TennisRanking.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tennis Players Ranking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Objective of this project is to rank all Tennis Players based on the matches they played in the year of 2018. Statistics of all matches are given including their scores in all the sets of all matches. This project comprises 4 approaches to rank Tennis players and I have tried to make these approaches more robust sequentially."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing libraries and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as ss\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:\\\\Users\\\\archd\\\\OneDrive\\\\Desktop\\\\UTR\\\\FINALutrdatascienceinternship2019.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>loserset4</th>\n",
       "      <th>loserset5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10979105</td>\n",
       "      <td>1/7/2018</td>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "      <th>4</th>\n",
       "      <td>10979111</td>\n",
       "      <td>1/7/2018</td>\n",
       "      <td>3534</td>\n",
       "      <td>3688</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   resultid resultdate  winner1id  loser1id  winnerset1  winnerset2  \\\n",
       "0  10979105   1/7/2018       3867     54552           6           6   \n",
       "1  10979106   1/7/2018       3671      4348           6           6   \n",
       "2  10979108   1/7/2018      52886      4021           6           7   \n",
       "3  10979110   1/7/2018      54846      3855           4           0   \n",
       "4  10979111   1/7/2018       3534      3688           6           6   \n",
       "\n",
       "   winnerset3  winnerset4  winnerset5  loserset1  loserset2  loserset3  \\\n",
       "0           0           0           0          4          3          0   \n",
       "1           0           0           0          3          4          0   \n",
       "2           0           0           0          4          5          0   \n",
       "3           0           0           0          2          0          0   \n",
       "4           0           0           0          3          2          0   \n",
       "\n",
       "   loserset4  loserset5  \n",
       "0          0          0  \n",
       "1          0          0  \n",
       "2          0          0  \n",
       "3          0          0  \n",
       "4          0          0  "
      ]
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     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resultid</th>\n",
       "      <th>resultdate</th>\n",
       "      <th>winner1id</th>\n",
       "      <th>loser1id</th>\n",
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       "      <th>9453</th>\n",
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       "      <td>4</td>\n",
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       "      <td>8/12/2018</td>\n",
       "      <td>70522</td>\n",
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       "      <th>9457</th>\n",
       "      <td>12974164</td>\n",
       "      <td>9/3/2018</td>\n",
       "      <td>76935</td>\n",
       "      <td>69327</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      resultid resultdate  winner1id  loser1id  winnerset1  winnerset2  \\\n",
       "9453  12599577  7/25/2018      52886     53840           6           6   \n",
       "9454  12599578  7/25/2018       3481     28096           4           7   \n",
       "9455  12599579  7/25/2018       3853    218159           6           4   \n",
       "9456  12775191  8/12/2018      70522    405461           6           6   \n",
       "9457  12974164   9/3/2018      76935     69327           7           6   \n",
       "\n",
       "      winnerset3  winnerset4  winnerset5  loserset1  loserset2  loserset3  \\\n",
       "9453           0           0           0          3          3          0   \n",
       "9454           6           0           0          6          6          4   \n",
       "9455           7           0           0          4          6          6   \n",
       "9456           6           0           0          4          3          1   \n",
       "9457           0           0           0          6          3          0   \n",
       "\n",
       "      loserset4  loserset5  \n",
       "9453          0          0  \n",
       "9454          0          0  \n",
       "9455          0          0  \n",
       "9456          0          0  \n",
       "9457          0          0  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9458, 14)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data contains statistics of total 9485 matches and I will rank the players based on these matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.resultdate = pd.to_datetime(df.resultdate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "246"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_match_day = max(df.resultdate.dt.dayofyear)\n",
    "latest_match_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Day_difference\"] = latest_match_day - df.resultdate.dt.dayofyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>resultid</th>\n",
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       "      <th>loserset5</th>\n",
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       "      <td>10979105</td>\n",
       "      <td>2018-01-07</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2018-01-07</td>\n",
       "      <td>52886</td>\n",
       "      <td>4021</td>\n",
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       "      <th>3</th>\n",
