1. David Warner vs Virat Kohli - IPL 2015-2020.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1011,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1012,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\utkhare\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3146: DtypeWarning: Columns (15) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "# IPL ball by ball data from 2008 - 2020 from kaggle. Kept only the data from 2015-2020 onwards and removed rest\n",
    "df = pd.read_csv(\"C:/Users/utkhare/Desktop/Documents/Onwards2015.csv\")\n",
    "\n",
    "\n",
    "# \"Delhi Capitals\" renamed as \"Delhi Daredevils\" for better team wise analysis\n",
    "# \"Rising Pune Supergiant\" renamed as \"Rising Pune Supergiants\" for better team wise analysis\n",
    "# Added extra columns for bowler type etc - manually. This results in the warning below\n",
    "# Removed some columns which I felt were not useful"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inital DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1013,
   "metadata": {},
   "outputs": [],
   "source": [
    "warner_df = df[df.batsman == 'DA Warner']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1014,
   "metadata": {},
   "outputs": [],
   "source": [
    "virat_df = df[df.batsman == 'V Kohli']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1015,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data from 2015 - 2020\n",
    "# Goals -: \n",
    "# Strike Rate Comparison in Power Play, Middle Overs and End\n",
    "# Runs Scored Comparison against other teams per innings (not average)\n",
    "# Runs Scored Per IPL edition Comparison \n",
    "# Top run getters of IPL between 2015 - 2020\n",
    "# Strike Rate against best IPL bowlers (Bumrah, ... ??)\n",
    "# Boundary Percentage in match phase \n",
    "# Number of innings in each run bracket (0-10), (11-20) (21-30) ... \n",
    "# Comparing Virat's 100 (58) with Warner's 100(55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1016,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'inning', 'over', 'ball', 'batsman', 'non_striker', 'bowler',\n",
       "       'batsman_runs', 'is_wicket', 'dismissal_kind', 'player_dismissed',\n",
       "       'batting_team', 'bowling_team', 'Bowler Type', 'Bowler Speed',\n",
       "       'Batsman Type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1016,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strike Rate Comparison in each phase "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1022,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1727, 1310, 254)"
      ]
     },
     "execution_count": 1022,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Warner's Score in each phase of innings\n",
    "# Overs 0 - 5 - PowerPlay \n",
    "# Overs 6 - 15 - Middle Overs\n",
    "# Overs 16 - 20 - Death Overs\n",
    "\n",
    "(warner_df[(warner_df.over < 6)].batsman_runs.sum() , \n",
    "warner_df[(warner_df.over >= 6) & (warner_df.over < 15)].batsman_runs.sum() , \n",
    "warner_df[(warner_df.over >= 15)].batsman_runs.sum() ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1023,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(138.82636655948554, 139.06581740976645, 180.1418439716312)"
      ]
     },
     "execution_count": 1023,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "warner_sr = (100 * warner_df[(warner_df.over < 6)].batsman_runs.sum() / \n",
    "                len (warner_df[(warner_df.over < 6)].batsman_runs), \n",
    "                                                                           \n",
    "             100 *   warner_df[(warner_df.over >= 6) & (warner_df.over < 15)].batsman_runs.sum() / \n",
    "                 len(warner_df[(warner_df.over >= 6) & (warner_df.over < 15)].batsman_runs) , \n",
    "                                                                           \n",
    "             100 *    warner_df[(warner_df.over >= 15)].batsman_runs.sum() / \n",
    "                 len(warner_df[(warner_df.over >= 15)].batsman_runs))\n",
    "\n",
    "warner_sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1020,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(123.58393408856848, 127.71503040834057, 190.72847682119206)"
      ]
     },
     "execution_count": 1020,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Virat's SR in each phase of innings\n",
    "# Overs 0 - 5 - PowerPlay \n",
    "# Overs 6 - 15 - Middle Overs\n",
    "# Overs 16 - 20 - Death Overs\n",
    "\n",
    "virat_sr = (100 * virat_df[(virat_df.over < 6)].batsman_runs.sum() / \n",
    "                len (virat_df[(virat_df.over < 6)].batsman_runs), \n",
    "                                                                           \n",
    "             100 *   virat_df[(virat_df.over >= 6) & (virat_df.over < 15)].batsman_runs.sum() / \n",
    "                len(virat_df[(virat_df.over >= 6) & (virat_df.over < 15)].batsman_runs) , \n",
    "                                                                           \n",
    "             100 *    virat_df[(virat_df.over >= 15)].batsman_runs.sum() / \n",
    "                len(virat_df[(virat_df.over >= 15)].batsman_runs))\n",
    "\n",
    "virat_sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1025,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Some Visualization \n",
    "\n",
    "X = ['PowerPlay','Middle Overs','Death Overs']\n",
    "X_axis = np.arange(len(X))\n",
    "  \n",
    "plt.bar(X_axis - 0.1, virat_sr, 0.2, label = 'Virat')\n",
    "plt.bar(X_axis + 0.1, warner_sr, 0.2, label = 'Warner')\n",
    "  \n",
    "plt.xticks(X_axis, X)\n",
    "plt.xlabel(\"Overs\")\n",
    "plt.ylabel(\"Strike Rate\")\n",
    "plt.title(\"Phase Wise SR Comparison\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performances against other teams (total runs, runs per innings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1026,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'inning', 'over', 'ball', 'batsman', 'non_striker', 'bowler',\n",
       "       'batsman_runs', 'is_wicket', 'dismissal_kind', 'player_dismissed',\n",
       "       'batting_team', 'bowling_team', 'Bowler Type', 'Bowler Speed',\n",
       "       'Batsman Type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1026,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1028,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bowling_team\n",
       "Chennai Super Kings      258\n",
       "Delhi Daredevils         372\n",
       "Gujarat Lions            336\n",
       "Kings XI Punjab          609\n",
       "Kolkata Knight Riders    578\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 1028,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Warner's Total score against each of the teams\n",
    "warner_score_each_team = warner_df.groupby('bowling_team').batsman_runs.sum()\n",
    "\n",
    "warner_score_each_team.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1029,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bowling_team\n",
       "Chennai Super Kings       6\n",
       "Delhi Daredevils         11\n",
       "Gujarat Lions             5\n",
       "Kings XI Punjab          10\n",
       "Kolkata Knight Riders    12\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 1029,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Warner's Total innings against each of the teams \n",
    "warner_innings_each_team = warner_df.groupby('bowling_team').id.nunique()\n",
    "\n",
    "warner_innings_each_team.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1030,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bowling_team\n",
       "Chennai Super Kings      43.000000\n",
       "Delhi Daredevils         33.818182\n",
       "Gujarat Lions            67.200000\n",
       "Kings XI Punjab          60.900000\n",
       "Kolkata Knight Riders    48.166667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1030,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Warner's RPI against each team \n",
    "warner_rpi = (warner_score_each_team / warner_innings_each_team)\n",
    "warner_rpi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1031,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bowling_team\n",
       "Chennai Super Kings      292\n",
       "Delhi Daredevils         463\n",
       "Gujarat Lions            283\n",
       "Kings XI Punjab          388\n",
       "Kolkata Knight Riders    513\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 1031,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Virat's Total score against each of the teams\n",
    "virat_score_each_team = virat_df.groupby('bowling_team').batsman_runs.sum()\n",
    "\n",
    "virat_score_each_team.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1032,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bowling_team\n",
