ipl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eeef0383",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "140ff341",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9df6f6bd",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28a4bb85",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery = pd.read_csv('deliveries.csv')\n",
    "match = pd.read_csv('matches.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "411828b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>id</th>\n",
       "      <th>Season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05-04-2017</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>06-04-2017</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>07-04-2017</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    Season       city        date                        team1  \\\n",
       "0   1  IPL-2017  Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
       "1   2  IPL-2017       Pune  06-04-2017               Mumbai Indians   \n",
       "2   3  IPL-2017     Rajkot  07-04-2017                Gujarat Lions   \n",
       "3   4  IPL-2017     Indore  08-04-2017       Rising Pune Supergiant   \n",
       "4   5  IPL-2017  Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3              Kings XI Punjab              Kings XI Punjab         field   \n",
       "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "1  normal           0       Rising Pune Supergiant            0   \n",
       "2  normal           0        Kolkata Knight Riders            0   \n",
       "3  normal           0              Kings XI Punjab            0   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
       "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
       "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "\n",
       "          umpire1        umpire2 umpire3  \n",
       "0     AY Dandekar       NJ Llong     NaN  \n",
       "1  A Nand Kishore         S Ravi     NaN  \n",
       "2     Nitin Menon      CK Nandan     NaN  \n",
       "3    AK Chaudhary  C Shamshuddin     NaN  \n",
       "4             NaN            NaN     NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa1ae0da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(756, 18)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de25b61e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "1            0            0             0             0           0   \n",
       "2            0            0             0             4           0   \n",
       "3            0            0             0             0           0   \n",
       "4            0            0             0             0           2   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "2           4               NaN            NaN     NaN  \n",
       "3           0               NaN            NaN     NaN  \n",
       "4           2               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "608ca29c",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_score_df =delivery.groupby(['match_id','inning']).sum()['total_runs'].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6751b73",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_score_df = total_score_df[total_score_df['inning']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f25e8d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df =match.merge(total_score_df[['match_id','total_runs']],left_on='id',right_on='match_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29db5184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sunrisers Hyderabad', 'Mumbai Indians', 'Gujarat Lions',\n",
       "       'Rising Pune Supergiant', 'Royal Challengers Bangalore',\n",
       "       'Kolkata Knight Riders', 'Delhi Daredevils', 'Kings XI Punjab',\n",
       "       'Chennai Super Kings', 'Rajasthan Royals', 'Deccan Chargers',\n",
       "       'Kochi Tuskers Kerala', 'Pune Warriors', 'Rising Pune Supergiants',\n",
       "       'Delhi Capitals'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df['team1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "52671528",
   "metadata": {},
   "outputs": [],
   "source": [
    "teams =[\n",
    "    'Sunrisers Hyderabad', 'Mumbai Indians','Royal Challengers Bangalore','Kolkata Knight Riders','Kings XI Punjab',\n",
    "    'Chennai Super Kings', 'Rajasthan Royals', 'Delhi Capitals'\n",
    "]\n",
    "match_df['team1']=match_df['team1'].str.replace('Deccan Chargers','Sunrisers Hyderabad')\n",
    "match_df['team2']=match_df['team2'].str.replace('Deccan Chargers','Sunrisers Hyderabad')\n",
    "\n",
    "match_df['team1']=match_df['team1'].str.replace('Delhi Daredevils','Delhi Capitals')\n",
    "match_df['team2']=match_df['team2'].str.replace('Delhi Daredevils','Delhi Capitals')\n",
    "match_df = match_df[match_df['team1'].isin(teams)]\n",
    "match_df = match_df[match_df['team2'].isin(teams)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "af3a99c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sunrisers Hyderabad', 'Royal Challengers Bangalore',\n",