       "      <td>10979110</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>54846</td>\n",
       "      <td>3855</td>\n",
       "      <td>4</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10979111</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>3534</td>\n",
       "      <td>3688</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   resultid resultdate  winner1id  loser1id  winnerset1  winnerset2  \\\n",
       "0  10979105 2018-01-07       3867     54552           6           6   \n",
       "1  10979106 2018-01-07       3671      4348           6           6   \n",
       "2  10979108 2018-01-07      52886      4021           6           7   \n",
       "3  10979110 2018-01-07      54846      3855           4           0   \n",
       "4  10979111 2018-01-07       3534      3688           6           6   \n",
       "\n",
       "   winnerset3  winnerset4  winnerset5  loserset1  loserset2  loserset3  \\\n",
       "0           0           0           0          4          3          0   \n",
       "1           0           0           0          3          4          0   \n",
       "2           0           0           0          4          5          0   \n",
       "3           0           0           0          2          0          0   \n",
       "4           0           0           0          3          2          0   \n",
       "\n",
       "   loserset4  loserset5  Day_difference  \n",
       "0          0          0             239  \n",
       "1          0          0             239  \n",
       "2          0          0             239  \n",
       "3          0          0             239  \n",
       "4          0          0             239  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All players' player ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_players = np.concatenate((df.winner1id, df.loser1id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_players = np.unique(all_players)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1052"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_players)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have total 1052 players and I will rank them accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of Match Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"sum_0\"] = (df[[\"winnerset1\", \"winnerset2\", \"winnerset3\", \"winnerset4\", \"winnerset5\", \"loserset1\", \"loserset2\", \"loserset3\", \"loserset4\", \"loserset5\"]] == 0).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    6\n",
       "2    6\n",
       "3    8\n",
       "4    6\n",
       "Name: sum_0, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"sum_0\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def total_rounds(x):\n",
    "    if (x == 10):\n",
    "        return 0\n",
    "    elif (x == 9 or x == 8):\n",
    "        return 1\n",
    "    elif (x == 7 or x == 6):\n",
    "        return 2\n",
    "    elif (x == 5 or x == 4):\n",
    "        return 3\n",
    "    elif (x == 3 or x == 2):\n",
    "        return 4\n",
    "    elif (x == 1 or x == 0):\n",
    "        return 5\n",
    "    else:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"total_rounds\"] = df[\"sum_0\"].apply(lambda x: total_rounds(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    2\n",
       "2    2\n",
       "3    1\n",
       "4    2\n",
       "Name: total_rounds, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"total_rounds\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    5863\n",
       "3    3385\n",
       "4     125\n",
       "5      73\n",
       "1      12\n",
       "Name: total_rounds, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"total_rounds\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"total_rounds\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find the score difference in all rounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_diff = pd.DataFrame()\n",
    "for i in range(4,9):\n",
    "    \n",
    "    df_0 = df.iloc[:,i] - df.iloc[:, i+5]\n",
    "    df_0 = df_0.reset_index(drop=True)\n",
    "    score_diff = score_diff.reset_index(drop=True)\n",
    "    score_diff = pd.concat([score_diff, df_0], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_diff.columns = [\"R1_score_diff\", \"R2_score_diff\", \"R3_score_diff\", \"R4_score_diff\", \"R5_score_diff\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>R1_score_diff</th>\n",
       "      <th>R2_score_diff</th>\n",
       "      <th>R3_score_diff</th>\n",
       "      <th>R4_score_diff</th>\n",
       "      <th>R5_score_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R1_score_diff  R2_score_diff  R3_score_diff  R4_score_diff  R5_score_diff\n",
       "0              2              3              0              0              0\n",
       "1              3              2              0              0              0\n",
       "2              2              2              0              0              0\n",
       "3              2              0              0              0              0\n",
       "4              3              4              0              0              0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_diff.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find how many sets winner won and how many loser won in a particular match based on score difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"winner_win_sets\"] = (score_diff[score_diff.columns] > 0).sum(axis = 1)\n",
    "df[\"loser_win_sets\"] = (score_diff[score_diff.columns] < 0).sum(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>resultid</th>\n",
