing'] = df2['AwayTeam']\n",
    "df2['opponent'] = df2['HomeTeam']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 892,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we need:\n",
    "# Columns describing:\n",
    "# 1. What the team_playing did. We will use these columns to compute rolling statistics of the team playing.\n",
    "# 2. Columns describing what the opponent did? Yes because does that represent the team_playing, and how oppositions have done against them right?\n",
    "#  \n",
    "\n",
    "df = df[['team_playing', 'opponent', 'HomeTeam', 'AwayTeam', 'FTHG', 'HTHG', 'HS', 'HST', 'HF', 'HC', 'HY', 'HR', \n",
    "        'Day', 'Month', 'Year', 'Hour',\n",
    "        'FTAG', 'HTAG', 'AS', 'AST', 'AF', 'AC', 'AY', 'AR', 'FTR', 'Referee']]\n",
    "\n",
    "df2 = df2[['team_playing', 'opponent','HomeTeam', 'AwayTeam','FTAG', 'HTAG', 'AS', 'AST', 'AF', 'AC', 'AY', 'AR', \n",
    "            'Day', 'Month', 'Year', 'Hour',\n",
    "            'FTHG', 'HTHG', 'HS', 'HST', 'HF', 'HC', 'HY', 'HR', 'FTR', 'Referee']]\n",
    "\n",
    "\n",
    "# We have two datasets. Each containing a team_playing column. df is when the team_playing is the home team and df2 is when \n",
    "# the team playing is the away team.\n",
    "\n",
    "# We need to convert these column names from home and way to team_playing stats and opponent stats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 893,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_playing</th>\n",
       "      <th>opponent</th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FT_TP_G</th>\n",
       "      <th>HT_TP_G</th>\n",
       "      <th>TP_S</th>\n",
       "      <th>TP_ST</th>\n",
       "      <th>TP_F</th>\n",
       "      <th>TP_C</th>\n",
       "      <th>TP_Y</th>\n",
       "      <th>TP_R</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Hour</th>\n",
       "      <th>FT_OP_G</th>\n",
       "      <th>HT_OP_G</th>\n",
       "      <th>OP_S</th>\n",
       "      <th>OP_ST</th>\n",
       "      <th>OP_F</th>\n",
       "      <th>OP_C</th>\n",
       "      <th>OP_Y</th>\n",
       "      <th>OP_R</th>\n",
       "      <th>FTR</th>\n",
       "      <th>Referee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>C Pawson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>M Oliver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>P Bankes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Luton</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>Luton</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>D Coote</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Everton</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>Everton</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>S Attwell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>Brentford</td>\n",
       "      <td>Man United</td>\n",
       "      <td>Brentford</td>\n",
       "      <td>Man United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    team_playing       opponent     HomeTeam       AwayTeam  FT_TP_G  HT_TP_G  \\\n",
       "0        Burnley       Man City      Burnley       Man City      0.0      0.0   \n",
       "1        Arsenal  Nott'm Forest      Arsenal  Nott'm Forest      2.0      2.0   \n",
       "2    Bournemouth       West Ham  Bournemouth       West Ham      1.0      0.0   \n",
       "3       Brighton          Luton     Brighton          Luton      4.0      1.0   \n",
       "4        Everton         Fulham      Everton         Fulham      0.0      0.0   \n",
       "..           ...            ...          ...            ...      ...      ...   \n",
       "284      Chelsea        Burnley      Chelsea        Burnley      NaN      NaN   \n",
       "285    Brentford     Man United    Brentford     Man United      NaN      NaN   \n",
       "286  Aston Villa         Wolves  Aston Villa         Wolves      NaN      NaN   \n",
       "287    Liverpool       Brighton    Liverpool       Brighton      NaN      NaN   \n",
       "288     Man City        Arsenal     Man City        Arsenal      NaN      NaN   \n",
       "\n",
       "     TP_S  TP_ST  TP_F  TP_C  TP_Y  TP_R  Day  Month  Year  Hour  FT_OP_G  \\\n",
       "0     6.0    1.0  11.0   6.0   0.0   1.0   11      8  2023    20      3.0   \n",
       "1    15.0    7.0  12.0   8.0   2.0   0.0   12      8  2023    12      1.0   \n",
       "2    14.0    5.0   9.0  10.0   1.0   0.0   12      8  2023    15      1.0   \n",
       "3    27.0   12.0  11.0   6.0   2.0   0.0   12      8  2023    15      1.0   \n",
       "4    19.0    9.0  12.0  10.0   0.0   0.0   12      8  2023    15      1.0   \n",
       "..    ...    ...   ...   ...   ...   ...  ...    ...   ...   ...      ...   \n",
       "284   NaN    NaN   NaN   NaN   NaN   NaN   30      3  2024    15      NaN   \n",
       "285   NaN    NaN   NaN   NaN   NaN   NaN   30      3  2024    20      NaN   \n",
       "286   NaN    NaN   NaN   NaN   NaN   NaN   30      3  2024    25      NaN   \n",
       "287   NaN    NaN   NaN   NaN   NaN   NaN   31      3  2024    14      NaN   \n",
       "288   NaN    NaN   NaN   NaN   NaN   NaN   31      3  2024    16      NaN   \n",
       "\n",
       "     HT_OP_G  OP_S  OP_ST  OP_F  OP_C  OP_Y  OP_R  FTR    Referee  \n",
       "0        2.0  17.0    8.0   8.0   5.0   0.0   0.0    A   C Pawson  \n",
       "1        0.0   6.0    2.0  12.0   3.0   2.0   0.0    H   M Oliver  \n",
       "2        0.0  16.0    3.0  14.0   4.0   4.0   0.0    D   P Bankes  \n",
       "3        0.0   9.0    3.0  12.0   7.0   2.0   0.0    H    D Coote  \n",
       "4        0.0   9.0    2.0   6.0   4.0   2.0   0.0    A  S Attwell  \n",
       "..       ...   ...    ...   ...   ...   ...   ...  ...        ...  \n",
       "284      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN        NaN  \n",
       "285      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN        NaN  \n",
       "286      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN        NaN  \n",
       "287      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN        NaN  \n",
       "288      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN        NaN  \n",
       "\n",
       "[289 rows x 26 columns]"
      ]
     },
     "execution_count": 893,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First we change the home team. So we need to rename the columns that represent the home teams to say TP for team playing.\n",
    "\n",
    "\n",
    "df = df.rename(columns = {'FTHG':'FT_TP_G', # goals for the team playing\n",
    "            'HTHG':'HT_TP_G', \n",
    "            'HS':'TP_S', \n",
    "            'HST':'TP_ST', \n",
    "            'HF':'TP_F',\n",
    "            'HC': 'TP_C',\n",
    "            'HY':'TP_Y', \n",
    "            'HR':'TP_R',\n",
    "            'FTAG':'FT_OP_G', \n",
    "            'HTAG':'HT_OP_G', \n",
    "            'AS':'OP_S', \n",
    "            'AST':'OP_ST', \n",
    "            'AF':'OP_F',\n",
    "            'AC': 'OP_C',\n",
    "            'AY':'OP_Y', \n",
    "            'AR':'OP_R'})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 894,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_playing</th>\n",
       "      <th>opponent</th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FT_TP_G</th>\n",
       "      <th>HT_TP_G</th>\n",
       "      <th>TP_S</th>\n",
       "      <th>TP_ST</th>\n",
       "      <th>TP_F</th>\n",
       "      <th>TP_C</th>\n",
       "      <th>TP_Y</th>\n",
       "      <th>TP_R</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Hour</th>\n",
       "      <th>FT_OP_G</th>\n",
       "      <th>HT_OP_G</th>\n",
       "      <th>OP_S</th>\n",
       "      <th>OP_ST</th>\n",
       "      <th>OP_F</th>\n",
       "      <th>OP_C</th>\n",
       "      <th>OP_Y</th>\n",