       "       'Kolkata Knight Riders', 'Kings XI Punjab', 'Delhi Capitals',\n",
       "       'Mumbai Indians', 'Chennai Super Kings', 'Rajasthan Royals'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df['team1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b87ce43",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d1869822",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "      <th>match_id</th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05-04-2017</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>09-04-2017</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>N Rana</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>10-04-2017</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>AR Patel</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>12-04-2017</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>JJ Bumrah</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    Season       city        date                        team1  \\\n",
       "0   1  IPL-2017  Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
       "4   5  IPL-2017  Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
       "6   7  IPL-2017     Mumbai  09-04-2017        Kolkata Knight Riders   \n",
       "7   8  IPL-2017     Indore  10-04-2017  Royal Challengers Bangalore   \n",
       "9  10  IPL-2017     Mumbai  12-04-2017          Sunrisers Hyderabad   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "4               Delhi Capitals  Royal Challengers Bangalore           bat   \n",
       "6               Mumbai Indians               Mumbai Indians         field   \n",
       "7              Kings XI Punjab  Royal Challengers Bangalore           bat   \n",
       "9               Mumbai Indians               Mumbai Indians         field   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "6  normal           0               Mumbai Indians            0   \n",
       "7  normal           0              Kings XI Punjab            0   \n",
       "9  normal           0               Mumbai Indians            0   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "6               4          N Rana                           Wankhede Stadium   \n",
       "7               8        AR Patel                     Holkar Cricket Stadium   \n",
       "9               4       JJ Bumrah                           Wankhede Stadium   \n",
       "\n",
       "        umpire1        umpire2 umpire3  match_id  total_runs  \n",
       "0   AY Dandekar       NJ Llong     NaN         1         207  \n",
       "4           NaN            NaN     NaN         5         157  \n",
       "6   Nitin Menon      CK Nandan     NaN         7         178  \n",
       "7  AK Chaudhary  C Shamshuddin     NaN         8         148  \n",
       "9   Nitin Menon      CK Nandan     NaN        10         158  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8216afc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df = match_df[match_df['dl_applied']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0417178f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a410d4f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df = match_df[['match_id','city','winner','total_runs']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e20e5b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df =match_df.merge(delivery,on='match_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1564e2a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df =delivery_df[delivery_df['inning']==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a64e8c4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>total_runs_x</th>\n",
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       "      <th>batting_team</th>\n",
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       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs_y</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
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       "      <th>126</th>\n",
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       "      <td>207</td>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>Mandeep Singh</td>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>Mandeep Singh</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id       city               winner  total_runs_x  inning  \\\n",
       "125         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "\n",
       "                    batting_team         bowling_team  over  ball  \\\n",
       "125  Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126  Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127  Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128  Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129  Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "\n",
       "           batsman  ... bye_runs legbye_runs  noball_runs  penalty_runs  \\\n",
       "125       CH Gayle  ...        0           0            0             0   \n",
