Web Scraping NFL Stats and Compare Quarterback Efficiencies.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\">Using Python to Scrape NFL Stats and Compare Quarterback Efficiencies</h1>\n",
    "\n",
    "<img src=\"https://miro.medium.com/max/3240/1*87qvjGMYcYSkShI1uZnRiA.jpeg\" align=\"center\" width=500>\n",
    "\n",
    "Article link: https://towardsdatascience.com/scraping-nfl-stats-to-compare-quarterback-efficiencies-4989642e02fe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Table of Contents</h2>\n",
    "\n",
    "<ul>\n",
    "    <li><a href=\"#intro\">Introduction</a></li>\n",
    "    <li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "    <li><a href=\"#radarcharts\">Generating Radar Charts</a></li>\n",
    "    <li><a href=\"#dataviz\">Visualizing Data</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"intro\"></a>\n",
    "<h2>Introduction</h2>\n",
    "\n",
    "The datasets is about quarterback stats from the 2019-20 season from Pro Footbal Reference, and will be used to create a radar charts to assess QB efficiency."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "<h2>Data Wrangling</h2>\n",
    "\n",
    "<h3>Gathering and Loading Data</h3>\n",
    "\n",
    "Data source: https://www.pro-football-reference.com/years/2019/passing.htm\n",
    "\n",
    "Interedted Statistical categories :\n",
    "- Completion percentage — Cmp%\n",
    "- Passing yards — Yds\n",
    "- Passing touchdowns — TD\n",
    "- Interceptions — Int\n",
    "- Yards per attempt — Y/A\n",
    "- Passer rating — Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import scraping modules\n",
    "from urllib.request import urlopen\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "#Import data manipulation modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#Import data visualization modules\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scrape Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Page URL\n",
    "url = 'https://www.pro-football-reference.com/years/2019/passing.htm'\n",
    "\n",
    "#open url and pass to BeautifulSoup\n",
    "html = urlopen(url)\n",
    "stats_page = BeautifulSoup(html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Rk', 'Player', 'Tm', 'Age', 'Pos', 'G', 'GS', 'QBrec', 'Cmp', 'Att', 'Cmp%', 'Yds', 'TD', 'TD%', 'Int', 'Int%', '1D', 'Lng', 'Y/A', 'AY/A', 'Y/C', 'Y/G', 'Rate', 'QBR', 'Sk', 'Yds', 'NY/A', 'ANY/A', 'Sk%', '4QC', 'GWD']\n"
     ]
    }
   ],
   "source": [
    "#Collecting table headers\n",
    "column_headers = stats_page.findAll('tr')[0]\n",
    "column_headers = [i.getText() for i in column_headers.findAll('th')]\n",
    "print(column_headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Jared Goff', 'LAR', '25', 'QB', '16', '16', '9-7-0', '394', '626', '62.9', '4638', '22', '3.5', '16', '2.6', '220', '66', '7.4', '7.0', '11.8', '289.9', '86.5', '', '22', '170', '6.90', '6.46', '3.4', '1', '2']\n"
     ]
    }
   ],
   "source": [
    "#Collecting table rows\n",
    "rows = stats_page.findAll('tr')[1:]\n",
    "\n",
    "#Getting stats from each row\n",
    "qb_stats = []\n",
    "for i in range(len(rows)):\n",
    "    qb_stats.append([col.getText() for col in rows[i].findAll('td')])\n",
    "    \n",
    "print(qb_stats[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Player</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pos</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>QBrec</th>\n",
       "      <th>Cmp</th>\n",
       "      <th>Att</th>\n",
       "      <th>Cmp%</th>\n",
       "      <th>...</th>\n",
       "      <th>Y/G</th>\n",
       "      <th>Rate</th>\n",
       "      <th>QBR</th>\n",
       "      <th>Sk</th>\n",
       "      <th>Yds</th>\n",
       "      <th>NY/A</th>\n",
       "      <th>ANY/A</th>\n",
       "      <th>Sk%</th>\n",
       "      <th>4QC</th>\n",
       "      <th>GWD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jared Goff</td>\n",
       "      <td>LAR</td>\n",
       "      <td>25</td>\n",
       "      <td>QB</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>9-7-0</td>\n",
       "      <td>394</td>\n",
       "      <td>626</td>\n",
       "      <td>62.9</td>\n",
       "      <td>...</td>\n",
       "      <td>289.9</td>\n",
       "      <td>86.5</td>\n",
       "      <td></td>\n",
       "      <td>22</td>\n",
       "      <td>170</td>\n",