       "Chennai Super Kings       9\n",
       "Delhi Daredevils         11\n",
       "Gujarat Lions             5\n",
       "Kings XI Punjab          11\n",
       "Kolkata Knight Riders    12\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 1032,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Virat's Total innings against each of the teams \n",
    "virat_innings_each_team = virat_df.groupby('bowling_team').id.nunique()\n",
    "\n",
    "virat_innings_each_team.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1033,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bowling_team\n",
       "Chennai Super Kings      32.444444\n",
       "Delhi Daredevils         42.090909\n",
       "Gujarat Lions            56.600000\n",
       "Kings XI Punjab          35.272727\n",
       "Kolkata Knight Riders    42.750000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1033,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Virat's RPI against each team \n",
    "virat_rpi = (virat_score_each_team / virat_innings_each_team)\n",
    "virat_rpi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1034,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Some Visualization \n",
    "\n",
    "X = ['CSK','DD','GL','KXIP','KKR','MI', 'RR','RPS','SRH/RCB']\n",
    "X_axis = np.arange(len(X))\n",
    "  \n",
    "plt.bar(X_axis - 0.1, virat_rpi, 0.2, label = 'Virat')\n",
    "plt.bar(X_axis + 0.1, warner_rpi, 0.2, label = 'Warner')\n",
    "  \n",
    "plt.xticks(X_axis, X)\n",
    "plt.xlabel(\"Opponent Team\")\n",
    "plt.ylabel(\"RPI\")\n",
    "plt.title(\"Team Comparison\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Runs Per IPL Edition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1035,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We don't have date record in the CSV \n",
    "# We can rely on id (matching the cricinfo match Id, \n",
    "# but that requires some bad coding with MAGIC numbers)\n",
    "# 829705 - 829823 : 2015 \n",
    "# 980901 - 981019 : 2016 \n",
    "# 1082591 - 1082650 : 2017 \n",
    "# 1136561- 1136620 : 2018 \n",
    "# 1175356 - 1181768 : 2019 \n",
    "# 1216492 - 1237181 : 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1036,
   "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>id</th>\n",
       "      <th>inning</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>is_wicket</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>Bowler Type</th>\n",
       "      <th>Bowler Speed</th>\n",
       "      <th>Batsman Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>829705</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Right arm pace</td>\n",
       "      <td>Fast</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  inning  over  ball    batsman non_striker      bowler  \\\n",
       "0  829705       2     0     2  G Gambhir  RV Uthappa  SL Malinga   \n",
       "\n",
       "   batsman_runs  is_wicket dismissal_kind player_dismissed  \\\n",
       "0             0          0            NaN              NaN   \n",
       "\n",
       "            batting_team    bowling_team     Bowler Type Bowler Speed  \\\n",
       "0  Kolkata Knight Riders  Mumbai Indians  Right arm pace         Fast   \n",
       "\n",
       "  Batsman Type  \n",
       "0         Left  "
      ]
     },
     "execution_count": 1036,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1037,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "562"
      ]
     },
     "execution_count": 1037,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample Check for 2015 Warner Runs Scored\n",
    "warner_df[(warner_df.id >= 829705) & (warner_df.id <= 829823)].batsman_runs.sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1038,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(562, 848, 641, 0, 692, 548)"
      ]
     },
     "execution_count": 1038,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Warner record in each IPL \n",
    "# Warner missed the 2018 edition due to ban\n",
    "\n",
    "warner_total = ( \n",
    "    warner_df[(warner_df.id >= 829705) & (warner_df.id <= 829823)].batsman_runs.sum() , \n",
    "    warner_df[(warner_df.id >= 980901) & (warner_df.id <= 981019)].batsman_runs.sum() , \n",
    "    warner_df[(warner_df.id >= 1082591) & (warner_df.id <= 1082650)].batsman_runs.sum() , \n",
    "    warner_df[(warner_df.id >= 1136561) & (warner_df.id <= 1136620)].batsman_runs.sum() , \n",
    "    warner_df[(warner_df.id >= 1175356) & (warner_df.id <= 1181768)].batsman_runs.sum() , \n",
    "    warner_df[(warner_df.id >= 1216492) & (warner_df.id <= 1237181)].batsman_runs.sum() , \n",
    ")\n",
    "\n",
    "warner_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1039,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(505, 973, 308, 530, 464, 466)"
      ]
     },
     "execution_count": 1039,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Virat record in each IPL \n",
    "\n",
    "virat_total = ( \n",
    "    virat_df[(virat_df.id >= 829705) & (virat_df.id <= 829823)].batsman_runs.sum() ,\n",
    "    virat_df[(virat_df.id >= 980901) & (virat_df.id <= 981019)].batsman_runs.sum() , \n",
    "    virat_df[(virat_df.id >= 1082591) & (virat_df.id <= 1082650)].batsman_runs.sum() , \n",
    "    virat_df[(virat_df.id >= 1136561) & (virat_df.id <= 1136620)].batsman_runs.sum() , \n",
    "    virat_df[(virat_df.id >= 1175356) & (virat_df.id <= 1181768)].batsman_runs.sum() , \n",
    "    virat_df[(virat_df.id >= 1216492) & (virat_df.id <= 1237181)].batsman_runs.sum() , \n",
    ")\n",
    "\n",
    "virat_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1040,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Some Visualization \n",
    "\n",
    "X = ['2015','2016','2017','2018','2019','2020']\n",
    "X_axis = np.arange(len(X))\n",
    "  \n",
    "plt.bar(X_axis - 0.1, virat_total, 0.2, label = 'Virat')\n",
    "plt.bar(X_axis + 0.1, warner_total, 0.2, label = 'Warner')\n",
    "  \n",
    "plt.xticks(X_axis, X)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Runs\")\n",
    "plt.title(\"Total Score Comparison\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Total Run Getters in IPL since 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1041,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'inning', 'over', 'ball', 'batsman', 'non_striker', 'bowler',\n",
       "       'batsman_runs', 'is_wicket', 'dismissal_kind', 'player_dismissed',\n",
       "       'batting_team', 'bowling_team', 'Bowler Type', 'Bowler Speed',\n",
       "       'Batsman Type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1041,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1044,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "batsman\n",
       "DA Warner         3291\n",
       "V Kohli           3246\n",
       "S Dhawan          2969\n",
       "AB de Villiers    2792\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 1044,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_batsmen = df.groupby('batsman').batsman_runs.sum().sort_values(ascending = False).head(10)\n",
    "\n",
    "top_batsmen.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1045,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='batsman'>"
      ]
     },
     "execution_count": 1045,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Some Visualization \n",
    "top_batsmen.iloc[::-1].plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strike Rate against best bowlers of IPL "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1046,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'inning', 'over', 'ball', 'batsman', 'non_striker', 'bowler',\n",
       "       'batsman_runs', 'is_wicket', 'dismissal_kind', 'player_dismissed',\n",
       "       'batting_team', 'bowling_team', 'Bowler Type', 'Bowler Speed',\n",
       "       'Batsman Type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1046,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1050,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bowler\n",
       "YS Chahal         109\n",
       "JJ Bumrah         101\n",
       "B Kumar            92\n",
       "Sandeep Sharma     83\n",
       "Rashid Khan        75\n",
       "DJ Bravo           74\n",
       "Imran Tahir        71\n",
       "MJ McClenaghan     71\n",
       "UT Yadav           65\n",
       "CH Morris          65\n",
       "Name: is_wicket, dtype: int64"
      ]
     },
     "execution_count": 1050,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best Bowler .. Bowlers with most wickets ? \n",
    "# Most Wickets \n",
    "\n",
    "wickets_without_runouts = df[(df.is_wicket == 1) & (df.dismissal_kind != 'run out') ]\n",
    "\n",