       "126  Mandeep Singh  ...        0           0            0             0   \n",
       "127  Mandeep Singh  ...        0           0            0             0   \n",
       "128  Mandeep Singh  ...        0           0            0             0   \n",
       "129  Mandeep Singh  ...        0           0            0             0   \n",
       "\n",
       "     batsman_runs  extra_runs  total_runs_y  player_dismissed  dismissal_kind  \\\n",
       "125             1           0             1               NaN             NaN   \n",
       "126             0           0             0               NaN             NaN   \n",
       "127             0           0             0               NaN             NaN   \n",
       "128             2           0             2               NaN             NaN   \n",
       "129             4           0             4               NaN             NaN   \n",
       "\n",
       "     fielder  \n",
       "125      NaN  \n",
       "126      NaN  \n",
       "127      NaN  \n",
       "128      NaN  \n",
       "129      NaN  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c6af5305",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['current_score']=delivery_df.groupby('match_id')['total_runs_y'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8415dd29",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['runs_left'] = delivery_df['total_runs_x']-delivery_df['current_score']+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "312f5538",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['balls_left']=126-(delivery_df['over']*6+delivery_df['ball'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "519c36b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>125</th>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>205</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>201</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id       city               winner  total_runs_x  inning  \\\n",
       "125         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "\n",
       "                    batting_team         bowling_team  over  ball  \\\n",
       "125  Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126  Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127  Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128  Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129  Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "\n",
       "           batsman  ... penalty_runs batsman_runs  extra_runs  total_runs_y  \\\n",
       "125       CH Gayle  ...            0            1           0             1   \n",
       "126  Mandeep Singh  ...            0            0           0             0   \n",
       "127  Mandeep Singh  ...            0            0           0             0   \n",
       "128  Mandeep Singh  ...            0            2           0             2   \n",
       "129  Mandeep Singh  ...            0            4           0             4   \n",
       "\n",
       "     player_dismissed  dismissal_kind  fielder  current_score  runs_left  \\\n",
       "125               NaN             NaN      NaN              1        207   \n",
       "126               NaN             NaN      NaN              1        207   \n",
       "127               NaN             NaN      NaN              1        207   \n",
       "128               NaN             NaN      NaN              3        205   \n",
       "129               NaN             NaN      NaN              7        201   \n",
       "\n",
       "     balls_left  \n",
       "125         119  \n",
       "126         118  \n",
       "127         117  \n",
       "128         116  \n",
       "129         115  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3da017dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['player_dismissed']=delivery_df['player_dismissed'].fillna(0)\n",
    "delivery_df['player_dismissed']=delivery_df['player_dismissed'].apply(lambda x : x if x ==0  else 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d4d8e4f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "wickets = delivery_df.groupby('match_id')['player_dismissed'].cumsum().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2c1f3289",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['wickets']=10- wickets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ff43e009",
   "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>city</th>\n",
       "      <th>winner</th>\n",
       "      <th>total_runs_x</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>...</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs_y</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "      <th>current_score</th>\n",
       "      <th>runs_left</th>\n",
       "      <th>balls_left</th>\n",
       "      <th>wickets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "      <td>119</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "      <td>118</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "      <td>117</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>205</td>\n",