       "      <th>resultdate</th>\n",
       "      <th>winner1id</th>\n",
       "      <th>loser1id</th>\n",
       "      <th>winnerset1</th>\n",
       "      <th>winnerset2</th>\n",
       "      <th>winnerset3</th>\n",
       "      <th>winnerset4</th>\n",
       "      <th>winnerset5</th>\n",
       "      <th>loserset1</th>\n",
       "      <th>loserset2</th>\n",
       "      <th>loserset3</th>\n",
       "      <th>loserset4</th>\n",
       "      <th>loserset5</th>\n",
       "      <th>Day_difference</th>\n",
       "      <th>sum_0</th>\n",
       "      <th>total_rounds</th>\n",
       "      <th>winner_win_sets</th>\n",
       "      <th>loser_win_sets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10979105</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>3867</td>\n",
       "      <td>54552</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10979106</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>3671</td>\n",
       "      <td>4348</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10979108</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>52886</td>\n",
       "      <td>4021</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10979110</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>54846</td>\n",
       "      <td>3855</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10979111</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>3534</td>\n",
       "      <td>3688</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10979112</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>4047</td>\n",
       "      <td>51330</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10979113</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>3671</td>\n",
       "      <td>3867</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10979114</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>52886</td>\n",
       "      <td>53149</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10979115</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>94283</td>\n",
       "      <td>54846</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10979116</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>4047</td>\n",
       "      <td>3534</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   resultid resultdate  winner1id  loser1id  winnerset1  winnerset2  \\\n",
       "0  10979105 2018-01-07       3867     54552           6           6   \n",
       "1  10979106 2018-01-07       3671      4348           6           6   \n",
       "2  10979108 2018-01-07      52886      4021           6           7   \n",
       "3  10979110 2018-01-07      54846      3855           4           0   \n",
       "4  10979111 2018-01-07       3534      3688           6           6   \n",
       "5  10979112 2018-01-07       4047     51330           7           7   \n",
       "6  10979113 2018-01-07       3671      3867           7           6   \n",
       "7  10979114 2018-01-07      52886     53149           7           7   \n",
       "8  10979115 2018-01-07      94283     54846           7           7   \n",
       "9  10979116 2018-01-07       4047      3534           6           7   \n",
       "\n",
       "   winnerset3  winnerset4  winnerset5  loserset1  loserset2  loserset3  \\\n",
       "0           0           0           0          4          3          0   \n",
       "1           0           0           0          3          4          0   \n",
       "2           0           0           0          4          5          0   \n",
       "3           0           0           0          2          0          0   \n",
       "4           0           0           0          3          2          0   \n",
       "5           0           0           0          6          6          0   \n",
       "6           7           0           0          6          7          6   \n",
       "7           0           0           0          5          6          0   \n",
       "8           0           0           0          6          6          0   \n",
       "9           0           0           0          2          5          0   \n",
       "\n",
       "   loserset4  loserset5  Day_difference  sum_0  total_rounds  winner_win_sets  \\\n",
       "0          0          0             239      6             2                2   \n",
       "1          0          0             239      6             2                2   \n",
       "2          0          0             239      6             2                2   \n",
       "3          0          0             239      8             1                1   \n",
       "4          0          0             239      6             2                2   \n",
       "5          0          0             239      6             2                2   \n",
       "6          0          0             239      4             3                2   \n",
       "7          0          0             239      6             2                2   \n",
       "8          0          0             239      6             2                2   \n",
       "9          0          0             239      6             2                2   \n",
       "\n",
       "   loser_win_sets  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  \n",
       "5               0  \n",
       "6               1  \n",
       "7               0  \n",
       "8               0  \n",
       "9               0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Approach 1:\n",