       "      <td>6.90</td>\n",
       "      <td>6.46</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jameis Winston</td>\n",
       "      <td>TAM</td>\n",
       "      <td>25</td>\n",
       "      <td>QB</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>7-9-0</td>\n",
       "      <td>380</td>\n",
       "      <td>626</td>\n",
       "      <td>60.7</td>\n",
       "      <td>...</td>\n",
       "      <td>319.3</td>\n",
       "      <td>84.3</td>\n",
       "      <td></td>\n",
       "      <td>47</td>\n",
       "      <td>282</td>\n",
       "      <td>7.17</td>\n",
       "      <td>6.15</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Matt Ryan</td>\n",
       "      <td>ATL</td>\n",
       "      <td>34</td>\n",
       "      <td>QB</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>7-8-0</td>\n",
       "      <td>408</td>\n",
       "      <td>616</td>\n",
       "      <td>66.2</td>\n",
       "      <td>...</td>\n",
       "      <td>297.7</td>\n",
       "      <td>92.1</td>\n",
       "      <td></td>\n",
       "      <td>48</td>\n",
       "      <td>316</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.08</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tom Brady</td>\n",
       "      <td>NWE</td>\n",
       "      <td>42</td>\n",
       "      <td>QB</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>12-4-0</td>\n",
       "      <td>373</td>\n",
       "      <td>613</td>\n",
       "      <td>60.8</td>\n",
       "      <td>...</td>\n",
       "      <td>253.6</td>\n",
       "      <td>88.0</td>\n",
       "      <td></td>\n",
       "      <td>27</td>\n",
       "      <td>185</td>\n",
       "      <td>6.05</td>\n",
       "      <td>6.24</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carson Wentz</td>\n",
       "      <td>PHI</td>\n",
       "      <td>27</td>\n",
       "      <td>QB</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>9-7-0</td>\n",
       "      <td>388</td>\n",
       "      <td>607</td>\n",
       "      <td>63.9</td>\n",
       "      <td>...</td>\n",
       "      <td>252.4</td>\n",
       "      <td>93.1</td>\n",
       "      <td></td>\n",
       "      <td>37</td>\n",
       "      <td>230</td>\n",
       "      <td>5.91</td>\n",
       "      <td>6.26</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Player   Tm Age Pos   G  GS   QBrec  Cmp  Att  Cmp%  ...    Y/G  \\\n",
       "0      Jared Goff  LAR  25  QB  16  16   9-7-0  394  626  62.9  ...  289.9   \n",
       "1  Jameis Winston  TAM  25  QB  16  16   7-9-0  380  626  60.7  ...  319.3   \n",
       "2       Matt Ryan  ATL  34  QB  15  15   7-8-0  408  616  66.2  ...  297.7   \n",
       "3       Tom Brady  NWE  42  QB  16  16  12-4-0  373  613  60.8  ...  253.6   \n",
       "4    Carson Wentz  PHI  27  QB  16  16   9-7-0  388  607  63.9  ...  252.4   \n",
       "\n",
       "   Rate QBR  Sk  Yds  NY/A ANY/A  Sk% 4QC GWD  \n",
       "0  86.5      22  170  6.90  6.46  3.4   1   2  \n",
       "1  84.3      47  282  7.17  6.15  7.0   2   2  \n",
       "2  92.1      48  316  6.25  6.08  7.2   3   2  \n",
       "3  88.0      27  185  6.05  6.24  4.2   1   1  \n",
       "4  93.1      37  230  5.91  6.26  5.7   2   4  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a Dataframe from our scraped data\n",
    "df = pd.DataFrame(qb_stats, columns=column_headers[1:])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Assessing and Cleaning Data</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Player', 'Tm', 'Age', 'Pos', 'G', 'GS', 'QBrec', 'Cmp', 'Att', 'Cmp%',\n",
       "       'Yds', 'TD', 'TD%', 'Int', 'Int%', '1D', 'Lng', 'Y/A', 'AY/A', 'Y/C',\n",
       "       'Y/G', 'Rate', 'QBR', 'Sk', 'Yds', 'NY/A', 'ANY/A', 'Sk%', '4QC',\n",
       "       'GWD'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are two columns titled `Yds`; one is for passing yards, and the other is yards lost due to being sacked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_columns = df.columns.values\n",
    "new_columns[-6] = 'Yds_Sack'\n",
    "df.columns = new_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Player', 'Tm', 'Age', 'Pos', 'G', 'GS', 'QBrec', 'Cmp', 'Att', 'Cmp%',\n",
       "       'Yds', 'TD', 'TD%', 'Int', 'Int%', '1D', 'Lng', 'Y/A', 'AY/A', 'Y/C',\n",
       "       'Y/G', 'Rate', 'QBR', 'Sk', 'Yds_Sack', 'NY/A', 'ANY/A', 'Sk%', '4QC',\n",