       "      <td>116</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>201</td>\n",
       "      <td>115</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id       city               winner  total_runs_x  inning  \\\n",
       "125         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "\n",
       "                    batting_team         bowling_team  over  ball  \\\n",
       "125  Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126  Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127  Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128  Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129  Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "\n",
       "           batsman  ... batsman_runs extra_runs  total_runs_y  \\\n",
       "125       CH Gayle  ...            1          0             1   \n",
       "126  Mandeep Singh  ...            0          0             0   \n",
       "127  Mandeep Singh  ...            0          0             0   \n",
       "128  Mandeep Singh  ...            2          0             2   \n",
       "129  Mandeep Singh  ...            4          0             4   \n",
       "\n",
       "     player_dismissed  dismissal_kind  fielder  current_score  runs_left  \\\n",
       "125                 0             NaN      NaN              1        207   \n",
       "126                 0             NaN      NaN              1        207   \n",
       "127                 0             NaN      NaN              1        207   \n",
       "128                 0             NaN      NaN              3        205   \n",
       "129                 0             NaN      NaN              7        201   \n",
       "\n",
       "     balls_left  wickets  \n",
       "125         119       10  \n",
       "126         118       10  \n",
       "127         117       10  \n",
       "128         116       10  \n",
       "129         115       10  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e665c7fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['crr']=delivery_df.current_score*6/(120-delivery_df.balls_left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a83ad106",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['rrr']=delivery_df.runs_left*6/delivery_df.balls_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e7269c6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df = delivery_df.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa79a08a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f2231b21",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df.drop(columns='index',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6d97a247",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_win(df):\n",
    "    winner = []\n",
    "    for item,row in df.iterrows():\n",
    "        if row.winner == row.batting_team:\n",
    "            winner.append(1)\n",
    "        else:\n",
    "            winner.append(0)\n",
    "\n",
    "    return winner\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "147f7168",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['winner']=is_win(delivery_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4302070a",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df =delivery_df[['match_id','batting_team','bowling_team','city','runs_left','balls_left','wickets','total_runs_x','crr','rrr','winner']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "822e15fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "final_df =final_df.sample(final_df.shape[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0b1e1bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['batting_team']=final_df['batting_team'].str.replace('Deccan Chargers','Sunrisers Hyderabad')\n",
    "final_df['bowling_team']=final_df['bowling_team'].str.replace('Deccan Chargers','Sunrisers Hyderabad')\n",
    "\n",
    "final_df['batting_team']=final_df['batting_team'].str.replace('Delhi Daredevils','Delhi Capitals')\n",
    "final_df['bowling_team']=final_df['bowling_team'].str.replace('Delhi Daredevils','Delhi Capitals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c0715581",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Delhi' 'Mumbai' 'Jaipur' 'Cape Town' 'Chennai' 'Bengaluru' 'Durban'\n",
      " 'Centurion' 'Mohali' 'Hyderabad' 'Kimberley' 'Chandigarh' 'Kolkata'\n",
      " 'Dharamsala' 'Bangalore' 'Cuttack' 'Pune' 'Ahmedabad' 'Bloemfontein'\n",
      " 'Ranchi' 'Indore' 'Visakhapatnam' nan 'Port Elizabeth' 'Raipur'\n",
      " 'Abu Dhabi' 'Johannesburg' 'Nagpur' 'East London' 'Sharjah']\n",
      "['Delhi Capitals' 'Chennai Super Kings' 'Royal Challengers Bangalore'\n",
      " 'Rajasthan Royals' 'Mumbai Indians' 'Kolkata Knight Riders'\n",
      " 'Kings XI Punjab' 'Sunrisers Hyderabad']\n"
     ]
    }
   ],
   "source": [
    "print(final_df.city.unique())\n",
    "print(final_df.batting_team.unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c1c9ffd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "cities_dict = {\n",
