7. IPL Batsmen Ranking With Topsis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d0f0240f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import ipywidgets as widgets\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option(\"display.precision\", 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0e76bd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read All IPL Data\n",
    "deliveres = pd.read_csv(\"C:/Users/utkar/OneDrive/Desktop/Analytics/Data/IPL Ball-by-Ball 2008-2020.csv\")\n",
    "matches = pd.read_csv(\"C:/Users/utkar/OneDrive/Desktop/Analytics/Data/IPL Matches 2008-2020.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eca77906",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Goal \n",
    "# Get Best Batsman against any team x, at venue y, during phase z \n",
    "# For example - Who are the best batsmen against 'Mumbai Indians' at 'Wankhede Stadium' during 'End' overs\n",
    "# I've used MCDM with Topsis to arrive at a score for each batsman\n",
    "# The Criterion considered are Strike Rate, Balls Per Dismissal, Runs Per Innings and Dot Percentage\n",
    "# A random(not really !!) weight is assigned to each of the above. \n",
    "# We've narrowed the dataset to atleast 2 innings and IPL 2016 onwards\n",
    "\n",
    "#Edit - Added Dropdown widget to make making choices simpler and remove typing mistakes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "261a6de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return match phase, based on over information\n",
    "def get_phase(over): \n",
    "    if (over < 6) : \n",
    "        return 'PowerPlay'\n",
    "    elif (over < 15) : \n",
    "        return 'Middle'\n",
    "    else:\n",
    "        return 'End'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f1fae93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Balls per dismissal\n",
    "def get_BPD(balls, dismissals): \n",
    "    if (dismissals == 0): \n",
    "        return balls/1\n",
    "    else: \n",
    "        return balls/dismissals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9da4510d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Balls per boundary\n",
    "def get_BPB(balls, boundries): \n",
    "    if (boundries == 0): \n",
    "        return balls/1\n",
    "    else: \n",
    "        return balls/boundries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aaf729ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a mega DataFrame for all batsmen, \n",
    "# adding all extra information about RPI, SR, BPB, BPD and DP\n",
    "# inputs are venue, opposition and match phase\n",
    "def getCustom(df, venue, opposition, phase): \n",
    "    \n",
    "    df = df[df.venue  == venue]\n",
    "    df = df[df.bowling_team == opposition]\n",
    "    df = df[df.phase == phase]\n",
    "    \n",
    "    df.reset_index(inplace = True, drop = True)\n",
    "    \n",
    "    df['isDot']   = df['batsman_runs'].apply(lambda x: 1 if x == 0 else 0)\n",
    "    df['isOne']   = df['batsman_runs'].apply(lambda x: 1 if x == 1 else 0)\n",
    "    df['isTwo']   = df['batsman_runs'].apply(lambda x: 1 if x == 2 else 0)\n",
    "    df['isThree'] = df['batsman_runs'].apply(lambda x: 1 if x == 3 else 0)\n",
    "    df['isFour']  = df['batsman_runs'].apply(lambda x: 1 if x == 4 else 0)\n",
    "    df['isSix']   = df['batsman_runs'].apply(lambda x: 1 if x == 6 else 0)\n",
    "\n",
    "    runs = df.groupby('batsman').batsman_runs.sum().reset_index().rename(columns = {'batsman_runs' : 'runs'})\n",
    "    balls = df.groupby('batsman').ball.count().reset_index().rename(columns = {'ball' : 'balls'})\n",
    "    innings = df.groupby('batsman').id.nunique().reset_index().rename(columns = {'id' : 'innings'})\n",
    "    dismissals = df.groupby('batsman').player_dismissed.count().reset_index().rename(columns = {'player_dismissed' : 'dismissals'})\n",
    "    sixes = df.groupby('batsman').isSix.sum().reset_index().rename(columns = {'isSix' : 'sixes'}) \n",
    "    fours = df.groupby('batsman').isFour.sum().reset_index().rename(columns = {'isFour' : 'fours'})\n",
    "    dots = df.groupby('batsman').isDot.sum().reset_index().rename(columns = {'isDot' : 'dots'})\n",
