RCB_analysis (7).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('E:/Hacka/train (1)/train/match_data.csv') # reading data\n",
    "#dataset can be found in dataset folder in repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rcb=df[df.team1_id=='RCB'] \n",
    "df_rcb_2=df[df.team2_id=='RCB']\n",
    "df=pd.concat([df_rcb,df_rcb_2]) #All the matches RCB played "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mayank\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: UserWarning:\n",
      "\n",
      "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[df.season==i]\n",
    "    season.append(i)\n",
    "    wins.append(len(seasonwisewins[df.winner=='RCB']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary = dict(zip(season, wins))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary #this dictionary gives us number of matches won by RCB in each season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dictionary' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
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      "\u001b[1;31mNameError\u001b[0m: name 'dictionary' is not defined"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "names = list(dictionary.keys())\n",
    "values = list(dictionary.values())\n",
    "\n",
    "#tick_label does the some work as plt.xticks()\n",
    "plt.bar(range(len(dictionary)),values,tick_label=names)\n",
    "plt.show()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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     "ename": "NameError",
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     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-3275e7a7405a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m data = [go.Bar(\n\u001b[0m\u001b[0;32m      2\u001b[0m             \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m             \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m             \u001b[0mmarker\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'#FF0000'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m             \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Wins\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'go' is not defined"
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   "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",
    "    width=700,\n",
    "    height=500,\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=14,\n",
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    "        )\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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "wins=df[df.winner=='RCB']  \n",
    "wins_At_home=wins[wins.city=='Bangalore']\n",
    "count_home=len(wins_At_home)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "wins_away=wins[wins.city!='Bangalore']\n",
    "count_away=len(wins_away)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 13,
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          "Home Wins",
          "Away Wins"
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   "source": [
    "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)"
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  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toss_wins_match=wins[wins.toss_winner=='RCB']\n",
    "len(toss_wins_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "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": 17,
   "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": 18,
   "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": 19,
   "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": 20,
   "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "man_of_the_match=wins_last_4_seasons['player_of_match'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "man_of_the_match_overall=wins['player_of_match'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chris Gayle         17\n",
       "AB De Villiers      13\n",
       "Virat Kohli         10\n",
       "Jacques Kallis       5\n",
       "Vinay Kumar          3\n",
       "Ross Taylor          2\n",
       "Kevin Pietersen      2\n",
       "Yuvraj Singh         2\n",
       "Manish Pandey        2\n",
       "Mark Boucher         2\n",
       "Anil Kumble          2\n",
       "Kedar Jadhav         1\n",
       "Sreenath Aravind     1\n",
       "Yuzvendra Chahal     1\n",
       "Jaydev Unadkat       1\n",
       "Shane Watson         1\n",
       "Harshal Patel        1\n",
       "Praveen Kumar        1\n",
       "Varun Aaron          1\n",
       "Parthiv Patel        1\n",
       "Mitchell Starc       1\n",
       "Robin Uthappa        1\n",
       "Rahul Dravid         1\n",
       "Mandeep Singh        1\n",
