Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pathlib import Path\n",
    "from scipy import stats\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import os\n",
    "import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up graphing defaults to look pretty\n",
    "sns.set_palette('pastel')\n",
    "sns.set_theme(rc={'axes.facecolor':'lightgrey'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis\n",
    "It's time to do our final analysis. Here, we can use all we learned from our EDA to answer our question:\n",
    "##### **In the English Premier League, does a managerial change postively affect a team's performance in the next ten games in terms of win/loss ratio, points per game, and shots per quantity of possession, among various other metrics?** \n",
    "\n",
    "To start, here's a summary of what we learned from EDA:\n",
    "- The representation in our dataset is left-skewed, and many metrics are right-skewed. This is mostly due to the way our data is set up: The higher performing a team is, the longer they'll stay in the League, and therefore the more represented they will be in our dataset. \n",
    "    - There are a few teams (ex: Crystal Palace, Arsenal, Tottenham) that are performing really well, getting super high metrics, and staying in the league for a long time.\n",
    "- Besides goals scored, the metrics that contributed the most to a positive result were Shots on Target, Pass Completion, Shots, and Corners. The metrics that negatively contributed to a positive result were Yellow Cards Earned and Fouls Committed.\n",
    "    - From this, we can safely define the former metrics as positive, and the latter as negative.\n",
    "    - In our analysis, we'll want to define an \"increase in performance\" as an increase in the positive metrics, and a decrease in the negative metrics. However, we can analyze both separately.\n",
    "\n",
    "Our final analysis will roughly follow this procedure:\n",
    "1. Combine the metrics and managers datasets, so we know the manager for each team for each match.\n",
    "2. Graph the changes in metrics for each manager over time, looking specifically to see if there's a jump in the beginning.\n",
    "3. Calculate the average metrics in the first 10 matches of a manager's term.\n",
    "4. Run a hypothesis test to see if there is significant improvement in performance than the entirety of the manager's *predecessor's* term.\n",
    "    - \"Significant Improvement\" will be defined as significantly higher positive metrics, and significantly lower negative metrics.\n",
    "    - More details on this process below.\n",
    "5. Repeat for every manager for every team.\n",
    "    - Using this, find the proportion of teams who experienced a significant new-manager bounce.\n",
    "6. Present results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Combining the datasets\n",
    "In order to proceed with our analysis, we'll have to put our manager data into our metrics data. To do this, we'll add a column into the metrics data, `'Manager'`, which simply gives the name of the manager for the team during the time of that match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>TotalGoalsScored</th>\n",
       "      <th>FullTimeResult</th>\n",
       "      <th>Team</th>\n",
       "      <th>Shots</th>\n",
       "      <th>ShotsOnTarget</th>\n",
       "      <th>Pass Cmp %</th>\n",
       "      <th>FoulsCommited</th>\n",
       "      <th>Corners</th>\n",
       "      <th>YellowCardsEarned</th>\n",
       "      <th>RedCardsEarned</th>\n",
       "      <th>IsHome</th>\n",
       "      <th>Won</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3499</th>\n",
       "      <td>08/12/18</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>76.2</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3500</th>\n",
       "      <td>08/12/18</td>\n",
       "      <td>4</td>\n",
       "      <td>H</td>\n",
       "      <td>Man United</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>83.4</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3501</th>\n",
       "      <td>08/12/18</td>\n",
       "      <td>3</td>\n",
       "      <td>H</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>78.1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3502</th>\n",
       "      <td>09/12/18</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>76.1</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3503</th>\n",
       "      <td>10/12/18</td>\n",
       "      <td>2</td>\n",
       "      <td>D</td>\n",
       "      <td>Everton</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>75.3</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  TotalGoalsScored FullTimeResult        Team  Shots  \\\n",
       "3499  08/12/18                 0              A   Leicester     11   \n",
       "3500  08/12/18                 4              H  Man United     20   \n",
       "3501  08/12/18                 3              H    West Ham     13   \n",
       "3502  09/12/18                 1              A   Newcastle     12   \n",
       "3503  10/12/18                 2              D     Everton     12   \n",
       "\n",
       "      ShotsOnTarget  Pass Cmp %  FoulsCommited  Corners  YellowCardsEarned  \\\n",
       "3499              3        76.2             12        6                  3   \n",
       "3500             11        83.4             11       10                  1   \n",
       "3501              6        78.1             10        5                  1   \n",
       "3502              4        76.1             10        4                  2   \n",
       "3503              5        75.3             13        6                  1   \n",
       "\n",
       "      RedCardsEarned  IsHome    Won  \n",
       "3499               0    True  False  \n",
       "3500               0    True   True  \n",
       "3501               0    True   True  \n",
       "3502               1    True  False  \n",
       "3503               0    True    NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = pd.read_csv(Path('data') / 'metrics_clean.csv')\n",
    "metrics.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Club</th>\n",
       "      <th>Predecessor</th>\n",
       "      <th>Join date</th>\n",
       "      <th>Coach</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>David Webb</td>\n",
       "      <td>30/6/1993</td>\n",
       "      <td>Glenn Hoddle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>Doug Livermore</td>\n",
       "      <td>19/6/1993</td>\n",
       "      <td>Osvaldo Ardiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>Ray Clemence</td>\n",
       "      <td>19/6/1993</td>\n",
       "      <td>Osvaldo Ardiles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>Steve Coppell</td>\n",
       "      <td>17/5/1993</td>\n",
       "      <td>Alan Smith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nottingham Forest</td>\n",
       "      <td>Brian Clough</td>\n",
       "      <td>8/5/1993</td>\n",
       "      <td>Frank Clark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Wolves</td>\n",
       "      <td>Bruno Lage</td>\n",
       "      <td>2/10/2022</td>\n",
       "      <td>Steve Davis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Andrew Crofts</td>\n",
       "      <td>17/9/2022</td>\n",
       "      <td>Roberto De Zerbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Thomas Tuchel</td>\n",
       "      <td>7/9/2022</td>\n",
       "      <td>Graham Potter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>Graham Potter</td>\n",
       "      <td>7/9/2022</td>\n",
       "      <td>Andrew Crofts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Scott Parker</td>\n",
       "      <td>30/8/2022</td>\n",
       "      <td>Gary O'Neil</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>409 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Club     Predecessor  Join date             Coach\n",
       "0              Chelsea      David Webb  30/6/1993      Glenn Hoddle\n",
       "1            Tottenham  Doug Livermore  19/6/1993   Osvaldo Ardiles\n",