    "    To rank players, I have found out a score matrix which contains a score based on player's performance in each match. I have computed score using following method.\n",
    "- The length of a match matters. If a match has 5 rounds, then it will reward or penalize a player more compared to a match having 3 rounds if a player wins or loses respectively.\n",
    "- All the matches played by a player are considered and the recent the match, more weightage is given to a match.\n",
    "\n",
    "Cons:\n",
    "- If a player plays lots of matches, he can have high score.\n",
    "- If a player wins or loses a most recent match, he will get rewarded (lucky guy!) or panelize (poor guy!) the highest.\n",
    "- Only Match's verdict is considered. The number of sets won by a winner or looser are not considered and it does not matter if a looser loses by 3-2 or 4-1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def approach1_score(player_df, player_id):\n",
    "    player_df = player_df.reset_index(drop = True)\n",
    "    matches = len(player_df)\n",
    "    max_weight = matches / 100\n",
    "    \n",
    "    score = 0\n",
    "    \n",
    "    for i in range(len(player_df)):\n",
    "        if player_df.winner1id[i] == player_id:\n",
    "            score = score + (player_df.total_rounds[i] * max_weight * i / matches)\n",
    "        if player_df.loser1id[i] == player_id:\n",
    "            score = score - (player_df.total_rounds[i] * max_weight * i / matches)\n",
    "    \n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "approach1_df_score = pd.DataFrame(columns = [\"Player_id\", \"score\", \"matches_played\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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",
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       "    }\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>Player_id</th>\n",
       "      <th>score</th>\n",
       "      <th>matches_played</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Player_id, score, matches_played]\n",
       "Index: []"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approach1_df_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in all_players:\n",
    "    temp_df = df[(df.winner1id == i) | (df.loser1id == i)]\n",
    "    approach1_df_score = approach1_df_score.append({\n",
    "                                                    \"Player_id\" : i,\n",
    "                                                    \"score\" : approach1_score(temp_df, i),\n",
    "                                                    \"matches_played\" : len(temp_df)\n",
    "                                                    }, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_id</th>\n",
       "      <th>score</th>\n",
       "      <th>matches_played</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3456.0</td>\n",
       "      <td>7.25</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3458.0</td>\n",
       "      <td>4.84</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3460.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3462.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3464.0</td>\n",
       "      <td>2.63</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3465.0</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3466.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3468.0</td>\n",
       "      <td>3.38</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3469.0</td>\n",
       "      <td>0.61</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3474.0</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player_id  score  matches_played\n",
       "0     3456.0   7.25            32.0\n",
       "1     3458.0   4.84            40.0\n",
       "2     3460.0   0.90            33.0\n",
       "3     3462.0   2.73            29.0\n",
       "4     3464.0   2.63            40.0\n",
       "5     3465.0  -1.64            33.0\n",
       "6     3466.0   2.31            29.0\n",
       "7     3468.0   3.38            37.0\n",
       "8     3469.0   0.61            31.0\n",
       "9     3474.0  -2.33            20.0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approach1_df_score.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "approach1_df_score = approach1_df_score.sort_values(by = [\"score\"], ascending= False)\n",
    "approach1_df_score[\"rank\"] = ss.rankdata(approach1_df_score[\"score\"], method='dense')\n",
    "approach1_df_score[\"rank\"] = max(approach1_df_score[\"rank\"]) + 1 - approach1_df_score[\"rank\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_id</th>\n",
       "      <th>score</th>\n",
       "      <th>matches_played</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>3927.0</td>\n",
       "      <td>14.19</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>4584.0</td>\n",
       "      <td>13.95</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>52294.0</td>\n",
       "      <td>13.70</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>4205.0</td>\n",
       "      <td>13.59</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>4515.0</td>\n",
       "      <td>13.47</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>3745.0</td>\n",
       "      <td>10.99</td>\n",
       "      <td>46.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>206792.0</td>\n",
       "      <td>10.86</td>\n",
       "      <td>52.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3478.0</td>\n",
       "      <td>10.73</td>\n",
       "      <td>34.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>5059.0</td>\n",
       "      <td>10.71</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>3695.0</td>\n",