       "Name: player_of_match, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "man_of_the_match_overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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)\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": "markdown",
   "metadata": {},
   "source": [
    "<b>Let us look at RCB performance against different teams</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RCB', 'MI', 'DC', 'KKR', 'CSK', 'DD', 'KXI', 'RR', 'KTK', 'PW',\n",
       "       'SRH', 'RPS', 'GL'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wins['team1_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
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        "height": 500,
        "showlegend": false,
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         "text": "RCB vs Different teams"
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        "width": 700,
        "xaxis": {
         "tickangle": -45,
         "tickmode": "linear",
         "title": {
          "font": {
           "size": 16
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          "text": "Teams"
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        },
        "yaxis": {
         "tickfont": {
          "size": 14
         },
         "title": {
          "font": {
           "size": 16
          },
          "text": "Number Of Wins"
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       "<div>\n",
       "        \n",
       "        \n",
       "            <div id=\"2bb141b0-0660-4ea1-827f-8da6ddcad13f\" class=\"plotly-graph-div\" style=\"height:500px; width:700px;\"></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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       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "data = [go.Bar(\n",
    "            x=labelss,\n",
    "            y=teams_loss,\n",
    "            marker=dict(color=['#F9F606','#0983FC','#FB5304','#4404FB','#FB04D9','#0939FA','#000000','#FA9B09']), \n",
    "            name=\"Wins\",            \n",
    "    )]\n",
    "layout = go.Layout(\n",
    "    width=700,\n",
    "    height=500,\n",
    "    showlegend=False,\n",
    "    xaxis=dict(tickangle=-45,tickmode='linear',title='Teams', titlefont=dict(\n",
    "            size=16,\n",
    "        \n",
    "        )),title='RCB vs Different teams',   \n",
    "         yaxis=dict(\n",
    "        title='Number Of Wins',\n",
    "        titlefont=dict(\n",
    "            size=16\n",
    "        ),\n",
    "        tickfont=dict(\n",
    "            size=14\n",
    "        )\n",
    "    ),\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "iplot(fig, filename='style-bar',show_link=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>It would be better if instead of number of wins we used win percentage</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "csk_matches=len(df[(df.team1_id =='CSK') | (df.team2_id=='CSK')]) \n",
    "mi_matches=len(df[(df.team1_id =='MI') | (df.team2_id=='MI')]) \n",
    "kxi_matches=len(df[(df.team1_id =='KXI') | (df.team2_id=='KXI')])\n",
    "dd_matches=len(df[(df.team1_id =='DD') | (df.team2_id=='DD')])\n",
    "rr_matches=len(df[(df.team1_id =='RR') | (df.team2_id=='RR')])\n",
    "dc_matches=len(df[(df.team1_id =='DC') | (df.team2_id=='DC')])\n",
    "kkr_matches=len(df[(df.team1_id =='KKR') | (df.team2_id=='KKR')])\n",
    "srh_matches=len(df[(df.team1_id =='SRH') | (df.team2_id=='SRH')])\n",
    "teams_matches=[csk_matches,mi_matches,kxi_matches,dd_matches,rr_matches,dc_matches,kkr_matches,srh_matches]\n",
    "labelss=['CSK','MI','KXI','DD','RR','DC','KKR','SRH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_list = [teams_loss[i]/teams_matches[i]*100 for i in range(len(teams_matches))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
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        "linkText": "Export to plot.ly",
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        "height": 500,
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        "title": {
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        },
        "width": 700,
        "xaxis": {
         "tickangle": -45,
         "tickmode": "linear",
         "title": {
          "font": {
           "size": 16
          },
          "text": "Teams"
         }
        },
        "yaxis": {
         "tickfont": {
          "size": 14
         },
         "title": {
          "font": {
           "size": 16
          },
          "text": "Win percentage"
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       "            <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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       "\n",
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     "metadata": {},