       "      <th>OP_R</th>\n",
       "      <th>FTR</th>\n",
       "      <th>Referee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>C Pawson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>M Oliver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>P Bankes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Luton</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>Luton</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>15</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>D Coote</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fulham</td>\n",
       "      <td>Everton</td>\n",
       "      <td>Everton</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>S Attwell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>Man United</td>\n",
       "      <td>Brentford</td>\n",
       "      <td>Brentford</td>\n",
       "      <td>Man United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Wolves</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      team_playing     opponent     HomeTeam       AwayTeam  FT_TP_G  HT_TP_G  \\\n",
       "0         Man City      Burnley      Burnley       Man City      3.0      2.0   \n",
       "1    Nott'm Forest      Arsenal      Arsenal  Nott'm Forest      1.0      0.0   \n",
       "2         West Ham  Bournemouth  Bournemouth       West Ham      1.0      0.0   \n",
       "3            Luton     Brighton     Brighton          Luton      1.0      0.0   \n",
       "4           Fulham      Everton      Everton         Fulham      1.0      0.0   \n",
       "..             ...          ...          ...            ...      ...      ...   \n",
       "284        Burnley      Chelsea      Chelsea        Burnley      NaN      NaN   \n",
       "285     Man United    Brentford    Brentford     Man United      NaN      NaN   \n",
       "286         Wolves  Aston Villa  Aston Villa         Wolves      NaN      NaN   \n",
       "287       Brighton    Liverpool    Liverpool       Brighton      NaN      NaN   \n",
       "288        Arsenal     Man City     Man City        Arsenal      NaN      NaN   \n",
       "\n",
       "     TP_S  TP_ST  TP_F  TP_C  TP_Y  TP_R  Day  Month  Year  Hour  FT_OP_G  \\\n",
       "0    17.0    8.0   8.0   5.0   0.0   0.0   11      8  2023    20      0.0   \n",
       "1     6.0    2.0  12.0   3.0   2.0   0.0   12      8  2023    12      2.0   \n",
       "2    16.0    3.0  14.0   4.0   4.0   0.0   12      8  2023    15      1.0   \n",
       "3     9.0    3.0  12.0   7.0   2.0   0.0   12      8  2023    15      4.0   \n",
       "4     9.0    2.0   6.0   4.0   2.0   0.0   12      8  2023    15      0.0   \n",
       "..    ...    ...   ...   ...   ...   ...  ...    ...   ...   ...      ...   \n",
       "284   NaN    NaN   NaN   NaN   NaN   NaN   30      3  2024    15      NaN   \n",
       "285   NaN    NaN   NaN   NaN   NaN   NaN   30      3  2024    20      NaN   \n",
       "286   NaN    NaN   NaN   NaN   NaN   NaN   30      3  2024    25      NaN   \n",
       "287   NaN    NaN   NaN   NaN   NaN   NaN   31      3  2024    14      NaN   \n",
       "288   NaN    NaN   NaN   NaN   NaN   NaN   31      3  2024    16      NaN   \n",
       "\n",
       "     HT_OP_G  OP_S  OP_ST  OP_F  OP_C  OP_Y  OP_R  FTR    Referee  \n",
       "0        0.0   6.0    1.0  11.0   6.0   0.0   1.0    A   C Pawson  \n",
       "1        2.0  15.0    7.0  12.0   8.0   2.0   0.0    H   M Oliver  \n",
       "2        0.0  14.0    5.0   9.0  10.0   1.0   0.0    D   P Bankes  \n",
       "3        1.0  27.0   12.0  11.0   6.0   2.0   0.0    H    D Coote  \n",
       "4        0.0  19.0    9.0  12.0  10.0   0.0   0.0    A  S Attwell  \n",
       "..       ...   ...    ...   ...   ...   ...   ...  ...        ...  \n",
       "284      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN        NaN  \n",
       "285      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN        NaN  \n",
       "286      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN        NaN  \n",
       "287      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN        NaN  \n",
       "288      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN        NaN  \n",
       "\n",
       "[289 rows x 26 columns]"
      ]
     },
     "execution_count": 894,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2.rename(columns = {'FTHG':'FT_OP_G',  # \n",
    "            'HTHG':'HT_OP_G', \n",
    "            'HS':'OP_S', \n",
    "            'HST':'OP_ST', \n",
    "            'HF':'OP_F',\n",
    "            'HC': 'OP_C',\n",
    "            'HY':'OP_Y', \n",
    "            'HR':'OP_R',\n",
    "            'FTAG':'FT_TP_G', \n",
    "            'HTAG':'HT_TP_G', \n",
    "            'AS':'TP_S', \n",
    "            'AST':'TP_ST', \n",
    "            'AF':'TP_F',\n",
    "            'AC': 'TP_C',\n",
    "            'AY':'TP_Y', \n",
    "            'AR':'TP_R'})\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 895,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_playing</th>\n",
       "      <th>opponent</th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FT_TP_G</th>\n",
       "      <th>HT_TP_G</th>\n",
       "      <th>TP_S</th>\n",
       "      <th>TP_ST</th>\n",
       "      <th>TP_F</th>\n",
       "      <th>TP_C</th>\n",
       "      <th>TP_Y</th>\n",
       "      <th>TP_R</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Hour</th>\n",
       "      <th>FT_OP_G</th>\n",
       "      <th>HT_OP_G</th>\n",
       "      <th>OP_S</th>\n",
       "      <th>OP_ST</th>\n",
       "      <th>OP_F</th>\n",
       "      <th>OP_C</th>\n",
       "      <th>OP_Y</th>\n",
       "      <th>OP_R</th>\n",
       "      <th>FTR</th>\n",
       "      <th>Referee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>C Pawson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>C Pawson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>M Oliver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>M Oliver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Luton</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>Luton</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>D Coote</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>Wolves</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>578 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      team_playing       opponent     HomeTeam       AwayTeam  FT_TP_G  \\\n",
       "0          Burnley       Man City      Burnley       Man City      0.0   \n",
       "1         Man City        Burnley      Burnley       Man City      3.0   \n",
       "2          Arsenal  Nott'm Forest      Arsenal  Nott'm Forest      2.0   \n",
       "3    Nott'm Forest        Arsenal      Arsenal  Nott'm Forest      1.0   \n",
       "4         Brighton          Luton     Brighton          Luton      4.0   \n",
       "..             ...            ...          ...            ...      ...   \n",
       "573         Wolves    Aston Villa  Aston Villa         Wolves      NaN   \n",
       "574      Liverpool       Brighton    Liverpool       Brighton      NaN   \n",
       "575       Brighton      Liverpool    Liverpool       Brighton      NaN   \n",
       "576       Man City        Arsenal     Man City        Arsenal      NaN   \n",
       "577        Arsenal       Man City     Man City        Arsenal      NaN   \n",
       "\n",
       "     HT_TP_G  TP_S  TP_ST  TP_F  TP_C  TP_Y  TP_R  Day  Month  Year  Hour  \\\n",
       "0        0.0   6.0    1.0  11.0   6.0   0.0   1.0   11      8  2023    20   \n",
       "1        2.0  17.0    8.0   8.0   5.0   0.0   0.0   11      8  2023    20   \n",
       "2        2.0  15.0    7.0  12.0   8.0   2.0   0.0   12      8  2023    12   \n",
       "3        0.0   6.0    2.0  12.0   3.0   2.0   0.0   12      8  2023    12   \n",