       "      <td>10.64</td>\n",
       "      <td>38.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Player_id  score  matches_played  rank\n",
       "170     3927.0  14.19            49.0     1\n",
       "291     4584.0  13.95            47.0     2\n",
       "501    52294.0  13.70            43.0     3\n",
       "236     4205.0  13.59            35.0     4\n",
       "280     4515.0  13.47            42.0     5\n",
       "105     3745.0  10.99            46.0     6\n",
       "879   206792.0  10.86            52.0     7\n",
       "12      3478.0  10.73            34.0     8\n",
       "334     5059.0  10.71            36.0     9\n",
       "85      3695.0  10.64            38.0    10"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approach1_df_score.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "approach1_df_score.to_excel(\"C:\\\\Users\\\\archd\\\\OneDrive\\\\Desktop\\\\UTR\\\\approach1_df_score.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Approach 2:\n",
    "    To rank players, I have found out a score matrix which contains a score based on player's performance in each match. I have computed score using following method.\n",
    "- The length of a match matters. If a match has 5 rounds, then it will reward or penalize a player more compared to a match having 3 rounds if a player wins or loses respectively.\n",
    "- Only recent 30 matches played by a player are considered and the recent the match, more weightage is given to a match.\n",
    "\n",
    "Cons:\n",
    "- If a player wins or loses a most recent match, he will get rewarded (lucky guy!) or panelize (poor guy!) the highest.\n",
    "- Only Match's verdict is considered. The number of sets won by a winner or looser are not considered and it does not matter if a looser loses by 3-2 or 4-1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def approach2_score(player_df, player_id):\n",
    "    matches = len(player_df)\n",
    "    if (matches>30):\n",
    "        player_df = player_df.iloc[matches-30 : matches, :]\n",
    "    \n",
    "    player_df = player_df.reset_index(drop = True)\n",
    "    max_weight = 30 / 100\n",
    "    \n",
    "    score = 0\n",
    "    \n",
    "    for i in range(len(player_df)):\n",
    "        if player_df.winner1id[i] == player_id:\n",
    "            score = score + (player_df.total_rounds[i] * max_weight * i / 30)\n",
    "        if player_df.loser1id[i] == player_id:\n",
    "            score = score - (player_df.total_rounds[i] * max_weight * i / 30)\n",
    "    \n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "approach2_df_score = pd.DataFrame(columns = [\"Player_id\", \"score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_id</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Player_id, score]\n",
       "Index: []"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approach2_df_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in all_players:\n",
    "    temp_df = df[(df.winner1id == i) | (df.loser1id == i)]\n",
    "    approach2_df_score = approach2_df_score.append({\n",
    "                                                    \"Player_id\" : i,\n",
    "                                                    \"score\" : approach2_score(temp_df, i)\n",
    "                                                    }, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_id</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3456.0</td>\n",
       "      <td>6.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3458.0</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3460.0</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3462.0</td>\n",
       "      <td>2.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3464.0</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3465.0</td>\n",
       "      <td>-1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3466.0</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3468.0</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3469.0</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3474.0</td>\n",
       "      <td>-2.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player_id  score\n",
       "0     3456.0   6.16\n",
       "1     3458.0   2.64\n",
       "2     3460.0   1.11\n",
       "3     3462.0   2.73\n",
       "4     3464.0   1.63\n",
       "5     3465.0  -1.38\n",
       "6     3466.0   2.31\n",
       "7     3468.0   2.11\n",
       "8     3469.0   0.45\n",
       "9     3474.0  -2.33"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approach2_df_score.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "approach2_df_score = approach2_df_score.sort_values(by = [\"score\"], ascending= False)\n",
    "approach2_df_score[\"rank\"] = ss.rankdata(approach2_df_score[\"score\"], method='dense')\n",
    "approach2_df_score[\"rank\"] = max(approach2_df_score[\"rank\"]) + 1 - approach2_df_score[\"rank\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_id</th>\n",
       "      <th>score</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>4205.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3478.0</td>\n",
       "      <td>9.20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>5059.0</td>\n",
       "      <td>7.34</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>4515.0</td>\n",
       "      <td>7.09</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>3695.0</td>\n",
       "      <td>6.78</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>52294.0</td>\n",
       "      <td>6.74</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3456.0</td>\n",