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   ],
   "source": [
    "data = [go.Bar(\n",
    "            x=labelss,\n",
    "            y=res_list,\n",
    "            marker=dict(color=['#F9F606','#0983FC','#FB5304','#4404FB','#FB04D9','#0939FA','#000000','#FA9B09']), \n",
    "            name=\"Wins\",            \n",
    "    )]\n",
    "layout = go.Layout(\n",
    "    height=500,\n",
    "    width=700,\n",
    "    showlegend=False,\n",
    "    xaxis=dict(tickangle=-45,tickmode='linear',title='Teams', titlefont=dict(\n",
    "            size=16,\n",
    "        \n",
    "        )),title='RCB vs Different teams',   \n",
    "         yaxis=dict(\n",
    "        title='Win percentage',\n",
    "        titlefont=dict(\n",
    "            size=16\n",
    "        ),\n",
    "        tickfont=dict(\n",
    "            size=14\n",
    "        )\n",
    "    ),\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "iplot(fig, filename='style-bar',show_link=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives a better insight of RCB performance against different teams we can see RCB has won 62 % games against DD and have lowest success rate against CSK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at the close matches RCB lost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df.win_by_runs>0) & (df.win_by_runs<10) & (df.winner!='RCB')] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>There have been 5 matches where RCB lost by less than 10 runs</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was the maximum info we could get from this dataset. We have another dataset which has ball by ball observation we will perform analysis on this data set and later merge the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ball_by_ball=pd.read_csv('E:/Hacka/train (1)/train/ball_by_ball_data.csv') # reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>batsman_id</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>non_striker_id</th>\n",
       "      <th>bowler</th>\n",
       "      <th>bowler_id</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>player_dismissed_id</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>is_super_over</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>David Warner</td>\n",
       "      <td>207</td>\n",
       "      <td>Shikhar Dhawan</td>\n",
       "      <td>57</td>\n",
       "      <td>Tymal Mills</td>\n",
       "      <td>621</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
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       "      <td>2</td>\n",
       "      <td>David Warner</td>\n",
       "      <td>207</td>\n",
       "      <td>Shikhar Dhawan</td>\n",
       "      <td>57</td>\n",
       "      <td>Tymal Mills</td>\n",
       "      <td>621</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>David Warner</td>\n",
       "      <td>207</td>\n",
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       "      <td>57</td>\n",
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       "      <td>621</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>David Warner</td>\n",
       "      <td>207</td>\n",
       "      <td>Shikhar Dhawan</td>\n",
       "      <td>57</td>\n",
       "      <td>Tymal Mills</td>\n",
       "      <td>621</td>\n",
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       "      <td>2017_1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  match_id  inning batting_team bowling_team  over  ball       batsman  \\\n",
       "0   2017_1       1          SRH          RCB     1     1  David Warner   \n",
       "1   2017_1       1          SRH          RCB     1     2  David Warner   \n",
       "2   2017_1       1          SRH          RCB     1     3  David Warner   \n",
       "3   2017_1       1          SRH          RCB     1     4  David Warner   \n",
       "4   2017_1       1          SRH          RCB     1     5  David Warner   \n",
       "\n",
       "   batsman_id     non_striker  non_striker_id       bowler  bowler_id  \\\n",
       "0         207  Shikhar Dhawan              57  Tymal Mills        621   \n",
       "1         207  Shikhar Dhawan              57  Tymal Mills        621   \n",
       "2         207  Shikhar Dhawan              57  Tymal Mills        621   \n",
       "3         207  Shikhar Dhawan              57  Tymal Mills        621   \n",
       "4         207  Shikhar Dhawan              57  Tymal Mills        621   \n",
       "\n",
       "   batsman_runs  extra_runs player_dismissed  player_dismissed_id  \\\n",
       "0             0           0              NaN                  NaN   \n",
       "1             0           0              NaN                  NaN   \n",
       "2             4           0              NaN                  NaN   \n",