    "bowler_most_wickets = wickets_without_runouts.groupby('bowler').is_wicket.sum().sort_values(ascending = False).head(10)\n",
    "\n",
    "bowler_most_wickets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1051,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to keep separate lists for Warner and Virat since the top bowlers (Chahal, Bhuvi, Rashid etc) \n",
    "# played for the same teams as them\n",
    "\n",
    "# Considering only Chahal, Bumrah, Bravo, Tahir and McClenaghan for Warner \n",
    "# Considering only Bumrah, Bhuvi, Sandeep Sharma, Rashid Khan, Bravo for Virat \n",
    "# Including Rabada for both since he has been amazing in the last two years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1052,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(207.69230769230768,\n",
       " 122.58064516129032,\n",
       " 100.0,\n",
       " 190.47619047619048,\n",
       " 157.57575757575756,\n",
       " 162.85714285714286)"
      ]
     },
     "execution_count": 1052,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "warner_sr = ( 100 * warner_df[warner_df.bowler == 'YS Chahal'].batsman_runs.sum() \n",
    "             / warner_df[warner_df.bowler == 'YS Chahal'].batsman_runs.count() , \n",
    "                  \n",
    "              100 *     warner_df[warner_df.bowler == 'JJ Bumrah'].batsman_runs.sum() \n",
    "             / warner_df[warner_df.bowler == 'JJ Bumrah'].batsman_runs.count() , \n",
    "             \n",
    "             100 *     warner_df[warner_df.bowler == 'DJ Bravo'].batsman_runs.sum() \n",
    "             / warner_df[warner_df.bowler == 'DJ Bravo'].batsman_runs.count()  , \n",
    "             \n",
    "             100 *     warner_df[warner_df.bowler == 'Imran Tahir'].batsman_runs.sum() \n",
    "             / warner_df[warner_df.bowler == 'Imran Tahir'].batsman_runs.count() , \n",
    "             \n",
    "             100 *     warner_df[warner_df.bowler == 'MJ McClenaghan'].batsman_runs.sum() \n",
    "             / warner_df[warner_df.bowler == 'MJ McClenaghan'].batsman_runs.count() , \n",
    "              \n",
    "             100 *     warner_df[warner_df.bowler == 'K Rabada'].batsman_runs.sum() \n",
    "             / warner_df[warner_df.bowler == 'K Rabada'].batsman_runs.count()\n",
    "            )\n",
    "\n",
    "warner_sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1053,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(146.3768115942029,\n",
       " 135.13513513513513,\n",
       " 122.91666666666667,\n",
       " 94.73684210526316,\n",
       " 132.0,\n",
       " 95.23809523809524)"
      ]
     },
     "execution_count": 1053,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "virat_sr = ( 100 * virat_df[virat_df.bowler == 'JJ Bumrah'].batsman_runs.sum() \n",
    "             / virat_df[virat_df.bowler == 'JJ Bumrah'].batsman_runs.count() , \n",
    "                  \n",
    "              100 *     virat_df[virat_df.bowler == 'B Kumar'].batsman_runs.sum() \n",
    "             / virat_df[virat_df.bowler == 'B Kumar'].batsman_runs.count() , \n",
    "             \n",
    "             100 *     virat_df[virat_df.bowler == 'Sandeep Sharma'].batsman_runs.sum() \n",
    "             / virat_df[virat_df.bowler == 'Sandeep Sharma'].batsman_runs.count()  , \n",
    "             \n",
    "             100 *     virat_df[virat_df.bowler == 'Rashid Khan'].batsman_runs.sum() \n",
    "             / virat_df[virat_df.bowler == 'Rashid Khan'].batsman_runs.count() , \n",
    "             \n",
    "             100 *     virat_df[virat_df.bowler == 'Imran Tahir'].batsman_runs.sum() \n",
    "             / virat_df[virat_df.bowler == 'Imran Tahir'].batsman_runs.count() , \n",
    "            \n",
    "             100 *     virat_df[virat_df.bowler == 'K Rabada'].batsman_runs.sum() \n",
    "             / virat_df[virat_df.bowler == 'K Rabada'].batsman_runs.count()\n",
    "            )\n",
    "\n",
    "virat_sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1054,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Some Visualization \n",
    "# This cannot be a exact comparative chart, since the bowlers are not all the same\n",
    "\n",
    "warner_X = ['Chahal','Bumrah','Bravo','Tahir','McClenaghan', 'Rabada']\n",
    "warner_X_axis = np.arange(len(warner_X))\n",
    "\n",
    "\n",
    "plt.bar(warner_X_axis - 0.1, warner_sr, 0.2)\n",
    "  \n",
    "plt.xticks(warner_X_axis, warner_X)\n",
    "plt.xlabel(\"Bowlers\")\n",
    "plt.ylabel(\"Warner S/R\")\n",
    "plt.title(\"S/R Comparison\")\n",
    "plt.show()\n",
    "\n",
    "virat_X = ['Bumrah','Bhuvi','Sandeep','Rashid','Tahir', 'Rabada']\n",
    "virat_X_axis = np.arange(len(virat_X))\n",
    "  \n",
    "plt.bar(virat_X_axis - 0.1, virat_sr, 0.2)\n",
    "  \n",
    "plt.xticks(virat_X_axis, virat_X)\n",
    "plt.xlabel(\"Bowlers\")\n",
    "plt.ylabel(\"Virat S/R\")\n",
    "plt.title(\"S/R Comparison\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boundary and Dot Ball Percentage in each match phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1055,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'inning', 'over', 'ball', 'batsman', 'non_striker', 'bowler',\n",
       "       'batsman_runs', 'is_wicket', 'dismissal_kind', 'player_dismissed',\n",
       "       'batting_team', 'bowling_team', 'Bowler Type', 'Bowler Speed',\n",
       "       'Batsman Type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1055,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1056,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "268"
      ]
     },
     "execution_count": 1056,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Warner Boundary Balls in first 6 overs\n",
    "warner_df[(warner_df.over < 6) & ((warner_df.batsman_runs == 4) | (warner_df.batsman_runs == 6))].ball.count() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1057,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1244"
      ]
     },
     "execution_count": 1057,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Warner Total Balls faced in first 6 overs\n",
    "warner_df[(warner_df.over < 6)].ball.count() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1058,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21.543408360128616, 14.225053078556263, 25.53191489361702)"
      ]
     },
     "execution_count": 1058,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "warner_boundary_percentages = (\n",
    "    100 * warner_df[(warner_df.over < 6) & ((warner_df.batsman_runs == 4) | (warner_df.batsman_runs == 6))].ball.count() / \n",
    "                warner_df[(warner_df.over < 6)].ball.count() , \n",
    "    \n",
    "    100 * warner_df[(warner_df.over >= 6) & (warner_df.over < 15) & ((warner_df.batsman_runs == 4) | (warner_df.batsman_runs == 6))].ball.count() / \n",
    "                warner_df[(warner_df.over >= 6) & (warner_df.over < 15)].ball.count(), \n",
    "    \n",
    "    100 * warner_df[(warner_df.over >= 15) & ((warner_df.batsman_runs == 4) | (warner_df.batsman_runs == 6))].ball.count() / \n",
    "                warner_df[(warner_df.over >= 15)].ball.count()\n",
    ")\n",
    "\n",
    "warner_boundary_percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1059,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17.30175077239959, 11.815812337098176, 24.172185430463575)"
      ]
     },
     "execution_count": 1059,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "virat_boundary_percentages = (\n",
    "    100 * virat_df[(virat_df.over < 6) & ((virat_df.batsman_runs == 4) | (virat_df.batsman_runs == 6))].ball.count() / \n",
    "                virat_df[(virat_df.over < 6)].ball.count() , \n",
    "    \n",
    "    100 * virat_df[(virat_df.over >= 6) & (virat_df.over < 15) & ((virat_df.batsman_runs == 4) | (virat_df.batsman_runs == 6))].ball.count() / \n",
    "                virat_df[(virat_df.over >= 6) & (virat_df.over < 15)].ball.count(), \n",
    "    \n",
    "    100 * virat_df[(virat_df.over >= 15) & ((virat_df.batsman_runs == 4) | (virat_df.batsman_runs == 6))].ball.count() / \n",
    "                virat_df[(virat_df.over >= 15)].ball.count()\n",
    ")\n",
    "\n",