       "       'GWD'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select stat categories\n",
    "categories = ['Cmp%', 'Yds', 'TD', 'Int', 'Y/A', 'Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Player</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Cmp%</th>\n",
       "      <th>Yds</th>\n",
       "      <th>TD</th>\n",
       "      <th>Int</th>\n",
       "      <th>Y/A</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jared Goff</td>\n",
       "      <td>LAR</td>\n",
       "      <td>62.9</td>\n",
       "      <td>4638</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>7.4</td>\n",
       "      <td>86.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jameis Winston</td>\n",
       "      <td>TAM</td>\n",
       "      <td>60.7</td>\n",
       "      <td>5109</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>8.2</td>\n",
       "      <td>84.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Matt Ryan</td>\n",
       "      <td>ATL</td>\n",
       "      <td>66.2</td>\n",
       "      <td>4466</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>7.3</td>\n",
       "      <td>92.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tom Brady</td>\n",
       "      <td>NWE</td>\n",
       "      <td>60.8</td>\n",
       "      <td>4057</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>6.6</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carson Wentz</td>\n",
       "      <td>PHI</td>\n",
       "      <td>63.9</td>\n",
       "      <td>4039</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>93.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Player   Tm  Cmp%   Yds  TD Int  Y/A  Rate\n",
       "0      Jared Goff  LAR  62.9  4638  22  16  7.4  86.5\n",
       "1  Jameis Winston  TAM  60.7  5109  33  30  8.2  84.3\n",
       "2       Matt Ryan  ATL  66.2  4466  26  14  7.3  92.1\n",
       "3       Tom Brady  NWE  60.8  4057  24   8  6.6  88.0\n",
       "4    Carson Wentz  PHI  63.9  4039  27   7  6.7  93.1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a subset dataframe for radar chart\n",
    "df_radar = df[['Player', 'Tm'] + categories]\n",
    "df_radar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Player    object\n",
       "Tm        object\n",
       "Cmp%      object\n",
       "Yds       object\n",
       "TD        object\n",
       "Int       object\n",
       "Y/A       object\n",
       "Rate      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking data types\n",
    "df_radar.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Converting data to numerical values\n",
    "for i in categories:\n",
    "    df_radar[i] = pd.to_numeric(df[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Player     object\n",
       "Tm         object\n",
       "Cmp%      float64\n",
       "Yds       float64\n",
       "TD        float64\n",
       "Int       float64\n",
       "Y/A       float64\n",
       "Rate      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test: Checking data types\n",
    "df_radar.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Removing ornamental characters for achievements\n",
    "df_radar['Player'] = df_radar['Player'].str.replace('*','')\n",
    "df_radar['Player'] = df_radar['Player'].str.replace('+','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
       "    .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</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Cmp%</th>\n",
       "      <th>Yds</th>\n",
       "      <th>TD</th>\n",
       "      <th>Int</th>\n",
       "      <th>Y/A</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jared Goff</td>\n",
       "      <td>LAR</td>\n",
       "      <td>62.9</td>\n",
       "      <td>4638.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>86.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jameis Winston</td>\n",
       "      <td>TAM</td>\n",
       "      <td>60.7</td>\n",
       "      <td>5109.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>84.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Matt Ryan</td>\n",
       "      <td>ATL</td>\n",
       "      <td>66.2</td>\n",
       "      <td>4466.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>92.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tom Brady</td>\n",
       "      <td>NWE</td>\n",
