a-league-penalty-conversion-rates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.ticker as mtick\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.dpi'] = 300\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_base = 'https://fbref.com/en/comps/65/'\n",
    "url_end = '-A-League-Men-Stats'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "BACKGROUND_COLOUR = '#323232'\n",
    "GREY_COLOUR = '#CCCCCC'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "COLOUR_BACKGROUND = '#F7F3ED'\n",
    "COLOUR_ALMOST_BLACK = '#252423'\n",
    "\n",
    "COLOUR_BLUE = '#228be6'\n",
    "COLOUR_PINK = '#e64980'\n",
    "\n",
    "colour_palette = {\n",
    "    'Sydney FC 2022/23' : COLOUR_PINK,\n",
    "    'All other seasons' : COLOUR_BLUE\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_season = 2005\n",
    "last_season = 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "seasons = []\n",
    "\n",
    "for season in range(first_season, last_season + 1):\n",
    "    season_string = str(season) + '-' + str(season + 1)\n",
    "    seasons.append(season_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_soup(season):\n",
    "    url_full = url_base + season + '/' + season + url_end\n",
    "    response = requests.get(url_full)\n",
    "\n",
    "    soup = BeautifulSoup(response.content, 'html.parser')\n",
    "\n",
    "    table = soup.find('table', {'id': 'stats_squads_standard_for'})\n",
    "\n",
    "    return table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scrape_data_from_soup(tbody):\n",
    "    \n",
    "    data = []\n",
    "    for row in tbody.find_all('tr'):\n",
    "        cols = row.find_all('td')\n",
    "        player = row.find('th', {'data-stat': 'team'})\n",
    "        cols = [player.text.strip()] + [col.text.strip() for col in cols]\n",
    "        data.append(cols)\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scrape_column_headers_from_soup(table):\n",
    "    \n",
    "    table_header = table.find('thead')\n",
    "    header_rows = table_header.find_all('tr')\n",
    "    for row in header_rows:\n",
    "        column_headers = row.find_all('th')\n",
    "        column_headers = [col.text.strip() for col in column_headers]\n",
    "\n",
    "    return column_headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_squad_standard_stats(season):\n",
    "\n",
    "    table = get_soup(season)\n",
    "    tbody = table.find('tbody')\n",
    "\n",
    "    data = scrape_data_from_soup(tbody)\n",
    "\n",
    "    column_headers = scrape_column_headers_from_soup(table)\n",
    "\n",
    "    df = pd.DataFrame(data, columns=column_headers)\n",
    "    df['Season'] = season\n",
    "    df['Squad Season'] = df['Squad'] + ' - ' + df['Season']\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, season in enumerate(seasons):\n",
    "    if i == 0:\n",
    "        df_stats = get_squad_standard_stats(season)\n",
    "    else:\n",
    "        df_stats = pd.concat([df_stats, get_squad_standard_stats(season)], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in ['PK', 'PKatt']:\n",
    "    df_stats[column] = df_stats[column].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_penalties = df_stats[['Squad Season', 'Squad', 'Season', 'PK', 'PKatt']].copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_penalties['Penalty Conversion Rate'] = df_penalties['PK'] / df_penalties['PKatt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FutureWarning: ['Squad Season', 'Squad', 'Season'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n",
      "  df_penalties_grouped = df_penalties.groupby(['PK', 'PKatt']).agg(['mean', 'count']).reset_index()\n"
     ]
    }
   ],
   "source": [
    "df_penalties_grouped = df_penalties.groupby(['PK', 'PKatt']).agg(['mean', 'count']).reset_index()\n",
    "df_penalties_grouped.columns = ['PK', 'PKatt', 'Penalty Conversion Rate', 'Number of Clubs / Seasons']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'79.5%'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_penalties_scored = df_penalties.agg({'PK' : 'sum', 'PKatt' : 'sum'})['PK']\n",
    "total_penalties_attempted = df_penalties.agg({'PK' : 'sum', 'PKatt' : 'sum'})['PKatt']\n",
    "\n",
    "average_success_rate = total_penalties_scored / total_penalties_attempted\n",
    "f'{average_success_rate:.1%}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "767"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_penalties_attempted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>PK</th>\n",
       "      <th>PKatt</th>\n",
       "      <th>Penalty Conversion Rate</th>\n",
       "      <th>Number of Clubs / Seasons</th>\n",
       "      <th>Season group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>6</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1</td>\n",
       "      <td>Sydney FC 2022/23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>14</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>14</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>3</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>1</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>15</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>4</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>2</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>7</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>5</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>2</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>1</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>4</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>2</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>1</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1</td>\n",
       "      <td>All other seasons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PK  PKatt  Penalty Conversion Rate  Number of Clubs / Seasons  \\\n",