    "virat_boundary_percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1060,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nnnty5513cvbZZ/Pqq6/y4osvsv3227+d+Jv3x3PwwQcDsOuuuzJr1iwAbrvtNqZNm8bVV18NpE7eZs6cydprr82wYcOc9M2sQ5z4V0FTO/8jjzzC4MGDGTRoEOeeey7rr78+Rx11FEcffTSNjY0MGjSIM8444x3dMFd21wywzjrrANCtW7e32/0jgvPPP/8dP+8Iqamn+evNzKrlNv5VMHz4cCZOnMhGG21Et27d2GijjVi4cCH33Xcfe+yxBwD9+/dnyZIlb9faV8b+++/PhRde+PYPtjz55JO88sornfoezKx81pwafwE/SNyeHXbYgfnz53P44Ye/o2zJkiX079+fY445hh122IGGhgZ22223lV7+0UcfzaxZs9hll12ICAYMGMD111/fie/AzMqosG6ZO5O7ZV553j5WZl3TZcPh7c/UGQroltlNPWZmJePEb2ZWMnWd+OuhmaoWvF3MrC11m/h79uzJggULnOSaiQgWLFhAz5513GesmRWqbu/q2WyzzZg9ezbz5s2rdSirnZ49e7LZZpvVOgwzW03VbeLv0aOHn1w1M+uAum3qMTOzjnHiNzMrGSd+M7OSqds2/tVOZ/8CWIvr6PpuKcxszeMav5lZyTjxm5mVjBO/mVnJOPGbmZWME7+ZWck48ZuZlYwTv5lZyTjxm5mVTGGJX9IgSXdKmiHpUUnH5fKNJN0uaWb+v2FRMZiZ2bsVWeNfBpwQEdsCuwPHStoOOAm4IyK2Au7I42Zm1kUKS/wR8XxEPJyHFwMzgIHAgcDlebbLgYOKisHMzN6tS9r4JTUAOwMPABtHxPOQvhyA93RFDGZmlhSe+CX1Bq4Bjo+IRSvxujGSGiU1+le2zMw6T6GJX1IPUtL/fURcm4tfkLRJnr4JMLel10bERRExNCKGDhgwoMgwzcxKpd3EL2ljSZdI+nMe307Sl6t4nYBLgBkRcV7FpBuA0Xl4NPCnlQ/bzMw6qpoa/2XArcCmefxJ4PgqXjccOALYV9LU/HcAcBbwEUkzgY/kcTMz6yLV/BBL/4i4StLJABGxTNLy9l4UEX8D1MrkD69EjGZm1omqqfG/IqkfEACSdgf8U1BmZnWqmhr/OFK7/JaS7gUGAIcUGpWZmRWm3cQfEQ9L2gf4AKnp5omIeLPwyMzMrBDtJn5JBzcr2lrSy8AjEdHirZhmZrb6qqap58vAHsCdeXwEcD/pC+DMiLiioNjMzKwA1ST+t4BtI+IFSPf1AxcCHwTuBpz4zczqSDV39TQ0Jf1sLrB1RLwIuK3fzKzOVFPjv0fSROCPefwzwN2SegELiwrMzMyKUU3iP5aU7IeT7ur5LXBNRAQwssDYzMysANXczhnA1fnPzMzqXDWdtO0u6UFJSyS9IWm5pKq7VzYzs9VLNRd3LwBGATOBdYGjgfOLDMrMzIpTTRs/EfGUpG4RsRz4jaTJBcdlZmYFqSbxvyppbWCqpLOB54FexYZlZmZFqaap54g831jgFWAQ0LwbBzMzqxPVJP6DIuL1iFgUEd+PiHHAJ4sOzMzMilFNU89o4OfNyo5soWy11HDSTV2ynlk9u2Q1ZmarrNXEL2kUcDiwhaQbKib1ARYUHZiZmRWjrRr/ZNKF3P7AuRXli4FpRQZlZmbFaTXxR8QzwDOkLpnNzGwNUc2TuwdLminpZUmLJC32k7tmZvWrmou7ZwOfiogZRQdjZmbFq+Z2zhec9M3M1hzV1PgbJU0ArgeWNhVGxLVFBWVmZsWpJvGvD7wKfLSiLAAnfjOzOlRNf/xf6opAzMysa1RzV8/Wku6QND2P7yjp1OJDMzOzIlRzcffXwMnkH1aPiGnA54oMyszMilNN4l8vIqY0K1tWRDBmZla8ahL/fElbki7oIukQUlcOZmZWh6q5q+dY4CJgG0nPAf8EvlBoVGZmVphq7up5GthPUi9grYhYXHxYZmZWlGru6vmxpA0i4pWIWCxpQ0k/rOJ1l0qa23Q3UC47Q9JzkqbmvwNW9Q2YmdnKqaaN/+MRsbBpJCJeAqpJ2JcBH2uh/L8jYkj+u7mqKM3MrNNUk/i7SVqnaUTSusA6bcwPQETcDby4CrGZmVkBqkn8vwPukPRlSUcBtwOXr8I6x0qalpuCNmxtJkljJDVKapw3b94qrM7MzCq1mfglCbgS+CGwLbA98IOIOLuD67sQ2BIYQrol9NzWZoyIiyJiaEQMHTBgQAdXZ2ZmzbV5V09EhKTrI2JX4JZVXVlEvNA0LOnXwMRVXaaZma2capp67pe0W2esTNImFaOfBqa3Nq+ZmRWjmge4RgJflTQLeAUQ6WRgx7ZeJOlKYATQX9Js4HRghKQhpKeAZwFf6WjgZmbWMdUk/o93ZMERMaqF4ks6siwzM+s81Ty5+4ykvYCtIuI3kgYAvYsPzawOnNG3i9bzctesx0qhmid3TwdOJHXNDNCDdIunmZnVoWou7n4a+E9S+z4RMQfoU2RQZmZWnGoS/xsREazolrlXsSGZmVmRqkn8V0n6FbCBpGOASaRf5TIzszpUzcXdcyR9BFgEfAA4LSJuLzwyMzMrRKuJX9JWwDmkLhYeAb4VEc91VWBmZlaMtpp6LiV1qfAZ4CHg/C6JyMzMCtVWU0+fiGhqy39C0sNdEZCZmRWrrcTfU9LOpC4aANatHI8IfxGYmdWhthL/88B5FeP/rhgPYN+igjIzs+K0mvgjYmRXBmJmZl2jmvv4zcxsDeLEb2ZWMk78ZmYlU03vnNdI+oQkf0mYma0BqknmFwKHAzMlnSVpm4JjMjOzArWb+CNiUkR8HtiF9HOJt0uaLOlLknoUHaCZmXWuqppvJPUDjgSOBv4O/Jz0ReDO2szM6ky7vXNKuhbYBrgC+FREPJ8nTZDUWGRwZmbW+dpM/PmC7tSIOLil6RExtJCozDpBw0k3Fb6OWT0LX4VZp2uzqSci3gI+3kWxmJlZF6imjf82SZ+RpPZnNTOz1V27bfzAOKAXsEzS66TeOSMi1i80MjMzK0Q1P73YpysCMTOzrlFNjR9JGwJbAW9fyoqIu4sKyszMilPN7ZxHA8cBmwFTgd2B+3B//GZmdamai7vHAbsBz+Q++ncG5hUalZmZFaaaxP96RLwOIGmdiHgc+ECxYZmZWVGqaeOfLWkD4HpSPz0vAXOKDMrMzIpTzV09n86DZ0i6E+gL3FJoVGZmVphWE7+kjVoofiT/7w28WEhEZmZWqLZq/A8BQXpg673AS3l4A+BfwBZtLVjSpcAngbkRMTiXbQRMABpIXTx/NiJeWpU3YGZmK6fVi7sRsUVEvA+4ldQrZ/+I6EdK5tdWsezLgI81KzsJuCMitgLuyONmZtaFqrmrZ7eIuLlpJCL+DOzT3ovyA17Nm4MOBC7Pw5cDB1UXppmZdZZqEv98SadKapC0uaTvAgs6uL6Nm/rzz//f09qMksZIapTUOG+eHxswM+ss1ST+UcAA4DrSLZ3vyWWFioiLImJoRAwdMGBA0aszMyuNam7nfJH09G5neEHSJhHxvKRNgLmdtFwzM6tSNX31bA18i3QnztvzR0RH+uq5ARgNnJX//6kDyzAzs1VQzZO7fwR+CVwMLK92wZKuBEYA/SXNBk4nJfyrJH2ZdEvooSsbsJmZrZpqEv+yiLhwZRccEa1dB/jwyi7LzMw6TzUXd2+U9HVJm0jaqOmv8MjMzKwQ1dT4R+f/364oC+B9nR+OmZkVrZq7etrsmsHMzOpLNXf1fLGl8oj4beeHY2ZmRaumqWe3iuGepIuzDwNO/GZmdaiapp5vVI5L6gtcUVhEZmZWqGru6mnuVWCrzg7EzMy6RjVt/DeS7uIB6AZsC1xVZFBmZlacatr4z6kYXgY8ExGzC4rHzMwK1m5TT0T8FXgc6ANsCLxRdFBmZlacdhO/pM8CU0j96nwWeEDSIUUHZmZmxaimqee7pF/hmgsgaQAwCbi6yMDMzKwY1dzVs1ZT0s8WVPk6MzNbDVVT479F0q3AlXn8MODmNuY3M7PVWDUPcH1b0sHAXoCAiyLiusIjMzOzQlRT4ycirgWuldSfjv/QupmZrQZabauXtLukuyRdK2lnSdOB6aTfzf1Y14VoZmadqa0a/wXAKUBf4C/AxyPifknbkNr7b+mC+MzMrJO1dXdO94i4LSL+CPw7Iu4HiIjHuyY0MzMrQluJ/62K4deaTQvMzKwutdXUs5OkRaQ7edbNw+TxnoVHZmZmhWg18UdEt64MxMzMuoafwDUzKxknfjOzknHiNzMrGSd+M7OSceI3MysZJ34zs5Jx4jczKxknfjOzknHiNzMrmar64+9skmYBi4HlwLKIGFqLOMzMyqgmiT8bGRHza7h+M7NSclOPmVnJ1CrxB3CbpIckjWlpBkljJDVKapw3b14Xh2dmtuaqVeIfHhG7AB8HjpW0d/MZIuKiiBgaEUMHDBjQ9RGama2hapL4I2JO/j8XuA4YVos4zMzKqMsTv6Rekvo0DQMfJf2Iu5mZdYFa3NWzMXCdpKb1/yEi/MPtZmZdpMsTf0Q8DezU1es1M7PEt3OamZWME7+ZWck48ZuZlYwTv5lZyTjxm5mVjBO/mVnJOPGbmZWME7+ZWck48ZuZlYwTv5lZyTjxm5mVjBO/mVnJOPGbmZWME7+ZWck48ZuZlYwTv5lZyTjxm5mVjBO/mVnJOPGbmZWME7+ZWck48ZuZlYwTv5lZyTjxm5mVjBO/mVnJOPGbmZWME7+ZWck48ZuZlYwTv5lZyTjxm5mVjBO/mVnJOPGbmZWME7+ZWcnUJPFL+pikJyQ9JemkWsRgZlZWXZ74JXUD/gf4OLAdMErSdl0dh5lZWdWixj8MeCoino6IN4DxwIE1iMPMrJS612CdA4FnK8ZnAx9sPpOkMcCYPLpE0hNdEFuHCfoD8wtdyfdV6OJt5XXJfgfv+9VQnez7zVsqrEXib+ldxLsKIi4CLio+nM4hqTEihtY6Duta3u/lVc/7vhZNPbOBQRXjmwFzahCHmVkp1SLxPwhsJWkLSWsDnwNuqEEcZmal1OVNPRGxTNJY4FagG3BpRDza1XEUoG6apaxTeb+XV93ue0W8q3ndzMzWYH5y18ysZJz4zcxKphSJX9JySVMlTZf0R0nr1XK9kpZ0xfrLQlJIuqJivLukeZIm5vH/bK1rkNb2haTLJB2Sh++SVPVte0pOlTRT0pOS7pS0/cq9K6tWxefsUUn/J2mcpA7lNkkbSPp6xfiIpuOondfV1T4vReIHXouIIRExGHgD+GqRK8vdUnT5ekvsFWCwpHXz+EeA55omRsQNEXFWF8ZzLLAnsFNEbA38BLhBUs9VXXDFsWUrNH3Otift+wOA0zu4rA2Ar7c3Uwvqap+XJfFXugd4v6SNJF0vaZqk+yXtCCDpkfytL0kLJH0xl18haT9J3ST9VNKD+bVfydNH5G/5PwCPtLbeygJJvSXdIenhvN4Dc/kPJB1XMd+PJP1XMZtjjfFn4BN5eBRwZdMESUdKuiAPbyHpvrz/flAxjyRdIOkxSTcB72lpJZI+ml//cD6L693CbCcC34iIVwEi4jZgMvB5SV+TdHaz2M7Pw1+QNCXXXn/V9IGXtETSmZIeAPaQdFaOc5qkczq8xdZAETGX9MT/2LxPW/u8tvjZA84Ctsz74Ke5rLekqyU9Lun3klp6CLW+9nlErPF/wJL8vzvwJ+BrwPnA6bl8X2BqHv4lKYEMJj1z8OtcPhPoTTqoTs1l6wCNwBbACFLNc4u21ttC+fp5uD/wFOnJ5gbg4Vy+FvAPoF+tt+Pq+gcsAXYErgZ6AlPz/piYpx8JXJCHbwC+mIePrdgXBwO3k24x3hRYCBySp90FDM376G6gVy4/ETitWSzrAy+2EONxwHnAAFJfVU3lfwb2ArYFbgR65PL/rYgzgM/m4Y2AJ1hxR94Gtd7+tf5r2ofNyl4CNm7j89rWZ296xXJGAC+THjRdC7gP2Kve93ktumyohXUlTc3D9wCXAA8AnwGIiL9I6iepb56+N/AMcCEwRtJA0o5dIumjwI7K7b9AX2ArUlPOlIj4ZzvrrSTgx5L2Bt4i9WO0cUTMymcbO5MO3r9HxIJO2RJrqIiYJqmBVNu/uY1Zh5P3O3AF8P/y8N7AlRGxHJgj6S8tvHZ3Uo+y9+ZK39qkRFANpTBjnqSnJe1Oqkx8ALiX9CW0K/BgXva6wNz82uXANXl4EfA6cHE+M2m3/bmkmmrlrX1eZ9PCZ6+VZU2JiNkA+fPcAPytyhhWy31elsT/WkQMqSxo5XQtSDW6Y4H3At8FPg0cQkrckHbmNyLi1mbLG0Gq8be53mY+T6oN7BoRb0qaRaqxAlxMqqn+B3BpG8uwFW4AziHV0vq1MV9rD6+091CLgNsjYlSrC45YJOkVSe+LiKcrJu0C/DUPTwA+CzwOXBcRkY/HyyPi5BYW+3r+QiLSA5DDgA+TnnofSzpjtUzS+0iJcy6tf16PpPXPXnNLK4aX0yxv1uM+L2Mbf5O7SYm3KWnPj4hFEfEs6dRvq7wT/wZ8ixWJ/1bga5J65NduLalXB2PoC8zNB95I3tmT3nXAx4Dd8jqtfZcCZ0ZES9dYmtxL+vBA3v/Z3cDncpvwJsDIFl57PzBc0vsBJK0naesW5vsp8Avli82S9iOd2v8hT78WOIh0djIhl90BHCLpPfk1G0l6V8+K+ZpC34i4GTgeGNLGey0dSQNIzbUXRGoXae3z2tpnbzHQpwOrrqt9XpYaf0vOAH4jaRrwKjC6YtoDpLZeSAn/J6w4tbuY3Aafv7HnkXZoR/weuFFSI6ld+vGmCRHxhqQ7gYVN3/zWtnw6/vN2ZjsO+IPSxfNrKsqvI9WiHgGeZEVNrXL583JN8UpJ6+TiU/P8lc4HNgQekbQc+DdwYES8lpfzkqTHgO0iYkoue0zSqcBtSrcivkk683ym2bL7AH9SultEwDfbeb9l0NSk2gNYRmrCOy9Pa+3z2uJnLyIWSLpX0nRSW/xNVcZQV/vcXTaspvKB8DBwaETMrHU8ZrbmKHNTz2pL6aconwLucNI3s87mGr+ZWcm4xm9mVjJO/GZmJePEb2ZWMk78tkZQOz10tvG6IZIOqGL57faomvtgmZf7XXlM0jG5/AxJ36rmfZh1BSd+W1O02UNnG4aQenPsLBPy09ojSF0CtNYNgFnNOPHbmqStHjqHSZos6e/5/wckrQ2cCRyWa+mHKfXa+BulHhunSfpMxTJ+pNTf+/3tJfRIvUT+gxVPhG6n1K//06roaVWph9iHlPqSH5PLuin9HsD0HMc3c/mWkm7J898jaZtV32RWRk78tiYZT+p2oSept84HKqY9DuwdETsDpwE/jog38vCESP25TwC+B7wcETtExI5AU2dtvYD7I2InUvcOx7QVSO4v5n2k5zEAtgH2B4YBpzd1IQAcFRG7knr//C9J/UhnIQMjYnBE7AD8Js97EanfmV1J3Yj870puHzOg3F022BqmnR46+wKXS9qK1BlbD1q2Hyv68iEiXsqDb7CiV8SHSE1JLTlM0l6kjr2+EhEvpp4CuCkilgJLJc0l9QQ5m5TsP51fO4jUc+QTwPuU+my/ifRIf2/SD338USv6F2zqNsJspTjx25qmtR46fwDcGRGfzl8Od7XyetFyL51vxoqnHd/VQ2OFCRExtoXyd/XwmDsH3A/YIyJelXQX0DP367IT6QzhWFKvjseT+m0a0sp6zarmph5b07TWQ2dfVlzsPbKivHlvjLeRur0FQNKGBcRYGdNLOelvQ+rvH0n9gbUi4hpS09MuEbEI+KekQ/M8yl8OZivNid/WKBExOyJa6qHzbOAnku5lRc+rAHeSLrxOlXQY8ENgw3xh9f9ouXvmznILqeY/jXRGcn8uHwjclXucvAxo6q/988CXc1yPAgdi1gHuq8fMrGRc4zczKxknfjOzknHiNzMrGSd+M7OSceI3MysZJ34zs5Jx4jczK5n/DzwDNbeLwT4uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Some Visualizations \n",
    "\n",
    "X = ['PowerPlay' , 'Middle Overs' , 'Death Overs']\n",
    "X_axis = np.arange(len(X))\n",
    "  \n",
    "plt.bar(X_axis - 0.1, virat_boundary_percentages, 0.2, label = 'Virat')\n",
    "plt.bar(X_axis + 0.1, warner_boundary_percentages, 0.2, label = 'Warner')\n",
    "  \n",
    "plt.xticks(X_axis, X)\n",
    "plt.xlabel(\"Match Phase\")\n",
    "plt.ylabel(\"Boundary Percentage\")\n",
    "plt.title(\"Boundary Percentage Comparison\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1061,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43.0064308681672, 26.53927813163482, 26.24113475177305)"
      ]
     },
     "execution_count": 1061,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "warner_dotball_percentages = (\n",
    "    100 * warner_df[(warner_df.over < 6) & (warner_df.batsman_runs == 0)].ball.count() / \n",
    "                warner_df[(warner_df.over < 6)].ball.count() , \n",
    "    \n",
    "    100 * warner_df[(warner_df.over >= 6) & (warner_df.over < 15) & (warner_df.batsman_runs == 0)].ball.count() / \n",
    "                warner_df[(warner_df.over >= 6) & (warner_df.over < 15)].ball.count(), \n",
    "    \n",
    "    100 * warner_df[(warner_df.over >= 15) & (warner_df.batsman_runs == 0)].ball.count() / \n",