       "4        1.0  27.0   12.0  11.0   6.0   2.0   0.0   12      8  2023    15   \n",
       "..       ...   ...    ...   ...   ...   ...   ...  ...    ...   ...   ...   \n",
       "573      NaN   NaN    NaN   NaN   NaN   NaN   NaN   30      3  2024    25   \n",
       "574      NaN   NaN    NaN   NaN   NaN   NaN   NaN   31      3  2024    14   \n",
       "575      NaN   NaN    NaN   NaN   NaN   NaN   NaN   31      3  2024    14   \n",
       "576      NaN   NaN    NaN   NaN   NaN   NaN   NaN   31      3  2024    16   \n",
       "577      NaN   NaN    NaN   NaN   NaN   NaN   NaN   31      3  2024    16   \n",
       "\n",
       "     FT_OP_G  HT_OP_G  OP_S  OP_ST  OP_F  OP_C  OP_Y  OP_R  FTR   Referee  \n",
       "0        3.0      2.0  17.0    8.0   8.0   5.0   0.0   0.0    A  C Pawson  \n",
       "1        0.0      0.0   6.0    1.0  11.0   6.0   0.0   1.0    A  C Pawson  \n",
       "2        1.0      0.0   6.0    2.0  12.0   3.0   2.0   0.0    H  M Oliver  \n",
       "3        2.0      2.0  15.0    7.0  12.0   8.0   2.0   0.0    H  M Oliver  \n",
       "4        1.0      0.0   9.0    3.0  12.0   7.0   2.0   0.0    H   D Coote  \n",
       "..       ...      ...   ...    ...   ...   ...   ...   ...  ...       ...  \n",
       "573      NaN      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN       NaN  \n",
       "574      NaN      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN       NaN  \n",
       "575      NaN      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN       NaN  \n",
       "576      NaN      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN       NaN  \n",
       "577      NaN      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN       NaN  \n",
       "\n",
       "[578 rows x 26 columns]"
      ]
     },
     "execution_count": 895,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = pd.concat([df, df2], ignore_index=True)\n",
    "df_final = df_final.sort_values(by = ['Year', 'Month', 'Day', 'Hour', 'Referee']).reset_index(drop = True)\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 896,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_playing</th>\n",
       "      <th>opponent</th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FT_TP_G</th>\n",
       "      <th>HT_TP_G</th>\n",
       "      <th>TP_S</th>\n",
       "      <th>TP_ST</th>\n",
       "      <th>TP_F</th>\n",
       "      <th>TP_C</th>\n",
       "      <th>TP_Y</th>\n",
       "      <th>TP_R</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Hour</th>\n",
       "      <th>FT_OP_G</th>\n",
       "      <th>HT_OP_G</th>\n",
       "      <th>OP_S</th>\n",
       "      <th>OP_ST</th>\n",
       "      <th>OP_F</th>\n",
       "      <th>OP_C</th>\n",
       "      <th>OP_Y</th>\n",
       "      <th>OP_R</th>\n",
       "      <th>FTR</th>\n",
       "      <th>Referee</th>\n",
       "      <th>is_home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>C Pawson</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>C Pawson</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>M Oliver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>M Oliver</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Luton</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>Luton</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>D Coote</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>Wolves</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2024</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>578 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      team_playing       opponent     HomeTeam       AwayTeam  FT_TP_G  \\\n",
       "0          Burnley       Man City      Burnley       Man City      0.0   \n",
       "1         Man City        Burnley      Burnley       Man City      3.0   \n",
       "2          Arsenal  Nott'm Forest      Arsenal  Nott'm Forest      2.0   \n",
       "3    Nott'm Forest        Arsenal      Arsenal  Nott'm Forest      1.0   \n",
       "4         Brighton          Luton     Brighton          Luton      4.0   \n",
       "..             ...            ...          ...            ...      ...   \n",
       "573         Wolves    Aston Villa  Aston Villa         Wolves      NaN   \n",
       "574      Liverpool       Brighton    Liverpool       Brighton      NaN   \n",
       "575       Brighton      Liverpool    Liverpool       Brighton      NaN   \n",
       "576       Man City        Arsenal     Man City        Arsenal      NaN   \n",
       "577        Arsenal       Man City     Man City        Arsenal      NaN   \n",
       "\n",
       "     HT_TP_G  TP_S  TP_ST  TP_F  TP_C  TP_Y  TP_R  Day  Month  Year  Hour  \\\n",
       "0        0.0   6.0    1.0  11.0   6.0   0.0   1.0   11      8  2023    20   \n",
       "1        2.0  17.0    8.0   8.0   5.0   0.0   0.0   11      8  2023    20   \n",
       "2        2.0  15.0    7.0  12.0   8.0   2.0   0.0   12      8  2023    12   \n",
       "3        0.0   6.0    2.0  12.0   3.0   2.0   0.0   12      8  2023    12   \n",
       "4        1.0  27.0   12.0  11.0   6.0   2.0   0.0   12      8  2023    15   \n",
       "..       ...   ...    ...   ...   ...   ...   ...  ...    ...   ...   ...   \n",
       "573      NaN   NaN    NaN   NaN   NaN   NaN   NaN   30      3  2024    25   \n",
       "574      NaN   NaN    NaN   NaN   NaN   NaN   NaN   31      3  2024    14   \n",
       "575      NaN   NaN    NaN   NaN   NaN   NaN   NaN   31      3  2024    14   \n",
       "576      NaN   NaN    NaN   NaN   NaN   NaN   NaN   31      3  2024    16   \n",
       "577      NaN   NaN    NaN   NaN   NaN   NaN   NaN   31      3  2024    16   \n",
       "\n",
       "     FT_OP_G  HT_OP_G  OP_S  OP_ST  OP_F  OP_C  OP_Y  OP_R  FTR   Referee  \\\n",
       "0        3.0      2.0  17.0    8.0   8.0   5.0   0.0   0.0    A  C Pawson   \n",
       "1        0.0      0.0   6.0    1.0  11.0   6.0   0.0   1.0    A  C Pawson   \n",
       "2        1.0      0.0   6.0    2.0  12.0   3.0   2.0   0.0    H  M Oliver   \n",
       "3        2.0      2.0  15.0    7.0  12.0   8.0   2.0   0.0    H  M Oliver   \n",
       "4        1.0      0.0   9.0    3.0  12.0   7.0   2.0   0.0    H   D Coote   \n",
       "..       ...      ...   ...    ...   ...   ...   ...   ...  ...       ...   \n",
       "573      NaN      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN       NaN   \n",
       "574      NaN      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN       NaN   \n",
       "575      NaN      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN       NaN   \n",
       "576      NaN      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN       NaN   \n",
       "577      NaN      NaN   NaN    NaN   NaN   NaN   NaN   NaN  NaN       NaN   \n",
       "\n",
       "     is_home  \n",
       "0          1  \n",
       "1          0  \n",
       "2          1  \n",
       "3          0  \n",
       "4          1  \n",
       "..       ...  \n",
       "573        0  \n",
       "574        1  \n",
       "575        0  \n",
       "576        1  \n",
       "577        0  \n",
       "\n",
       "[578 rows x 27 columns]"
      ]
     },
     "execution_count": 896,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create is_home feature.\n",
    "\n",
    "df_final['is_home'] = (df_final['team_playing'] == df_final['HomeTeam']).astype(int)\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 897,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename the columns. \n",
    "\n",
    "rename_columns = {\n",
    "    'FT_TP_G': 'team_playing_ft_goals',  # Full-Time Goals by Team Playing\n",
    "    'HT_TP_G': 'team_playing_ht_goals',  # Half-Time Goals by Team Playing\n",
    "    'TP_S': 'team_playing_total_shots',  # Total Shots by Team Playing\n",