       "3             0           0              NaN                  NaN   \n",
       "4             0           2              NaN                  NaN   \n",
       "\n",
       "  dismissal_kind  is_super_over  \n",
       "0            NaN              0  \n",
       "1            NaN              0  \n",
       "2            NaN              0  \n",
       "3            NaN              0  \n",
       "4            NaN              0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ball_by_ball.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>First let us extract all the matches played by RCB</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcb_matches=df_ball_by_ball[(df_ball_by_ball.batting_team =='RCB') | (df_ball_by_ball.bowling_team=='RCB')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>batsman_id</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>non_striker_id</th>\n",
       "      <th>bowler</th>\n",
       "      <th>bowler_id</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>player_dismissed_id</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>is_super_over</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>David Warner</td>\n",
       "      <td>207</td>\n",
       "      <td>Shikhar Dhawan</td>\n",
       "      <td>57</td>\n",
       "      <td>Tymal Mills</td>\n",
       "      <td>621</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>David Warner</td>\n",
       "      <td>207</td>\n",
       "      <td>Shikhar Dhawan</td>\n",
       "      <td>57</td>\n",
       "      <td>Tymal Mills</td>\n",
       "      <td>621</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>David Warner</td>\n",
       "      <td>207</td>\n",
       "      <td>Shikhar Dhawan</td>\n",
       "      <td>57</td>\n",
       "      <td>Tymal Mills</td>\n",
       "      <td>621</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>David Warner</td>\n",
       "      <td>207</td>\n",
       "      <td>Shikhar Dhawan</td>\n",
       "      <td>57</td>\n",
       "      <td>Tymal Mills</td>\n",
       "      <td>621</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>David Warner</td>\n",
       "      <td>207</td>\n",
       "      <td>Shikhar Dhawan</td>\n",
       "      <td>57</td>\n",
       "      <td>Tymal Mills</td>\n",
       "      <td>621</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  match_id  inning batting_team bowling_team  over  ball       batsman  \\\n",
       "0   2017_1       1          SRH          RCB     1     1  David Warner   \n",
       "1   2017_1       1          SRH          RCB     1     2  David Warner   \n",
       "2   2017_1       1          SRH          RCB     1     3  David Warner   \n",
       "3   2017_1       1          SRH          RCB     1     4  David Warner   \n",
       "4   2017_1       1          SRH          RCB     1     5  David Warner   \n",
       "\n",
       "   batsman_id     non_striker  non_striker_id       bowler  bowler_id  \\\n",
       "0         207  Shikhar Dhawan              57  Tymal Mills        621   \n",
       "1         207  Shikhar Dhawan              57  Tymal Mills        621   \n",
       "2         207  Shikhar Dhawan              57  Tymal Mills        621   \n",
       "3         207  Shikhar Dhawan              57  Tymal Mills        621   \n",
       "4         207  Shikhar Dhawan              57  Tymal Mills        621   \n",
       "\n",
       "   batsman_runs  extra_runs player_dismissed  player_dismissed_id  \\\n",
       "0             0           0              NaN                  NaN   \n",
       "1             0           0              NaN                  NaN   \n",
       "2             4           0              NaN                  NaN   \n",
       "3             0           0              NaN                  NaN   \n",
       "4             0           2              NaN                  NaN   \n",
       "\n",
       "  dismissal_kind  is_super_over  \n",
       "0            NaN              0  \n",
       "1            NaN              0  \n",
       "2            NaN              0  \n",
       "3            NaN              0  \n",
       "4            NaN              0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcb_matches.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#total_matches_rcb_played\n",
    "unique_matches=rcb_matches['match_id'].unique()\n",
    "#Total 152 matches played by RCB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2017_1', '2017_5', '2017_8', '2017_12', '2017_17', '2017_20',\n",
       "       '2017_27', '2017_30', '2017_33', '2017_37', '2017_42', '2017_45',\n",
       "       '2017_55', '2008_1', '2008_4', '2008_11', '2008_15', '2008_17',\n",