    "                warner_df[(warner_df.over >= 15)].ball.count()\n",
    ")\n",
    "\n",
    "warner_dotball_percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1063,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40.988671472708546, 25.89052997393571, 22.8476821192053)"
      ]
     },
     "execution_count": 1063,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "virat_dotball_percentages = (\n",
    "    100 * virat_df[(virat_df.over < 6) & (virat_df.batsman_runs == 0)].ball.count() / \n",
    "                virat_df[(virat_df.over < 6)].ball.count() , \n",
    "    \n",
    "    100 * virat_df[(virat_df.over >= 6) & (virat_df.over < 15) & (virat_df.batsman_runs == 0)].ball.count() / \n",
    "                virat_df[(virat_df.over >= 6) & (virat_df.over < 15)].ball.count(), \n",
    "    \n",
    "    100 * virat_df[(virat_df.over >= 15) & (virat_df.batsman_runs == 0)].ball.count() / \n",
    "                virat_df[(virat_df.over >= 15)].ball.count()\n",
    ")\n",
    "\n",
    "virat_dotball_percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1064,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Some Visualizations \n",
    "\n",
    "X = ['PowerPlay' , 'Middle Overs' , 'Death Overs']\n",
    "X_axis = np.arange(len(X))\n",
    "  \n",
    "plt.bar(X_axis - 0.1, virat_dotball_percentages, 0.2, label = 'Virat')\n",
    "plt.bar(X_axis + 0.1, warner_dotball_percentages, 0.2, label = 'Warner')\n",
    "  \n",
    "plt.xticks(X_axis, X)\n",
    "plt.xlabel(\"Match Phase\")\n",
    "plt.ylabel(\"Dotball Percentage\")\n",
    "plt.title(\"Dotball Percentage Comparison\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scores in each bracket (0-10) (11-20) ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1065,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'inning', 'over', 'ball', 'batsman', 'non_striker', 'bowler',\n",
       "       'batsman_runs', 'is_wicket', 'dismissal_kind', 'player_dismissed',\n",
       "       'batting_team', 'bowling_team', 'Bowler Type', 'Bowler Speed',\n",
       "       'Batsman Type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1065,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1067,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "829711    53\n",
       "829719    57\n",
       "829727    21\n",
       "829731    28\n",
       "829743    91\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 1067,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "warner_innings = warner_df.groupby('id').batsman_runs.sum()\n",
    "\n",
    "warner_innings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 892,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 7, 8, 5, 8, 12, 8, 3, 5, 3, 2)"
      ]
     },
     "execution_count": 892,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "warner_score_dist = ( \n",
    "    warner_innings[warner_innings.values <= 10].count() , \n",
    "    warner_innings[(warner_innings.values > 10) & ((warner_innings.values <= 20))].count() , \n",
    "    warner_innings[(warner_innings.values > 20) & ((warner_innings.values <= 30))].count() , \n",
    "    warner_innings[(warner_innings.values > 30) & ((warner_innings.values <= 40))].count() , \n",
    "    warner_innings[(warner_innings.values > 40) & ((warner_innings.values <= 50))].count() , \n",
    "    warner_innings[(warner_innings.values > 50) & ((warner_innings.values <= 60))].count() , \n",
    "    warner_innings[(warner_innings.values > 60) & ((warner_innings.values <= 70))].count() , \n",
    "    warner_innings[(warner_innings.values > 70) & ((warner_innings.values <= 80))].count() , \n",
    "    warner_innings[(warner_innings.values > 80) & ((warner_innings.values <= 90))].count() , \n",
    "    warner_innings[(warner_innings.values > 90) & ((warner_innings.values <= 99))].count() , \n",
    "    warner_innings[warner_innings.values >= 100].count() )\n",
    "\n",
    "warner_score_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1068,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "829713    13\n",
       "829719    41\n",
       "829737    18\n",
       "829745    51\n",
       "829749    62\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 1068,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "virat_innings  = virat_df.groupby('id').batsman_runs.sum()\n",
    "\n",
    "virat_innings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1069,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 14, 7, 8, 10, 7, 6, 5, 3, 1, 5)"
      ]
     },
     "execution_count": 1069,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "virat_score_dist = ( \n",
    "    virat_innings[virat_innings.values <= 10].count() , \n",
    "    virat_innings[(virat_innings.values > 10) & ((virat_innings.values <= 20))].count() , \n",
    "    virat_innings[(virat_innings.values > 20) & ((virat_innings.values <= 30))].count() , \n",
    "    virat_innings[(virat_innings.values > 30) & ((virat_innings.values <= 40))].count() , \n",
    "    virat_innings[(virat_innings.values > 40) & ((virat_innings.values <= 50))].count() , \n",
    "    virat_innings[(virat_innings.values > 50) & ((virat_innings.values <= 60))].count() , \n",
    "    virat_innings[(virat_innings.values > 60) & ((virat_innings.values <= 70))].count() , \n",
    "    virat_innings[(virat_innings.values > 70) & ((virat_innings.values <= 80))].count() , \n",
    "    virat_innings[(virat_innings.values > 80) & ((virat_innings.values <= 90))].count() , \n",
    "    virat_innings[(virat_innings.values > 90) & ((virat_innings.values <= 99))].count() , \n",
    "    virat_innings[virat_scores.values >= 100].count() )\n",
    "\n",
    "virat_score_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1070,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Some Visualizations \n",
    "\n",
    "X = ['0-10' , '11-20' , '21-30' ,'31-40' ,'41-50' ,'51-60' ,'61-70' ,'71-80' ,'81-90' ,'91-99' , '100+']\n",
    "X_axis = np.arange(len(X))\n",
    "\n",
    "plt.figure(figsize=(15, 3))\n",
    "\n",
    "plt.bar(X_axis - 0.1, virat_score_dist, 0.2, label = 'Virat')\n",
    "plt.bar(X_axis + 0.1, warner_score_dist, 0.2, label = 'Warner')\n",
    "\n",
    "\n",
    "plt.xticks(X_axis, X)\n",
    "plt.xlabel(\"Runs Bracket\")\n",
    "plt.ylabel(\"Number of Innings\")\n",
    "plt.title(\"Number of Innings in each Runs Bracket\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Virat's 100 (58) with Warner's 100(55) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1071,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Match Id - 1175366 \n",
    "# SRH vs RCB (https://www.espncricinfo.com/series/ipl-2019-1165643/\n",
    "# sunrisers-hyderabad-vs-royal-challengers-bangalore-11th-match-1175366/full-scorecard)\n",
    "# Warner 100(55)\n",
    "\n",
    "# Match Id 1178410 \n",
    "# RCB vs KKR (https://www.espncricinfo.com/series/ipl-2019-1165643/\n",
    "# kolkata-knight-riders-vs-royal-challengers-bangalore-35th-match-1178410/full-scorecard)\n",
    "# Virat - 100(58)\n",
    "\n",
    "# Comparing the scores two batsmen during the same points in their innings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1072,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4,   5,   6,   7,   8,   8,  14,  15,  19,  25,  27,  29,  30,\n",
       "        32,  33,  37,  37,  43,  43,  43,  44,  46,  46,  48,  50,  54,\n",
       "        55,  56,  57,  58,  59,  60,  61,  63,  64,  66,  66,  67,  68,\n",
       "        69,  70,  72,  73,  79,  81,  81,  83,  84,  86,  92,  94,  95,\n",
       "        96,  96, 100], dtype=int64)"
      ]
     },
     "execution_count": 1072,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# warner_srs = ( 100 * warner_df[warner_df.id == 1175366].batsman_runs.cumsum() /\n",
    "#              warner_df[warner_df.id == 1175366].batsman_runs.expanding().count())\n",
    "\n",
    "warner_srs = warner_df[warner_df.id == 1175366].batsman_runs.cumsum() \n",
    "              \n",
    "(warner_srs.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1073,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   3,   3,   4,   5,   6,  10,  11,  13,  13,  13,  13,  17,\n",
       "        18,  19,  23,  23,  24,  26,  26,  27,  31,  31,  35,  36,  36,\n",
       "        37,  38,  39,  41,  41,  41,  47,  48,  49,  50,  51,  52,  53,\n",
       "        54,  58,  59,  63,  65,  69,  75,  76,  77,  83,  84,  86,  92,\n",
       "        96,  98,  99, 100, 100, 100], dtype=int64)"
      ]
     },
     "execution_count": 1073,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# virat_srs = ( 100 * virat_df[virat_df.id == 1178410].batsman_runs.cumsum() / \n",
    "#              virat_df[virat_df.id == 1178410].batsman_runs.expanding().count() )\n",
    "\n",