    "    'Royal Challengers Bangalore':'Bengaluru',\n",
    "    'Chennai Super Kings':'Chennai',\n",
    "    'Kings XI Punjab':'Mumbai',\n",
    "    'Kolkata Knight Riders':'Kolkata',\n",
    "    'Delhi Capitals':'Delhi',\n",
    "    'Rajasthan Royals':'Jaipur',\n",
    "    'Mumbai Indians':'Mumbai',\n",
    "    'Sunrisers Hyderabad':'Hyderabad',\n",
    "}\n",
    "final_df['city']=final_df['city'].fillna(0)\n",
    "def fill_city(x):\n",
    "    if x.city == 0:\n",
    "        team = [x.batting_team,x.bowling_team][random.randint(0,1)]\n",
    "        return cities_dict[team]\n",
    "    else :\n",
    "        return x.city\n",
    "        \n",
    "\n",
    "final_df['city']=final_df.apply(fill_city,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "02a6fd7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.dropna(inplace=True)\n",
    "final_df =final_df[final_df.balls_left !=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "03a4a5f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = final_df.drop(columns=['winner','match_id'])\n",
    "y = final_df['winner']\n",
    "X_copy =final_df.drop(columns=['winner'])\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "84b4cf0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b7760f36",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ohe = OneHotEncoder()\n",
    "ohe.fit_transform(X.iloc[:,:3])\n",
    "category = ohe.categories_\n",
    "trf = ColumnTransformer([\n",
    "    ('trf',OneHotEncoder(categories=category,sparse=False,drop='first'),['batting_team','bowling_team','city'])\n",
    "],remainder='passthrough')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1dc56f5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Rajat\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;trf&#x27;,\n",
       "                                                  OneHotEncoder(categories=[array([&#x27;Chennai Super Kings&#x27;, &#x27;Delhi Capitals&#x27;, &#x27;Kings XI Punjab&#x27;,\n",
       "       &#x27;Kolkata Knight Riders&#x27;, &#x27;Mumbai Indians&#x27;, &#x27;Rajasthan Royals&#x27;,\n",
       "       &#x27;Royal Challengers Bangalore&#x27;, &#x27;Sunrisers Hyderabad&#x27;], dtype=object),\n",
       "                                                                            array([&#x27;Chennai Super Kings&#x27;, &#x27;Delhi Capitals&#x27;, &#x27;Kings XI Punjab...\n",
       "       &#x27;Cape Town&#x27;, &#x27;Centurion&#x27;, &#x27;Chandigarh&#x27;, &#x27;Chennai&#x27;, &#x27;Cuttack&#x27;,\n",
       "       &#x27;Delhi&#x27;, &#x27;Dharamsala&#x27;, &#x27;Durban&#x27;, &#x27;East London&#x27;, &#x27;Hyderabad&#x27;,\n",
       "       &#x27;Indore&#x27;, &#x27;Jaipur&#x27;, &#x27;Johannesburg&#x27;, &#x27;Kimberley&#x27;, &#x27;Kolkata&#x27;,\n",
       "       &#x27;Mohali&#x27;, &#x27;Mumbai&#x27;, &#x27;Nagpur&#x27;, &#x27;Port Elizabeth&#x27;, &#x27;Pune&#x27;, &#x27;Raipur&#x27;,\n",
       "       &#x27;Ranchi&#x27;, &#x27;Sharjah&#x27;, &#x27;Visakhapatnam&#x27;], dtype=object)],\n",
       "                                                                drop=&#x27;first&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [&#x27;batting_team&#x27;,\n",
       "                                                   &#x27;bowling_team&#x27;, &#x27;city&#x27;])])),\n",
       "                (&#x27;step2&#x27;, LogisticRegression(solver=&#x27;liblinear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;trf&#x27;,\n",
       "                                                  OneHotEncoder(categories=[array([&#x27;Chennai Super Kings&#x27;, &#x27;Delhi Capitals&#x27;, &#x27;Kings XI Punjab&#x27;,\n",
       "       &#x27;Kolkata Knight Riders&#x27;, &#x27;Mumbai Indians&#x27;, &#x27;Rajasthan Royals&#x27;,\n",
       "       &#x27;Royal Challengers Bangalore&#x27;, &#x27;Sunrisers Hyderabad&#x27;], dtype=object),\n",
       "                                                                            array([&#x27;Chennai Super Kings&#x27;, &#x27;Delhi Capitals&#x27;, &#x27;Kings XI Punjab...\n",
       "       &#x27;Cape Town&#x27;, &#x27;Centurion&#x27;, &#x27;Chandigarh&#x27;, &#x27;Chennai&#x27;, &#x27;Cuttack&#x27;,\n",
       "       &#x27;Delhi&#x27;, &#x27;Dharamsala&#x27;, &#x27;Durban&#x27;, &#x27;East London&#x27;, &#x27;Hyderabad&#x27;,\n",
       "       &#x27;Indore&#x27;, &#x27;Jaipur&#x27;, &#x27;Johannesburg&#x27;, &#x27;Kimberley&#x27;, &#x27;Kolkata&#x27;,\n",
       "       &#x27;Mohali&#x27;, &#x27;Mumbai&#x27;, &#x27;Nagpur&#x27;, &#x27;Port Elizabeth&#x27;, &#x27;Pune&#x27;, &#x27;Raipur&#x27;,\n",
       "       &#x27;Ranchi&#x27;, &#x27;Sharjah&#x27;, &#x27;Visakhapatnam&#x27;], dtype=object)],\n",
       "                                                                drop=&#x27;first&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [&#x27;batting_team&#x27;,\n",
       "                                                   &#x27;bowling_team&#x27;, &#x27;city&#x27;])])),\n",