       "       '2008_21', '2008_25', '2008_29', '2008_31', '2008_35', '2008_42',\n",
       "       '2008_45', '2008_48', '2008_52', '2009_2', '2009_5', '2009_7',\n",
       "       '2009_10', '2009_12', '2009_17', '2009_22', '2009_26', '2009_31',\n",
       "       '2009_36', '2009_39', '2009_42', '2009_50', '2009_54', '2009_56',\n",
       "       '2009_57', '2010_4', '2010_7', '2010_10', '2010_14', '2010_18',\n",
       "       '2010_23', '2010_28', '2010_31', '2010_35', '2010_40', '2010_43',\n",
       "       '2010_46', '2010_49', '2010_52', '2010_57', '2010_59', '2011_3',\n",
       "       '2011_8', '2011_11', '2011_14', '2011_24', '2011_29', '2011_34',\n",
       "       '2011_46', '2011_49', '2011_54', '2011_57', '2011_62', '2011_68',\n",
       "       '2011_70', '2011_72', '2011_73', '2012_5', '2012_10', '2012_13',\n",
       "       '2012_19', '2012_22', '2012_26', '2012_31', '2012_37', '2012_43',\n",
       "       '2012_49', '2012_53', '2012_55', '2012_60', '2012_65', '2012_69',\n",
       "       '2013_2', '2013_7', '2013_11', '2013_15', '2013_20', '2013_26',\n",
       "       '2013_30', '2013_36', '2013_39', '2013_45', '2013_48', '2013_51',\n",
       "       '2013_57', '2013_60', '2013_67', '2013_71', '2014_2', '2014_5',\n",
       "       '2014_11', '2014_14', '2014_18', '2014_24', '2014_27', '2014_31',\n",
       "       '2014_35', '2014_38', '2014_42', '2014_46', '2014_49', '2014_53',\n",
       "       '2015_5', '2015_8', '2015_17', '2015_21', '2015_23', '2015_26',\n",
       "       '2015_29', '2015_33', '2015_37', '2015_40', '2015_45', '2015_49',\n",
       "       '2015_51', '2015_54', '2015_57', '2015_58', '2016_4', '2016_11',\n",
       "       '2016_14', '2016_16', '2016_19', '2016_27', '2016_30', '2016_35',\n",
       "       '2016_39', '2016_41', '2016_44', '2016_48', '2016_50', '2016_56',\n",
       "       '2016_57', '2016_60'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_matches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are all ids of all the matches RCB played. We will perform analysis on all matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcb_matches_batting= rcb_matches[(rcb_matches.batting_team=='RCB')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>batsman_id</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>non_striker_id</th>\n",
       "      <th>bowler</th>\n",
       "      <th>bowler_id</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>player_dismissed_id</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>is_super_over</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chris Gayle</td>\n",
       "      <td>79</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>373</td>\n",
       "      <td>Ashish Nehra</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>373</td>\n",
       "      <td>Chris Gayle</td>\n",
       "      <td>79</td>\n",
       "      <td>Ashish Nehra</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>373</td>\n",
       "      <td>Chris Gayle</td>\n",
       "      <td>79</td>\n",
       "      <td>Ashish Nehra</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>373</td>\n",
       "      <td>Chris Gayle</td>\n",
       "      <td>79</td>\n",
       "      <td>Ashish Nehra</td>\n",
       "      <td>119</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2017_1</td>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>373</td>\n",
       "      <td>Chris Gayle</td>\n",
       "      <td>79</td>\n",
       "      <td>Ashish Nehra</td>\n",
       "      <td>119</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  inning batting_team bowling_team  over  ball        batsman  \\\n",
       "125   2017_1       2          RCB          SRH     1     1    Chris Gayle   \n",
       "126   2017_1       2          RCB          SRH     1     2  Mandeep Singh   \n",
       "127   2017_1       2          RCB          SRH     1     3  Mandeep Singh   \n",
       "128   2017_1       2          RCB          SRH     1     4  Mandeep Singh   \n",
       "129   2017_1       2          RCB          SRH     1     5  Mandeep Singh   \n",
       "\n",
       "     batsman_id    non_striker  non_striker_id        bowler  bowler_id  \\\n",
       "125          79  Mandeep Singh             373  Ashish Nehra        119   \n",
       "126         373    Chris Gayle              79  Ashish Nehra        119   \n",
       "127         373    Chris Gayle              79  Ashish Nehra        119   \n",
       "128         373    Chris Gayle              79  Ashish Nehra        119   \n",
       "129         373    Chris Gayle              79  Ashish Nehra        119   \n",
       "\n",
       "     batsman_runs  extra_runs player_dismissed  player_dismissed_id  \\\n",