    "   \n",
    "    mega = pd.merge(runs, balls, on = 'batsman', how = 'left').merge(innings, on = 'batsman').merge(dismissals, on = 'batsman').merge(fours, on = 'batsman').merge(sixes , on = 'batsman').merge(dots, on = 'batsman')\n",
    "    mega['RPI'] = mega['runs'] / mega['innings']\n",
    "    mega['SR'] = 100 * (mega['runs'] / mega['balls'])\n",
    "    mega['BPB'] = mega.apply(lambda x: get_BPB(x['balls'], x['fours'] + x['sixes']) , axis=1)\n",
    "    mega['BPD'] = mega.apply(lambda x: get_BPD(x['balls'], x['dismissals']) , axis=1)\n",
    "    mega['DP'] = mega.apply(lambda x: 100 * (x['dots'] / x['balls']) , axis=1)\n",
    "    \n",
    "    return mega"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d989469d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getWeights(phase): \n",
    "    if (phase == 'PowerPlay'): \n",
    "        # Giving preference to Strike Rate and Dot Percentage in PowerPlay. \n",
    "        # The onus should be on scoring lots of runs at high SR. Dismissals and Dot Percentage should matter less\n",
    "        wt_sr, wt_rpi, wt_bpd, wt_dp = 0.3 , 0.3 , 0.2, 0.2\n",
    "    elif (phase == 'Middle'): \n",
    "        # Giving preference to RPI, BPD and Dot Percentage during middle \n",
    "        # Players who can bat long and score runs, compared to heavy strikers\n",
    "        wt_sr, wt_rpi, wt_bpd, wt_dp = 0.1 , 0.3 , 0.3, 0.3\n",
    "    else:\n",
    "        # Major preference to Strike Rate and Dot Percentage\n",
    "        # RPI and BPD are immaterial as most teams would go for runs at this stage\n",
    "        wt_sr, wt_rpi, wt_bpd, wt_dp = 0.5, 0.1, 0.1, 0.3\n",
    "        \n",
    "    return (wt_sr, wt_rpi, wt_bpd, wt_dp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "000837de",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getTopsisScore(mydf, phase):\n",
    "    \n",
    "    if mydf.empty: \n",
    "        print (\"Empty DataFrame\")\n",
    "        return mydf\n",
    "    \n",
    "    # Square the required fields and store in another column\n",
    "    mydf['calc_SR']  = mydf['SR'].apply(lambda x: x * x)\n",
    "    mydf['calc_RPI'] = mydf['RPI'].apply(lambda x: x * x)\n",
    "    mydf['calc_BPD']  = mydf['BPD'].apply(lambda x: x * x)\n",
    "    mydf['calc_DP']  = mydf['DP'].apply(lambda x: x * x)\n",
    "\n",
    "    # Get Weights depending on phase\n",
    "    wt_sr, wt_rpi, wt_bpd, wt_dp = getWeights(phase)\n",
    "    \n",
    "    # get SQRT of sum of squares\n",
    "    sr_sqrt , rpi_sqrt , bpd_sqrt , dp_sqrt = np.sqrt(mydf[['calc_SR' , 'calc_RPI' , 'calc_BPD' , 'calc_DP']].sum(axis=0))\n",
    "\n",
    "    #divide calculated squares with sum of squares\n",
    "    mydf['calc_SR'] = mydf['calc_SR'].apply(lambda x : x / sr_sqrt)\n",
    "    mydf['calc_RPI'] = mydf['calc_RPI'].apply(lambda x : x / rpi_sqrt)\n",
    "    mydf['calc_BPD'] = mydf['calc_BPD'].apply(lambda x : x / bpd_sqrt)\n",
    "    mydf['calc_DP'] = mydf['calc_DP'].apply(lambda x : x / dp_sqrt)\n",
    "\n",
    "    # multiply each calculated value with it's weight \n",
    "    mydf['calc_SR'] = mydf['calc_SR'].apply(lambda x : wt_sr * x)\n",
    "    mydf['calc_RPI'] = mydf['calc_RPI'].apply(lambda x : wt_rpi * x)\n",
    "    mydf['calc_BPD'] = mydf['calc_BPD'].apply(lambda x : wt_bpd * x)\n",
    "    mydf['calc_DP'] = mydf['calc_DP'].apply(lambda x : wt_dp * x)\n",
    "\n",
    "    # Get best and worst of each criterion\n",
    "    best_sr , worst_sr   = max(mydf['calc_SR']) , min(mydf['calc_SR'])\n",
    "    best_rpi , worst_rpi = max(mydf['calc_RPI']) , min(mydf['calc_RPI'])\n",
    "    best_bpd , worst_bpd = max(mydf['calc_BPD']) , min(mydf['calc_BPD'])\n",
    "    best_dp , worst_dp   = min(mydf['calc_DP']) , max(mydf['calc_DP'])\n",
    "    \n",
    "    # Deviation from best - Square of difference \n",
    "    mydf['dev_best_sr']   = mydf['calc_SR'].apply(lambda x : (x-best_sr) * (x-best_sr))\n",
    "    mydf['dev_best_rpi']  = mydf['calc_RPI'].apply(lambda x : (x-best_rpi) * (x-best_rpi))\n",
    "    mydf['dev_best_bpd']  = mydf['calc_BPD'].apply(lambda x : (x-best_bpd) * (x-best_bpd))\n",