       "                (&#x27;step2&#x27;, LogisticRegression(solver=&#x27;liblinear&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">step1: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;trf&#x27;,\n",
       "                                 OneHotEncoder(categories=[array([&#x27;Chennai Super Kings&#x27;, &#x27;Delhi Capitals&#x27;, &#x27;Kings XI Punjab&#x27;,\n",
       "       &#x27;Kolkata Knight Riders&#x27;, &#x27;Mumbai Indians&#x27;, &#x27;Rajasthan Royals&#x27;,\n",
       "       &#x27;Royal Challengers Bangalore&#x27;, &#x27;Sunrisers Hyderabad&#x27;], dtype=object),\n",
       "                                                           array([&#x27;Chennai Super Kings&#x27;, &#x27;Delhi Capitals&#x27;, &#x27;Kings XI Punjab&#x27;,\n",
       "       &#x27;Kolkata Knight Riders&#x27;, &#x27;...\n",
       "                                                           array([&#x27;Abu Dhabi&#x27;, &#x27;Ahmedabad&#x27;, &#x27;Bangalore&#x27;, &#x27;Bengaluru&#x27;, &#x27;Bloemfontein&#x27;,\n",
       "       &#x27;Cape Town&#x27;, &#x27;Centurion&#x27;, &#x27;Chandigarh&#x27;, &#x27;Chennai&#x27;, &#x27;Cuttack&#x27;,\n",
       "       &#x27;Delhi&#x27;, &#x27;Dharamsala&#x27;, &#x27;Durban&#x27;, &#x27;East London&#x27;, &#x27;Hyderabad&#x27;,\n",
       "       &#x27;Indore&#x27;, &#x27;Jaipur&#x27;, &#x27;Johannesburg&#x27;, &#x27;Kimberley&#x27;, &#x27;Kolkata&#x27;,\n",
       "       &#x27;Mohali&#x27;, &#x27;Mumbai&#x27;, &#x27;Nagpur&#x27;, &#x27;Port Elizabeth&#x27;, &#x27;Pune&#x27;, &#x27;Raipur&#x27;,\n",
       "       &#x27;Ranchi&#x27;, &#x27;Sharjah&#x27;, &#x27;Visakhapatnam&#x27;], dtype=object)],\n",
       "                                               drop=&#x27;first&#x27;, sparse=False),\n",
       "                                 [&#x27;batting_team&#x27;, &#x27;bowling_team&#x27;, &#x27;city&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">trf</label><div class=\"sk-toggleable__content\"><pre>[&#x27;batting_team&#x27;, &#x27;bowling_team&#x27;, &#x27;city&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(categories=[array([&#x27;Chennai Super Kings&#x27;, &#x27;Delhi Capitals&#x27;, &#x27;Kings XI Punjab&#x27;,\n",
       "       &#x27;Kolkata Knight Riders&#x27;, &#x27;Mumbai Indians&#x27;, &#x27;Rajasthan Royals&#x27;,\n",
       "       &#x27;Royal Challengers Bangalore&#x27;, &#x27;Sunrisers Hyderabad&#x27;], dtype=object),\n",
       "                          array([&#x27;Chennai Super Kings&#x27;, &#x27;Delhi Capitals&#x27;, &#x27;Kings XI Punjab&#x27;,\n",
       "       &#x27;Kolkata Knight Riders&#x27;, &#x27;Mumbai Indians&#x27;, &#x27;Rajasthan Royals&#x27;,\n",
       "       &#x27;Royal Challengers Bangalore&#x27;, &#x27;S...\n",
       "                          array([&#x27;Abu Dhabi&#x27;, &#x27;Ahmedabad&#x27;, &#x27;Bangalore&#x27;, &#x27;Bengaluru&#x27;, &#x27;Bloemfontein&#x27;,\n",
       "       &#x27;Cape Town&#x27;, &#x27;Centurion&#x27;, &#x27;Chandigarh&#x27;, &#x27;Chennai&#x27;, &#x27;Cuttack&#x27;,\n",
       "       &#x27;Delhi&#x27;, &#x27;Dharamsala&#x27;, &#x27;Durban&#x27;, &#x27;East London&#x27;, &#x27;Hyderabad&#x27;,\n",
       "       &#x27;Indore&#x27;, &#x27;Jaipur&#x27;, &#x27;Johannesburg&#x27;, &#x27;Kimberley&#x27;, &#x27;Kolkata&#x27;,\n",
       "       &#x27;Mohali&#x27;, &#x27;Mumbai&#x27;, &#x27;Nagpur&#x27;, &#x27;Port Elizabeth&#x27;, &#x27;Pune&#x27;, &#x27;Raipur&#x27;,\n",
       "       &#x27;Ranchi&#x27;, &#x27;Sharjah&#x27;, &#x27;Visakhapatnam&#x27;], dtype=object)],\n",
       "              drop=&#x27;first&#x27;, sparse=False)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;runs_left&#x27;, &#x27;balls_left&#x27;, &#x27;wickets&#x27;, &#x27;total_runs_x&#x27;, &#x27;crr&#x27;, &#x27;rrr&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('step1',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('trf',\n",
       "                                                  OneHotEncoder(categories=[array(['Chennai Super Kings', 'Delhi Capitals', 'Kings XI Punjab',\n",
       "       'Kolkata Knight Riders', 'Mumbai Indians', 'Rajasthan Royals',\n",
       "       'Royal Challengers Bangalore', 'Sunrisers Hyderabad'], dtype=object),\n",
       "                                                                            array(['Chennai Super Kings', 'Delhi Capitals', 'Kings XI Punjab...\n",
       "       'Cape Town', 'Centurion', 'Chandigarh', 'Chennai', 'Cuttack',\n",
       "       'Delhi', 'Dharamsala', 'Durban', 'East London', 'Hyderabad',\n",
       "       'Indore', 'Jaipur', 'Johannesburg', 'Kimberley', 'Kolkata',\n",
       "       'Mohali', 'Mumbai', 'Nagpur', 'Port Elizabeth', 'Pune', 'Raipur',\n",
       "       'Ranchi', 'Sharjah', 'Visakhapatnam'], dtype=object)],\n",
       "                                                                drop='first',\n",
       "                                                                sparse=False),\n",
       "                                                  ['batting_team',\n",
