IPL_RCB_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pandas and numpy package\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading match_analysis\n",
    "df_match=pd.read_csv('C:/Users/Mayank Gupta/Desktop/IPL_analysis/Datasets/match_data.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#All the matches RCB played\n",
    "df_rcb=df_match[df_match.team1_id=='RCB'] \n",
    "df_rcb_2=df_match[df_match.team2_id=='RCB']\n",
    "df_rcb_matches=pd.concat([df_rcb,df_rcb_2]) #All the matches RCB played "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mayank Gupta\\AppData\\Local\\Continuum\\Anaconds\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#to find the season wise wins of RCB\n",
    "import sys\n",
    "wins=[]\n",
    "season=[]\n",
    "for i in range(2008,2018):\n",
    "    seasonwisewins=df_rcb_matches[df_rcb_matches.season==i]\n",
    "    season.append(i)\n",
    "    wins.append(len(seasonwisewins[df_rcb_matches.winner=='RCB']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary_rcb_wins = dict(zip(season, wins)) #dictionary containing season and number wins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2008: 4,\n",
       " 2009: 9,\n",
       " 2010: 8,\n",
       " 2011: 10,\n",
       " 2012: 8,\n",
       " 2013: 9,\n",
       " 2014: 5,\n",
       " 2015: 8,\n",
       " 2016: 9,\n",
       " 2017: 3}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary_rcb_wins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "season=list(map(str,season)) #convert list of int to list of strings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Clearly we can see that 2009,2011 and 2016 were the best seasons for RCB let us see why this was the case. We will start season by season and later see why it has failed badly from previous 2 seasons. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
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       "<Figure size 432x288 with 1 Axes>"
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   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "names = list(dictionary_rcb_wins.keys())\n",
    "values = list(dictionary_rcb_wins.values())\n",
    "\n",
    "#tick_label does the some work as plt.xticks()\n",
    "plt.bar(range(len(dictionary_rcb_wins)),values,tick_label=names,color='red')\n",
    "plt.show()"
   ]
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    "# Standard plotly imports\n",
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    "df['wins'].iplot()"
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   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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          "text": "Number Of Wins"
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       "<div>\n",
       "        \n",
       "        \n",
       "            <div id=\"f3372a3e-9b37-49cd-837a-318955ab8bfc\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    window.PLOTLYENV.BASE_URL='https://plot.ly';\n",
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       "                if (document.getElementById(\"f3372a3e-9b37-49cd-837a-318955ab8bfc\")) {\n",
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       "                        [{\"marker\": {\"color\": \"#FF0000\"}, \"name\": \"Wins\", \"type\": \"bar\", \"uid\": \"e98f08a2-ee8a-443b-a282-19e2764f1e0c\", \"x\": [2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017], \"y\": [4, 9, 8, 10, 8, 9, 5, 8, 9, 3]}],\n",
       "                        {\"showlegend\": false, \"title\": {\"text\": \"Season Wise Wins\"}, \"xaxis\": {\"tickangle\": -45, \"tickmode\": \"linear\", \"title\": {\"font\": {\"color\": \"rgb(107, 107, 107)\", \"size\": 16}, \"text\": \"Year\"}}, \"yaxis\": {\"tickfont\": {\"color\": \"rgb(107, 107, 107)\", \"size\": 14}, \"title\": {\"font\": {\"color\": \"rgb(107, 107, 107)\", \"size\": 16}, \"text\": \"Number Of Wins\"}}},\n",
       "                        {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
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       "            Plotly.purge(gd);\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [go.Bar(\n",
    "            x=names,\n",
    "            y=values,\n",
    "            marker=dict(color='#FF0000'), \n",
    "            name=\"Wins\",\n",
    "            \n",
    "    )]\n",
    "layout = go.Layout(\n",
    "    showlegend=False,\n",
    "    xaxis=dict(tickangle=-45,tickmode='linear',title='Year', titlefont=dict(\n",
    "            size=16,\n",
    "            color='rgb(107, 107, 107)'\n",
    "        )),title='Season Wise Wins',   \n",
    "         yaxis=dict(\n",
    "        title='Number Of Wins',\n",
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    "            size=16,\n",
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    "            size=14,\n",
    "            color='rgb(107, 107, 107)'\n",
    "        )\n",
    "    ),\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "iplot(fig, filename='style-bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Let us see RCB performance in and away from home Did it had a huge impact on their success Let us Find Out..</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "labels": [
          "Home Wins",
          "Away Wins"
         ],
         "type": "pie",
         "uid": "02e48356-26a5-441c-b59d-2bf532ec7e82",
         "values": [
          30,
          43
         ]
        }
       ],
       "layout": {}
      },
      "text/html": [
       "<div>\n",
       "        \n",
       "        \n",
       "            <div id=\"a6579048-c0ce-4e59-8de6-e61ffd32a488\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    window.PLOTLYENV.BASE_URL='https://plot.ly';\n",
       "                    \n",
       "                if (document.getElementById(\"a6579048-c0ce-4e59-8de6-e61ffd32a488\")) {\n",
       "                    Plotly.newPlot(\n",
       "                        'a6579048-c0ce-4e59-8de6-e61ffd32a488',\n",
       "                        [{\"labels\": [\"Home Wins\", \"Away Wins\"], \"type\": \"pie\", \"uid\": \"02e48356-26a5-441c-b59d-2bf532ec7e82\", \"values\": [30, 43]}],\n",