       "      <td>6.16</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>3927.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>3691.0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>3907.0</td>\n",
       "      <td>5.42</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Player_id  score  rank\n",
       "236     4205.0  10.00     1\n",
       "12      3478.0   9.20     2\n",
       "334     5059.0   7.34     3\n",
       "280     4515.0   7.09     4\n",
       "85      3695.0   6.78     5\n",
       "501    52294.0   6.74     6\n",
       "0       3456.0   6.16     7\n",
       "170     3927.0   6.00     8\n",
       "84      3691.0   5.52     9\n",
       "165     3907.0   5.42    10"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approach2_df_score.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "approach2_df_score.to_excel(\"C:\\\\Users\\\\archd\\\\OneDrive\\\\Desktop\\\\UTR\\\\approach2_df_score.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Approach 3:\n",
    "    To rank players, I have found out a score matrix which contains a score based on player's performance in each match. I have computed score using following method.\n",
    "- The length of a match matters. If a match has 5 rounds, then it will reward or penalize a player more compared to a match having 3 rounds if a player wins or loses respectively.\n",
    "- Only recent 30 matches played by a player are considered. Recent match get high weightage compared to past matches but weightage is a step function with step length is equal to 3. So, a player's rank does not fall or climb rapidly just because good or poor performance in the last match. \n",
    "\n",
    "Cons:\n",
    "- Only Match's verdict is considered. The number of sets won by a winner or looser are not considered and it does not matter if a looser loses by 3-2 or 4-1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def approach3_score(player_df, player_id):\n",
    "    matches = len(player_df)\n",
    "    if (matches>30):\n",
    "        player_df = player_df.iloc[matches-30 : matches, :]\n",
    "    \n",
    "    player_df = player_df.reset_index(drop = True)\n",
    "    max_weight = 30 / 100\n",
    "    \n",
    "    score = 0\n",
    "    \n",
    "    for i in range(len(player_df)):\n",
    "        if player_df.winner1id[i] == player_id:\n",
    "            score = score + (player_df.total_rounds[i] * max_weight * (int(i/3) + 1) / 10)\n",
    "        if player_df.loser1id[i] == player_id:\n",
    "            score = score - (player_df.total_rounds[i] * max_weight * (int(i/3) + 1) / 10)\n",
    "    \n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "approach3_df_score = pd.DataFrame(columns = [\"Player_id\", \"score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_id</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Player_id, score]\n",
       "Index: []"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approach3_df_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in all_players:\n",
    "    temp_df = df[(df.winner1id == i) | (df.loser1id == i)]\n",
    "    approach3_df_score = approach3_df_score.append({\n",
    "                                                    \"Player_id\" : i,\n",
    "                                                    \"score\" : approach3_score(temp_df, i)\n",
    "                                                    }, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_id</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3456.0</td>\n",
       "      <td>7.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3458.0</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3460.0</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3462.0</td>\n",
       "      <td>3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3464.0</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3465.0</td>\n",
       "      <td>-1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3466.0</td>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3468.0</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3469.0</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3474.0</td>\n",
       "      <td>-2.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player_id  score\n",
       "0     3456.0   7.38\n",
       "1     3458.0   2.91\n",
       "2     3460.0   1.23\n",
       "3     3462.0   3.03\n",
       "4     3464.0   1.80\n",
       "5     3465.0  -1.59\n",
       "6     3466.0   2.61\n",
       "7     3468.0   2.31\n",
       "8     3469.0   0.81\n",
       "9     3474.0  -2.58"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approach3_df_score.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "approach3_df_score = approach3_df_score.sort_values(by = [\"score\"], ascending= False)\n",
    "approach3_df_score[\"rank\"] = ss.rankdata(approach3_df_score[\"score\"], method='dense')\n",
    "approach3_df_score[\"rank\"] = max(approach3_df_score[\"rank\"]) + 1 - approach3_df_score[\"rank\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_id</th>\n",
       "      <th>score</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>4205.0</td>\n",
       "      <td>11.43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3478.0</td>\n",
       "      <td>9.93</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>5059.0</td>\n",
       "      <td>8.46</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>52294.0</td>\n",
       "      <td>7.89</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>4515.0</td>\n",
       "      <td>7.80</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>3695.0</td>\n",