    "    'TP_ST': 'team_playing_shots_on_target',  # Shots on Target by Team Playing\n",
    "    'TP_F': 'team_playing_fouls_committed',  # Fouls Committed by Team Playing\n",
    "    'TP_C': 'team_playing_corners_won',  # Corners Won by Team Playing\n",
    "    'TP_Y': 'team_playing_yellow_cards',  # Yellow Cards Received by Team Playing\n",
    "    'TP_R': 'team_playing_red_cards',  # Red Cards Received by Team Playing\n",
    "    'FT_OP_G': 'opponent_ft_goals',  # Full-Time Goals by Opponent (Goals Conceded by Team Playing)\n",
    "    'HT_OP_G': 'opponent_ht_goals',  # Half-Time Goals by Opponent\n",
    "    'OP_S': 'opponent_total_shots',  # Total Shots by Opponent\n",
    "    'OP_ST': 'opponent_shots_on_target',  # Shots on Target by Opponent\n",
    "    'OP_F': 'opponent_fouls_committed',  # Fouls Committed by Opponent (Fouls Won by Team Playing)\n",
    "    'OP_C': 'opponent_corners_won',  # Corners Won by Opponent\n",
    "    'OP_Y': 'opponent_yellow_cards',  # Yellow Cards Received by Opponent\n",
    "    'OP_R': 'opponent_red_cards',  # Red Cards Received by Opponent\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "df_final = df_final.rename(columns=rename_columns)#.drop(columns = ['opponent_yellow_cards', 'opponent_red_cards', 'team_playing_yellow_cards', 'team_playing_red_cards', 'opponent_ht_goals', 'team_playing_ht_goals'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 898,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_playing</th>\n",
       "      <th>opponent</th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>team_playing_ft_goals</th>\n",
       "      <th>team_playing_ht_goals</th>\n",
       "      <th>team_playing_total_shots</th>\n",
       "      <th>team_playing_shots_on_target</th>\n",
       "      <th>team_playing_fouls_committed</th>\n",
       "      <th>team_playing_corners_won</th>\n",
       "      <th>team_playing_yellow_cards</th>\n",
       "      <th>team_playing_red_cards</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Hour</th>\n",
       "      <th>opponent_ft_goals</th>\n",
       "      <th>opponent_ht_goals</th>\n",
       "      <th>opponent_total_shots</th>\n",
       "      <th>opponent_shots_on_target</th>\n",
       "      <th>opponent_fouls_committed</th>\n",
       "      <th>opponent_corners_won</th>\n",
       "      <th>opponent_yellow_cards</th>\n",
       "      <th>opponent_red_cards</th>\n",
       "      <th>FTR</th>\n",
       "      <th>Referee</th>\n",
       "      <th>is_home</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>C Pawson</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Man City</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>C Pawson</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>M Oliver</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>M Oliver</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Luton</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>Luton</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>D Coote</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Luton</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>Luton</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>15</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>H</td>\n",
       "      <td>D Coote</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sheffield United</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>Sheffield United</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2023</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>J Brooks</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       team_playing        opponent          HomeTeam        AwayTeam  \\\n",
       "0           Burnley        Man City           Burnley        Man City   \n",
       "1          Man City         Burnley           Burnley        Man City   \n",
       "2           Arsenal   Nott'm Forest           Arsenal   Nott'm Forest   \n",
       "3     Nott'm Forest         Arsenal           Arsenal   Nott'm Forest   \n",
       "4          Brighton           Luton          Brighton           Luton   \n",
       "5             Luton        Brighton          Brighton           Luton   \n",
       "6  Sheffield United  Crystal Palace  Sheffield United  Crystal Palace   \n",
       "\n",
       "   team_playing_ft_goals  team_playing_ht_goals  team_playing_total_shots  \\\n",
       "0                    0.0                    0.0                       6.0   \n",
       "1                    3.0                    2.0                      17.0   \n",
       "2                    2.0                    2.0                      15.0   \n",
       "3                    1.0                    0.0                       6.0   \n",
       "4                    4.0                    1.0                      27.0   \n",
       "5                    1.0                    0.0                       9.0   \n",
       "6                    0.0                    0.0                       8.0   \n",
       "\n",
       "   team_playing_shots_on_target  team_playing_fouls_committed  \\\n",
       "0                           1.0                          11.0   \n",
       "1                           8.0                           8.0   \n",
       "2                           7.0                          12.0   \n",
       "3                           2.0                          12.0   \n",
       "4                          12.0                          11.0   \n",
       "5                           3.0                          12.0   \n",
       "6                           1.0                          18.0   \n",
       "\n",
       "   team_playing_corners_won  team_playing_yellow_cards  \\\n",
       "0                       6.0                        0.0   \n",
       "1                       5.0                        0.0   \n",
       "2                       8.0                        2.0   \n",
       "3                       3.0                        2.0   \n",
       "4                       6.0                        2.0   \n",
       "5                       7.0                        2.0   \n",
       "6                       5.0                        3.0   \n",
       "\n",
       "   team_playing_red_cards  Day  Month  Year  Hour  opponent_ft_goals  \\\n",
       "0                     1.0   11      8  2023    20                3.0   \n",
       "1                     0.0   11      8  2023    20                0.0   \n",
       "2                     0.0   12      8  2023    12                1.0   \n",
       "3                     0.0   12      8  2023    12                2.0   \n",
       "4                     0.0   12      8  2023    15                1.0   \n",
       "5                     0.0   12      8  2023    15                4.0   \n",
       "6                     0.0   12      8  2023    15                1.0   \n",
       "\n",
       "   opponent_ht_goals  opponent_total_shots  opponent_shots_on_target  \\\n",
       "0                2.0                  17.0                       8.0   \n",
       "1                0.0                   6.0                       1.0   \n",
       "2                0.0                   6.0                       2.0   \n",
       "3                2.0                  15.0                       7.0   \n",
       "4                0.0                   9.0                       3.0   \n",
       "5                1.0                  27.0                      12.0   \n",
       "6                0.0                  24.0                       8.0   \n",
       "\n",
       "   opponent_fouls_committed  opponent_corners_won  opponent_yellow_cards  \\\n",
       "0                       8.0                   5.0                    0.0   \n",
       "1                      11.0                   6.0                    0.0   \n",