       "0    0      0                      NaN                          0   \n",
       "1    0      1                 0.000000                          3   \n",
       "2    0      2                 0.000000                          1   \n",
       "3    1      1                 1.000000                         10   \n",
       "4    1      2                 0.500000                          3   \n",
       "5    1      3                 0.333333                          6   \n",
       "6    1      7                 0.142857                          1   \n",
       "7    2      2                 1.000000                         16   \n",
       "8    2      3                 0.666667                         14   \n",
       "9    2      4                 0.500000                          6   \n",
       "10   2      5                 0.400000                          1   \n",
       "11   3      3                 1.000000                         18   \n",
       "12   3      4                 0.750000                         14   \n",
       "13   3      5                 0.600000                          3   \n",
       "14   3      6                 0.500000                          1   \n",
       "15   3      7                 0.428571                          1   \n",
       "16   4      4                 1.000000                         14   \n",
       "17   4      5                 0.800000                         15   \n",
       "18   4      6                 0.666667                          4   \n",
       "19   4      7                 0.571429                          2   \n",
       "20   4      8                 0.500000                          1   \n",
       "21   5      5                 1.000000                          9   \n",
       "22   5      6                 0.833333                          7   \n",
       "23   5      7                 0.714286                          5   \n",
       "24   5      8                 0.625000                          2   \n",
       "25   5      9                 0.555556                          1   \n",
       "26   6      6                 1.000000                          9   \n",
       "27   6      7                 0.857143                          4   \n",
       "28   6      8                 0.750000                          2   \n",
       "29   8      8                 1.000000                          2   \n",
       "30   8      9                 0.888889                          1   \n",
       "31   8     12                 0.666667                          1   \n",
       "32   9     10                 0.900000                          1   \n",
       "33  10     11                 0.909091                          1   \n",
       "\n",
       "         Season group  \n",
       "0   All other seasons  \n",
       "1   All other seasons  \n",
       "2   All other seasons  \n",
       "3   All other seasons  \n",
       "4   All other seasons  \n",
       "5   All other seasons  \n",
       "6   Sydney FC 2022/23  \n",
       "7   All other seasons  \n",
       "8   All other seasons  \n",
       "9   All other seasons  \n",
       "10  All other seasons  \n",
       "11  All other seasons  \n",
       "12  All other seasons  \n",
       "13  All other seasons  \n",
       "14  All other seasons  \n",
       "15  All other seasons  \n",
       "16  All other seasons  \n",
       "17  All other seasons  \n",
       "18  All other seasons  \n",
       "19  All other seasons  \n",
       "20  All other seasons  \n",
       "21  All other seasons  \n",
       "22  All other seasons  \n",
       "23  All other seasons  \n",
       "24  All other seasons  \n",
       "25  All other seasons  \n",
       "26  All other seasons  \n",
       "27  All other seasons  \n",
       "28  All other seasons  \n",
       "29  All other seasons  \n",
       "30  All other seasons  \n",
       "31  All other seasons  \n",
       "32  All other seasons  \n",
       "33  All other seasons  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_penalties_grouped['Season group'] = np.where((df_penalties_grouped['PK']==1) & (df_penalties_grouped['PKatt']==7), 'Sydney FC 2022/23', 'All other seasons')\n",
    "df_penalties_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3300x2100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(11,7))\n",
    "\n",
    "fig.patch.set_facecolor(COLOUR_BACKGROUND)\n",
    "ax.patch.set_facecolor(COLOUR_BACKGROUND)\n",
    "\n",
    "ax.spines['bottom'].set_color(COLOUR_ALMOST_BLACK)  \n",
    "ax.spines['left'].set_color(COLOUR_ALMOST_BLACK)  \n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.xaxis.label.set_color(COLOUR_ALMOST_BLACK)\n",
    "ax.yaxis.label.set_color(COLOUR_ALMOST_BLACK)\n",
    "\n",
    "sns.scatterplot(data=df_penalties_grouped, x='PKatt', y='Penalty Conversion Rate', size='Number of Clubs / Seasons', legend='full', sizes=(20, 400), zorder=2, hue='Season group', palette=colour_palette, ec=None)\n",
    "\n",
    "ax.legend().remove()\n",
    "\n",
    "ax.axhline(y=average_success_rate, ls='dotted', alpha=1, zorder=1, color='#CCCCCC')\n",
    "\n",
    "yticks = mtick.PercentFormatter(xmax=1.0, decimals=None, symbol='%')\n",
    "ax.yaxis.set_major_formatter(yticks)\n",
    "\n",
    "ax.set_xlabel('Penalties attempted')\n",
    "ax.set_ylabel('Penalty conversion rate')\n",
    "\n",
    "ax.xaxis.set_major_locator(mtick.MultipleLocator(base=1))\n",
    "\n",
    "fig.suptitle(\"Penalty Conversion Rates\\nA-League Men's Competition History\\nPoint size represents number of clubs/seasons\", ha='left', x=0.13)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig('img/penalty_conversion_rates.pdf', bbox_inches='tight')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py311",
   "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": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}