       "2            Tottenham    Ray Clemence  19/6/1993   Osvaldo Ardiles\n",
       "3       Crystal Palace   Steve Coppell  17/5/1993        Alan Smith\n",
       "4    Nottingham Forest    Brian Clough   8/5/1993       Frank Clark\n",
       "..                 ...             ...        ...               ...\n",
       "404             Wolves      Bruno Lage  2/10/2022       Steve Davis\n",
       "405           Brighton   Andrew Crofts  17/9/2022  Roberto De Zerbi\n",
       "406            Chelsea   Thomas Tuchel   7/9/2022     Graham Potter\n",
       "407           Brighton   Graham Potter   7/9/2022     Andrew Crofts\n",
       "408        Bournemouth    Scott Parker  30/8/2022       Gary O'Neil\n",
       "\n",
       "[409 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "managers = pd.read_csv(Path('data')/'managers_clean.csv')\n",
    "managers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert everything into Timestamps first, to make comparisons easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-01-10 00:00:00')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime('10/01/17', format='%d/%m/%y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics['Date'] = pd.to_datetime(metrics['Date'], format='%d/%m/%y')\n",
    "managers['Join date'] = pd.to_datetime(managers['Join date'], format='%d/%m/%Y')\n",
    "metrics['Date'].iloc[-1].month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To combine the two, we'll add a preliminary column with a list of tuples of the form `(join_date, manager, predecessor)` for each team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>All Managers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>[(1995-02-21 00:00:00, Stewart Houston, George...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>[(1994-11-10 00:00:00, Brian Little, Ron Atkin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Birmingham</td>\n",
       "      <td>[(2007-11-18 00:00:00, Eric Black, Steve Bruce...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Blackburn</td>\n",
       "      <td>[(1995-06-30 00:00:00, Ray Harford, Sir Kenny ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bolton</td>\n",
       "      <td>[(1996-01-07 00:00:00, Phil Brown, Roy McFarla...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          Team                                       All Managers\n",
       "0      Arsenal  [(1995-02-21 00:00:00, Stewart Houston, George...\n",
       "1  Aston Villa  [(1994-11-10 00:00:00, Brian Little, Ron Atkin...\n",
       "2   Birmingham  [(2007-11-18 00:00:00, Eric Black, Steve Bruce...\n",
       "3    Blackburn  [(1995-06-30 00:00:00, Ray Harford, Sir Kenny ...\n",
       "4       Bolton  [(1996-01-07 00:00:00, Phil Brown, Roy McFarla..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Helper function to get list of man/date tuples for a given df\n",
    "def get_man_dates(df):\n",
    "    all_mans = list(zip(df['Join date'], df['Coach'], df['Predecessor']))\n",
    "    sorted_mans = sorted(all_mans, key=lambda x: x[0])\n",
    "    result = []\n",
    "    name_counts = dict()\n",
    "    for date, name, pred in sorted_mans:\n",
    "        if name in name_counts:\n",
    "            name_counts[name] += 1\n",
    "            name = f'{name}_{name_counts[name]}'\n",
    "        else:\n",
    "            name_counts[name] = 1\n",
    "        if pred in name_counts and name_counts[pred] > 1:\n",
    "            pred = f'{pred}_{name_counts[pred]}'\n",
    "        result.append((date, name, pred))\n",
    "    return result\n",
    "all_managers = managers.groupby('Club')[['Join date', 'Coach', 'Predecessor']].apply(get_man_dates).reset_index()\n",
    "all_managers.columns = ['Team', 'All Managers']\n",
    "all_managers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It should be noted for the special case where managers leave and are rehired, their names are numbered by their term number. For example, Keith Millen got rehired twice for Crystal Palace, so his terms are labeled \"Keith Millen\", \"Keith Millen_2\", and \"Keith Millen_3\". This is reflected in the predecessor column as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(Timestamp('1993-05-17 00:00:00'), 'Alan Smith', 'Steve Coppell'),\n",
       " (Timestamp('1995-05-15 00:00:00'), 'Steve Coppell', 'Alan Smith'),\n",
       " (Timestamp('1998-03-13 00:00:00'), 'Attilio Lombardo', 'Steve Coppell'),\n",
       " (Timestamp('1998-04-29 00:00:00'), 'Ray Lewington', 'Attilio Lombardo'),\n",
       " (Timestamp('1998-05-10 00:00:00'), 'Terry Venables', 'Ray Lewington'),\n",
       " (Timestamp('2013-10-23 00:00:00'), 'Keith Millen', 'Ian Holloway'),\n",
       " (Timestamp('2013-11-23 00:00:00'), 'Tony Pulis', 'Keith Millen'),\n",
       " (Timestamp('2014-08-14 00:00:00'), 'Keith Millen_2', 'Tony Pulis'),\n",
       " (Timestamp('2014-08-27 00:00:00'), 'Neil Warnock', 'Keith Millen_2'),\n",
       " (Timestamp('2014-12-27 00:00:00'), 'Keith Millen_3', 'Neil Warnock'),\n",
       " (Timestamp('2015-01-02 00:00:00'), 'Alan Pardew', 'Keith Millen_3'),\n",
       " (Timestamp('2016-12-22 00:00:00'), 'Sam Allardyce', 'Alan Pardew'),\n",
       " (Timestamp('2017-05-23 00:00:00'), 'Frank de Boer', 'Sam Allardyce'),\n",
       " (Timestamp('2017-09-11 00:00:00'), 'Roy Hodgson', 'Frank de Boer'),\n",
       " (Timestamp('2021-06-30 00:00:00'), 'Patrick Vieira', 'Roy Hodgson'),\n",
       " (Timestamp('2023-03-17 00:00:00'), 'Patrick McCarthy', 'Patrick Vieira'),\n",
       " (Timestamp('2023-03-20 00:00:00'), 'Roy Hodgson_2', 'Patrick McCarthy')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_managers.iloc[13]['All Managers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>TotalGoalsScored</th>\n",
       "      <th>FullTimeResult</th>\n",
       "      <th>Team</th>\n",
       "      <th>Shots</th>\n",
       "      <th>ShotsOnTarget</th>\n",
       "      <th>Pass Cmp %</th>\n",
       "      <th>FoulsCommited</th>\n",
       "      <th>Corners</th>\n",
       "      <th>YellowCardsEarned</th>\n",
       "      <th>RedCardsEarned</th>\n",
       "      <th>IsHome</th>\n",
       "      <th>Won</th>\n",
       "      <th>All Managers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[(1993-05-17 00:00:00, Alan Smith, Steve Coppe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-14</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[(1993-05-17 00:00:00, Alan Smith, Steve Coppe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>[(1993-05-17 00:00:00, Alan Smith, Steve Coppe...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  TotalGoalsScored FullTimeResult            Team  Shots  \\\n",
       "0 2017-01-01                 0              H  Crystal Palace      7   \n",
       "1 2017-01-14                 0              H  Crystal Palace     10   \n",
       "2 2017-01-31                 2              A  Crystal Palace     11   \n",
       "\n",
       "   ShotsOnTarget  Pass Cmp %  FoulsCommited  Corners  YellowCardsEarned  \\\n",
       "0              4         NaN              7        4                  1   \n",
       "1              1         NaN             14        2                  2   \n",
       "2              4         NaN             23        3                  4   \n",
       "\n",
       "   RedCardsEarned  IsHome    Won  \\\n",
       "0               0   False  False   \n",
       "1               0   False  False   \n",
       "2               0   False   True   \n",
       "\n",
       "                                        All Managers  \n",
       "0  [(1993-05-17 00:00:00, Alan Smith, Steve Coppe...  \n",
       "1  [(1993-05-17 00:00:00, Alan Smith, Steve Coppe...  \n",
       "2  [(1993-05-17 00:00:00, Alan Smith, Steve Coppe...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_managers = metrics.merge(all_managers, on='Team', how='inner')\n",