       "                        {},\n",
       "                        {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('a6579048-c0ce-4e59-8de6-e61ffd32a488');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wins=df_rcb_matches[df_rcb_matches.winner=='RCB']  \n",
    "wins_At_home=wins[wins.city=='Bangalore']\n",
    "count_home=len(wins_At_home)\n",
    "wins_away=wins[wins.city!='Bangalore']\n",
    "count_away=len(wins_away)\n",
    "labels = ['Home Wins','Away Wins']\n",
    "values = [count_home,count_away]\n",
    "trace = go.Pie(labels=labels, values=values)\n",
    "iplot([trace], filename='basic_pie_chart',show_link=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Of all the matches RCB won there 59% wins were from away games <br>Now Let us see toss dependency on RCB success</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toss_wins_match=wins[wins.toss_winner=='RCB']\n",
    "len(toss_wins_match)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_wins_match=wins[wins.toss_winner!='RCB']\n",
    "len(loss_wins_match)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Toss doesnot matter much for RCB. They have won 35 matches when the won the toss and 38 while losing the toss</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches_won_batting_first=wins[wins.win_by_runs>0]\n",
    "batting_first=len(matches_won_batting_first)\n",
    "matches_won_chasing=wins[wins.win_by_wickets>0]\n",
    "batting_second=len(matches_won_chasing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n",
      "42\n"
     ]
    }
   ],
   "source": [
    "print(batting_first)\n",
    "print(batting_second)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>So RCB has won 30 matches batting first and 42 matches while chasing. Let us see what happened in last 4 seasons</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "wins_season_2014=wins[wins.season==2014]\n",
    "wins_season_2015=wins[wins.season==2015]\n",
    "wins_season_2016=wins[wins.season==2016]\n",
    "wins_season_2017=wins[wins.season==2017]\n",
    "wins_last_4_seasons=pd.concat([wins_season_2014,wins_season_2015,wins_season_2016,wins_season_2017])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches_won_batting_first=wins_last_4_seasons[wins_last_4_seasons.win_by_runs>0]\n",
    "batting_first=len(matches_won_batting_first)\n",
    "matches_won_chasing=wins_last_4_seasons[wins_last_4_seasons.win_by_wickets>0]\n",
    "batting_second=len(matches_won_chasing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "13\n"
     ]
    }
   ],
   "source": [
    "print(batting_first)\n",
    "print(batting_second)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>In last four seasons RCB has won 13 matches while chasing and 12 while batting first. So batting first or second didnot matter much in RCB success</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "man_of_the_match=wins_last_4_seasons['player_of_match'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "man_of_the_match.plot.bar(figsize=(10,3),xticks=man_of_the_match,yticks=man_of_the_match)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "man_of_the_match_overall=wins['player_of_match'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "man_of_the_match_overall.plot.bar(figsize=(12,8),xticks=man_of_the_match_overall,yticks=man_of_the_match_overall,color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>The Previous two graphs shows RCB dependency on their Three batsman Chris Gayle, AB Devilleres and Virat Kohli. Other than them there were not too many match winners specially in last 4 seasons and after dropping Gayle they are left with two. You cannot win games with just two players.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RCB', 'MI', 'DC', 'KKR', 'CSK', 'DD', 'KXI', 'RR', 'KTK', 'PW',\n",
       "       'SRH', 'RPS', 'GL'], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wins['team1_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "csk_loss=len(wins[(wins.team1_id =='CSK') | (wins.team2_id=='CSK')]) \n",
    "mi_loss=len(wins[(wins.team1_id =='MI') | (wins.team2_id=='MI')]) \n",
    "kxi_loss=len(wins[(wins.team1_id =='KXI') | (wins.team2_id=='KXI')])\n",
    "dd_loss=len(wins[(wins.team1_id =='DD') | (wins.team2_id=='DD')])\n",
    "rr_loss=len(wins[(wins.team1_id =='RR') | (wins.team2_id=='RR')])\n",
    "dc_loss=len(wins[(wins.team1_id =='DC') | (wins.team2_id=='DC')])\n",
    "kkr_loss=len(wins[(wins.team1_id =='KKR') | (wins.team2_id=='KKR')])\n",
    "srh_loss=len(wins[(wins.team1_id =='SRH') | (wins.team2_id=='SRH')])\n",
    "teams_loss=[csk_loss,mi_loss,kxi_loss,dd_loss,rr_loss,dc_loss,kkr_loss,srh_loss]\n",
    "labelss=['CSK','MI','KXI','DD','RR','DC','KKR','SRH']"
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   "execution_count": 62,
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    "csk_matches=len(df_rcb_matches[(df_rcb_matches.team1_id =='CSK') | (df_rcb_matches.team2_id=='CSK')]) \n",
    "mi_matches=len(df_rcb_matches[(df_rcb_matches.team1_id =='MI') | (df_rcb_matches.team2_id=='MI')]) \n",
    "kxi_matches=len(df_rcb_matches[(df_rcb_matches.team1_id =='KXI') | (df_rcb_matches.team2_id=='KXI')])\n",
    "dd_matches=len(df_rcb_matches[(df_rcb_matches.team1_id =='DD') | (df_rcb_matches.team2_id=='DD')])\n",
    "rr_matches=len(df_rcb_matches[(df_rcb_matches.team1_id =='RR') | (df_rcb_matches.team2_id=='RR')])\n",
    "dc_matches=len(df_rcb_matches[(df_rcb_matches.team1_id =='DC') | (df_rcb_matches.team2_id=='DC')])\n",
    "kkr_matches=len(df_rcb_matches[(df_rcb_matches.team1_id =='KKR') | (df_rcb_matches.team2_id=='KKR')])\n",
    "srh_matches=len(df_rcb_matches[(df_rcb_matches.team1_id =='SRH') | (df_rcb_matches.team2_id=='SRH')])\n",
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   "execution_count": 63,
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    "res_list = [teams_loss[i]/teams_matches[i]*100 for i in range(len(teams_matches))]"
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