       "                                                   'bowling_team', 'city'])])),\n",
       "                ('step2', LogisticRegression(solver='liblinear'))])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([\n",
    "    ('step1',trf),\n",
    "    ('step2',LogisticRegression(solver='liblinear'))\n",
    "#     ('step2',RandomForestClassifier(n_jobs=-1))\n",
    "])\n",
    "pipe.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fd160489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.49037175, 0.50962825],\n",
       "       [0.11876839, 0.88123161],\n",
       "       [0.97385002, 0.02614998],\n",
       "       ...,\n",
       "       [0.80629293, 0.19370707],\n",
       "       [0.02263366, 0.97736634],\n",
       "       [0.08590339, 0.91409661]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = pipe.predict_proba(X_test)\n",
    "# accuracy_score(y_test,y_pred)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6c47b48b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_progression(x_df,match_id,pipe):\n",
    "    match = x_df[x_df['match_id'] == match_id]\n",
    "    match = match[(match['ball'] == 6)]\n",
    "    temp_df = match[['batting_team','bowling_team','city','runs_left','balls_left','wickets','total_runs_x','crr','rrr']].dropna()\n",
    "    temp_df = temp_df[temp_df['balls_left'] != 0]\n",
    "    result = pipe.predict_proba(temp_df)\n",
    "    temp_df['lose'] = np.round(result.T[0]*100,1)\n",
    "    temp_df['win'] = np.round(result.T[1]*100,1)\n",
    "    temp_df['end_of_over'] = range(1,temp_df.shape[0]+1)\n",
    "    \n",
    "    target = temp_df['total_runs_x'].values[0]\n",
    "    runs = list(temp_df['runs_left'].values)\n",
    "    new_runs = runs[:]\n",
    "    runs.insert(0,target)\n",
    "    temp_df['runs_after_over'] = np.array(runs)[:-1] - np.array(new_runs)\n",
    "    wickets = list(temp_df['wickets'].values)\n",
    "    new_wickets = wickets[:]\n",
    "    new_wickets.insert(0,10)\n",
    "    wickets.append(0)\n",
    "    w = np.array(wickets)\n",
    "    nw = np.array(new_wickets)\n",
    "    temp_df['wickets_in_over'] = (nw - w)[0:temp_df.shape[0]]\n",
    "    \n",
    "    print(\"Target-\",target)\n",
    "    temp_df = temp_df[['end_of_over','runs_after_over','wickets_in_over','lose','win']]\n",
    "    return temp_df,target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "232d9805",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target- 189\n"
     ]
    }
   ],
   "source": [
    "temp_df ,target =match_progression(delivery_df,513,pipe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5b6a3468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Target-189')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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HH+rYsWPq3r27pkyZoosuusgNaYHzc7bn9/PPP+/0U7uLFi1Sr169yjkdUDbnev0+U926dTVy5EiNHDmy/MPhgnhksxoAAAAAAAAAULl41JrVAAAAAAAAAIDKiWY1AAAAAAAAAMDtaFYDAAAAAAAAANyOZjUAAAAAAAAAwO1oVgMAAAAAAAAA3I5mNQAAAAAAAADA7WhWAwAAAAAAAADcjmY1AAAAAAAAAMDtaFYDAAAAAAAAANyOZjUAAAAAAAAAwO1oVgMAAAAAAAAA3I5mNQAAAAAAAADA7f4fWAZyC6WXNKMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(18,8))\n",
    "plt.plot(temp_df['end_of_over'],temp_df['wickets_in_over'],color='yellow',linewidth=3)\n",
    "plt.plot(temp_df['end_of_over'],temp_df['win'],color='#00a65a',linewidth=4)\n",
    "plt.plot(temp_df['end_of_over'],temp_df['lose'],color='red',linewidth=4)\n",
    "plt.bar(temp_df['end_of_over'],temp_df['runs_after_over'])\n",
    "plt.title('Target-' + str(target))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ed71e155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Hyderabad', 'Bangalore', 'Mumbai', 'Indore', 'Kolkata', 'Delhi',\n",
       "       'Chandigarh', 'Jaipur', 'Chennai', 'Cape Town', 'Port Elizabeth',\n",
       "       'Durban', 'Centurion', 'East London', 'Johannesburg', 'Kimberley',\n",
       "       'Bloemfontein', 'Ahmedabad', 'Cuttack', 'Nagpur', 'Dharamsala',\n",
       "       'Visakhapatnam', 'Pune', 'Raipur', 'Ranchi', 'Abu Dhabi',\n",
       "       'Sharjah', nan, 'Mohali', 'Bengaluru'], dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "delivery_df['city'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5e7f7384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sunrisers Hyderabad',\n",
       " 'Mumbai Indians',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kings XI Punjab',\n",
       " 'Chennai Super Kings',\n",
       " 'Rajasthan Royals',\n",
       " 'Delhi Capitals']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7470e2c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(pipe,open('pipe.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2f1ea693",
   "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>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>city</th>\n",
       "      <th>runs_left</th>\n",
       "      <th>balls_left</th>\n",
       "      <th>wickets</th>\n",
       "      <th>total_runs_x</th>\n",
       "      <th>crr</th>\n",