    "    mydf['dev_best_dp']   = mydf['calc_DP'].apply(lambda x : (x-best_dp) * (x-best_dp))\n",
    "\n",
    "    # SquareRoot of sum of all deviations from best\n",
    "    mydf['dev_best_sqrt'] = np.sqrt(mydf.apply(lambda x: x['dev_best_sr'] + x['dev_best_rpi'] + x['dev_best_bpd'] + x['dev_best_dp'] , axis=1))\n",
    "    \n",
    "    # Deviation from Worst - Square of difference \n",
    "    mydf['dev_worst_sr']   = mydf['calc_SR'].apply(lambda x : (x-worst_sr) * (x-worst_sr))\n",
    "    mydf['dev_worst_rpi']  = mydf['calc_RPI'].apply(lambda x : (x-worst_rpi) * (x-worst_rpi))\n",
    "    mydf['dev_worst_bpd']  = mydf['calc_BPD'].apply(lambda x : (x-worst_bpd) * (x-worst_bpd))\n",
    "    mydf['dev_worst_dp']   = mydf['calc_DP'].apply(lambda x : (x-worst_dp) * (x-worst_dp))\n",
    "\n",
    "    # SquareRoot of sum of all deviations from worst\n",
    "    mydf['dev_worst_sqrt'] = np.sqrt(mydf.apply(lambda x: x['dev_worst_sr'] + x['dev_worst_rpi'] + x['dev_worst_bpd'] + x['dev_worst_dp'] , axis=1))\n",
    "    \n",
    "    # Calculate Score - worst / (worst + best) .. shows how far from worst are you (i.e - closer to best)\n",
    "    mydf['score'] = mydf.apply(lambda x : x['dev_worst_sqrt'] / (x['dev_worst_sqrt'] + x['dev_best_sqrt']) , axis=1)\n",
    "    \n",
    "    return mydf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "11b152ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Main - Start\n",
    "\n",
    "# Create Copies\n",
    "del_df = deliveres.copy()\n",
    "match_df = matches.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fcef519a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge two dataframes\n",
    "comb = pd.merge(del_df , match_df, on = 'id', how = 'left') \n",
    "\n",
    "# Get Data from IPL 2016 onwards\n",
    "comb = comb[(comb.id > 980901)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bce0a792",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Match Phase\n",
    "comb['phase'] = comb['over'].apply(lambda x : get_phase(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "98edf9ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "venue_list = comb['venue'].unique().tolist()\n",
    "team_list = comb['bowling_team'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e92c695a",
   "metadata": {},
   "outputs": [],
   "source": [
    "venue_drop = widgets.Dropdown(options = venue_list , description='Venue' , value='Wankhede Stadium')\n",
    "team_drop = widgets.Dropdown(options = team_list , description='Bowling Team' , value='Mumbai Indians')\n",
    "phase_drop = widgets.Dropdown(options = ['PowerPlay' , 'Middle' , 'End'] , description='Match Phase', value='End')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "db0d6640",
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_change_venue(change):\n",
    "    if change['type'] == 'change' and change['name'] == 'value':\n",
    "        print(\"Venue changed to %s\" % change['new'])\n",
    "        \n",
    "def on_change_team(change):\n",
    "    if change['type'] == 'change' and change['name'] == 'value':\n",
    "        print(\"Bowling Team changed to %s\" % change['new'])\n",
    "        \n",
    "def on_change_phase(change):\n",
    "    if change['type'] == 'change' and change['name'] == 'value':\n",
    "        print(\"Phase changed to %s\" % change['new'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "975cabf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2900fcf44ec6401daa76a2406dc68db6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Venue', index=6, options=('Eden Gardens', 'Punjab Cricket Association IS Bindra Stadium,…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "787ed1b4c3af49059ba243c65f2be5e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Bowling Team', index=6, options=('Delhi Daredevils', 'Kolkata Knight Riders', 'Gujarat L…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ec493da3077436784f4728a0fd01056",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Match Phase', index=2, options=('PowerPlay', 'Middle', 'End'), value='End')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "venue_drop.observe(on_change_venue)\n",