    "metrics_managers.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll search within these dictionaries for every match for the manager with the latest join date that is still before the match's date. This will tell us which manager managed the team at the time of that match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Jürgen Klopp', 'Brendan Rodgers')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Helper function: returns the manager name for a single match\n",
    "row = metrics_managers.iloc[877]\n",
    "def get_manager(row):\n",
    "    match_date = row['Date']\n",
    "    manager_tups = row['All Managers']\n",
    "    if not manager_tups:\n",
    "        return np.NaN\n",
    "    for i in range(len(manager_tups)):\n",
    "        date, _, _ = manager_tups[i]\n",
    "        if date > match_date:\n",
    "            return manager_tups[i-1][1:]\n",
    "    return manager_tups[-1][1:]\n",
    "get_manager(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(Timestamp('1994-01-28 00:00:00'), 'Roy Evans', 'Graeme Souness'),\n",
       " (Timestamp('1998-11-11 00:00:00'), 'Gérard Houllier', 'Roy Evans'),\n",
       " (Timestamp('2004-06-30 00:00:00'), 'Rafael Benítez', 'Gérard Houllier'),\n",
       " (Timestamp('2010-06-30 00:00:00'), 'Roy Hodgson', 'Rafael Benítez'),\n",
       " (Timestamp('2011-01-07 00:00:00'), 'Sir Kenny Dalglish', 'Roy Hodgson'),\n",
       " (Timestamp('2012-05-16 00:00:00'), 'Brendan Rodgers', 'Sir Kenny Dalglish'),\n",
       " (Timestamp('2015-10-04 00:00:00'), 'Jürgen Klopp', 'Brendan Rodgers')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row['All Managers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Date</th>\n",
       "      <th>TotalGoalsScored</th>\n",
       "      <th>FullTimeResult</th>\n",
       "      <th>Team</th>\n",
       "      <th>Shots</th>\n",
       "      <th>ShotsOnTarget</th>\n",
       "      <th>Pass Cmp %</th>\n",
       "      <th>FoulsCommited</th>\n",
       "      <th>Corners</th>\n",
       "      <th>YellowCardsEarned</th>\n",
       "      <th>RedCardsEarned</th>\n",
       "      <th>IsHome</th>\n",
       "      <th>Won</th>\n",
       "      <th>Manager</th>\n",
       "      <th>Predecessor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Sam Allardyce</td>\n",
       "      <td>Alan Pardew</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-14</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Sam Allardyce</td>\n",
       "      <td>Alan Pardew</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Sam Allardyce</td>\n",
       "      <td>Alan Pardew</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-02-11</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Sam Allardyce</td>\n",
       "      <td>Alan Pardew</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-04</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Sam Allardyce</td>\n",
       "      <td>Alan Pardew</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  TotalGoalsScored FullTimeResult            Team  Shots  \\\n",
       "0 2017-01-01                 0              H  Crystal Palace      7   \n",
       "1 2017-01-14                 0              H  Crystal Palace     10   \n",
       "2 2017-01-31                 2              A  Crystal Palace     11   \n",
       "3 2017-02-11                 0              H  Crystal Palace      6   \n",
       "4 2017-03-04                 2              A  Crystal Palace     10   \n",
       "\n",
       "   ShotsOnTarget  Pass Cmp %  FoulsCommited  Corners  YellowCardsEarned  \\\n",
       "0              4         NaN              7        4                  1   \n",
       "1              1         NaN             14        2                  2   \n",
       "2              4         NaN             23        3                  4   \n",
       "3              1         NaN             14        5                  3   \n",
       "4              4         NaN             12        5                  1   \n",
       "\n",
       "   RedCardsEarned  IsHome    Won        Manager  Predecessor  \n",
       "0               0   False  False  Sam Allardyce  Alan Pardew  \n",
       "1               0   False  False  Sam Allardyce  Alan Pardew  \n",
       "2               0   False   True  Sam Allardyce  Alan Pardew  \n",
       "3               0   False  False  Sam Allardyce  Alan Pardew  \n",
       "4               0   False   True  Sam Allardyce  Alan Pardew  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_managers['Manager_pred'] = metrics_managers.apply(get_manager, axis=1)\n",
    "metrics_managers['Manager'] = metrics_managers['Manager_pred'].apply(lambda x: x[0])\n",
    "metrics_managers['Predecessor'] = metrics_managers['Manager_pred'].apply(lambda x: x[1])\n",
    "metrics_managers = metrics_managers.drop(['All Managers', 'Manager_pred'], axis=1)\n",
    "metrics_managers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sam Allardyce', 'Roy Hodgson', 'Patrick Vieira', 'Frank de Boer',\n",
       "       'Mauricio Pochettino', 'José Mourinho', 'Ryan Mason',\n",
       "       'Nuno Espírito Santo', 'Antonio Conte', 'Claude Puel',\n",
       "       'Ralph Hasenhüttl', 'Mauricio Pellegrino', 'Mark Hughes',\n",
       "       'Brian Laws', 'Mike Jackson', 'Claudio Ranieri',\n",
       "       'Craig Shakespeare_2', 'Brendan Rodgers_2', 'Michael Appleton',\n",
       "       'Craig Shakespeare', 'Jürgen Klopp', 'Mike Phelan', 'Marco Silva',\n",
       "       'Ole Gunnar Solskjær_2', 'Michael Carrick', 'Ralf Rangnick',\n",
       "       'Arsène Wenger', 'Mikel Arteta', 'Unai Emery', 'Freddie Ljungberg',\n",
       "       'Paul Clement', 'Leon Britton', 'Carlos Carvalhal',\n",
       "       'Walter Mazzarri', 'Vladimir Ivic', 'Javi Gracia',\n",
       "       'Quique Sánchez Flores', 'Hayden Mullins', 'Nigel Pearson',\n",
       "       'Frank Lampard', 'Thomas Tuchel', 'Maurizio Sarri',\n",
       "       'Andoni Iraola', 'Paul Lambert', 'Tony Pulis', 'Darren Moore',\n",
       "       'Gary Megson', 'Alan Pardew', 'Colin Cooper', 'Steve Agnew',\n",
       "       'Pep Guardiola', 'Ronald Koeman', 'Carlo Ancelotti',\n",
       "       'Rafael Benítez', 'Duncan Ferguson', 'David Unsworth_3',\n",
       "       'Duncan Ferguson_2', 'David Moyes', 'David Moyes_3',\n",
       "       'Manuel Pellegrini', 'David Moyes_2', 'Kevin Blackwell_2',\n",
       "       'Jesse Marsch', 'Steve Bruce', 'Graeme Jones', 'Eddie Howe',\n",
       "       'Dean Saunders', 'Bruno Lage', 'Scott Parker', 'Felix Magath',\n",
       "       'Graham Potter', 'Roberto De Zerbi', 'Roberto Di Matteo',\n",
       "       'Steven Gerrard', 'Neil Adams', 'Dean Smith', 'Jan Siewert',\n",
       "       'Ole Gunnar Solskjær'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_managers['Manager'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's add the PointsWonByResult column back in, and drop `'Won'` and `'FullTimeResult'`, since the info in those two is also in PWBR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Date</th>\n",
       "      <th>TotalGoalsScored</th>\n",
       "      <th>Team</th>\n",
       "      <th>Shots</th>\n",
       "      <th>ShotsOnTarget</th>\n",
       "      <th>Pass Cmp %</th>\n",
       "      <th>FoulsCommited</th>\n",
       "      <th>Corners</th>\n",
       "      <th>YellowCardsEarned</th>\n",
       "      <th>RedCardsEarned</th>\n",
       "      <th>IsHome</th>\n",
       "      <th>Manager</th>\n",
       "      <th>Predecessor</th>\n",
       "      <th>PointsWonByResult</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>Sam Allardyce</td>\n",
       "      <td>Alan Pardew</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-14</td>\n",
       "      <td>0</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>Sam Allardyce</td>\n",
       "      <td>Alan Pardew</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>2</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>Sam Allardyce</td>\n",
       "      <td>Alan Pardew</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-02-11</td>\n",
       "      <td>0</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>Sam Allardyce</td>\n",
       "      <td>Alan Pardew</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-04</td>\n",