       "2                      12.0                   3.0                    2.0   \n",
       "3                      12.0                   8.0                    2.0   \n",
       "4                      12.0                   7.0                    2.0   \n",
       "5                      11.0                   6.0                    2.0   \n",
       "6                      11.0                   5.0                    0.0   \n",
       "\n",
       "   opponent_red_cards FTR   Referee  is_home  target  \n",
       "0                 0.0   A  C Pawson        1       0  \n",
       "1                 1.0   A  C Pawson        0       1  \n",
       "2                 0.0   H  M Oliver        1       1  \n",
       "3                 0.0   H  M Oliver        0       0  \n",
       "4                 0.0   H   D Coote        1       1  \n",
       "5                 0.0   H   D Coote        0       0  \n",
       "6                 0.0   A  J Brooks        1       0  "
      ]
     },
     "execution_count": 898,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we need to create a binary target column that tells us if the team_playing won the match.\n",
    "\n",
    "df_final['target'] = ((((df_final['is_home']==1) & (df_final['FTR']=='H')) | ((df_final['is_home']==0) & (df_final['FTR']=='A')))).astype(int)\n",
    "df_final.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 899,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding in the matches for future predictions. the matches that havnt happened yet.\n",
    "# just a few matches. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "\n",
    "Calculating Rolling Statistics. We want to calculate rolling stats. At the moment, these features are telling us what happened during each particular match. When making predictions we wont know these stats. We want to be able to use what happened in the past to predict future games."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 900,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grouped = df_final.groupby('team_playing')\n",
    "\n",
    "#df_final.drop(columns = ['AwayTeam', 'FTR', 'HomeTeam', 'Referee', 'opponent', 'team_playing', 'Day', 'Month', 'Year']).rolling(window = 3, closed = 'left').mean()\n",
    "\n",
    "# We grouped the data by team_playing. Now we can calculate rolling stats for each team respectivly. \n",
    "# We will exclude non numeric columns and time columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 901,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arsenal\n",
      "Aston Villa\n",
      "Bournemouth\n",
      "Brentford\n",
      "Brighton\n",
      "Burnley\n",
      "Chelsea\n",
      "Crystal Palace\n",
      "Everton\n",
      "Fulham\n",
      "Liverpool\n",
      "Luton\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Man City\n",
      "Man United\n",
      "Newcastle\n",
      "Nott'm Forest\n",
      "Sheffield United\n",
      "Tottenham\n",
      "West Ham\n",
      "Wolves\n"
     ]
    }
   ],
   "source": [
    "cols_to_drop = ['AwayTeam', 'FTR', 'HomeTeam', 'Referee', 'opponent', 'team_playing', 'Day', 'Month', 'Year', 'Hour', 'is_home']\n",
    "\n",
    "window = 5\n",
    "data = []\n",
    "\n",
    "for name, group in df_grouped:\n",
    "    print(name)\n",
    "    group.sort_values(by = ['Year', 'Month', 'Day', 'Hour', 'Referee']).reset_index(drop = True, inplace=True)\n",
    "    mean_ = group.drop(columns =cols_to_drop).rolling(window = window, closed = 'left').mean().add_suffix('_rolling_mean')\n",
    "    sum_ = group.drop(columns =cols_to_drop).rolling(window = window, closed = 'left').sum().add_suffix('_rolling_sum')\n",
    "    \n",
    "    std_ = group.drop(columns =cols_to_drop).rolling(window = window, closed = 'left').std().add_suffix('_rolling_std')\n",
    "    min_ = group.drop(columns =cols_to_drop).rolling(window = window, closed = 'left').min().add_suffix('_rolling_min')\n",
    "    max_ = group.drop(columns =cols_to_drop).rolling(window = window, closed = 'left').max().add_suffix('_rolling_max')\n",
    "    median_ = group.drop(columns=cols_to_drop).rolling(window=window, closed='left').median().add_suffix('_rolling_median')\n",
    "    \n",
    "    \n",
    "\n",
    "    d = pd.concat([group[['team_playing', 'opponent', 'is_home', 'target', 'Year', 'Month', 'Day', 'Hour', 'Referee']], \n",
    "                   mean_,                \n",
    "                    #sum_, \n",
    "                   # std_, \n",
    "                    #min_, \n",
    "                    #max_, \n",
    "                   #median_, \n",
    "                   ], axis = 1).bfill()\n",
    "    data.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 902,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a top 6 binary feature to indicate if the team_playing or opponent is considered top 6 team.\n",
    "final_data = pd.concat(data).sort_index()\n",
    "#top_6 = ['Arsenal', 'Liverpool', 'Man City', 'Man United', 'Chelsea', 'Tottenham']\n",
    "#final_data['TP_top6'] = final_data['team_playing'].apply(lambda x:1 if x in top_6 else 0)\n",
    "#final_data['OP_top6'] = final_data['opponent'].apply(lambda x:1 if x in top_6 else 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 903,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data['Date'] = pd.to_datetime(final_data[['Year', 'Month', 'Day', 'Hour']])\n",
    "final_data.sort_values(by = ['Date', 'Referee'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 904,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = final_data.sort_values(by = ['Year', 'Month', 'Day', 'Hour', 'Referee']).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 905,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(578, 27)"
      ]
     },
     "execution_count": 905,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 906,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(516, 27)\n",
      "(50, 27)\n",
      "(12, 27)\n"
     ]
    }
   ],
   "source": [
    "train_df = final_data[final_data['Date']<=pd.Timestamp('2024-03-01')]\n",
    "test_df = final_data[(final_data['Date'] > pd.Timestamp('2024-03-01')) & (final_data['Date'] <= pd.Timestamp('2024-03-29'))]\n",
    "future_prediction = final_data[final_data['Date']>=pd.Timestamp('2024-03-20')]\n",
    "\n",
    "print(train_df.shape)\n",
    "print(test_df.shape)\n",
    "print(future_prediction.shape)\n",
    "# I am going t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 907,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.to_csv('data/train_df.csv', index = False)\n",
    "test_df.to_csv('data/test_df.csv', index = False)\n",
    "future_prediction.to_csv('data/future_prediction.csv', index = False)\n",
    "final_data.to_csv('data/final_data.csv', index = False) # get the enocding here too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 908,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6209/1164267158.py:5: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(train_df.drop(columns=['Day', 'Month', 'Year', 'Hour', 'Date']).corr().round(2), annot=True, cmap = 'coolwarm')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Correlation Matrix')"
      ]
     },
     "execution_count": 908,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "sns.heatmap(train_df.drop(columns=['Day', 'Month', 'Year', 'Hour', 'Date']).corr().round(2), annot=True, cmap = 'coolwarm')\n",
    "plt.title('Feature Correlation Matrix')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 909,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['team_playing', 'opponent', 'is_home', 'target', 'Referee',\n",
       "       'team_playing_ft_goals_rolling_mean',\n",
       "       'team_playing_ht_goals_rolling_mean',\n",
       "       'team_playing_total_shots_rolling_mean',\n",
       "       'team_playing_shots_on_target_rolling_mean',\n",