       "      <th>rrr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39700</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>198</td>\n",
       "      <td>98</td>\n",
       "      <td>9</td>\n",
       "      <td>223</td>\n",
       "      <td>7.090909</td>\n",
       "      <td>12.122449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11235</th>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Centurion</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>143</td>\n",
       "      <td>7.425743</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56069</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>62</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "      <td>170</td>\n",
       "      <td>8.493506</td>\n",
       "      <td>8.651163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47815</th>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>118</td>\n",
       "      <td>61</td>\n",
       "      <td>7</td>\n",
       "      <td>179</td>\n",
       "      <td>6.305085</td>\n",
       "      <td>11.606557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20349</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>76</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>165</td>\n",
       "      <td>6.136364</td>\n",
       "      <td>14.250000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66453</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mohali</td>\n",
       "      <td>117</td>\n",
       "      <td>66</td>\n",
       "      <td>9</td>\n",
       "      <td>187</td>\n",
       "      <td>7.888889</td>\n",
       "      <td>10.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51342</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>41</td>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>130</td>\n",
       "      <td>7.826087</td>\n",
       "      <td>4.823529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28054</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>185</td>\n",
       "      <td>99</td>\n",
       "      <td>8</td>\n",
       "      <td>205</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>11.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2737</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>159</td>\n",
       "      <td>116</td>\n",
       "      <td>10</td>\n",
       "      <td>167</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>8.224138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18050</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>120</td>\n",
       "      <td>67</td>\n",
       "      <td>9</td>\n",
       "      <td>171</td>\n",
       "      <td>5.886792</td>\n",
       "      <td>10.746269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>57737 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      batting_team                 bowling_team        city  \\\n",
       "39700          Sunrisers Hyderabad          Chennai Super Kings   Hyderabad   \n",
       "11235             Rajasthan Royals               Delhi Capitals   Centurion   \n",
       "56069               Mumbai Indians  Royal Challengers Bangalore      Mumbai   \n",
       "47815             Rajasthan Royals              Kings XI Punjab  Chandigarh   \n",
       "20349               Mumbai Indians          Chennai Super Kings     Chennai   \n",
       "...                            ...                          ...         ...   \n",
       "66453              Kings XI Punjab               Mumbai Indians      Mohali   \n",
       "51342  Royal Challengers Bangalore             Rajasthan Royals   Ahmedabad   \n",
       "28054  Royal Challengers Bangalore          Chennai Super Kings     Chennai   \n",
       "2737         Kolkata Knight Riders              Kings XI Punjab  Chandigarh   \n",
       "18050          Chennai Super Kings  Royal Challengers Bangalore   Bangalore   \n",
       "\n",
       "       runs_left  balls_left  wickets  total_runs_x        crr        rrr  \n",
       "39700        198          98        9           223   7.090909  12.122449  \n",
       "11235         19          19        5           143   7.425743   6.000000  \n",
       "56069         62          43        8           170   8.493506   8.651163  \n",
       "47815        118          61        7           179   6.305085  11.606557  \n",
       "20349         76          32        3           165   6.136364  14.250000  \n",
       "...          ...         ...      ...           ...        ...        ...  \n",
       "66453        117          66        9           187   7.888889  10.636364  \n",
       "51342         41          51        9           130   7.826087   4.823529  \n",
       "28054        185          99        8           205   6.000000  11.212121  \n",
       "2737         159         116       10           167  13.500000   8.224138  \n",
       "18050        120          67        9           171   5.886792  10.746269  \n",
       "\n",
       "[57737 rows x 9 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  }
 ],
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