       "      <td>2</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>Sam Allardyce</td>\n",
       "      <td>Alan Pardew</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  TotalGoalsScored            Team  Shots  ShotsOnTarget  \\\n",
       "0 2017-01-01                 0  Crystal Palace      7              4   \n",
       "1 2017-01-14                 0  Crystal Palace     10              1   \n",
       "2 2017-01-31                 2  Crystal Palace     11              4   \n",
       "3 2017-02-11                 0  Crystal Palace      6              1   \n",
       "4 2017-03-04                 2  Crystal Palace     10              4   \n",
       "\n",
       "   Pass Cmp %  FoulsCommited  Corners  YellowCardsEarned  RedCardsEarned  \\\n",
       "0         NaN              7        4                  1               0   \n",
       "1         NaN             14        2                  2               0   \n",
       "2         NaN             23        3                  4               0   \n",
       "3         NaN             14        5                  3               0   \n",
       "4         NaN             12        5                  1               0   \n",
       "\n",
       "   IsHome        Manager  Predecessor  PointsWonByResult  \n",
       "0   False  Sam Allardyce  Alan Pardew                0.0  \n",
       "1   False  Sam Allardyce  Alan Pardew                0.0  \n",
       "2   False  Sam Allardyce  Alan Pardew                3.0  \n",
       "3   False  Sam Allardyce  Alan Pardew                0.0  \n",
       "4   False  Sam Allardyce  Alan Pardew                3.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_points = metrics_managers['Won'].replace({\n",
    "    True: 3.0,\n",
    "    False: 0.0\n",
    "}).fillna(1.0)\n",
    "metrics_managers['PointsWonByResult'] = result_points\n",
    "metrics_managers = metrics_managers.drop(['Won', 'FullTimeResult'], axis=1)\n",
    "metrics_managers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we finally have one dataframe with all our metrics AND all our managers (including their predecessors)! We can finally get into the meat of our analysis, and work towards answering our question."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Graphing changes in metrics under each manager over time\n",
    "This is a large task, and more for our own viewing purposes. If we did this for every manager for every team, this would take ages, and would produce a ton of graphs to look at. Let's first define a function to generate a graph like this, given a manager and a team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining our quantitative/qualitative columns to make it easier to do analysis later\n",
    "quant_columns = ['TotalGoalsScored', 'Shots', 'ShotsOnTarget', 'Pass Cmp %', 'FoulsCommited', 'Corners', 'YellowCardsEarned', 'RedCardsEarned', 'PointsWonByResult']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arsène Wenger\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "manager = 'Unai Emery'\n",
    "team = 'Arsenal'\n",
    "def graph_met_over_time(manager, team, specific_met=None):\n",
    "    metrics_under_manager = (metrics_managers\n",
    "                             [(metrics_managers['Manager'] == manager) \n",
    "                              & (metrics_managers['Team'] == team)])\n",
    "    quant_mum = metrics_under_manager[quant_columns + ['Date']].copy()\n",
    "    quant_mum['Position'] = [manager] * quant_mum.shape[0]\n",
    "    predecessor = metrics_under_manager['Predecessor'].unique()[0]\n",
    "    if predecessor == manager:\n",
    "        predecessor += '_prev'\n",
    "    metrics_under_pred = (metrics_managers\n",
    "                          [(metrics_managers['Manager'] == predecessor) \n",
    "                           & (metrics_managers['Team'] == team)])\n",
    "    quant_mup = metrics_under_pred[quant_columns + ['Date']].copy()\n",
    "    quant_mup['Position'] = ['Pred: '+predecessor] * quant_mup.shape[0]\n",
    "    mum_mup = pd.concat([quant_mum, quant_mup])\n",
    "    print(predecessor)\n",
    "    if specific_met:\n",
    "        fig = sns.lineplot(data=mum_mup, x='Date', y=specific_met, hue='Position')\n",
    "        fig.set(title=f'Metric for {team}\\'s {manager}')\n",
    "    else:\n",
    "        plt.subplot(1, 2, 2,)\n",
    "        fig = sns.lineplot(data=(quant_mum\n",
    "                                 .drop('Position', axis=1)\n",
    "                                 .melt('Date')), \n",
    "                           x='Date', y='value', hue='variable')\n",
    "        plt.xticks(rotation=45, ha = 'right')\n",
    "        plt.subplot(1, 2, 1)\n",
    "        fig2 = sns.lineplot(data=(quant_mup\n",
    "                                 .drop('Position', axis=1)\n",
    "                                 .melt('Date')), \n",
    "                            x='Date', y='value', hue='variable')\n",
    "        plt.xticks(rotation=45, ha = 'right')\n",
    "        plt.legend([],[], frameon=False)\n",
    "        fig.set(title=f'Metrics for {team}\\'s {manager}', ylabel='')\n",
    "        fig2.set(title=f'Metrics for Pred: {predecessor}')\n",
    "    sns.move_legend(fig, 'upper left', bbox_to_anchor=(1,1))\n",
    "    plt.xticks(rotation=45, ha = 'right')\n",
    "    return fig\n",
    "graph_met_over_time(manager, team);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this manager, there were sporadic jumps throughout in all metrics. Let's look at a couple different teams and managers, and see what we can find."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bournemouth Andoni Iraola\n",
      "Gary O'Neil_2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "team = np.random.choice(metrics_managers['Team'].unique())\n",
    "manager = np.random.choice(metrics_managers[metrics_managers['Team'] == team]['Manager'].unique())\n",
    "print(team, manager)\n",
    "graph_met_over_time(manager, team, specific_met='TotalGoalsScored'); # Graphing only one metric\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Calculating average metrics for the first 10 games\n",
    "Moving onto the next step, let's pick a manager and team and calculate the metrics for their first 10 games. We want to make sure the manager we pick has a predecessor with data in our dataset- otherwise, there's nothing to compare them to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Southampton Mark Hughes\n",
      "Mauricio Pellegrino\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "team = 'Southampton'\n",
    "manager = 'Mark Hughes'\n",
    "print(team, manager)\n",
    "graph_met_over_time(manager, team, specific_met='TotalGoalsScored');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the next two steps, we'll work with Southampton's Mark Hughes, comparing him with his predecessor, Mauricio Pellegrino.\n",
    "\n",
    "First off, we want to calculate Mark's metrics for his first 10 games. The process we'll follow along with the reasons is outlined below:\n",
    "1. Standardize all quantitative metrics based on Southampton's mean and std\n",
    "    - This is so our metrics don't have higher influence depending on the units. For example, Pass Cmp % right now has higher raw values than any other column, since it stores percents that range from 0 to 100.\n",
    "    - Doing this process by team also ensures that we're comparing scores within the scope of the team, not in the overall League.\n",
    "    - We'll apply the following formula to each value in every quantitative column:\n",
    "    $$\\text{new value}=\\frac{\\text{old value} - \\text{column mean}}{\\text{column std}}$$\n",
    "2. Take the mean of the means of all positive columns to get the Positive Metric Aggregate (PMA), and do the same to the negative columns to get the Negative Metric Aggregate (NMA)\n",
    "    - This aggregation of the positive and negative columns ensures both positive and negative metrics are taken into account. Since there are more positive than negative metrics, it also avoids weighing the positive metrics higher.\n",