       "125             1           0              NaN                  NaN   \n",
       "126             0           0              NaN                  NaN   \n",
       "127             0           0              NaN                  NaN   \n",
       "128             2           0              NaN                  NaN   \n",
       "129             4           0              NaN                  NaN   \n",
       "\n",
       "    dismissal_kind  is_super_over  \n",
       "125            NaN              0  \n",
       "126            NaN              0  \n",
       "127            NaN              0  \n",
       "128            NaN              0  \n",
       "129            NaN              0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcb_matches_batting.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "batsman_run = rcb_matches_batting.groupby('batsman')['batsman_runs'].sum()\n",
    "trimmedData = {key:val for key, val in batsman_run.items() if val >= 1000}\n",
    "sortedData_rcb = sorted(trimmedData.items(), key=lambda kv: kv[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Jacques Kallis', 1132),\n",
       " ('AB De Villiers', 2815),\n",
       " ('Chris Gayle', 3175),\n",
       " ('Virat Kohli', 4423)]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sortedData_rcb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>There have been only 4 players with 1000+ runs for RCB. This has been RCB issue they have not retained many players. We will compare this stats with players of other teams. Let us compare with CSK, MI </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Faf du Plessis', 1081),\n",
       " ('Matthew Hayden', 1107),\n",
       " ('S Badrinath', 1441),\n",
       " ('Murali Vijay', 1600),\n",
       " ('Michael Hussey', 1768),\n",
       " ('Mahendra Singh Dhoni', 2986),\n",
       " ('Suresh Raina', 3707)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csk_matches=df_ball_by_ball[(df_ball_by_ball.batting_team =='CSK') | (df_ball_by_ball.bowling_team=='CSK')]\n",
    "csk_matches_batting= csk_matches[(csk_matches.batting_team=='CSK')]\n",
    "batsman_run = csk_matches_batting.groupby('batsman')['batsman_runs'].sum()\n",
    "trimmedData = {key:val for key, val in batsman_run.items() if val >= 1000}\n",
    "sortedData_csk = sorted(trimmedData.items(), key=lambda kv: kv[1])\n",
    "sortedData_csk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Lendl Simmons', 1079),\n",
       " ('Sachin Tendulkar', 2334),\n",
       " ('Kieron Pollard', 2354),\n",
       " ('Ambati Rayudu', 2416),\n",
       " ('Rohit Sharma', 3037)]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mi_matches=df_ball_by_ball[(df_ball_by_ball.batting_team =='MI') | (df_ball_by_ball.bowling_team=='MI')]\n",
    "mi_matches_batting= mi_matches[(mi_matches.batting_team=='MI')]\n",
    "batsman_run = mi_matches_batting.groupby('batsman')['batsman_runs'].sum()\n",
    "trimmedData = {key:val for key, val in batsman_run.items() if val >= 1000}\n",
    "sortedData_mi = sorted(trimmedData.items(), key=lambda kv: kv[1])\n",
    "sortedData_mi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('J P Duminy', 1015),\n",
       " ('Dinesh Karthik', 1036),\n",
       " ('Gautam Gambhir', 1097),\n",
       " ('David Warner', 1435),\n",
       " ('Virender Sehwag', 2174)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_matches=df_ball_by_ball[(df_ball_by_ball.batting_team =='DD') | (df_ball_by_ball.bowling_team=='DD')]\n",
    "dd_matches_batting= dd_matches[(dd_matches.batting_team=='DD')]\n",
    "batsman_run = dd_matches_batting.groupby('batsman')['batsman_runs'].sum()\n",
    "trimmedData = {key:val for key, val in batsman_run.items() if val >= 1000}\n",
    "sortedData_dd = sorted(trimmedData.items(), key=lambda kv: kv[1])\n",
    "sortedData_dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "s=wins['team1_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RCB\n",
      "4\n",
      "MI\n",
      "5\n",
      "DC\n",
      "2\n",
      "KKR\n",
      "7\n",
      "CSK\n",
      "7\n",
      "DD\n",
      "5\n",
      "KXI\n",
      "5\n",
      "RR\n",
      "4\n",
      "KTK\n",
      "0\n",
      "PW\n",
      "1\n",
      "SRH\n",
      "2\n",
      "RPS\n",
      "0\n",
      "GL\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "sorteddata=[]\n",
    "z=[]\n",
    "for i in range(0,13):\n",
    "    print(s[i])\n",
    "    matches=df_ball_by_ball[(df_ball_by_ball.batting_team ==s[i]) | (df_ball_by_ball.bowling_team==s[i])]\n",
    "    matches_batting= matches[(matches.batting_team==s[i])]\n",
    "    batsman_run = matches_batting.groupby('batsman')['batsman_runs'].sum()\n",
    "    trimmedData = {key:val for key, val in batsman_run.items() if val >= 1000}\n",