       "       'team_playing_fouls_committed_rolling_mean',\n",
       "       'team_playing_corners_won_rolling_mean',\n",
       "       'team_playing_yellow_cards_rolling_mean',\n",
       "       'team_playing_red_cards_rolling_mean', 'opponent_ft_goals_rolling_mean',\n",
       "       'opponent_ht_goals_rolling_mean', 'opponent_total_shots_rolling_mean',\n",
       "       'opponent_shots_on_target_rolling_mean',\n",
       "       'opponent_fouls_committed_rolling_mean',\n",
       "       'opponent_corners_won_rolling_mean',\n",
       "       'opponent_yellow_cards_rolling_mean', 'opponent_red_cards_rolling_mean',\n",
       "       'target_rolling_mean'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 909,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.drop(columns=['Day', 'Month', 'Year', 'Hour', 'Date']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 910,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['is_home',\n",
       " 'team_playing_ht_goals_rolling_mean',\n",
       " 'team_playing_total_shots_rolling_mean',\n",
       " 'team_playing_fouls_committed_rolling_mean',\n",
       " 'team_playing_corners_won_rolling_mean',\n",
       " 'team_playing_yellow_cards_rolling_mean',\n",
       " 'team_playing_red_cards_rolling_mean',\n",
       " 'opponent_ft_goals_rolling_mean',\n",
       " 'opponent_ht_goals_rolling_mean',\n",
       " 'opponent_fouls_committed_rolling_mean',\n",
       " 'opponent_corners_won_rolling_mean',\n",
       " 'opponent_yellow_cards_rolling_mean',\n",
       " 'opponent_red_cards_rolling_mean',\n",
       " 'target_rolling_mean']"
      ]
     },
     "execution_count": 910,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from statsmodels.tools.tools import add_constant\n",
    "\n",
    "df_with_const = add_constant(train_df.drop(columns=['Day', 'Month', 'Year', 'Hour', 'Date', 'target', 'team_playing', 'opponent', 'Referee']))\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "vif[\"Variable\"] = df_with_const.columns\n",
    "vif[\"VIF\"] = [variance_inflation_factor(df_with_const.values, i) for i in range(df_with_const.shape[1])]\n",
    "\n",
    "vif[vif['VIF']<5]['Variable'].values.tolist()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choosing features. \n",
    "\n",
    "1. Features with high correlation to the target.\n",
    "2. Minimal multicolinerality with the target.\n",
    "\n",
    "\n",
    "Chosen:\n",
    "- is_home : low multicolinearaoity and high correlation with target -> 0.12.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 911,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prep X_train, y_train, X_test and y_test\n",
    "# For simplicity, only going to use 4 features,\n",
    "\n",
    "features = ['is_home', # If the team playing is home or not.\n",
    "            'team_playing_ft_goals_rolling_mean', # full time goals rolling averages\n",
    "            'team_playing_total_shots_rolling_mean', # rolling averages of total shots taken.\n",
    "            'team_playing_corners_won_rolling_mean', # rolling averages of total corners won.\n",
    "            'target_rolling_mean', # rolling averages of prev results.\n",
    "            'opponent_fouls_committed_rolling_mean' # rolling averages of number of fouls won.\n",
    "            ]\n",
    "\n",
    "X_train = train_df[features]\n",
    "y_train = train_df['target']\n",
    "\n",
    "X_test = test_df[features]\n",
    "y_test = test_df['target']\n",
    "\n",
    "X_future = future_prediction[features]\n",
    "\n",
    "\n",
    "\n",
    "X_train.to_csv('data/X_train.csv', index = False)\n",
    "y_train.to_csv('data/y_train.csv', index = False)\n",
    "\n",
    "X_test.to_csv('data/X_test.csv', index = False)\n",
    "y_test.to_csv('data/y_test.csv', index = False)\n",
    "\n",
    "X_future.to_csv('data/X_future.csv', index = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 918,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_playing</th>\n",
       "      <th>opponent</th>\n",
       "      <th>is_home</th>\n",
       "      <th>team_playing_ft_goals_rolling_mean</th>\n",
       "      <th>team_playing_total_shots_rolling_mean</th>\n",
       "      <th>team_playing_corners_won_rolling_mean</th>\n",
       "      <th>target_rolling_mean</th>\n",
       "      <th>opponent_fouls_committed_rolling_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Everton</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>Everton</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>11.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brentford</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>10.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Brentford</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>11.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>11.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fulham</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>15.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Newcastle</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Wolves</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Luton</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>16.6</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Luton</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Man United</td>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Man United</td>\n",
       "      <td>Man City</td>\n",
       "      <td>0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>18.4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sheffield United</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Sheffield United</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>6.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Man United</td>\n",
       "      <td>Everton</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>14.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Everton</td>\n",
       "      <td>Man United</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Sheffield United</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>13.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Sheffield United</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>Luton</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>9.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Luton</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>15.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Wolves</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>13.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Fulham</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>13.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Brentford</td>\n",
       "      <td>1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>19.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Brentford</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>11.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>14.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>13.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>10.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>16.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>11.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Man City</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>20.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Man City</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>23.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>11.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Newcastle</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Luton</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>18.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Luton</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Brentford</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Brentford</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>11.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Luton</td>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>12.6</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Nott'm Forest</td>\n",