       "      <td>7.65</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3456.0</td>\n",
       "      <td>7.38</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>3927.0</td>\n",
       "      <td>6.54</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>3691.0</td>\n",
       "      <td>6.36</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>3907.0</td>\n",
       "      <td>6.06</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Player_id  score  rank\n",
       "236     4205.0  11.43     1\n",
       "12      3478.0   9.93     2\n",
       "334     5059.0   8.46     3\n",
       "501    52294.0   7.89     4\n",
       "280     4515.0   7.80     5\n",
       "85      3695.0   7.65     6\n",
       "0       3456.0   7.38     7\n",
       "170     3927.0   6.54     8\n",
       "84      3691.0   6.36     9\n",
       "165     3907.0   6.06    10"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approach3_df_score.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "approach3_df_score.to_excel(\"C:\\\\Users\\\\archd\\\\OneDrive\\\\Desktop\\\\UTR\\\\approach3_df_score.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Approach 4:\n",
    "    To rank players, I have found out a score matrix which contains a score based on player's performance in each match. I have computed score using following method.\n",
    "- The length of a match matters. If a match has 5 rounds, then it will reward or penalize a player more compared to a match having 3 rounds if a player wins or loses respectively.\n",
    "- Only recent 30 matches played by a player are considered. Recent match get high weightage compared to past matches but weightage is a step function with step length is equal to 3. So, a player's rank does not fall or climb rapidly just because good or poor performance in the last match. \n",
    "- The number of sets won by a winner or looser are considered so if a player loses by 3-2, he gets panelize less compared to if he loses by 4-1. Same way, winner gets rewarded more if he wins a match by 4-1 compared to 3-2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def approach4_score(player_df, player_id):\n",
    "    matches = len(player_df)\n",
    "    if (matches>30):\n",
    "        player_df = player_df.iloc[matches-30 : matches, :]\n",
    "    \n",
    "    player_df = player_df.reset_index(drop = True)\n",
    "    max_weight = 30 / 100\n",
    "    \n",
    "    score = 0\n",
    "    \n",
    "    for i in range(len(player_df)):\n",
    "        if player_df.winner1id[i] == player_id:\n",
    "            score = score + (player_df.winner_win_sets[i] * max_weight * (int(i/3) + 1) / 30)\n",
    "        if player_df.loser1id[i] == player_id:\n",
    "            score = score - (player_df.winner_win_sets[i] * max_weight * (int(i/3) + 1) / 30)\n",
    "    \n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "approach4_df_score = pd.DataFrame(columns = [\"Player_id\", \"score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Player_id</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Player_id, score]\n",
       "Index: []"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approach4_df_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in all_players:\n",
    "    temp_df = df[(df.winner1id == i) | (df.loser1id == i)]\n",
    "    approach4_df_score = approach4_df_score.append({\n",
    "                                                    \"Player_id\" : i,\n",
    "                                                    \"score\" : approach4_score(temp_df, i)\n",
    "                                                    }, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_id</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3456.0</td>\n",
       "      <td>2.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3458.0</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3460.0</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3462.0</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3464.0</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3465.0</td>\n",
       "      <td>-0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3466.0</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3468.0</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3469.0</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3474.0</td>\n",
       "      <td>-0.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player_id  score\n",
       "0     3456.0   2.51\n",
       "1     3458.0   0.98\n",
       "2     3460.0   0.34\n",
       "3     3462.0   0.51\n",
       "4     3464.0   0.59\n",
       "5     3465.0  -0.35\n",
       "6     3466.0   0.78\n",
       "7     3468.0   0.67\n",
       "8     3469.0   0.05\n",
       "9     3474.0  -0.54"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "approach4_df_score.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "approach4_df_score = approach4_df_score.sort_values(by = [\"score\"], ascending= False)\n",
    "approach4_df_score[\"rank\"] = ss.rankdata(approach4_df_score[\"score\"], method='dense')\n",
    "approach4_df_score[\"rank\"] = max(approach4_df_score[\"rank\"]) + 1 - approach4_df_score[\"rank\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Player_id</th>\n",
       "      <th>score</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>4205.0</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3478.0</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>5059.0</td>\n",
       "      <td>2.63</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3456.0</td>\n",
