{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scrape Data from Pro Football Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pro Football Reference has a list of all QBs who have ever played. They have a metric (AV == Approximate Value) that tries to determine the career value of a QB. No surprises here, Brady is #1. "
   ]
  },
  {
   "attachments": {
    "qbs.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![qbs.png](attachment:qbs.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our goal will be to make QB tiers out of this AV and try to predict the tier a QB will land in, given his rookie stats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://www.pro-football-reference.com/\"\n",
    "r = requests.get(\"https://www.pro-football-reference.com/players/qbindex.htm\")\n",
    "soup = BeautifulSoup(r.content, 'html.parser')\n",
    "parsed_table = soup.find_all('table')[0]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For each QB, get their stats page and filter out stats from their table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will define \"rookie year\" as the first year where the QB started > 2 games in a season.\n",
    "I will also arbitraly decide to filter out QBs who played less than 2 years in the league, with a year being defined as starting > 2 games in a season. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 1035/1035 [07:27<00:00,  2.31it/s]\n"
     ]
    }
   ],
   "source": [
    "rookie_year = []\n",
    "for i,row in enumerate(tqdm(parsed_table.find_all('tr')[1:])):\n",
    "        try:\n",
    "            name = row.a.get_text()\n",
    "            stub = row.a.get('href')\n",
    "            av = row.find('td', \n",
    "                attrs={'data-stat': 'av'}).get_text()\n",
    "            year_min = row.find('td', \n",
    "                attrs={'data-stat': 'year_min'}).get_text()        \n",
    "            if int(year_min) >= 1969:\n",
    "                qb_data = pd.read_html(url + stub)[0]   \n",
    "                # Replace strings with 0s\n",
    "                qb_data = qb_data.replace(r'[^0-9]', 0, regex=True)\n",
    "                # Select 1st year where they started > 2 games as 'Rookie Year' \n",
    "                qb_data_filtered = qb_data[qb_data[\"GS\"] > 2]\n",
    "                # Add name and career_av to data \n",
    "                qb_data_filtered.insert(0, \"Name\", name)\n",
    "                last_row = len(qb_data_filtered.iloc[0])\n",
    "                qb_data_filtered.insert(last_row, \"Career_AV\", int(av))\n",
    "                # Filter out QBs who started only a few games in their career\n",
    "                if len(qb_data_filtered) >= 2:\n",
    "                    rookie_year.append(qb_data_filtered.iloc[0])\n",
    "                \n",
    "        except Exception:\n",
    "            pass\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rookie_year = pd.DataFrame(rookie_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "406"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rookie_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means there are 406 QBs that qualify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Pos</th>\n",
       "      <th>No.</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>QBrec</th>\n",
       "      <th>Cmp</th>\n",
       "      <th>...</th>\n",
       "      <th>Yds.1</th>\n",
       "      <th>Sk%</th>\n",
       "      <th>NY/A</th>\n",
       "      <th>ANY/A</th>\n",
       "      <th>4QC</th>\n",
       "      <th>GWD</th>\n",
       "      <th>AV</th>\n",
       "      <th>Awards</th>\n",
       "      <th>Career_AV</th>\n",
       "      <th>Tier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tom Brady</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>264</td>\n",
       "      <td>...</td>\n",
       "      <td>216</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.79</td>\n",
       "      <td>5.39</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>316</td>\n",
       "      <td>Elite Career QB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Drew Brees</td>\n",
       "      <td>2002</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>320</td>\n",
       "      <td>...</td>\n",
       "      <td>180</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.64</td>\n",
       "      <td>4.95</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>277</td>\n",
       "      <td>Elite Career QB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brett Favre</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>302</td>\n",
       "      <td>...</td>\n",
       "      <td>208</td>\n",
       "      <td>6.7</td>\n",
       "      <td>5.98</td>\n",
       "      <td>5.53</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>259</td>\n",
       "      <td>Elite Career QB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Philip Rivers</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.66</td>\n",
       "      <td>6.73</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>218</td>\n",
       "      <td>Elite Career QB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aaron Rodgers</td>\n",
       "      <td>2008</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>341</td>\n",
       "      <td>...</td>\n",
       "      <td>231</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.68</td>\n",
       "      <td>6.64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>218</td>\n",
       "      <td>Elite Career QB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name  Year Age   Tm  Pos   No.   G    GS  QBrec  Cmp  ...  Yds.1  \\\n",
       "1      Tom Brady     0  24  0.0  0.0  12.0  15  14.0    0.0  264  ...    216   \n",
       "1     Drew Brees  2002  23  0.0  0.0   9.0  16  16.0    0.0  320  ...    180   \n",
       "1    Brett Favre     0  23  0.0  0.0   4.0  15  13.0    0.0  302  ...    208   \n",
       "2  Philip Rivers     0  25  0.0  0.0  17.0  16  16.0    0.0  284  ...    144   \n",
       "3  Aaron Rodgers  2008  25  0.0  0.0  12.0  16  16.0    0.0  341  ...    231   \n",
       "\n",
       "   Sk%  NY/A  ANY/A  4QC  GWD  AV  Awards  Career_AV             Tier  \n",
       "1  9.0  5.79   5.39  3.0  3.0  12     0.0        316  Elite Career QB  \n",
       "1  4.4  5.64   4.95  2.0  4.0  10     0.0        277  Elite Career QB  \n",
       "1  6.7  5.98   5.53  3.0  3.0  13     0.0        259  Elite Career QB  \n",
       "2  5.5  6.66   6.73  4.0  4.0  18     0.0        218  Elite Career QB  \n",
       "3  6.0  6.68   6.64  1.0  2.0  15     0.0        218  Elite Career QB  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rookie_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rookie_year = rookie_year.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will then assume the Career_AV is a normal distribution, and create zscore \"tiers\" for each QB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = statistics.NormalDist.from_samples(rookie_year[\"Career_AV\"])\n",
    "def pred_to_tier(row):\n",
    "    zscore = dist.zscore(row[\"Career_AV\"])\n",
    "    # Decided to influence the zscore a bit. \n",
    "    # Any QB with negative Career AV deserves bottom tier\n",
    "    if row[\"Career_AV\"] < 0:\n",
    "        tier = \"Poor Career QB\"\n",
    "    elif zscore > 1.5:\n",
    "        tier = \"Elite Career QB\"\n",
    "    elif zscore > 0.5 and zscore < 1.5:\n",
    "        tier = \"Above Average Career QB\"\n",
    "    elif zscore > -0.5 and zscore < 0.5:\n",
    "        tier = \"Average Career QB\"\n",
    "    elif zscore > -1.5 and zscore < -0.5:\n",
    "        tier = \"Below-Average Career QB\"\n",
    "    else:\n",
    "        tier = \"Poor Career QB\"\n",
    "    return tier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rookie_year['Tier'] = rookie_year.apply(lambda row: pred_to_tier(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Below-Average Career QB    179\n",
       "Average Career QB          137\n",
       "Above Average Career QB     52\n",
       "Elite Career QB             35\n",
       "Poor Career QB               3\n",
       "Name: Tier, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rookie_year[\"Tier\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "rookie_year.to_csv(\"rookie_year.csv\") # Save as CSV for next Step"
   ]
  }
 ],
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