    "display(venue_drop)\n",
    "\n",
    "team_drop.observe(on_change_team)\n",
    "display(team_drop)\n",
    "\n",
    "phase_drop.observe(on_change_phase)\n",
    "display(phase_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "af06eb18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Venue =  Wankhede Stadium\n",
      "Bowling Team =  Mumbai Indians\n",
      "Match Phase =  End\n"
     ]
    }
   ],
   "source": [
    "print (\"Venue = \" , venue_drop.value)\n",
    "print (\"Bowling Team = \", team_drop.value)\n",
    "print (\"Match Phase = \" , phase_drop.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "71dc6a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inputs\n",
    "venue = venue_drop.value\n",
    "bowlingTeam = team_drop.value\n",
    "phase = phase_drop.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b1f116d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get per batsman DF for stadium, oppostion, phase data\n",
    "mydf = getCustom(comb , venue , bowlingTeam , phase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7fb01714",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Atleast 2 innings\n",
    "mydf = mydf[(mydf.innings > 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "af249379",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = getTopsisScore(mydf,  phase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8c70eeae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Venue =  Wankhede Stadium\n",
      "Bowling Team =  Mumbai Indians\n",
      "Match Phase =  End\n"
     ]
    },
    {
     "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>batsman</th>\n",
       "      <th>runs</th>\n",
       "      <th>SR</th>\n",
       "      <th>RPI</th>\n",
       "      <th>BPD</th>\n",
       "      <th>DP</th>\n",
       "      <th>innings</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>76</td>\n",
       "      <td>230.30</td>\n",
       "      <td>38.00</td>\n",
       "      <td>33.0</td>\n",
       "      <td>18.18</td>\n",
       "      <td>2</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>72</td>\n",
       "      <td>225.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>32.0</td>\n",
       "      <td>15.62</td>\n",
       "      <td>2</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>56</td>\n",
       "      <td>215.38</td>\n",
       "      <td>28.00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>38.46</td>\n",
       "      <td>2</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SV Samson</td>\n",
       "      <td>29</td>\n",
       "      <td>181.25</td>\n",
       "      <td>14.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>37.50</td>\n",
       "      <td>2</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JJ Roy</td>\n",
       "      <td>41</td>\n",
       "      <td>178.26</td>\n",
       "      <td>20.50</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.43</td>\n",
       "      <td>2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AJ Finch</td>\n",
       "      <td>32</td>\n",
       "      <td>177.78</td>\n",
       "      <td>16.00</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.67</td>\n",
       "      <td>2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MK Tiwary</td>\n",
       "      <td>48</td>\n",
       "      <td>177.78</td>\n",
       "      <td>16.00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.93</td>\n",
       "      <td>3</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>P Negi</td>\n",
       "      <td>33</td>\n",
       "      <td>165.00</td>\n",
       "      <td>16.50</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>64</td>\n",
       "      <td>152.38</td>\n",
       "      <td>32.00</td>\n",
       "      <td>42.0</td>\n",
       "      <td>33.33</td>\n",
       "      <td>2</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>29</td>\n",
       "      <td>152.63</td>\n",
       "      <td>14.50</td>\n",
       "      <td>19.0</td>\n",
       "      <td>42.11</td>\n",
       "      <td>2</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AD Russell</td>\n",