       "      <td>Luton</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>11.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Fulham</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>12.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>13.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        team_playing          opponent  is_home  \\\n",
       "0            Everton          West Ham        1   \n",
       "1           West Ham           Everton        0   \n",
       "2          Tottenham    Crystal Palace        1   \n",
       "3     Crystal Palace         Tottenham        0   \n",
       "4          Brentford           Chelsea        1   \n",
       "5            Chelsea         Brentford        0   \n",
       "6      Nott'm Forest         Liverpool        1   \n",
       "7          Liverpool     Nott'm Forest        0   \n",
       "8             Fulham          Brighton        1   \n",
       "9           Brighton            Fulham        0   \n",
       "10         Newcastle            Wolves        1   \n",
       "11            Wolves         Newcastle        0   \n",
       "12             Luton       Aston Villa        1   \n",
       "13       Aston Villa             Luton        0   \n",
       "14           Burnley       Bournemouth        1   \n",
       "15       Bournemouth           Burnley        0   \n",
       "16          Man City        Man United        1   \n",
       "17        Man United          Man City        0   \n",
       "18  Sheffield United           Arsenal        1   \n",
       "19           Arsenal  Sheffield United        0   \n",
       "20        Man United           Everton        1   \n",
       "21           Everton        Man United        0   \n",
       "22       Bournemouth  Sheffield United        1   \n",
       "23  Sheffield United       Bournemouth        0   \n",
       "24    Crystal Palace             Luton        1   \n",
       "25             Luton    Crystal Palace        0   \n",
       "26            Wolves            Fulham        1   \n",
       "27            Fulham            Wolves        0   \n",
       "28           Arsenal         Brentford        1   \n",
       "29         Brentford           Arsenal        0   \n",
       "30       Aston Villa         Tottenham        1   \n",
       "31         Tottenham       Aston Villa        0   \n",
       "32          West Ham           Burnley        1   \n",
       "33           Burnley          West Ham        0   \n",
       "34          Brighton     Nott'm Forest        1   \n",
       "35     Nott'm Forest          Brighton        0   \n",
       "36         Liverpool          Man City        1   \n",
       "37          Man City         Liverpool        0   \n",
       "38           Chelsea         Newcastle        1   \n",
       "39         Newcastle           Chelsea        0   \n",
       "40       Bournemouth             Luton        1   \n",
       "41             Luton       Bournemouth        0   \n",
       "42           Burnley         Brentford        1   \n",
       "43         Brentford           Burnley        0   \n",
       "44             Luton     Nott'm Forest        1   \n",
       "45     Nott'm Forest             Luton        0   \n",
       "46            Fulham         Tottenham        1   \n",
       "47         Tottenham            Fulham        0   \n",
       "48          West Ham       Aston Villa        1   \n",
       "49       Aston Villa          West Ham        0   \n",
       "\n",
       "    team_playing_ft_goals_rolling_mean  team_playing_total_shots_rolling_mean  \\\n",
       "0                                  0.8                                   13.0   \n",
       "1                                  1.0                                   12.6   \n",
       "2                                  2.0                                   15.0   \n",
       "3                                  1.8                                   11.4   \n",
       "4                                  1.2                                   10.6   \n",
       "5                                  1.6                                   11.8   \n",
       "6                                  1.6                                   11.8   \n",
       "7                                  3.2                                   21.4   \n",
       "8                                  1.6                                   15.8   \n",
       "9                                  2.2                                   15.0   \n",
       "10                                 2.6                                   12.0   \n",
       "11                                 2.0                                   14.4   \n",
       "12                                 2.2                                   16.6   \n",
       "13                                 2.6                                   15.4   \n",
       "14                                 0.8                                    7.8   \n",
       "15                                 1.0                                   13.6   \n",
       "16                                 1.6                                   23.0   \n",
       "17                                 2.4                                   18.4   \n",
       "18                                 1.0                                    8.8   \n",
       "19                                 4.0                                   18.6   \n",
       "20                                 1.8                                   14.8   \n",
       "21                                 1.0                                   13.2   \n",
       "22                                 1.2                                   13.8   \n",
       "23                                 0.6                                    7.8   \n",
       "24                                 1.4                                    9.8   \n",
       "25                                 1.8                                   15.6   \n",
       "26                                 1.4                                   13.6   \n",
       "27                                 2.2                                   13.2   \n",
       "28                                 4.8                                   19.2   \n",
       "29                                 1.4                                   11.6   \n",
       "30                                 3.0                                   15.4   \n",
       "31                                 2.2                                   14.6   \n",
       "32                                 1.4                                   13.2   \n",
       "33                                 0.6                                   10.2   \n",
       "34                                 2.2                                   16.2   \n",
       "35                                 1.4                                   11.6   \n",
       "36                                 2.6                                   20.2   \n",
       "37                                 1.6                                   23.4   \n",
       "38                                 1.8                                   11.8   \n",
       "39                                 2.6                                   12.0   \n",
       "40                                 1.4                                   18.4   \n",
       "41                                 1.2                                   15.0   \n",