       "      <td>2.51</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>52294.0</td>\n",
       "      <td>2.08</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>4515.0</td>\n",
       "      <td>2.03</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>3695.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>3907.0</td>\n",
       "      <td>1.98</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>28096.0</td>\n",
       "      <td>1.82</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>3700.0</td>\n",
       "      <td>1.81</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Player_id  score  rank\n",
       "236     4205.0   3.62     1\n",
       "12      3478.0   3.03     2\n",
       "334     5059.0   2.63     3\n",
       "0       3456.0   2.51     4\n",
       "501    52294.0   2.08     5\n",
       "280     4515.0   2.03     6\n",
       "85      3695.0   2.00     7\n",
       "165     3907.0   1.98     8\n",
       "381    28096.0   1.82     9\n",
       "89      3700.0   1.81    10"
      ]
     },
     "execution_count": 113,
     "metadata": {},
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   ],
   "source": [
    "approach4_df_score.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "approach4_df_score.to_excel(\"C:\\\\Users\\\\archd\\\\OneDrive\\\\Desktop\\\\UTR\\\\approach4_df_score.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "approach1_df_score = approach1_df_score.sort_index()\n",
    "approach2_df_score = approach2_df_score.sort_index()\n",
    "approach3_df_score = approach3_df_score.sort_index()\n",
    "approach4_df_score = approach4_df_score.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ranks_compare_df = pd.DataFrame(columns = [\"Player_id\", \"approach1_rank\", \"approach2_rank\", \"approach3_rank\", \"approach4_rank\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Player_id</th>\n",
       "      <th>approach1_rank</th>\n",
       "      <th>approach2_rank</th>\n",
       "      <th>approach3_rank</th>\n",
       "      <th>approach4_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Player_id, approach1_rank, approach2_rank, approach3_rank, approach4_rank]\n",
       "Index: []"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "Ranks_compare_df"
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  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ranks_compare_df[\"Player_id\"] = all_players\n",
    "Ranks_compare_df[\"approach1_rank\"] = approach1_df_score[\"rank\"]\n",
    "Ranks_compare_df[\"approach2_rank\"] = approach2_df_score[\"rank\"]\n",
    "Ranks_compare_df[\"approach3_rank\"] = approach3_df_score[\"rank\"]\n",
    "Ranks_compare_df[\"approach4_rank\"] = approach4_df_score[\"rank\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_id</th>\n",
       "      <th>approach1_rank</th>\n",
       "      <th>approach2_rank</th>\n",
       "      <th>approach3_rank</th>\n",
       "      <th>approach4_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3456</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3458</td>\n",
       "      <td>46</td>\n",
       "      <td>71</td>\n",
       "      <td>65</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3460</td>\n",
       "      <td>188</td>\n",
       "      <td>152</td>\n",
       "      <td>127</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3462</td>\n",
       "      <td>100</td>\n",
       "      <td>69</td>\n",
       "      <td>60</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3464</td>\n",
       "      <td>102</td>\n",
       "      <td>116</td>\n",
       "      <td>105</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3465</td>\n",
       "      <td>609</td>\n",
       "      <td>589</td>\n",
       "      <td>428</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3466</td>\n",
       "      <td>114</td>\n",
       "      <td>88</td>\n",
       "      <td>77</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3468</td>\n",
       "      <td>77</td>\n",
       "      <td>98</td>\n",
       "      <td>86</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3469</td>\n",
       "      <td>213</td>\n",
       "      <td>222</td>\n",
       "      <td>163</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3474</td>\n",
       "      <td>653</td>\n",
       "      <td>657</td>\n",
       "      <td>476</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Player_id  approach1_rank  approach2_rank  approach3_rank  approach4_rank\n",
       "0       3456              20               7               7               4\n",
       "1       3458              46              71              65              49\n",
       "2       3460             188             152             127             114\n",
       "3       3462             100              69              60              94\n",
       "4       3464             102             116             105              86\n",
       "5       3465             609             589             428             322\n",
       "6       3466             114              88              77              64\n",
       "7       3468              77              98              86              75\n",
       "8       3469             213             222             163             187\n",
       "9       3474             653             657             476             356"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ranks_compare_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ranks_compare_df.to_excel(\"C:\\\\Users\\\\archd\\\\OneDrive\\\\Desktop\\\\UTR\\\\Ranks_compare_df.xlsx\")"
   ]
  }
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