       "      <td>60.8</td>\n",
       "      <td>4057.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carson Wentz</td>\n",
       "      <td>PHI</td>\n",
       "      <td>63.9</td>\n",
       "      <td>4039.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>93.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Player   Tm  Cmp%     Yds    TD   Int  Y/A  Rate\n",
       "0      Jared Goff  LAR  62.9  4638.0  22.0  16.0  7.4  86.5\n",
       "1  Jameis Winston  TAM  60.7  5109.0  33.0  30.0  8.2  84.3\n",
       "2       Matt Ryan  ATL  66.2  4466.0  26.0  14.0  7.3  92.1\n",
       "3       Tom Brady  NWE  60.8  4057.0  24.0   8.0  6.6  88.0\n",
       "4    Carson Wentz  PHI  63.9  4039.0  27.0   7.0  6.7  93.1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering quarterbacks who threw more than 1500 yards\n",
    "df_radar_filtered = df_radar.query('Yds > 1500')\n",
    "df_radar_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \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>Player</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Cmp%</th>\n",
       "      <th>Yds</th>\n",
       "      <th>TD</th>\n",
       "      <th>Int</th>\n",
       "      <th>Y/A</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Cmp%_Rank</th>\n",
       "      <th>Yds_Rank</th>\n",
       "      <th>TD_Rank</th>\n",
       "      <th>Int_Rank</th>\n",
       "      <th>Y/A_Rank</th>\n",
       "      <th>Rate_Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jared Goff</td>\n",
       "      <td>LAR</td>\n",
       "      <td>62.9</td>\n",
       "      <td>4638.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>86.5</td>\n",
       "      <td>0.43750</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.109375</td>\n",
       "      <td>0.593750</td>\n",
       "      <td>0.343750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jameis Winston</td>\n",
       "      <td>TAM</td>\n",
       "      <td>60.7</td>\n",
       "      <td>5109.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>84.3</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.968750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.859375</td>\n",
       "      <td>0.203125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Matt Ryan</td>\n",
       "      <td>ATL</td>\n",
       "      <td>66.2</td>\n",
       "      <td>4466.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>92.1</td>\n",
       "      <td>0.81250</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.718750</td>\n",
       "      <td>0.171875</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.593750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tom Brady</td>\n",
       "      <td>NWE</td>\n",
       "      <td>60.8</td>\n",
       "      <td>4057.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.18750</td>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.609375</td>\n",
       "      <td>0.546875</td>\n",
       "      <td>0.140625</td>\n",
       "      <td>0.453125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carson Wentz</td>\n",
       "      <td>PHI</td>\n",
       "      <td>63.9</td>\n",
       "      <td>4039.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>93.1</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.843750</td>\n",
       "      <td>0.593750</td>\n",
       "      <td>0.234375</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Player   Tm  Cmp%     Yds    TD   Int  Y/A  Rate  Cmp%_Rank  \\\n",
       "0      Jared Goff  LAR  62.9  4638.0  22.0  16.0  7.4  86.5    0.43750   \n",
       "1  Jameis Winston  TAM  60.7  5109.0  33.0  30.0  8.2  84.3    0.15625   \n",
       "2       Matt Ryan  ATL  66.2  4466.0  26.0  14.0  7.3  92.1    0.81250   \n",
       "3       Tom Brady  NWE  60.8  4057.0  24.0   8.0  6.6  88.0    0.18750   \n",
       "4    Carson Wentz  PHI  63.9  4039.0  27.0   7.0  6.7  93.1    0.50000   \n",
       "\n",
       "   Yds_Rank   TD_Rank  Int_Rank  Y/A_Rank  Rate_Rank  \n",
       "0    0.9375  0.500000  0.109375  0.593750   0.343750  \n",
       "1    1.0000  0.968750  0.000000  0.859375   0.203125  \n",
       "2    0.8750  0.718750  0.171875  0.562500   0.593750  \n",
       "3    0.8125  0.609375  0.546875  0.140625   0.453125  \n",
       "4    0.7500  0.843750  0.593750  0.234375   0.625000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating columns with percentile rank\n",