       "42                                 0.6                                   10.0   \n",
       "43                                 1.2                                   11.6   \n",
       "44                                 1.6                                   12.6   \n",
       "45                                 1.2                                   11.8   \n",
       "46                                 2.0                                   14.8   \n",
       "47                                 2.4                                   12.6   \n",
       "48                                 1.8                                   13.2   \n",
       "49                                 2.0                                   14.6   \n",
       "\n",
       "    team_playing_corners_won_rolling_mean  target_rolling_mean  \\\n",
       "0                                     4.2                  0.0   \n",
       "1                                     3.0                  0.2   \n",
       "2                                     8.4                  0.4   \n",
       "3                                     6.4                  0.4   \n",
       "4                                     4.2                  0.2   \n",
       "5                                     5.0                  0.4   \n",
       "6                                     3.8                  0.2   \n",
       "7                                     8.0                  0.8   \n",
       "8                                     8.4                  0.4   \n",
       "9                                     6.8                  0.4   \n",
       "10                                    4.6                  0.4   \n",
       "11                                    4.2                  0.6   \n",
       "12                                    6.8                  0.2   \n",
       "13                                    6.6                  0.6   \n",
       "14                                    3.0                  0.0   \n",
       "15                                    7.0                  0.0   \n",
       "16                                   10.4                  0.8   \n",
       "17                                    6.8                  0.8   \n",
       "18                                    3.2                  0.2   \n",
       "19                                    6.8                  1.0   \n",
       "20                                    6.2                  0.6   \n",
       "21                                    3.8                  0.0   \n",
       "22                                    6.4                  0.2   \n",
       "23                                    3.0                  0.2   \n",
       "24                                    4.4                  0.2   \n",
       "25                                    7.0                  0.0   \n",
       "26                                    4.4                  0.6   \n",
       "27                                    5.8                  0.6   \n",
       "28                                    6.0                  1.0   \n",
       "29                                    2.6                  0.2   \n",
       "30                                    6.2                  0.8   \n",
       "31                                    8.0                  0.6   \n",
       "32                                    3.8                  0.4   \n",
       "33                                    3.8                  0.0   \n",
       "34                                    7.2                  0.4   \n",
       "35                                    4.0                  0.2   \n",
       "36                                    8.8                  0.8   \n",
       "37                                   10.8                  0.8   \n",
       "38                                    5.0                  0.2   \n",
       "39                                    3.8                  0.4   \n",
       "40                                    6.2                  0.2   \n",
       "41                                    7.8                  0.0   \n",
       "42                                    4.2                  0.0   \n",
       "43                                    2.6                  0.0   \n",
       "44                                    5.8                  0.0   \n",
       "45                                    3.4                  0.2   \n",
       "46                                    4.0                  0.6   \n",
       "47                                    7.8                  0.6   \n",
       "48                                    3.0                  0.4   \n",
       "49                                    6.6                  0.6   \n",
       "\n",
       "    opponent_fouls_committed_rolling_mean  \n",
       "0                                     9.4  \n",
       "1                                    10.8  \n",
       "2                                    11.4  \n",
       "3                                    10.8  \n",
       "4                                    10.0  \n",
       "5                                    11.2  \n",
       "6                                    10.8  \n",
       "7                                    11.6  \n",
       "8                                     9.4  \n",
       "9                                    11.6  \n",
       "10                                   15.2  \n",
       "11                                    9.2  \n",
       "12                                   15.0  \n",
       "13                                   11.2  \n",
       "14                                    7.4  \n",
       "15                                   10.2  \n",
       "16                                    9.0  \n",
       "17                                    8.4  \n",
       "18                                    7.0  \n",
       "19                                   10.2  \n",
       "20                                    7.4  \n",
       "21                                    9.4  \n",
       "22                                    9.2  \n",
       "23                                    6.2  \n",
       "24                                    8.6  \n",
       "25                                   16.0  \n",
       "26                                    8.8  \n",
       "27                                    8.8  \n",
       "28                                   11.2  \n",
       "29                                   11.0  \n",
       "30                                   10.4  \n",
       "31                                   13.0  \n",
       "32                                   10.4  \n",
       "33                                    9.4  \n",
       "34                                   12.2  \n",
       "35                                   11.8  \n",
       "36                                   10.6  \n",
       "37                                   10.6  \n",
       "38                                   11.4  \n",
       "39                                   14.6  \n",
       "40                                    9.0  \n",
       "41                                   17.4  \n",
       "42                                   10.2  \n",
       "43                                   10.6  \n",
       "44                                   18.2  \n",
       "45                                   10.8  \n",
       "46                                    8.0  \n",
       "47                                   12.2  \n",
       "48                                   12.0  \n",
       "49                                   11.4  "
      ]
     },
     "execution_count": 918,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.drop(columns = ['Year', 'Month', 'Day', 'Hour', 'Referee']).reset_index(drop = True)[['team_playing', 'opponent']+features]"
   ]
  }
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