    "virat_srs = virat_df[virat_df.id == 1178410].batsman_runs.cumsum() \n",
    "             \n",
    "virat_srs.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1074,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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86WJmbzjnZu7rMY3siYiIiIjEolAH7NwUDnLru8JcoBTqt+y5b8ZI8BdTmX8m/9qUwqv1WWSPncoXz59DUd6wqJQvh05hT0RERERkoHIOgoE9w1zNBgishx3l0NHatW/yUPAXQ8EJkF0M2eMgezxkFVHVksCPnl7DP9/ayqhhqXz7sinMn5qnpRAGOIU9EREREZGBwDmoXgvrn4PK97xAV7MBWuq69olPgqwi8I+HiQu8MJdd7H35/B+YIKWtI8S9yyq4c9F6WttDfHlOMV+aU0xqUnwfvzmJBIU9EREREZH+qr0VNr4M657xvnZUeNuHjPZG6WZcFA5z472RumFjIK5nQe3Vshq+89gq1lbuYvbEHL5zzlQK/b79P1EGDIU9EREREZH+JBiA9c/DuqehdAm07oKEFCg8xZvdcsICGDrqoF++sr6ZHz5VwmMrtzFqWCp3feooTpuils1YpLAnIiIiIhJNzkFVSdfo3eblgIP04TDtQph4hhf0ktIO6TBtHSHuebmCOxetoy3kuPbUYr4wWy2bsUxhLwK+9rWvMXbsWK677joA5s+fT35+Pn/84x8B+MY3vsGoUaP4+te/HsUqRURERCSqGmvhPz+Btf/2Zs0EGHE4zL4JJsyH4YdBXFyvHOrtzTv55sNvs76qgTnhls0CtWzGPIW9CDj++ON5+OGHue666wiFQgQCAerr6zsfX7ZsGXfeeed+X6e9vZ2EhIP/EXV0dBAfr7/UiIiIiPQ7OzfB3z4GteVQPA9O+gaMnw9DRvT6oRaXVPKl+98k25fMH66YybzJuWrZHCR6508FsocTTjiBZcuWAbBq1SqmTZtGRkYGO3bsoKWlhZKSEp599lmOPvpopk2bxjXXXMPuxe1nz57NLbfcwimnnMIvfvELZs+ezY033sisWbOYMGEC//3vfwEvyF1//fUcffTRzJgxg9///vcALF26lDlz5nDZZZcxffr06HwAIiIiIvLhKlfBn06HhkpY+Dhc9gAcdWVEgt6Dr2/imr++wYS8DB778gm6Nm+Qie2Rvadvgvff7d3XHD4dzrjjI3cZOXIkCQkJbNq0iWXLlnHcccexdetWXnnlFYYOHcqMGTP48pe/zK233grApz71KZ588knOOeccAHbu3Ml//vMfAJ544gna29tZvnw5Tz31FLfffjuLFi3iT3/6E0OHDuX111+npaWFE044gdNPPx2A5cuX895771FYWNi7711EREREDk3FS/D3yyDJB59+BvKmROQwzjl+vaSUnz2/jpMn5PDbTx6JLzm2f/WXD9JPPEJ2j+4tW7aMr3/962zdupVly5YxdOhQjj/+eF544QV+8pOf0NjYSG1tLVOnTu0MexdffPEer3XhhRcCcNRRR1FRUQHAc889xzvvvMM//vEPAOrq6li/fj1JSUnMmjVLQU9ERESkv1n1L3j0am8dvMsfgaGjI3KYjpDj1sfe477XNnHhkaP48cdmkBivhr7BKLbD3n5G4CLp+OOPZ9myZbz77rtMmzaN/Px8fvaznzFkyBCuuuoqPvvZz7JixQry8/O57bbbaG5u7nyuz7fnxbLJyckAxMfH097eDnh/rfnVr37F/Pnz99h36dKlH3i+iIiIiETZa3fB0zfAmGPhkvshLSsih2lu6+Dav7/Fc6sr+cLscdwwf6LaNgcxRfwIOeGEE3jyySfJysoiPj6erKwsdu7cySuvvMJxxx0HgN/vp6GhoXN07kDMnz+f3/72t7S1tQGwbt06gsFgr74HERERETlEzsGi2+Hp62HSWfCpf0Ys6O1sbOXyP77G8yWVfOecKdy4YJKC3iAX2yN7UTR9+nQCgQCXXXbZHtsaGhrw+/1cffXVTJ8+nYKCAo4++ugDfv3PfvazVFRUcOSRR+KcIycnh3/961+9+A5ERERE5JB0tMHj18Lb98NRn4azfgZxkZkpfdvOJhbevZyNNY386tIjOHvGyIgcRwYW2z0L5EA0c+ZMt2LFij22lZSUMHny5ChV1P/p8xERERHpA61BeGghlD4Pc/4HTr4eIjTKtvb9XSy8eznBlnbuumImx43LjshxpH8yszecczP39ZhG9kREREREelMwAPddBNtXwjm/hKMWRuxQr5XVcPVfVpCSGM9Dnz+OySOGROxYMvAo7ImIiIiI9JbacvjbhVC/3ZuIZeIZETvUM+9t59oHVjI6M5W/XDWL0ZlpETuWDEwxGfacc7oYdR8GcsuuiIiISL+3baU3ohdq8xZLz58VsUP99ZUKbn18FUfkD+NPC48m05cUsWPJwBVzYS8lJYWamhqys7MV+LpxzlFTU0NKSkq0SxERERGJLfXbYc2TsOg2SM2Ey/8NORN6/TDOOVZtq+ehFZv5yysbmTc5l19deiSpSZGZ9EUGvpgLe6NHj2bLli1UV1dHu5R+JyUlhdGjI7N4p4iIiMigEQp51+OtexbWPQ3b3/a2jzwCLvk7DBnRa4dqbutg2YYAi0qqWFJSxfv1zZjBJ48Zw+3nTiVBi6XLR4i5sJeYmEhhYWG0yxARERGRWNLaCGVLYd0zXshreB8sDkbPgrnf8a7Ny5nUKzNuVtY3s2RNFYtLKnmpNEBzWwhfUjwnjc/h1Mm5zJmYS05G8qG/J4l5MRf2RERERER6Rd2W8OjdM1D+IrQ3Q1IGFM/1wl3xaeA79GUOnHO8t7WexWsqWVxSxbtb6wAYNSyVi2fmM3dyHscUZZGcoHZNOTAKeyIiIiIyuDkHjbVQUwo166F6LZS9AO+/6z2eWeAtij5xAYw5HhIOfjKUUMixdWcT5YEg5YEgq7fVs3RdFZX1LZjBkWMyuX7+ROZNzmNCXrrmoJBDorAnIiIiIoNDayPUlnmBrqYUajZAIHy7eWfXfnEJMPpomHe7N4Lnn3DA7Zm1wVbKAw2UVQcpCwQpr/bCXXlNkNb2UOd+GSkJnDTez6mT8pgzMYfsdLVnSu9R2BMRERGR2NPS4I3Olf0HAuu8YFe/Zc99MkaCvximXQjZxZA9HrLHwbCxEH9gvya/WlbDQys2d47Y7Wxs63wsIc4Yk51Gkd/HKRNzKPT7KPT7KPL7yMlI1uidRIzCnoiIiIjEhp2bYO0z3jV2Ff+FjlZISoeciVBwQjjQhb+yiiA5vVcO+8+3tnD9w+8wJDWRScMzOGv6CAr9PsblpFPo9zE6M1WzZkpUKOyJiIiIyMAU6oAtK8IzZD4DVau97dnFMOsamLAAxhwL8YkRObxzjrteLONHT6/h+HHZ/P5TR5GREpljiRwMhT0RERERGTia62HDEi/crX8OGmvA4mHs8XD6D7yA5y+OeBmhkOP7/y7h7pfLOeewkfz0ohmaLVP6naiEPTP7GvBZwAHvAp8G0oAHgQKgAviEc25HNOoTERERkQjoaIctr8OGxV5oOyDOu/au4mUItUHKMBh/mhfuiudB6rAIFLxvLe0dfOOht3nyne1cdUIh3zprMnFxuu5O+p8+D3tmNgq4FpjinGsys4eAS4ApwGLn3B1mdhNwE3BjX9cnIiIiIr2ouQ5KF3nr1a1/Dpp2eCNxyRkH/loZw+HYL3gBL/+YA55EpTfUN7fxub+8wStlNdxy5iSuPqlIE6xIvxWtNs4EINXM2vBG9LYBNwOzw4/fCyxFYU9ERERk4KnZ0HUd3cZlEGqH1CwYP99bq27cqZAyNNpVHrCq+mYW/vl11lfu4n8vPowLjhgd7ZJEPlKfhz3n3FYz+ymwCWgCnnPOPWdmec657eF9tptZ7r6eb2bXANcAjBkzpq/KFhEREZEP09EOm1+DdU97s2HWrPe250yG47/ijcSNPhriBu41bRuqG7jiT8vZ0djK3VcezckTcqJdksh+RaONMxM4DygEdgIPm9nlPX2+c+4u4C6AmTNnukjUKCIiIiI9UFsOL/zQa89s3glxiVBwIsy6GibMh8yCaFfYK97atIOr7nmd+DjjwWuOY/rogTcqKYNTNNo45wHlzrlqADN7FDgeqDSzEeFRvRFAVRRqExEREZGe2LYS7vs4tLfApLO72jMP5lq8fmxxSSVfuv9N8oak8JerZjE22xftkkR6LBphbxNwrJml4bVxzgVWAEFgIXBH+PtjUahNRERERPZnwxJ48FOQmglXPgU5E6JdUUQ89Ppmbv7nu0wZMYQ/f/po/OnJ0S5J5IBE45q918zsH8CbQDvwFl5bZjrwkJl9Bi8QXtTXtYmIiIjIfrzzEPzrC5AzCT75DxgyItoV9TrnHL9eUsrPnl/HSeP9/O7yo/Ala3lqGXiictY6574DfGevzS14o3wiIiIi0h8t+xU89y0oOAkuuW9Azqi5L845qna1UFYdpDwQ5OUNAf79znYuOGIUP/7YDJIS4qJdoshB0Z8oREREROSjhUJeyHv1NzDlfLjwLkgYeC2N9c1tVASClFUHKQt4wa480EB5dZBga0fnfskJcXxpzji+cdpELZYuA5rCnoiIiIh8uPYW+NcX4b1/wKzPwYI7IK7/jnS1tofYVNtIWXVDOMx1hbtAQ0vnfnEGozPTKPT7mDk2i6IcH0X+dApzfIwYkqKQJzFBYU9ERERE9q25Hh68HMr/A/NugxOuA4t+CAqFHO/XN3tBLhCkvDpIWcALd5trGwl1W5wr25dEUY6PUyflUOhPD4c6H2Oy00hOGLjr/on0hMKeiIiIiHzQrkq472NQVQLn/w4OvzSq5SwuqeTRN7dSFghSEQjS1NbVdpmaGE+h38e0UUM597CRFOX4KPSnU5jtY2haYhSrFokuhT0RERER2VOgFP52AQRr4NIHYfy8qJZzz8vl3P7kavIyUpg8IoPjirIpzPExzu+jMMdHXobaLkX2RWFPRERERLpsWQH3fwIwuPIJGHVU1EpxzvGTZ9fy26UbOH1KHr+89AhSEtV6KdJTCnsiIiIi4ln3HDy8EHw58Kl/Qva4qJXS1hHixkfe4dE3t3LZMWP43nnTiNfoncgBUdgTERERGayCNVBT6n1Vvgev/R6GT/MWS0/PjV5ZLe184b43eXFdNV8/bQJfObUY6wcTw4gMNAp7IiIiIrGstRFqy8Khbj3UbOgKeE07uvaLS4SJZ8AFv4PkjKiVG2ho4ap7Xue9rXXcceF0Lpk1Jmq1iAx0CnsiIiIisaK9BSr+C6WLoWq1F+zqNu+5T8ZI8BfD1Asguxiyx3vtmsPGQnx0fzXcVNPIFXe/xvv1zdz1qZnMm5IX1XpEBjqFPREREZGBrKEK1j8Ha5+GDS9AWxASUiF3Mow5DvxXeGEuuxiyxkFyerQr3qf3ttZx5Z+X0x5y3PfZYzlqbGa0SxIZ8BT2RERERAYS57zr69Y+A+uega1vAA6GjILDLoYJC6DwZEhMjXalPfbf9dV8/q9vMCwtiQeumkVxbv8MpCIDjcKeiIiISH/X1uy1Z659GtY9C/VbvO2jjoI5t3gBb/h0GICTmPzrra188+G3Kc5N596rZpE3JCXaJYnEDIU9ERERkf6kpQFqw5OoBEph+0ooWwptjZDog3FzYPZNMP50yBjY17T94cUyfvBUCccWZXHXFTMZkpIY7ZJEYorCnoiIiEhf62iDnZvCgW591+yYNaWwa/ue+2YWwOGXwYQzoOBESBz4I1+hkOMHT5Xwp5fKOWvGCH7+icNITtBi6SK9TWFPREREJBKcg4bKvcLcBm/5gx0VEGrv2jc105sVs2hO12Qq/vGQWQhJaVF7C5GwI9jKtx57j3+/s50rjy/g1rOnEKfF0kUiQmFPRERE5FA013cLcqV7rmfX2tC1X0IKZBVB7hSYfG5XoMsuhrSs6NXfR0Ihx4MrNvOTZ9ZQ39zOTWdM4nMnF2mxdJEIUtgTERER2Z/2Vti58YMtlzWl3uhdJ4NhY7wAl39sOMyFR+qGjIa4uKi9hWh6e/NObn3sPd7eUsesgiy+e/5UJg0fEu2yRGKewp6IiIgIeG2Xu7Z3u45uQ9co3Y6N4Dq69k3L9toui0/zFijPDn9lFsbENXW9pTbYyv97dg0PvL4Zf3oyd158OOcdPlKjeSJ9RGFPREREBpfmuj3bLjtH6zZ4C5LvlpDqBbjhM2Dax7oCXfY47xo7+VAdIcffl2/ip8+tZVdzO585oZCvzhtPhmbbFOlTCnsiIiISe9pbvElQ9g5zNaUQrOraz+LCbZfjvZkud7dcZhdDxshB23Z5KN7atINbH1vFu1vrOLYoi++eN40JeRnRLktkUFLYExERkf6juR42LPEWEG9rPsAnO2io8gLdzo3gQl0P+XK9ADdxQbcRuvHesgYJSb35DgatmoYWfvLMWh5csZm8Icn88tIjOGfGCLVsikSRwp6IiIhE144KWPsMrHsGKl6CUBskZUDK0AN/rbQsGHUkzPjEnm2XB/Na0iMdIcf9r23kp8+tI9jSzjUnF3Ht3PGkJ+vXTJFo03+FIiIi0rdCHbB5uRfu1j0D1Wu87f4JcOznvcXD84+BeP2a0p9V1TezZE0Vf311I6u21XP8uGxuP3cq49WyKdJv6F9RERERibzmOihd7IW79c9DUy3EJcDY4+HIhTBhvjcCJ/2Wc45V2+pZXFLF4jWVvLOlDoAxWWn8+rIjOGu6WjZF+huFPREREeldwZo916HbugI2LoNQO6RmwfjTvXBXPFftlf1cU2sHyzYEWFRSxZI1lVTWt2AGR+QP4/r5E5k7OZeJeRkKeSL9lMKeiIiIHLjWRqgt61qHrnNNulJo2tG1X1wC+CfCcV+GCQsgfxbExUevbtmv9+uaWbymkiUlVbxUGqClPYQvKZ6TJ+Qwd3Iesyfm4E9PjnaZItIDCnsiIiLy0TraYfOrsO5ZeP8dL9jVbd5zn4yRXhvm1Au6TYxSDMPG6tq7AaC9I8R9r23ioRWbWbWtHoDRmalcOmsMcyfnMqswi+QEhXSRgUb/+oqIiMgHNe3wrrFb+zSUPu9dcxefBHnTvOvsds9ymV0MWeMgOT3aFctBeq2shu88voo17+/isPxh3LhgEnMn5zI+N13tmSIDnMKeiIiIeALrvXC37lnY9Aq4Dkjzw6SzvRbMcXMgWTMtxoqq+mZ++FQJ/1q5jVHDUvnd5Ucxf2qeAp5IDFHYExERGaw62rxQt3uNu9oN3va8aXDi17yAN+ooiIuLbp3Sq9o6Qty7rII7F62ntT3EV04t5ouzi0lNUpumSKyJStgzs2HAH4FpgAOuAtYCDwIFQAXwCefcjn2/goiIiByQ1mB4EpXwZCqV78GGpdASbs8sPBmO/YIX8IblR7taiZBXy2q49bH3WFfZwJyJOXznnKkU+H3RLktEIiRaI3u/AJ5xzn3czJKANOAWYLFz7g4zuwm4CbgxSvWJiIgMPB3tsHPjnsse1JRCoBR2bdtz36FjYMq5Xrgrmq1r7mJcZX0zP/h3CY+/vY3Rman84YqZzJucq5ZNkRjX52HPzIYAJwNXAjjnWoFWMzsPmB3e7V5gKQp7IiIie3IOGqrCI3TdwlxNKewo99ay2y01E7LHe2Fu92Qq/vGQVQSJqVF7C9J32jpC/Pnlcn6xaD1tIce1c8fzxdnjSElUy6bIYBCNkb0ioBr4s5kdBrwBfBXIc85tB3DObTez3H092cyuAa4BGDNmTN9ULCIi0tdadoXDXHj9usD6rvutu7r2S0jxwlvuZJh8jhfmdi97kJYVvfol6paVBrj18VWUVjUwd1Iut54zhbHZatkUGUyiEfYSgCOBrzjnXjOzX+C1bPaIc+4u4C6AmTNnusiUKCIi0gc62mBHRbcRum6Lkze8321H866jyx4P+ceER+jCgW7IaE2gIjS3dVBRE6SsOkh5IMibG3eweE0VY7LS+NPCmcydnBftEkUkCqIR9rYAW5xzr4Xv/wMv7FWa2YjwqN4IoCoKtYmIiPQu52DX9g+2XNaUekHPdXTtm5rljcwVz+0anfOPh8xCSEyJ2luQ/qEj5Ni2s4myQJCy6gbKA16wK6sOsq2uCdftT+B5Q5K5bt54Pn+KWjZFBrM+D3vOuffNbLOZTXTOrQXmAqvDXwuBO8LfH+vr2kRERA5ac11Xm2Wg2/V0NRugLdi1X0Kqd/3c8Gkw9YKuUJc9Tm2X8gH/XV/Nfa9uoizQQEVNI63toc7HMpITKMzxcXRBJoX+fApzfBT5fRT4faQna3UtEYnebJxfAe4Lz8RZBnwaiAMeMrPPAJuAi6JUm4iIyL61t3S1XXa/hq5mPQSru/azOBia743KjT1hz8lRMkaq7VL2a+vOJr7/5Gqefu99hg9JYfroocyZmEuh30dRTjqFfh/+9CTNpikiHykqYc85txKYuY+H5vZxKSIiInsKhbxlCva+hq5mPezcBK5rZAVfjncd3YQFe7VdFkBCctTeggxcLe0d/PG/5fx6SSkOxzdPn8DVJxeRnKBWTBE5cBrjFxGRwalpx57Xz3Vvu2xv6tovMc0bmRt5JEz/RNfkKFnjIHVY1MqX2LN0bRW3P7Ga8kCQBVOH862zJzM6My3aZYnIAKawJyIisautGWrL9gxyu9ena6zp2s/ivdG47