    "    - \"positive\" and \"negative\" were determined in the EDA process, and are defined as follows:\n",
    "\n",
    "<center>\n",
    "\n",
    "| Effect | Metrics |\n",
    "| --- | --- |\n",
    "| Positive |Total Goals, Shots, Shots on Target, Pass Completion, and Corners |\n",
    "| Negative |Yellow Cards Earned and Fouls Committed |\n",
    "</center>\n",
    "\n",
    "Later on, we'll compare the PMA, NMA, and average PWBR of each manager with those of their predecessor to see if PMA and PWBR are significantly higher, and NMA is significantly lower.\n",
    "\n",
    "With all that in mind, let's get started!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22, 14)\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>Date</th>\n",
       "      <th>TotalGoalsScored</th>\n",
       "      <th>Team</th>\n",
       "      <th>Shots</th>\n",
       "      <th>ShotsOnTarget</th>\n",
       "      <th>Pass Cmp %</th>\n",
       "      <th>FoulsCommited</th>\n",
       "      <th>Corners</th>\n",
       "      <th>YellowCardsEarned</th>\n",
       "      <th>RedCardsEarned</th>\n",
       "      <th>IsHome</th>\n",
       "      <th>Manager</th>\n",
       "      <th>Predecessor</th>\n",
       "      <th>PointsWonByResult</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>0</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>71.6</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>Mark Hughes</td>\n",
       "      <td>Mauricio Pellegrino</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>2018-04-08</td>\n",
       "      <td>2</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>69.1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>Mark Hughes</td>\n",
       "      <td>Mauricio Pellegrino</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>2018-04-19</td>\n",
       "      <td>0</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>Mark Hughes</td>\n",
       "      <td>Mauricio Pellegrino</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>2018-05-05</td>\n",
       "      <td>1</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>71.9</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>Mark Hughes</td>\n",
       "      <td>Mauricio Pellegrino</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>2018-05-08</td>\n",
       "      <td>1</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>Mark Hughes</td>\n",
       "      <td>Mauricio Pellegrino</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  TotalGoalsScored         Team  Shots  ShotsOnTarget  \\\n",
       "427 2018-03-31                 0  Southampton      9              0   \n",
       "428 2018-04-08                 2  Southampton     15              8   \n",
       "429 2018-04-19                 0  Southampton      6              2   \n",
       "430 2018-05-05                 1  Southampton     11              5   \n",
       "431 2018-05-08                 1  Southampton     13              8   \n",
       "\n",
       "     Pass Cmp %  FoulsCommited  Corners  YellowCardsEarned  RedCardsEarned  \\\n",
       "427        71.6             16        9                  3               0   \n",
       "428        69.1              7        6                  1               1   \n",
       "429        71.2             10        3                  0               0   \n",
       "430        71.9             12        4                  4               1   \n",
       "431        68.9             15        6                  1               0   \n",
       "\n",
       "     IsHome      Manager          Predecessor  PointsWonByResult  \n",
       "427   False  Mark Hughes  Mauricio Pellegrino                0.0  \n",
       "428   False  Mark Hughes  Mauricio Pellegrino                0.0  \n",
       "429   False  Mark Hughes  Mauricio Pellegrino                1.0  \n",
       "430   False  Mark Hughes  Mauricio Pellegrino                1.0  \n",
       "431   False  Mark Hughes  Mauricio Pellegrino                3.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hughes_sh_df = metrics_managers[(metrics_managers['Manager'] == 'Mark Hughes') & (metrics_managers['Team'] == 'Southampton')]\n",
    "print(hughes_sh_df.shape)\n",
    "hughes_sh_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 14)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hughes_first10 = hughes_sh_df.sort_values('Date', ascending=True).iloc[:10]\n",
    "hughes_first10.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the metrics for Hughes' first 10 matches, we can go through the process to calculate the PMA, NMA, and average PWBR. \n",
    "\n",
    "First, let's standardize the metrics, based on all of Southampton's metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>TotalGoalsScored</th>\n",
       "      <th>Shots</th>\n",
       "      <th>ShotsOnTarget</th>\n",
       "      <th>Pass Cmp %</th>\n",
       "      <th>FoulsCommited</th>\n",
       "      <th>Corners</th>\n",
       "      <th>YellowCardsEarned</th>\n",
       "      <th>RedCardsEarned</th>\n",
       "      <th>PointsWonByResult</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.084256</td>\n",
       "      <td>-0.696580</td>\n",
       "      <td>-1.971202</td>\n",
       "      <td>-0.304109</td>\n",
       "      <td>1.356033</td>\n",
       "      <td>1.262226</td>\n",
       "      <td>1.135562</td>\n",
       "      <td>-0.223607</td>\n",
       "      <td>-0.852163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.871876</td>\n",
       "      <td>0.494640</td>\n",
       "      <td>1.659960</td>\n",
       "      <td>-0.693224</td>\n",
       "      <td>-1.141922</td>\n",
       "      <td>0.181200</td>\n",
       "      <td>-0.467046</td>\n",
       "      <td>3.689512</td>\n",
       "      <td>-0.852163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.871876</td>\n",
       "      <td>-0.498043</td>\n",
       "      <td>1.206064</td>\n",
       "      <td>-0.335238</td>\n",
       "      <td>0.523381</td>\n",
       "      <td>-0.539483</td>\n",
       "      <td>2.738169</td>\n",
       "      <td>-0.223607</td>\n",
       "      <td>-0.852163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.084256</td>\n",
       "      <td>-1.292189</td>\n",
       "      <td>-1.063412</td>\n",
       "      <td>-0.366367</td>\n",
       "      <td>-0.309271</td>\n",
       "      <td>-0.899825</td>\n",
       "      <td>-1.268349</td>\n",
       "      <td>-0.223607</td>\n",
       "      <td>-0.028249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.871876</td>\n",
       "      <td>0.296103</td>\n",
       "      <td>0.752169</td>\n",
       "      <td>-0.677660</td>\n",
       "      <td>0.245831</td>\n",
       "      <td>-0.179141</td>\n",
       "      <td>2.738169</td>\n",
       "      <td>-0.223607</td>\n",
       "      <td>1.619581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TotalGoalsScored     Shots  ShotsOnTarget  Pass Cmp %  FoulsCommited  \\\n",
       "0         -1.084256 -0.696580      -1.971202   -0.304109       1.356033   \n",
       "1          0.871876  0.494640       1.659960   -0.693224      -1.141922   \n",
       "2          0.871876 -0.498043       1.206064   -0.335238       0.523381   \n",
       "3         -1.084256 -1.292189      -1.063412   -0.366367      -0.309271   \n",
       "4          0.871876  0.296103       0.752169   -0.677660       0.245831   \n",
       "\n",
       "    Corners  YellowCardsEarned  RedCardsEarned  PointsWonByResult  \n",
       "0  1.262226           1.135562       -0.223607          -0.852163  \n",
       "1  0.181200          -0.467046        3.689512          -0.852163  \n",
       "2 -0.539483           2.738169       -0.223607          -0.852163  \n",
       "3 -0.899825          -1.268349       -0.223607          -0.028249  \n",
       "4 -0.179141           2.738169       -0.223607           1.619581  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sh_scaler = StandardScaler()\n",
    "sh_quant_mets = metrics_managers[metrics_managers['Team'] == 'Southampton'][quant_columns]\n",