       "      <td>24</td>\n",
       "      <td>150.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>37.50</td>\n",
       "      <td>2</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AR Patel</td>\n",
       "      <td>33</td>\n",
       "      <td>143.48</td>\n",
       "      <td>11.00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.09</td>\n",
       "      <td>3</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>34</td>\n",
       "      <td>136.00</td>\n",
       "      <td>11.33</td>\n",
       "      <td>12.5</td>\n",
       "      <td>36.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>32</td>\n",
       "      <td>139.13</td>\n",
       "      <td>16.00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>52.17</td>\n",
       "      <td>2</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CR Woakes</td>\n",
       "      <td>17</td>\n",
       "      <td>121.43</td>\n",
       "      <td>8.50</td>\n",
       "      <td>7.0</td>\n",
       "      <td>35.71</td>\n",
       "      <td>2</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SP Narine</td>\n",
       "      <td>6</td>\n",
       "      <td>100.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SA Yadav</td>\n",
       "      <td>3</td>\n",
       "      <td>60.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.5</td>\n",
       "      <td>40.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AD Nath</td>\n",
       "      <td>3</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>5</td>\n",
       "      <td>62.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>UT Yadav</td>\n",
       "      <td>1</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.50</td>\n",
       "      <td>3.0</td>\n",
       "      <td>66.67</td>\n",
       "      <td>2</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MP Stoinis</td>\n",
       "      <td>1</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         batsman  runs      SR    RPI   BPD     DP  innings  score\n",
       "0       KL Rahul    76  230.30  38.00  33.0  18.18        2   0.98\n",
       "1      MK Pandey    72  225.00  36.00  32.0  15.62        2   0.95\n",
       "2       DJ Bravo    56  215.38  28.00  13.0  38.46        2   0.86\n",
       "3      SV Samson    29  181.25  14.50   8.0  37.50        2   0.62\n",
       "4         JJ Roy    41  178.26  20.50  23.0  30.43        2   0.60\n",
       "5       AJ Finch    32  177.78  16.00  18.0  16.67        2   0.60\n",
       "6      MK Tiwary    48  177.78  16.00  27.0  25.93        3   0.60\n",
       "7         P Negi    33  165.00  16.50  10.0  35.00        2   0.52\n",
       "8     KD Karthik    64  152.38  32.00  42.0  33.33        2   0.45\n",
       "9      YK Pathan    29  152.63  14.50  19.0  42.11        2   0.44\n",
       "10    AD Russell    24  150.00  12.00   8.0  37.50        2   0.43\n",
       "11      AR Patel    33  143.48  11.00  23.0  26.09        3   0.41\n",
       "12     KM Jadhav    34  136.00  11.33  12.5  36.00        3   0.37\n",
       "13      MS Dhoni    32  139.13  16.00  11.5  52.17        2   0.37\n",
       "14     CR Woakes    17  121.43   8.50   7.0  35.71        2   0.30\n",
       "15     SP Narine     6  100.00   3.00   3.0  50.00        2   0.21\n",
       "16      SA Yadav     3   60.00   1.50   2.5  40.00        2   0.15\n",
       "17       AD Nath     3   50.00   1.50   3.0  50.00        2   0.12\n",
       "18  Yuvraj Singh     5   62.50   2.50   4.0  75.00        2   0.06\n",
       "19      UT Yadav     1   33.33   0.50   3.0  66.67        2   0.05\n",
       "20    MP Stoinis     1   25.00   0.50   2.0  75.00        2   0.00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# debug \n",
    "print (\"Venue = \" , venue_drop.value)\n",
    "print (\"Bowling Team = \", team_drop.value)\n",
    "print (\"Match Phase = \" , phase_drop.value)\n",
    "\n",
    "final_df[['batsman','runs' , 'SR' , 'RPI' , 'BPD' , 'DP' , 'innings', 'score']].sort_values(by = 'score', ascending = False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fc82b52",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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