GIoPMULd7uXMMgYAWqVkwjasqOR7z25mmdXVVLo93HvVbM4ZUJOtMsSkRjQ47BnZqnAmPCkKiIiIv1DKAR1m/dck253oNu5Geg2RWF6ntd2OensrpbL7GIYNhYSkqL2FmRwam7r4A8vlvGbpaUYxvXzJ/LZkwrVsikivaZHYc/MzgF+CiQBhWZ2OPBd59y5EaxNRESkS2PtXrNc7r6ebgN0tHTtl5ThjcyNngWHf7JrpsuscZAyJHr1i3TzwpoqbntiFRtrGjlz+nD+56wpjBqWGu2yRCTG9HRk7zZgFrAUvAlWzKwgMiWJiMig1dbktV3uPdNlTal3jd1ucQnhtsu91qTLHg/puWq7lH5rc20jtz+xmkUllRTl+PjrZ2Zx0ni1bIpIZPQ07LU75+o0va+IiPSqjnZ448+w5t9eoKvbwh5tlxkjvVG5Ked3tVzubruM12Xn0r+1dYTYXNvYufj5uspdPLZyG/Fxxk1nTOKqEwpJStAyHCISOT39P+V7ZnYZEG9m44FrgWWRK0tERGLeplfh39+EynchdyqMOa5rpsvs8GyXyenRrlLkIznnqNrVQll1MBzqGjpvb6ptpD3U9ceLzLREFkwbzk1nTGLEULVsikjk9TTsfQX4H6AFuB94Fvh+pIoSEZEY1lAFz98Kb/8dhoyGT/wFJp+r1kvp13Y1t3WO0O0Oc2WBBsqrgwRbOzr3S06Io9DvY9KIDM6YPpwifzqFOT4Ks31k+jQJkIj0rf2GPTOLBx53zs3DC3wiIiIHrqMdXv8DvPBDaG+Gk77hfSX5ol2ZCACt7SE2dbZdeiN0ZeGAV72raxKgOINRmakU+dOZOTaLcTk+CsOhbsSQFOLi9IcLEekf9hv2nHMdZtZoZkOdc3V9UZSIiMSYipfhqeuhahWMmwtn/MRr1xTpY8453q9vprxbkCurbqA8EGTzjiY6urVdZvuSKMrxMWdiDoX+dIpyfBT5fYzJTtPyCCIyIPS0jbMZeNfMngeCuzc6566NSFUiIhIbdr0Pz30b3n0IhubDxX/z1rhTy6ZEWF1T2wdH6MLtl01tXW2XKYlxFGT7mDpyKOccNpJCv49Cv48ifzpD0xKj+A5ERA5dT8Pev8NfIiIi+9fRBsvvghd+5K2Bd9I3wy2badGuTGJIS3sHm2oaKeu8jq6h85q6mmBr535xBvlZaRT6fRxblE1heISu0O9juNouRSSG9SjsOefuNbMkYEJ401rnXFvkyhIRkQGr4iVvls3qEig+Dc74sbd8gshBCIUc2zvbLhv2mBxl644munVd4k9Ppsjv47QpeV0jdDnpjMlK0xIHIjIo9Sjsmdls4F6gAjAg38wWOudejFhlIiIycDTXwYYl8N4jUPIEDBsDl9wPE89Uy6b0yM7G1n2O0JUHgrS0hzr3S0uKp9Dv4/D8TC44YjRFfh9FOT4K/D6GpKjtUkSku562cf4MON05txbAzCYAfweOilRhIiLSz9WWwdpnYN0zsPFlCLVDaiacciOc+DVI1Dpisqfmtg421jRSVt3wgclRdjR2NQzFxxljstIo8vs4sdgfbrv0JkjJzUjG9AcEEZEe6WnYS9wd9ACcc+vMTH8+ExEZTDraYctyWPu0F/AC67ztOZPguC/BhDNg9NEQ39P/tUgs6gg5tu1sCk+IEh6hC4/SbatrwnVru8wbkkyh38eCaSM6R+gK/T7ys9JIjFfbpYjIoerp/5FXmNmfgL+G738SeCMyJYmISL/RtBNKF8G6Z6H0eWjaAXGJUHACzPwMTJgPWYXRrlL6mHOO2mBrZ5DrPkJXUdNIa7e2y/TkBAr9PmYWZFLoH01RTjpFfq/tMj1ZfxgQEYmknv4r+wXgS8C1eNfsvQj8X6SKEhGRKAqUeiN3656BjcvAdUBatjdyN2E+jDsVUoZEu0rpA02tHeHlCz64yHhdU1fbZWK813ZZ6E9nzsTczslRCnN85KSr7VJEJFp6GvYSgF84534OYGbxQHLEqhIRkb7T0QabXu0KeDWl3vbcKXDCV2HCAhg9E+K0iHQs6gg5tu5oYkOgoXMdurLw7W11zXvsO2JoCoV+H+ccNsJbZDwc6kZnppKgtksRkX6np2FvMTAPaAjfTwWeA46PRFEiIhJhTTtg/SIv3JU+782mGZ8EBSfCrM95I3iZY6NdpUTItp1N/OjpNazZXs/GmkZaO7raLjNSEijKSeeYomwvzIWvoyv0+0hLUtuliMhA0tN/tVOcc7uDHs65BjPTyrgiIgNJYH14cpVnYdMr4fZMP0w62xu9GzcHkjOiXaVE2Nr3d7Hw7uU0tLRz/LhsTp2cyzh/emeoy/Ylqe1SRCRG9DTsBc3sSOfcmwBmNhNoilxZIiKyh8ZaWP+8t5Zda8P+9+/OOaheA7UbvPt507ylESYsgFFHQZza7waL18pquPovK0hJjOfhzx/H5BG69lJEJJb1NOxdBzxsZtsAB4wELo5UUSIig55zUL226zq6za+BC4EvB9LzDvz1sorg2C947ZnDxvR+vdLvPf3udr764EpGZ6byl6tmMTpTDToiIrHuI8OemR0NbHbOvW5mk4DPARcCzwDlfVCfiMjg0d7qLU6+O+DtqPC2D58BJ30TJi6AEUdoJE4O2F9fqeDWx1dxRP4w/rTwaDJ9SdEuSURE+sD+RvZ+jzcxC8BxwC3AV4DDgbuAj0esMhGRwSAYgPXPhSdKWQKtuyAhBQpPgeOv9Voth46KdpUyQDnn+Olza/nNCxuYNzmXX116JKlJmlVVRGSw2F/Yi3fO1YZvXwzc5Zx7BHjEzFZGtDIRkVgS6oC6LVCzHmo2eMsbbFsJW14HHKQPh2kXwsQzvKCXpBY7OTRtHSFuefRdHn5jC5ccnc/3z5+m5RFERAaZ/YY9M0twzrUDc4FrDuC5IiKDi3PeRCo1670wt/srUAq1ZdDR0rVvUgbkTIRTbvTaM4cfpvZM6TWNre186b43eWFtNV+dO57r5o3XDJsiIoPQ/gLb34H/mFkAb/bN/wKYWTFQF+HaREQOXsVL8OpvvRG1iHNeO2ZNKTTv7NoclwhZhZBdDOPnQfZ473Z2MaTngn75lgioaWjhqntX8O6Wnfzggml88hitlygiMlh9ZNhzzv3AzBYDI4DnnHMu/FAc3rV7IiL9z7v/gH9+HtKyDm7myoORMtRrw+wMdONg2FiIVxOE9J3NtY1ccfdytu1s4reXH8X8qcOjXZKIiETRfn8Lcc69uo9t6yJTjojIIXrl/+DZm2HM8XDp/ZCaGe2KRPrEe1vr+PQ9r9PaHuK+zx7DzIKsaJckIiJRpj85i0hsCIVg0Xdg2S9h8rlw4R8gMSXaVYn0iZdLA3zur28wJCWB+z9/HOPzMqJdkoiI9ANRC3tmFg+sALY65842syzgQaAAqAA+4ZzbEa36RGQAaW+Fx78M7zwIR18NZ/wY4jS9vERHRSDIopJKXq+opa3D7f8Jh8g5x0ulAYr86dxz1dGMGJoa8WOKiMjAEM2Rva8CJcCQ8P2bgMXOuTvM7Kbw/RujVZyIDBAtu+ChK2DDEjj123DSNzTxifSp9o4Qb2zcweI1VSwqqaSsOghAQXYaGSmJfVLDgmkj+P750xia2jfHExGRgSEqYc/MRgNnAT8Avh7efB4wO3z7XmApCnsi8lEaquC+j8P778F5v4EjLo92RTJI1DW28Z/11SwuqWTp2mrqmtpIjDeOLcrmimPHcuqkPMZka61EERGJrmiN7N0J3AB0v6ggzzm3HcA5t93Mcvf1RDO7hvB6f2PGjIlwmSLSb9VsgL9dCLsq4dK/w4T50a5IYlxZdQNLwqN3r1fsoCPkyPIlMW9yHvMm53LieH+fjeSJiIj0RJ+HPTM7G6hyzr1hZrMP9PnOubuAuwBmzpwZ+YshRKT/2fom3HcRuBBc+SSMnhntiiSGNLd1sLGmkfJAAxuqg5QHgry5cQdlAa89c2JeBp87uYi5k/M4PH8Y8XFqGxYRkf4pGiN7JwDnmtmZQAowxMz+BlSa2YjwqN4IoCoKtYlIf1e6CB68AnzZcPmj4B8f7YpkAOoIObbtbKI8EKSsusH7HghSVh1kW10TrtufEnMzkpk8YggLjy/g1Em55GepPVNERAaGPg97zrmbgZsBwiN733TOXW5m/w9YCNwR/v5YX9cmIv3c2w/AY1+C3MnwyX9AhhaMlg/nnGNHY9seI3Tl1UHKAg1U1DTS2h7q3Dc9OYGiHB8zCzIp9I+m0O9jXE46BX4f6clapUhERAam/vR/sDuAh8zsM8Am4KIo1yMi/YVz8PIvvHX0Ck+Gi++DlCH7f54MCk2tHV6QCwQpDzR0jtCVB4LUNbV17pcYb+RnpVHkT2f2xFyK/D4K/T4Kc3zkpCdjmsVVRERiTFTDnnNuKd6smzjnaoC50axHRCJs+9vw0p3QUn9gz2tpgM2vwrSPwfm/hYTkiJQn/VdHyLFlRyNl4dE5r+2ygfLqINvqmvfYd8TQFAr9Ps6eMYKinPTOUDc6M5WE+LgovQMREZG+159G9kQkVjXtgCXfhxV3Q8pQyCo68Nc45UY45SaI0y/rsco5R6ChtWuErtq7jq48EGRTTSOtHV1tlxkpCRT5fRxTlO2FuZzwKJ3fR1qS/tcmIiICCnsiEkmhEKy8z2u/bNoBR18Nc26B1GHRrkx6gXOO6oaW8HVwXiirCARp7nYtXE/VNbZSFgiyq7m9c1tSfBxjs9Mo8vuYNzmvM9QV+X1k+ZLUdikiIrIfCnsiEhnb3oJ/fxO2roD8Y+HM/wcjZkS7KjkIDS3tVASCbAjPWlne7Zq4hpZu4SwhjrFZafgOYkKTjJRELjhiVOfoXJE/nVGZqVrWQERE5BAo7IlI72qshSXfgxV/Bl8OnP87OOwS0CjMgOGc4/nVldyzrILSqgaqdrV0PmYGI4emUpTj42NHeuGsKCedQr+PkcMUzkRERPoThT0R6R2hELz1F1h0OzTXwTGfhzk3e9foyYBREQhy2xOrWLq2moLsNE6ekBMeafNC3djsNFIS46NdpoiIiPSAwp6IHLqtb3gtm9vehDHHey2bw6dFuyo5AE2tHfzf0lJ+/58ykhLi+NZZk1l4fAGJmr1SRERkwFLYE5GD11gLi2+HN+6F9Fy48A8w/SK1bA4gzjmeXVXJ955czdadTZx/+EhuOXMyuUNSol2aiIiIHCKFPRHpmbYmqC2DwHqoKYWaDbDuaWiuh2O/CLNv0kLnA0xZdQO3PbGaF9dVM2l4Bg9ecyzHFGVHuywRERHpJQp7ItIl1AF1m70wFygNh7rwV93mPffNGAFjjoNTvw15U6JTrxyUxtZ2fr2klD/+t5zkhDhuPXsKVxw3VguOi4iIxBiFPZHBrGkHlC6G9c/B9ne8kbuOrpkXSR4C2eO8UJf9Ke+2f7y3KHpyRvTqloPinOOZ997ne0+uZltdMxceMYqbzpxEboZaNkVERGKRwp7IYBMo9dov1z0LG5eB64C0bMg/BsafBtnFXqDLLvaWTtD1dzFhQ3UDtz2+iv+uDzBpeAa/uPQIji7IinZZIiIiEkEKeyKxrqMNNr0K656BtU9D7QZve+5UOOGrMPEMGHUUxGk6/f5ufeUufrmklNpgy/537iYUghUba0lJiOe2c6Zw+bFq2RQRERkMFPZEYlFjLZQu8gJe6SJv3bv4JCg4CY79Aow/HTLHRrtK6aGGlnZ+sWgdf365gtSkeCbmHXgL7UUz8/navAnkZCRHoEIRERHpjxT2RAayjnbYuXHPiVQqV8GW18GFvDbMSefAxAVQNFvX2Q0wzjkef3sbP3yqhMr6Fi6emc8NCyaSna7AJiIiIvunsCfS3zkHDZV7BrrdM2XuKIdQe9e+KcPAPwFO+gZMWAAjj4Q4tesNROsqd3HrY+/xalkt00cN5XeXH8URYzKjXZaIiIgMIAp7Iv1NeytsfNlrwdz0qreeXeuursfjk71ZMXMnweSzITs8mUp2Mfi0RtpAt6u5jV8sWs+fl1WQnpzADy6YxiVHjyE+ThPliIiIyIFR2BPpD4IBWP+8N0tm6RIv3CWkeDNkHn5pONCN8wLd0NGaTCUGOed4bOU2fvBUCYGGFi45Op/r508iy5cU7dJERERkgFLYE4kG56CqxBu9W/cMbF4OOEgfDtMu9GbILDwFktKiXan0gTXv13PrY6tYXl7LYaOH8scrZnJY/rBolyUiIiIDnMKeSF9pb4GKl7oC3s5N3vYRh8EpN3qTqAw/TNfYDSL1zW387/Pr+MsrGxmSksCPLpzOxTPziVPLpoiIiPQChT2RSGqohvXPee2ZG16A1gZISPVmxjzx6zBhPgwZGe0qpQ80tXZQHgiGvxooqw7y4voANcEWLps1hm+ePpFMtWyKiIhIL1LYE+lNznlLH+wevduyAnCQMRKmfxwmnAGFJ6s9M0Z1hBxbdzSxIdBAebUX7MrCt7fVNe+x74ihKRw2eihfnTeeGaOHRadgERERiWkKeyKHqq053J75NKx7Fuo2e9tHHgGzbw63Z84AU2tef1YRCLKopJKXSwM0tnYc0HMdUBtsZVNNI60doc7tGSkJFOWkc0xRNkV+H4U5Por86RT400hL0j+/IiIiEln6bUPkYOyqDLdnPuO1Z7YFITENiubAydd77ZkZw6NdpXyE9o4QKzbuYMmaKhaVVFJWHQRgXI4P/wEuWm5Aod/H3Mm5jPOnU5jjo9DvI9uXhCnki4iISJQo7IkciMrV8MRXYcty7/6QUXDYJd4C5oUnQWJqdOuTj1TX2MbSdVUsLqli6doq6pvbSYw3ji3K5opjx3LqpDzGZKvFVkRERGKDwp5IT1W8DH+/FBJTYM63vPbMvGlqz+znNlQ3sKTEG71bsXEHHSFHti+J06cOZ+6kXE6akEN6sv4pFBERkdij33BEemL1Y/DI1TBsDHzqUe+79Cu7mtuoCDRSFp7psjwQ5N2tdZQHvPbMScMz+PwpRZw6KY/D84cRr+UNREREJMYp7Insz/I/wFPXw+ij4bIHIS0r2hUNWq3tITbvaKS8OjzLZSBIWXWQskCQ6l0tnfuZwahhqUzIy+DTJxRw6qRcRmeqPVNEREQGF4U9kQ/jHCz5Hvz3Z96SCR+/W0smHKIVFbX839IN7GpuO6DnOQeBhhY272iiI+Q6t2f5kijy+5g9ISc806WPopx0xmSlkZIY39vli4iIiAwoCnsi+9LRBk9cByv/BkdeAWf9L8TrP5eDVb2rhR89XcKjb24lNyOZ4tz0A36NKSOHcPaMkRT6fRSFZ7sclqZFyEVEREQ+jH57FdlbaxAevtJbWuGUm2D2TZqE5SC1d4T466sb+flz62hu7+ALs8fxlVOLtcaciIiISB/Qb1wi3QVr4P6LYNtbcPb/wsyrol3RgLW8vJZbH3uPNe/v4qTxfm47dyrjcg58RE9EREREDk6fhz0zywf+AgwHQsBdzrlfmFkW8CBQAFQAn3DO7ejr+mQQ21EBf/sY1G2BT/wVJp8d7YoGpKpdzdzx1BoefWsro4al8rvLj2T+1OFaXFxERESkj0VjZK8d+IZz7k0zywDeMLPngSuBxc65O8zsJuAm4MYo1CeD0fZ34L6PQ3sLXPEYjDk22hUNOO0dIe59ZSN3Pr+OlvYQX55TzJfmFJOapIlSRERERKKhz8Oec247sD18e5eZlQCjgPOA2eHd7gWWorAnfaHsP/DAJyFlKFz1OOROinZFA85rZTXc+tgq1lbu4pQJOdx27lQK/b5olyUiIiIyqEX1mj0zKwCOAF4D8sJBEOfcdjPL/ZDnXANcAzBmjBa2lkP07j/gn58H/3j45D9g6KhoVzSgVNU388OnSvjXym2MGpbK7z91FKdPyVPLpoiIiEg/ELWwZ2bpwCPAdc65+p7+cuicuwu4C2DmzJluP7uL7FtbE7z4U/jvT2HsCXDJ/ZA6LNpV9Ws7G1vZUB2kPBCkPLyg+YvrArR2hLj21GK+MFstmyIiIiL9SVTCnpkl4gW9+5xzj4Y3V5rZiPCo3gigKhq1SYxzDtY+Bc/cBDs3wWGXwtl3QmJKtCvrF5rbOqioCVJeHaQsEKSsuivY7WjsWgg9Ic4Yk5XG3Mm5fG3eBArUsikiIiLS70RjNk4D/gSUOOd+3u2hx4GFwB3h74/1dW0S42o2wNM3QunzkDMZFj4JhSdFu6o+1xFybNvZFA5zDeGROi/YbatrwnUbL88bkkyh38cZ00dQ5PeFFzRPZ3RmKonxcdF7EyIiIiKyX9EY2TsB+BTwrpmtDG+7BS/kPWRmnwE2ARdFoTaJRa2N8NLP4eVfQHwynP4DOOZzEJ8Y7coixjlHbbCVskDXKF15oIGy6iAbaxpp7Qh17puRnEBhjo+ZBZkU+fMpzPFR5PdR4PeRnqylOEVEREQGqmjMxvkS8GEX6M3ty1okxjkHa56EZ26Buk0w/RNw+vcgY3i0K4uY/6yr5jdLSlnzfj31ze2d2xPjjbHZ3sjcqZNyO0foCv0+/OlJmlBFREREJAbpz/YSmwKl8PQNsGEx5E6FK5+CghOiXVXEbN3ZxPeeWM0zq95nTFYa5x4+kiJ/euco3ahhqSSo7VJERERkUFHYk9jSGvRm2Xzl15CQAgvugKOvhvjYPNVb2jv4w4tl/PqFUgCunz+Rz55USHKCZsUUERERGexi8zdgGXycg5LHvZbN+i3eLJvzboeMvGhXFjEvrK3i9sdXUVHTyBnThvOts6cwalhqtMsSERERkX5CYU8GnvZW2LkRakohsN77vv1t2L4S8qbBx/4IY4+LdpURs7m2ke8+uZrnV1dS5Pfxl6tmcfKEnGiXJSIiIiL9jMKe9E/Owa7t3QLdBu92TSnsqADX0bVvmh+yi+GM/wczr4rZls3mtg7uerGM37xQSpwZNyyYyGdOVMumiIiIiOxbbP5WLAPX5uXegudVa6At2LU9IdULdMOnw9QLwD/eu589DlIzo1dvH1myppLbn1jNxppGzpo+gv85azIj1bIpIiIiIh9BYU/6j7VPw8OfhvQcOPIKL8jtDnUZIyFu8M0mubm2kdufWM2ikkqKcnz87TPHcOJ4f7TLEhEREZEBQGFP+oc37oUnr4MRh8NlD3mBL0J2Nbfx4roAi9dUUlrVELHj9Ia17+8iPs646YxJXHVCIUkJgy/wioiIiMjBUdiT6HIO/vMTWPpDKD4NLroHktN7/TAba4IsLqli8ZpKXiurpT3kGJaWyIzRw4jvx+uJX3jkaK6dW8yIoWrZFBEREZEDo7An0RPqgH9/A974Mxx2GZz7S4hP7JWXbu8I8eamnSwuqWTxmqrOEbzi3HQ+c1Ih8ybncUT+MC00LiIiIiIxS2FPoqOtCR75LKx5Ek78Osy9FezQhtjqmtr4z7pqlpRUsnRdNTsb20iMN44pzOayWWOYOzmXsdm+XnoDIiIiIiL9m8Ke9L3GWvj7pbD5NW+5hGOuOaCnN7d1sKm2kbLqIOWBIGXVDWyobuCdLXW0hxxZviROnZTLvMl5nDTeT0ZK74wWioiIiIgMJAp70rd2boa/fQx2lHvX5009f5+7hUKObXVNlAd2B7ogZYEg5YEGtuxowrmufXMykinM9nH1yUXMm5zL4fmZxMf14wvxRERERET6gMKe9J3K1V7Qaw3C5Y9C4Ul7POyc47GV27jrxTI2VDfQ0h7qfMyXFE9hjo8j8jO58IjRFOX4KPR7Xxq5ExERERH5IIU96RsVL3utm0lpcNXTkDd1j4fXvF/PrY+tYnl5LVNGDOGK48ZS6E+nKMdHkd9HTkYydojX9ImIiIiIDCYKexJ5qx+DR66GzLHeiN6w/M6H6pvbuPP59dz7SgVDUhL40YXTuXhmPnFqwxQREREROSQKexJZy/8AT10P+bPg0gcgLQvwWjb/+dZWfvjUGmqCLVw2awzfPH0imb6kKBcsIiIiIhIbFPbkg1oboXYD1JSGvzZ4X+1NB/Y6oQ6oWg0Tz4SP3w2J3sLgJdvrufWx93i9YgeH5Q/j7itnMmP0sN5/HyIiIiIig5jC3mAV6oCdG8NBLhzqAuu9+/Vb9tx3yCjIKgKf/8CPM/kcOPkGiE+grqmN/31+HX99dSNDUxP58cemc9FRatkUEREREYkEhb1Y5hwEq7uN0JVCIPx9Rzl0tHbtmzwU/MVQcAJkj4fscZBd7H1POrSFyEMhx6NvbOGOp0uoCbZy+TFj+cbpExiWppZNEREREZFIUdiLBS0NXW2XgdI92y9b6rr2i0/yRuj842HiGeEwV+zdT8uGCMx2uWpbHbc+too3Nu7giDHDuOfTs5g2amivH0dERERERPaksDdQdLTBzk3d2i27hbpd2/fcd2i+NyI34xPdAl2xtz0uPiLltXeE2LqzyVv4vDpIWaCBsuogr5bVkJmWxE8+PoOPHzlaLZsiIiIiIn1EYa8/cQ4aKvcKdBugZj3sqIBQe9e+KcO8Ebmi2XuO0GUWemvZRaQ8R6ChlfJAkLLqBu97IEh5IMjGmiBtHa5z3yEpCRTlpPPZk4r40uxihqZp4XMRERERkb6ksBcNzfVe2+UeLZfhYNe6q2u/+GRvhC53Ckw+tyvQZRd3LmEQCcGW9q4gVx2kPNDQeXtXS1fgTIqPo8CfxrgcH/Mm51Hk91GU46PQ7yPLl6RF0EVEREREokhhL1I62rzRuH2N0jVUdtvRYNgYL9TlH9PVcpldDENGQ1xcRMpr6wixubaR8vDIXFm30brK+pau6gxGDk2l0O/jgiNHUej3UZSTTpHfx8hhqcSrLVNEREREpF9S2Ottqx+HRbd5Qc91dG1P83sBrvi0rjCXXey1XSamRKQU5xxVu1ooqw6GQ11D5+1NtY20h7raLjPTEin0+zixOIeiHB9Ffh+FOT4Ksn2kJEbmOj8REREREYkchb3elpYFI2bAtAu7Al32OEjNjNghdzW3dY3QVe++jq6B8uogwdauwJmcEEeh38fE4RmcMX04hf50b6TO7yPTp2UQRERERERiicJebys40fvqgVDIsWpbPYtKKvnv+moaul0P11M7Gtuo3rVn2+XozFQK/enMHJvVeQ1dUU46I4akaDZMEREREZFBQmGvjzW1dvByaYDFaypZXFJF1a4WzODw/GGMy0k/4NdLT/ZmvSz0+xiX4yM/K01tlyIiIiIiorDXF7bXNbG4pIola6p4uTRAS3uI9OQETp7gZ+6kPGZPzCE7PTnaZYqIiIiISAxR2IuAUMjx7tY6FpdUsnhNFau21QOQn5XKpbPGMG9yHrMKs0hKiMxMmyIiIiIiIv0u7JnZAuAXQDzwR+fcHVEu6YA89e52vvP4Kqp3tRBncNTYTG5cMIl5k3Mpzk3X2nMiIiIiItIn+lXYM7N44DfAacAW4HUze9w5tzq6lfVc3pAUZhVmMXdSLrMn5pKlWS5FRERERCQK+lXYA2YBpc65MgAzewA4DxgwYe+osZkcNTZyyyyIiIiIiIj0RH+7aGwUsLnb/S3hbZ3M7BozW2FmK6qrq/u0OBERERERkYGiv4W9fV3Q5va449xdzrmZzrmZOTk5fVSWiIiIiIjIwNLfwt4WIL/b/dHAtijVIiIiIiIiMmD1t7D3OjDezArNLAm4BHg8yjWJiIiIiIgMOP1qghbnXLuZfRl4Fm/phbudc6uiXJaIiIiIiMiA06/CHoBz7ingqWjXISIiIiIiMpD1tzZOERERERER6QUKeyIiIiIiIjHInHP736ufMrNqYGO069gHPxCIdhHS7+i8kL3pnJB90Xkhe9M5Ifui80J2G+uc2+eadAM67PVXZrbCOTcz2nVI/6LzQvamc0L2ReeF7E3nhOyLzgvpCbVxioiIiIiIxCCFPRERERERkRiksBcZd0W7AOmXdF7I3nROyL7ovJC96ZyQfdF5Ifula/ZERERERERikEb2REREREREYpDCnoiIiIiISAxS2OtlZrbAzNaaWamZ3RTteiQ6zOxuM6sys/e6bcsys+fNbH34e2Y0a5S+ZWb5ZvaCmZWY2Soz+2p4u86LQcrMUsxsuZm9HT4nbg9v1zkxyJlZvJm9ZWZPhu/rnBjkzKzCzN41s5VmtiK8TeeF7JfCXi8ys3jgN8AZwBTgUjObEt2qJEruARbste0mYLFzbjywOHxfBo924BvOucnAscCXwv8+6LwYvFqAU51zhwGHAwvM7Fh0Tgh8FSjpdl/nhADMcc4d3m1tPZ0Xsl8Ke71rFlDqnCtzzrUCDwDnRbkmiQLn3ItA7V6bzwPuDd++Fzi/L2uS6HLObXfOvRm+vQvvF7lR6LwYtJynIXw3Mfzl0DkxqJnZaOAs4I/dNuuckH3ReSH7pbDXu0YBm7vd3xLeJgKQ55zbDt4v/kBulOuRKDGzAuAI4DV0Xgxq4Xa9lUAV8LxzTueE3AncAIS6bdM5IQ54zszeMLNrwtt0Xsh+JUS7gBhj+9imtS1EpJOZpQOPANc55+rN9vXPhgwWzrkO4HAzGwb808ymRbkkiSIzOxuocs69YWazo1yO9C8nOOe2mVku8LyZrYl2QTIwaGSvd20B8rvdHw1si1It0v9UmtkIgPD3qijXI33MzBLxgt59zrlHw5t1XgjOuZ3AUrxrfXVODF4nAOeaWQXepSCnmtnf0Dkx6DnntoW/VwH/xLt0SOeF7JfCXu96HRhvZoVmlgRcAjwe5Zqk/3gcWBi+vRB4LIq1SB8zbwjvT0CJc+7n3R7SeTFImVlOeEQPM0sF5gFr0DkxaDnnbnbOjXbOFeD9DrHEOXc5OicGNTPzmVnG7tvA6cB76LyQHjDn1GXYm8zsTLx++3jgbufcD6JbkUSDmf0dmA34gUrgO8C/gIeAMcAm4CLn3N6TuEiMMrMTgf8C79J1Lc4teNft6bwYhMxsBt6kCvF4f3x9yDn3XTPLRufEoBdu4/ymc+5snRODm5kV4Y3mgXcJ1v3OuR/ovJCeUNgTERERERGJQWrjFBERERERiUEKeyIiIiIiIjFIYU9ERERERCQGKeyJiIiIiIjEIIU9ERERERGRGKSwJyIiMcfMOsxspZm9bWZvmtnxPXhOQ/h7gZm9dwDHqjCzd8PHW9mTYx0MM1tqZjMj8doiIhKbEqJdgIiISAQ0OecOBzCz+cCPgFMieLw5zrlABF9fRETkgGlkT0REYt0QYAeAmaWb2eLwaN+7ZnbeRz3RzKaa2fLwiN07Zja+Jwc0s3+Z2RtmtsrMrum2fUH42G+b2eLwNp+Z3W1mr5vZW7trMrNUM3sgfNwHgdSD/QBERGRw0sieiIjEolQzWwmkACOAU8Pbm4ELnHP1ZuYHXjWzx51z7kNe5/PAL5xz95lZEhD/Ifu9YGYdQItz7hjgKudcrZmlAq+b2SN4f2D9A3Cyc67czLLCz/0fYIlz7iozGwYsN7NFwOeARufcDDObAbx58B+HiIgMRgp7IiISi7q3cR4H/MXMpgEG/NDMTgZCwCggD3j/Q17nFeB/zGw08Khzbv2H7Ld3G+e1ZnZB+HY+MB7IAV50zpUDOOdqw4+fDpxrZt8M308BxgAnA78M7/uOmb3T43cvIiKCwp6IiMQ459wr4VG8HODM8PejnHNtZlaBF64+7Ln3m9lrwFnAs2b2Wefcko86npnNBuYBxznnGs1safgYBuxrBNGAjznn1u71OnzI/iIiIj2ia/ZERCSmmdkkvPbLGmAoUBUOenOAsft5bhFQ5pz7JfA4MKMHhxwK7AgHvUnAseHtrwCnmFlh+LV3t3E+C3zFwunOzI4Ib38R+GR427QeHltERKSTRvZERCQW7b5mD7yRs4XOuQ4zuw94wsxWACuBNft5nYuBy82sDa/V87s9OPYzwOfDbZdrgVcBnHPV4claHjWzOKAKOA34HnAn8E448FUAZwO/Bf4cfp2VwPIeHFtERKSTffg16SIiIiIiIjJQqY1TREREREQkBinsiYiIiIiIxCCFPRERERERkRiksCciIiIiIhKDFPZERERERERikMKeiIiIiIhIDFLYExERERERiUH/Hw42ixv5NN45AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Some Visualizations \n",
    "\n",
    "plt.figure(figsize=(15, 4))\n",
    "plt.plot(virat_srs.values,label='Virat')\n",
    "plt.plot(warner_srs.values, label='Warner')\n",
    "plt.xlabel(\"Balls Faced\")\n",
    "plt.ylabel(\"Score\")\n",
    "\n",
    "plt.title(\"Scoring Rate Comparison\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1075,
   "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>id</th>\n",
       "      <th>inning</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>is_wicket</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>Bowler Type</th>\n",
       "      <th>Bowler Speed</th>\n",
       "      <th>Batsman Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>829705</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Right arm pace</td>\n",
       "      <td>Fast</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>829705</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Right arm pace</td>\n",
       "      <td>Fast</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>829705</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Right arm pace</td>\n",
       "      <td>Fast</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>829705</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Right arm pace</td>\n",
       "      <td>Fast</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>829705</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>R Vinay Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Right arm pace</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  inning  over  ball     batsman non_striker         bowler  \\\n",
       "0  829705       2     0     2   G Gambhir  RV Uthappa     SL Malinga   \n",
       "1  829705       2     0     1  RV Uthappa   G Gambhir     SL Malinga   \n",
       "2  829705       2     0     6  RV Uthappa   G Gambhir     SL Malinga   \n",
       "3  829705       2     0     7  RV Uthappa   G Gambhir     SL Malinga   \n",
       "4  829705       2     1     1   G Gambhir  RV Uthappa  R Vinay Kumar   \n",
       "\n",
       "   batsman_runs  is_wicket dismissal_kind player_dismissed  \\\n",
       "0             0          0            NaN              NaN   \n",
       "1             1          0            NaN              NaN   \n",
       "2             0          0            NaN              NaN   \n",
       "3             0          0            NaN              NaN   \n",
       "4             0          0            NaN              NaN   \n",
       "\n",
       "            batting_team    bowling_team     Bowler Type Bowler Speed  \\\n",
       "0  Kolkata Knight Riders  Mumbai Indians  Right arm pace         Fast   \n",
       "1  Kolkata Knight Riders  Mumbai Indians  Right arm pace         Fast   \n",
       "2  Kolkata Knight Riders  Mumbai Indians  Right arm pace         Fast   \n",
       "3  Kolkata Knight Riders  Mumbai Indians  Right arm pace         Fast   \n",
       "4  Kolkata Knight Riders  Mumbai Indians  Right arm pace       Medium   \n",
       "\n",
       "  Batsman Type  \n",
       "0         Left  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  \n",
       "4          NaN  "
      ]
     },
     "execution_count": 1075,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1076,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1], dtype=int64)"
      ]
     },
     "execution_count": 1076,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.inning.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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