    "sh_scaler.fit(sh_quant_mets)\n",
    "hughes_f10_std_mets = pd.DataFrame(sh_scaler.transform(hughes_first10[quant_columns]), columns=quant_columns)\n",
    "hughes_f10_std_mets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we'll use these standardized metrics to calculate our positive/negative scores. We'll also calculate the avg PWBR here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.2026613365710534, -0.11064000702262042, 0.6121331267111735)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_metrics = ['TotalGoalsScored', 'Shots', 'ShotsOnTarget', 'Pass Cmp %', 'Corners']\n",
    "negative_metrics = ['FoulsCommited', 'YellowCardsEarned']\n",
    "hughes_f10_pma = hughes_f10_std_mets[positive_metrics].mean().mean()\n",
    "hughes_f10_nma = hughes_f10_std_mets[negative_metrics].mean().mean()\n",
    "hughes_f10_pwbr = hughes_f10_std_mets['PointsWonByResult'].mean()\n",
    "hughes_f10_pma, hughes_f10_pwbr, hughes_f10_nma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These scores alone tell us that Hughes seems to have underperformed as a manager by Southampton standards, since his PMA and PWBR are lower than the average, and his NMA is higher. However, it *doesn't* tell us anything about his bounce: we have to compare his scores to that of his predecessor to get that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Comparing to the Predecessor\n",
    "First we have to find the metrics for his predecessor, Mauricio Pellegrino."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30, 14)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Date</th>\n",
       "      <th>TotalGoalsScored</th>\n",
       "      <th>Team</th>\n",
       "      <th>Shots</th>\n",
       "      <th>ShotsOnTarget</th>\n",
       "      <th>Pass Cmp %</th>\n",
       "      <th>FoulsCommited</th>\n",
       "      <th>Corners</th>\n",
       "      <th>YellowCardsEarned</th>\n",
       "      <th>RedCardsEarned</th>\n",
       "      <th>IsHome</th>\n",
       "      <th>Manager</th>\n",
       "      <th>Predecessor</th>\n",
       "      <th>PointsWonByResult</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>2017-08-26</td>\n",
       "      <td>0</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>75.5</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>Mauricio Pellegrino</td>\n",
       "      <td>Claude Puel</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>2017-09-16</td>\n",
       "      <td>1</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>78.5</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>Mauricio Pellegrino</td>\n",
       "      <td>Claude Puel</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>2017-09-30</td>\n",
       "      <td>1</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>83.6</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>Mauricio Pellegrino</td>\n",
       "      <td>Claude Puel</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>2017-10-29</td>\n",
       "      <td>1</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>77.4</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>Mauricio Pellegrino</td>\n",
       "      <td>Claude Puel</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>2017-11-18</td>\n",
       "      <td>0</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>Mauricio Pellegrino</td>\n",
       "      <td>Claude Puel</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  TotalGoalsScored         Team  Shots  ShotsOnTarget  \\\n",
       "413 2017-08-26                 0  Southampton      6              3   \n",
       "414 2017-09-16                 1  Southampton     13              4   \n",
       "415 2017-09-30                 1  Southampton     21              3   \n",
       "416 2017-10-29                 1  Southampton      6              1   \n",
       "417 2017-11-18                 0  Southampton      5              0   \n",
       "\n",
       "     Pass Cmp %  FoulsCommited  Corners  YellowCardsEarned  RedCardsEarned  \\\n",
       "413        75.5             10        4                  1               0   \n",
       "414        78.5             13        5                  1               0   \n",
       "415        83.6             10        6                  2               0   \n",
       "416        77.4             10        7                  1               0   \n",
       "417        71.0              9        2                  1               0   \n",
       "\n",
       "     IsHome              Manager  Predecessor  PointsWonByResult  \n",
       "413   False  Mauricio Pellegrino  Claude Puel                1.0  \n",
       "414   False  Mauricio Pellegrino  Claude Puel                3.0  \n",
       "415   False  Mauricio Pellegrino  Claude Puel                0.0  \n",
       "416   False  Mauricio Pellegrino  Claude Puel                1.0  \n",
       "417   False  Mauricio Pellegrino  Claude Puel                0.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pelle_sh_df = metrics_managers[(metrics_managers['Manager'] == 'Mauricio Pellegrino') & (metrics_managers['Team'] == 'Southampton')]\n",
    "print(pelle_sh_df.shape)\n",
    "pelle_sh_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll follow the same process: Standardize, then calculate scores. The good news is, we already have a scaler fit for Southampton."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>TotalGoalsScored</th>\n",
       "      <th>Shots</th>\n",
       "      <th>ShotsOnTarget</th>\n",
       "      <th>Pass Cmp %</th>\n",
       "      <th>FoulsCommited</th>\n",
       "      <th>Corners</th>\n",
       "      <th>YellowCardsEarned</th>\n",
       "      <th>RedCardsEarned</th>\n",
       "      <th>PointsWonByResult</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.084256</td>\n",
       "      <td>-1.292189</td>\n",
       "      <td>-0.609516</td>\n",
       "      <td>0.302912</td>\n",
       "      <td>-0.309271</td>\n",
       "      <td>-0.539483</td>\n",
       "      <td>-0.467046</td>\n",
       "      <td>-0.223607</td>\n",
       "      <td>-0.028249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.106190</td>\n",
       "      <td>0.097567</td>\n",
       "      <td>-0.155621</td>\n",
       "      <td>0.769850</td>\n",
       "      <td>0.523381</td>\n",
       "      <td>-0.179141</td>\n",
       "      <td>-0.467046</td>\n",
       "      <td>-0.223607</td>\n",
       "      <td>1.619581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.106190</td>\n",
       "      <td>1.685859</td>\n",
       "      <td>-0.609516</td>\n",
       "      <td>1.563646</td>\n",
       "      <td>-0.309271</td>\n",
       "      <td>0.181200</td>\n",
       "      <td>0.334258</td>\n",
       "      <td>-0.223607</td>\n",
       "      <td>-0.852163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.106190</td>\n",
       "      <td>-1.292189</td>\n",
       "      <td>-1.517307</td>\n",
       "      <td>0.598639</td>\n",
       "      <td>-0.309271</td>\n",
       "      <td>0.541542</td>\n",
       "      <td>-0.467046</td>\n",
       "      <td>-0.223607</td>\n",
       "      <td>-0.028249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.084256</td>\n",
       "      <td>-1.490726</td>\n",
       "      <td>-1.971202</td>\n",
       "      <td>-0.397497</td>\n",
       "      <td>-0.586821</td>\n",
       "      <td>-1.260167</td>\n",
       "      <td>-0.467046</td>\n",
       "      <td>-0.223607</td>\n",
       "      <td>-0.852163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TotalGoalsScored     Shots  ShotsOnTarget  Pass Cmp %  FoulsCommited  \\\n",
       "0         -1.084256 -1.292189      -0.609516    0.302912      -0.309271   \n",
       "1         -0.106190  0.097567      -0.155621    0.769850       0.523381   \n",
       "2         -0.106190  1.685859      -0.609516    1.563646      -0.309271   \n",
       "3         -0.106190 -1.292189      -1.517307    0.598639      -0.309271   \n",
       "4         -1.084256 -1.490726      -1.971202   -0.397497      -0.586821   \n",
       "\n",