    "for i in categories:\n",
    "    df_radar_filtered[i + '_Rank'] = df_radar_filtered[i].rank(pct=True)\n",
    "    \n",
    "# We need to flip the rank for interceptions\n",
    "df_radar_filtered['Int_Rank'] = 1 - df_radar_filtered['Int_Rank']\n",
    "\n",
    "df_radar_filtered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='radarcharts'></a>\n",
    "<h3>Generating Radar Charts</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# General plot parameters\n",
    "mpl.rcParams['font.family'] = 'Avenir'\n",
    "mpl.rcParams['font.size'] = 16\n",
    "mpl.rcParams['axes.linewidth'] = 0\n",
    "mpl.rcParams['xtick.major.pad'] = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting Teams color\n",
    "team_colors = {'ARI':'#97233f', 'ATL':'#a71930', 'BAL':'#241773', 'BUF':'#00338d', 'CAR':'#0085ca', 'CHI':'#0b162a', 'CIN':'#fb4f14', 'CLE':'#311d00', 'DAL':'#041e42', 'DEN':'#002244', 'DET':'#0076b6', 'GNB':'#203731', 'HOU':'#03202f', 'IND':'#002c5f', 'JAX':'#006778', 'KAN':'#e31837', 'LAC':'#002a5e', 'LAR':'#003594', 'MIA':'#008e97', 'MIN':'#4f2683', 'NWE':'#002244', 'NOR':'#d3bc8d', 'NYG':'#0b2265', 'NYJ':'#125740', 'OAK':'#000000', 'PHI':'#004c54', 'PIT':'#ffb612', 'SFO':'#aa0000', 'SEA':'#002244', 'TAM':'#d50a0a', 'TEN':'#0c2340', 'WAS':'#773141'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"The angles at which we plot all our points will be dependent on the number of statistical categories we have. In our example, we have 6 categories, so we will plot our points every 2π/6 radians, or 60 degrees. We can calculate this as follows, using numpy.linspace() :\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating angles for radar chart\n",
    "offset = np.pi/6\n",
    "angles = np.linspace(0, 2*np.pi, len(categories) + 1) + offset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"We are adding the offset term to adjust where the labels appear on the circle (instead of the first label at 0 radians, it now appears at π/6).\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to create radar chart\n",
    "def create_radar_chart(ax, angles, player_data, color='blue'):\n",
    "    \n",
    "    # Plot data and fill with team color\n",
    "    ax.plot(angles, np.append(player_data[-(len(angles)-1):], \n",
    "            player_data[-(len(angles)-1)]), color=color, linewidth=2)\n",
    "    ax.fill(angles, np.append(player_data[-(len(angles)-1):], \n",
    "            player_data[-(len(angles)-1)]), color=color, alpha=0.2)\n",
    "    \n",
    "    # Set category labels\n",
    "    ax.set_xticks(angles[:-1])\n",
    "    ax.set_xticklabels(categories)\n",
    "    \n",
    "    # Remove radial labels\n",
    "    ax.set_yticklabels([])\n",
    "\n",
    "    # Add player name\n",
    "    ax.text(np.pi/2, 1.7, player_data[0], ha='center', va='center', \n",
    "            size=18, color=color)\n",
    "    \n",
    "    # Use white grid\n",
    "    ax.grid(color='white', linewidth=1.5)\n",
    "\n",
    "    # Set axis limits\n",
    "    ax.set(xlim=(0, 2*np.pi), ylim=(0, 1))\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get QB data\n",
    "def get_qb_data(data, team):\n",
    "  return np.asarray(data[data['Tm'] == team])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='dataviz'></a>\n",
    "<h3>Visualizing Data</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NFC West"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Avenir'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Avenir'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating figure\n",
    "fig = plt.figure(figsize=(8,8), facecolor='white')\n",
    "\n",
    "# Adding subplots\n",
    "ax1 = fig.add_subplot(221, projection='polar', facecolor='#ededed')\n",
    "ax2 = fig.add_subplot(222, projection='polar', facecolor='#ededed')\n",
    "ax3 = fig.add_subplot(223, projection='polar', facecolor='#ededed')\n",
    "ax4 = fig.add_subplot(224, projection='polar', facecolor='#ededed')\n",
    "\n",
    "# Adjusting space between subplots\n",
    "plt.subplots_adjust(hspace=0.8, wspace=0.5)\n",
    "\n",
    "# Getting QB Data\n",
    "sf_data = get_qb_data(df_radar_filtered, 'SFO')\n",
    "sea_data = get_qb_data(df_radar_filtered, 'SEA')\n",
    "ari_data = get_qb_data(df_radar_filtered, 'ARI')\n",
    "lar_data = get_qb_data(df_radar_filtered, 'LAR')\n",