    "    sortedData= len(sorted(trimmedData.items(), key=lambda kv: kv[1]))\n",
    "    print(sortedData)\n",
    "    z.append(sortedData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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        "linkText": "Export to plot.ly",
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       },
       "data": [
        {
         "marker": {
          "color": "#000000"
         },
         "name": "Wins",
         "type": "bar",
         "uid": "396fd562-b66c-4178-a02e-54ba5ab55349",
         "x": [
          "RCB",
          "MI",
          "DC",
          "KKR",
          "CSK",
          "DD",
          "KXI",
          "RR",
          "KTK",
          "PW",
          "SRH",
          "RPS",
          "GL"
         ],
         "y": [
          4,
          5,
          2,
          7,
          7,
          5,
          5,
          4,
          0,
          1,
          2,
          0,
          0
         ]
        }
       ],
       "layout": {
        "showlegend": false,
        "title": {
         "text": "More than 1000 runs"
        },
        "xaxis": {
         "tickangle": -45,
         "tickmode": "linear",
         "title": {
          "font": {
           "color": "rgb(107, 107, 107)",
           "size": 16
          },
          "text": "Teams"
         }
        },
        "yaxis": {
         "title": {
          "font": {
           "color": "rgb(107, 107, 107)",
           "size": 16
          },
          "text": "Number Of Players"
         }
        }
       }
      },
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       "        \n",
       "        \n",
       "            <div id=\"8eda8e5b-4dad-4acb-85db-15b03aea55b3\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
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       "                        '8eda8e5b-4dad-4acb-85db-15b03aea55b3',\n",
       "                        [{\"marker\": {\"color\": \"#000000\"}, \"name\": \"Wins\", \"type\": \"bar\", \"uid\": \"396fd562-b66c-4178-a02e-54ba5ab55349\", \"x\": [\"RCB\", \"MI\", \"DC\", \"KKR\", \"CSK\", \"DD\", \"KXI\", \"RR\", \"KTK\", \"PW\", \"SRH\", \"RPS\", \"GL\"], \"y\": [4, 5, 2, 7, 7, 5, 5, 4, 0, 1, 2, 0, 0]}],\n",
       "                        {\"showlegend\": false, \"title\": {\"text\": \"More than 1000 runs\"}, \"xaxis\": {\"tickangle\": -45, \"tickmode\": \"linear\", \"title\": {\"font\": {\"color\": \"rgb(107, 107, 107)\", \"size\": 16}, \"text\": \"Teams\"}}, \"yaxis\": {\"title\": {\"font\": {\"color\": \"rgb(107, 107, 107)\", \"size\": 16}, \"text\": \"Number Of Players\"}}},\n",
       "                        {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
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       "                            \n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
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       "\n",
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       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [go.Bar(\n",
    "            x=s,\n",
    "            y=z,\n",
    "            marker=dict(color='#000000'), \n",
    "            name=\"Wins\",\n",
    "            \n",
    "    )]\n",
    "layout = go.Layout(\n",
    "    showlegend=False,\n",
    "    xaxis=dict(tickangle=-45,tickmode='linear',title='Teams', titlefont=dict(\n",
    "            size=16,\n",
    "            color='rgb(107, 107, 107)'\n",
    "        )),title='More than 1000 runs',   \n",
    "         yaxis=dict(\n",
    "        title='Number Of Players',\n",
    "        titlefont=dict(\n",
    "            size=16,\n",
    "            color='rgb(107, 107, 107)'\n",
    "        ),),)\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "iplot(fig, filename='style-bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>We see among the teams which has played all the seasons of IPL RCB has least number of players who have scored more than 1000 runs. While CSK and KKR have most number of players with 1000+ runs. </b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "caught                   4373\n",
       "bowled                   1382\n",
       "run out                   755\n",
       "lbw                       455\n",
       "stumped                   243\n",
       "caught and bowled         211\n",
       "retired hurt                9\n",
       "hit wicket                  9\n",
       "obstructing the field       1\n",
       "Name: dismissal_kind, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ball_by_ball['dismissal_kind'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> These are the count of different types of dismissals along with the counts in the IPL</b>"
   ]
  }
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