       "    Corners  YellowCardsEarned  RedCardsEarned  PointsWonByResult  \n",
       "0 -0.539483          -0.467046       -0.223607          -0.028249  \n",
       "1 -0.179141          -0.467046       -0.223607           1.619581  \n",
       "2  0.181200           0.334258       -0.223607          -0.852163  \n",
       "3  0.541542          -0.467046       -0.223607          -0.028249  \n",
       "4 -1.260167          -0.467046       -0.223607          -0.852163  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pelle_std_mets = pd.DataFrame(sh_scaler.transform(pelle_sh_df[quant_columns]), columns=quant_columns)\n",
    "pelle_std_mets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0026866722542562282, -0.0831761754921827, -0.13485803607109872)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pelle_pms = pelle_std_mets[positive_metrics].mean().mean()\n",
    "pelle_nms = pelle_std_mets[negative_metrics].mean().mean()\n",
    "pelle_pwbr = pelle_std_mets['PointsWonByResult'].mean()\n",
    "pelle_pms, pelle_pwbr, pelle_nms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems Pellegrino actually performed a bit *better* than Hughes. But how significant is this difference? Was there a bounce?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis Testing\n",
    "Here, we'll outline the procedure for our hypothesis test. We will be testing to see if the PMA and PWBR for a new manager (in this case, Hughes) in their first 10 matches are significantly higher than those of their predecessor (in this case, Pellegrino), and if the NMA is significantly *lower*.\n",
    "\n",
    "PMA first:\n",
    "rough procedure:\n",
    "- assume that the pma does not depend on whether it was hughes or pelle\n",
    "- create df with all of pelle's matches and hugh's first 10\n",
    "- calculate diff in pma between hughes first 10 and pelle all\n",
    "- permute 'manager' column, recalculate diff in pma (repeat many times)\n",
    "- see if the proportion of simulated diff pma's that are higher than hughes pma (which is our p-value) is less than 0.05\n",
    "***\n",
    "$H_0$: The PMA of Hughes in his first 10 matches is equal to the PMA of Pellegrino.<br>\n",
    "     $\\quad\\text{PMA}_{Hughes}-\\text{PMA}_{Pellegrino} = 0$<br>\n",
    "$H_a$: The PMA of Hughes is higher than the PMA of Pellegrino.<br>\n",
    "     $\\quad\\text{PMA}_{Hughes}-\\text{PMA}_{Pellegrino} > 0$<br>\n",
    "\n",
    "Significance level: $\\alpha = 0.05$\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The P-value was 0.808'"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pelle = pelle_std_mets.assign(Manager = ['AAA'] * pelle_std_mets.shape[0])\n",
    "hughes = hughes_f10_std_mets.assign(Manager = ['BBB'] * hughes_f10_std_mets.shape[0])\n",
    "pop = pd.concat([pelle, hughes]).reset_index(drop=True)\n",
    "# pma calculation function\n",
    "def pma(df):\n",
    "    return df[positive_metrics].mean().mean()\n",
    "\n",
    "# observed test stat\n",
    "obs_diff_pmas = pop.groupby('Manager').apply(pma).diff().iloc[-1]\n",
    "\n",
    "# simulate test stats\n",
    "N = 1000\n",
    "sim_diffs = []\n",
    "for _ in range(N):\n",
    "    pop['perm_man'] = np.random.permutation(pop['Manager'])\n",
    "    sim_diff_pmas = pop.groupby('perm_man').apply(pma).diff().iloc[-1]\n",
    "    sim_diffs.append(sim_diff_pmas)\n",
    "\n",
    "# sns.histplot(sim_diffs)\n",
    "# calc p-val\n",
    "pma_p_val = (np.array(sim_diffs) >= obs_diff_pmas).mean()\n",
    "f\"The P-value was {pma_p_val}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen, the p-value was very high, meaning we fail to rejetc the null. Hughes' PMA in his first 10 matches was *not* significantly higher (nor actually higher at all) than his predecessor. \n",
    "\n",
    "What about the other stats, NMA and PWBR? We'll run very similar hypothesis tests for those.\n",
    "***\n",
    "$H_0$: The average PWBR of Hughes in his first 10 matches is equal to the average PWBR of Pellegrino.<br>\n",
    "     $\\quad\\text{PWBR}_{Hughes}-\\text{PMA}_{Pellegrino} = 0$<br>\n",
    "$H_a$: The average PWBR of Hughes is higher than the PWBR of Pellegrino.<br>\n",
    "     $\\quad\\text{PWBR}_{Hughes}-\\text{PWBR}_{Pellegrino} > 0$<br>\n",
    "\n",
    "Significance level: $\\alpha = 0.05$\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.027463831530437702\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'The P-value was 0.556'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# observed test stat\n",
    "obs_diff_pwbr = pop.groupby('Manager')['PointsWonByResult'].mean().diff().iloc[-1] * -1\n",
    "print(obs_diff_pwbr)\n",
    "# simulate test stats\n",
    "N = 1000\n",
    "sim_diffs = []\n",
    "for _ in range(N):\n",
    "    pop['perm_man'] = np.random.permutation(pop['Manager'])\n",
    "    sim_diff_pwbr = pop.groupby('perm_man')['PointsWonByResult'].mean().diff().iloc[-1] * -1\n",
    "    sim_diffs.append(sim_diff_pwbr)\n",
    "\n",
    "# sns.histplot(sim_diffs)\n",
    "# calc p-val\n",
    "pwbr_p_val = (np.array(sim_diffs) >= obs_diff_pwbr).mean()\n",
    "f\"The P-value was {pwbr_p_val}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "$H_0$: The NMA of Hughes in his first 10 matches is equal to the NMA of Pellegrino.<br>\n",
    "     $\\quad\\text{NMA}_{Hughes}-\\text{NMA}_{Pellegrino} = 0$<br>\n",
    "$H_a$: The NMA of Hughes is lower than the NMA of Pellegrino.<br>\n",
    "     $\\quad\\text{NMA}_{Hughes}-\\text{NMA}_{Pellegrino} < 0$<br>\n",
    "\n",
    "Significance level: $\\alpha = 0.05$\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7469911627822722\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'The P-value was 0.985'"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# observed test stat\n",
    "def nma(df):\n",
    "    return df[negative_metrics].mean().mean()\n",
    "obs_diff_nma = pop.groupby('Manager').apply(nma).diff().iloc[-1]\n",
    "print(obs_diff_nma)\n",
    "\n",
    "# simulate test stats\n",
    "N = 1000\n",
    "sim_diffs = []\n",
    "for _ in range(N):\n",
    "    pop['perm_man'] = np.random.permutation(pop['Manager'])\n",
    "    sim_diff_nma = pop.groupby('perm_man').apply(nma).diff().iloc[-1]\n",
    "    sim_diffs.append(sim_diff_nma)\n",
    "\n",
    "sns.histplot(sim_diffs)\n",
    "# calc p-val\n",
    "nma_p_val = (np.array(sim_diffs) <= obs_diff_nma).mean()\n",
    "f\"The P-value was {nma_p_val}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.808, 0.556, 0.017)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pma_p_val, pwbr_p_val, nma_p_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's reflect. ALL of our p-values were (much) higher than our chosen significance level of 0.05. From this, we can determine that Hughes showed no significant improvement over Pellegrino in his first ten matches. However, this makes sense: When we looked at the PMA, NMA, and PWBR separately, they were all worse for Hughes' first 10 than for Pellegrino. So we know Hughes did *not* have a bounce. What about the other managers, for the other teams?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Repeat hypothesis tests for all managers, across all teams\n",
    "This is the biggest step of our analysis. How many of the managers we have data on experienced a significant bounce in their metrics? To determine this, we'll have to repeat the above processes for every manager, within every team."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make this process easier, we'll define a function that runs a permutation test given a dataframe of metrics for a single team `'df'`, a manager/predecessor name pair `'man_pred'`, and stat to calculate `'stat'`, that's either `PMA`, `NMA`, or `PWBR`. It'll go through steps 3 and 4 of our analysis for the given manager and give us the resulting p-value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.984"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def manager_perm_test(df, man_pred, stat, N = 1000, showplot=False):\n",
    "    \"\"\"Runs a permutation test, determining if the manager/predecessor pair given had a bounce\n",