    "\n",
    "# Plotting QB Data\n",
    "ax1 = create_radar_chart(ax1, angles, lar_data, team_colors['LAR'])\n",
    "ax2 = create_radar_chart(ax2, angles, ari_data, team_colors['ARI'])\n",
    "ax3 = create_radar_chart(ax3, angles, sea_data, team_colors['SEA'])\n",
    "ax4 = create_radar_chart(ax4, angles, sf_data, team_colors['SFO'])\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Russell Wilson is on an elite level, reaching at least 75th percentiles in all statistical categories. This analysis also doesn’t take into account what he brings with his scrambling ability.\n",
    "\n",
    "- Jimmy Garoppolo is generally a solid QB, but throws a few boneheaded interceptions from time-to-time.\n",
    "\n",
    "- Jared Goff gets a lot of yards, but it does not seem to translate to a lot of production (i.e. “empty yards”).\n",
    "\n",
    "- Kyler Murray is hard to judge because he was a rookie, but his efficiency looks to be the lowest of the four. Like Wilson, his mobility is not accounted for in this plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MVP Race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MVP Race\n",
    "# Creating figure\n",
    "fig = plt.figure(figsize=(8, 4), facecolor='white')\n",
    "\n",
    "# Adding subplots\n",
    "ax1 = fig.add_subplot(121, projection='polar', facecolor='#ededed')\n",
    "ax2 = fig.add_subplot(122, projection='polar', facecolor='#ededed')\n",
    "\n",
    "# Adjusting space between subplots\n",
    "plt.subplots_adjust(hspace=0.8, wspace=0.5)\n",
    "\n",
    "# Getting QB data\n",
    "bal_data = get_qb_data(df_radar_filtered, 'BAL')\n",
    "sea_data = get_qb_data(df_radar_filtered, 'SEA')\n",
    "\n",
    "# Plotting QB data\n",
    "ax1 = create_radar_chart(ax1, angles, sea_data, team_colors['SEA'])\n",
    "ax2 = create_radar_chart(ax2, angles, bal_data, team_colors['BAL'])\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From a pure passer standpoint, Russell Wilson has more balanced stats, but Lamar Jackson clearly had the more “bang-for-buck” passing yards, as shown by his number of passing TDs and quarterback rating. Additionally, Lamar Jackson set the QB rushing record for a season, which is not captured in this chart."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Teams who drafted 1st round QBs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1st Round Draft Picks\n",
    "# Creating figure\n",
    "fig = plt.figure(figsize=(8, 8), facecolor='white')\n",
    "\n",
    "# Adding subplots\n",
    "ax1 = fig.add_subplot(221, projection='polar', facecolor='#ededed')\n",
    "ax2 = fig.add_subplot(222, projection='polar', facecolor='#ededed')\n",
    "ax3 = fig.add_subplot(223, projection='polar', facecolor='#ededed')\n",
    "ax4 = fig.add_subplot(224, projection='polar', facecolor='#ededed')\n",
    "\n",
    "# Adjusting space between subplots\n",
    "plt.subplots_adjust(hspace=0.8, wspace=0.5)\n",
    "\n",
    "# Getting QB data\n",
    "cin_data = get_qb_data(df_radar_filtered, 'CIN')\n",
    "mia_data = get_qb_data(df_radar_filtered, 'MIA')\n",
    "lac_data = get_qb_data(df_radar_filtered, 'LAC')\n",
    "gnb_data = get_qb_data(df_radar_filtered, 'GNB')\n",
    "\n",
    "# Plotting QB data\n",
    "ax1 = create_radar_chart(ax1, angles, cin_data, team_colors['CIN'])\n",
    "ax2 = create_radar_chart(ax2, angles, mia_data, team_colors['MIA'])\n",
    "ax3 = create_radar_chart(ax3, angles, lac_data, team_colors['LAC'])\n",
    "ax4 = create_radar_chart(ax4, angles, gnb_data, team_colors['GNB'])\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Andy Dalton had a terrible season by all metrics — it clearly appears that it was time to move on.\n",
    "\n",
    "- Ryan Fitzpatrick was below average in all six statistical metrics and had a fairly mediocre season. Considering Josh Rosen was benched for Fitzpatrick, Miami needed to invest in Tua for the future.\n",
    "\n",
    "- Philip Rivers actually performed average to above average in most categories, but clearly had some bad decision-making, judging by the number of interceptions he threw.\n",
    "\n",
    "- Aaron Rodgers had what appears to be an above average season, but still below average by the lofty expectations we have for him. The Jordan Love pick was a shock for everyone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}