    "\n",
    "    Args:\n",
    "        df (DataFrame): df of metrics for just one team\n",
    "        man_pred (tuple): (manager name, predecessor name)\n",
    "        stat (str): name of statistic ('PMA', 'NMA', or 'PWBR')\n",
    "        N (int, optional): Number of iterations to run. Defaults to 1000.\n",
    "        showplot (bool, optional): Whether or not to display the histogram of \n",
    "            simulated test statistics. Defaults to False.\n",
    "    \"\"\"\n",
    "    def fit_stdscaler(df):\n",
    "        team_scaler = StandardScaler()\n",
    "        team_quant_mets = df[quant_columns]\n",
    "        team_scaler.fit(team_quant_mets)\n",
    "        return sh_scaler\n",
    "\n",
    "    def get_std_df(df, scaler, manager, first10=False):\n",
    "        man_team = (df\n",
    "            [(df['Manager'] == manager)]\n",
    "            .sort_values('Date'))\n",
    "        if man_team.empty:\n",
    "            return pd.DataFrame()\n",
    "        if first10:\n",
    "            man_team = man_team.iloc[:10]\n",
    "        man_team_std = pd.DataFrame(\n",
    "            scaler.transform(man_team[quant_columns]),\n",
    "            columns=quant_columns)\n",
    "        return man_team_std\n",
    "\n",
    "    def get_stat_func(stat):\n",
    "        positive_metrics = ['TotalGoalsScored', 'Shots', 'ShotsOnTarget', 'Pass Cmp %', 'Corners']\n",
    "        negative_metrics = ['FoulsCommited', 'YellowCardsEarned'] # remind ourselves what we're considering positive and negative\n",
    "        if stat == 'PMA':\n",
    "            return lambda df: df[positive_metrics].mean().mean()\n",
    "        if stat == 'NMA':\n",
    "            return lambda df: df[negative_metrics].mean().mean()\n",
    "        if stat == 'PWBR':\n",
    "            return lambda df: df['PointsWonByResult'].mean()\n",
    "        raise ValueError('Invalid stat')\n",
    "\n",
    "    manager, pred_name = man_pred\n",
    "    scaler = fit_stdscaler(df)\n",
    "    man_f10_std = get_std_df(df, scaler, manager, first10=True)\n",
    "    pred_std = get_std_df(df, scaler, pred_name)\n",
    "    if pred_std.empty:\n",
    "        return 1\n",
    "\n",
    "    pred = pred_std.assign(Manager = ['AAA'] * pred_std.shape[0])\n",
    "    man = man_f10_std.assign(Manager = ['BBB'] * man_f10_std.shape[0])\n",
    "    pop = pd.concat([pred, man]).reset_index(drop=True)\n",
    "    func = get_stat_func(stat)\n",
    "\n",
    "    # observed test stat\n",
    "    obs_stats = pop.groupby('Manager').apply(func)\n",
    "    obs_ts = obs_stats.diff().iloc[-1]\n",
    "    if showplot:\n",
    "        print(obs_ts)\n",
    "    # simulate test stats\n",
    "    sim_ts_list = []\n",
    "    for _ in range(N):\n",
    "        pop['perm_man'] = np.random.permutation(pop['Manager'])\n",
    "        stats = pop.groupby('perm_man').apply(func)\n",
    "        sim_ts = stats.diff().iloc[-1]\n",
    "        sim_ts_list.append(sim_ts)\n",
    "\n",
    "    if showplot:\n",
    "        sns.histplot(sim_ts_list)\n",
    "        print(sim_ts_list)\n",
    "    # calc p-val\n",
    "    if stat == 'NMA':\n",
    "        p_val = (np.array(sim_ts_list) <= obs_ts).mean()\n",
    "    else:\n",
    "        p_val = (np.array(sim_ts_list) >= obs_ts).mean()\n",
    "    return p_val\n",
    "sh_df = metrics_managers[metrics_managers['Team'] == 'Southampton']\n",
    "manager_perm_test(sh_df, ('Mark Hughes', 'Mauricio Pellegrino'), 'NMA') # Should be around 0.99"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll loop through all manager-team pairs and calculate the p-values for all of them. Let's narrow it down to one team, Southampton, for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Claude Puel', 'Ronald Koeman'),\n",
       " ('Ralph Hasenhüttl', 'Kelvin Davis'),\n",
       " ('Mauricio Pellegrino', 'Claude Puel'),\n",
       " ('Mark Hughes', 'Mauricio Pellegrino')]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh_man_pred = list(zip(sh_df['Manager'].unique(), sh_df['Predecessor'].unique()))\n",
    "sh_man_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 0.419, 0.822]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_vals = [manager_perm_test(sh_df, man_pred, 'PMA') for man_pred in sh_man_pred]\n",
    "p_vals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "None of the managers in Southampton had a bounce, by the looks of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 0.798, 0.192, 0.494, 0.884]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_pvals(team_df, stat):\n",
    "    team_man_pred = list(zip(team_df['Manager'].unique(), team_df['Predecessor'].unique()))\n",
    "    p_vals = [manager_perm_test(team_df, man_pred, stat) for man_pred in team_man_pred]\n",
    "    return p_vals\n",
    "get_pvals(tot_df, 'NMA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nor did any managers in tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n"
     ]
    }
   ],
   "source": [
    "nma_pvals = metrics_managers.groupby('Team').apply(lambda df: get_pvals(df, 'NMA'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 0.976, 0.897, 0.043]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nma_pvals['Arsenal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Team\n",
       "Arsenal      0.053\n",
       "Chelsea      0.039\n",
       "Everton      0.093\n",
       "Everton      0.091\n",
       "Newcastle    0.087\n",
       "Watford      0.098\n",
       "dtype: object"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nma_pvals_ser = all_pvals.explode()\n",
    "nma_pvals_ser[nma_pvals_ser<0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Arsène Wenger', 'Pat Rice'),\n",
       " ('Mikel Arteta', 'Freddie Ljungberg'),\n",
       " ('Unai Emery', 'Arsène Wenger'),\n",
       " ('Freddie Ljungberg', 'Unai Emery')]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_df = metrics_managers[metrics_managers['Team']=='Arsenal']\n",
    "team_man_pred = list(zip(team_df['Manager'].unique(), team_df['Predecessor'].unique()))\n",
    "team_man_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frank Lampard\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_met_over_time('Thomas Tuchel', 'Chelsea', specific_met='FoulsCommited');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n"
     ]
    }
   ],
   "source": [
    "pma_pvals = metrics_managers.groupby('Team').apply(lambda df: get_pvals(df, 'PMA'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Team\n",
       "Arsenal      0.053\n",
       "Chelsea      0.039\n",
       "Everton      0.093\n",
       "Everton      0.091\n",
       "Newcastle    0.087\n",
       "Watford      0.098\n",
       "dtype: object"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pma_pvals_ser = all_pvals.explode()\n",
    "pma_pvals_ser[pma_pvals_ser<0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:985: RuntimeWarning: invalid value encountered in true_divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:990: RuntimeWarning: invalid value encountered in true_divide\n",
      "  T = new_sum / new_sample_count\n",
      "/Users/tzhang04/miniforge3/envs/dsc80/lib/python3.8/site-packages/sklearn/utils/extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide\n",
      "  new_unnormalized_variance -= correction ** 2 / new_sample_count\n"
     ]
    }
   ],
   "source": [
    "pwbr_pvals = metrics_managers.groupby('Team').apply(lambda df: get_pvals(df, 'PWBR'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Team\n",
       "Arsenal      0.053\n",
       "Chelsea      0.039\n",
       "Everton      0.093\n",
       "Everton      0.091\n",
       "Newcastle    0.087\n",
       "Watford      0.098\n",
       "dtype: object"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwbr_pvals_ser = all_pvals.explode()\n",
    "pwbr_pvals_ser[pma_pvals_ser<0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dsc80",
   "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": "3.8.18"
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