FIFA World Cup 2018.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "# FIFA World Cup 2018\n",
    "\n",
    "I'm going to treat the entire country as a black box entity that somehow just finds players that fairly consistently do well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stephen/miniconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/stephen/miniconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n",
      "/home/stephen/miniconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/stephen/miniconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import itertools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "from rosey.helpers import suppress_warnings\n",
    "\n",
    "import matplotlib.pyplot as graph\n",
    "import seaborn as sns\n",
    "\n",
    "from pprint import pprint\n",
    "\n",
    "import trueskill\n",
    "from trueskill import Rating, rate_1vs1, quality_1vs1, BETA\n",
    "from IPython.display import display\n",
    "\n",
    "graph.style.use('fivethirtyeight')\n",
    "suppress_warnings()\n",
    "\n",
    "\n",
    "def win_prob(team_a, team_b):\n",
    "    delta_mu = team_a.mu - team_b.mu\n",
    "    sum_std = team_a.sigma ** 2 + team_b.sigma ** 2\n",
    "    n_players = 2\n",
    "    return trueskill.global_env().cdf(delta_mu / (math.sqrt(n_players * (BETA ** 2) + sum_std)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>City</th>\n",
       "      <th>Home Team Name</th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Away Team Name</th>\n",
       "      <th>Win conditions</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Half-time Home Goals</th>\n",
       "      <th>Half-time Away Goals</th>\n",
       "      <th>Referee</th>\n",
       "      <th>Assistant 1</th>\n",
       "      <th>Assistant 2</th>\n",
       "      <th>RoundID</th>\n",
       "      <th>MatchID</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>13 Jul 1930 - 15:00</td>\n",
       "      <td>Group 1</td>\n",
       "      <td>Pocitos</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>France</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td></td>\n",
       "      <td>4444.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>LOMBARDI Domingo (URU)</td>\n",
       "      <td>CRISTOPHE Henry (BEL)</td>\n",
       "      <td>REGO Gilberto (BRA)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>FRA</td>\n",
       "      <td>MEX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>13 Jul 1930 - 15:00</td>\n",
       "      <td>Group 4</td>\n",
       "      <td>Parque Central</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>USA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Belgium</td>\n",
       "      <td></td>\n",
       "      <td>18346.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MACIAS Jose (ARG)</td>\n",
       "      <td>MATEUCCI Francisco (URU)</td>\n",
       "      <td>WARNKEN Alberto (CHI)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>BEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>14 Jul 1930 - 12:45</td>\n",
       "      <td>Group 2</td>\n",
       "      <td>Parque Central</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td></td>\n",
       "      <td>24059.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TEJADA Anibal (URU)</td>\n",
       "      <td>VALLARINO Ricardo (URU)</td>\n",
       "      <td>BALWAY Thomas (FRA)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1093.0</td>\n",
       "      <td>YUG</td>\n",
       "      <td>BRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>14 Jul 1930 - 14:50</td>\n",
       "      <td>Group 3</td>\n",
       "      <td>Pocitos</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Romania</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Peru</td>\n",
       "      <td></td>\n",
       "      <td>2549.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WARNKEN Alberto (CHI)</td>\n",
       "      <td>LANGENUS Jean (BEL)</td>\n",
       "      <td>MATEUCCI Francisco (URU)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>ROU</td>\n",
       "      <td>PER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>15 Jul 1930 - 16:00</td>\n",
       "      <td>Group 1</td>\n",
       "      <td>Parque Central</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>France</td>\n",
       "      <td></td>\n",
       "      <td>23409.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>REGO Gilberto (BRA)</td>\n",
       "      <td>SAUCEDO Ulises (BOL)</td>\n",
       "      <td>RADULESCU Constantin (ROU)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>ARG</td>\n",
       "      <td>FRA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year              Datetime    Stage         Stadium         City  \\\n",
       "0  1930.0  13 Jul 1930 - 15:00   Group 1         Pocitos  Montevideo    \n",
       "1  1930.0  13 Jul 1930 - 15:00   Group 4  Parque Central  Montevideo    \n",
       "2  1930.0  14 Jul 1930 - 12:45   Group 2  Parque Central  Montevideo    \n",
       "3  1930.0  14 Jul 1930 - 14:50   Group 3         Pocitos  Montevideo    \n",
       "4  1930.0  15 Jul 1930 - 16:00   Group 1  Parque Central  Montevideo    \n",
       "\n",
       "  Home Team Name  Home Team Goals  Away Team Goals Away Team Name  \\\n",
       "0         France              4.0              1.0         Mexico   \n",
       "1            USA              3.0              0.0        Belgium   \n",
       "2     Yugoslavia              2.0              1.0         Brazil   \n",
       "3        Romania              3.0              1.0           Peru   \n",
       "4      Argentina              1.0              0.0         France   \n",
       "\n",
       "  Win conditions  Attendance  Half-time Home Goals  Half-time Away Goals  \\\n",
       "0                     4444.0                   3.0                   0.0   \n",
       "1                    18346.0                   2.0                   0.0   \n",
       "2                    24059.0                   2.0                   0.0   \n",
       "3                     2549.0                   1.0                   0.0   \n",
       "4                    23409.0                   0.0                   0.0   \n",
       "\n",
       "                  Referee               Assistant 1  \\\n",
       "0  LOMBARDI Domingo (URU)     CRISTOPHE Henry (BEL)   \n",
       "1       MACIAS Jose (ARG)  MATEUCCI Francisco (URU)   \n",
       "2     TEJADA Anibal (URU)   VALLARINO Ricardo (URU)   \n",
       "3   WARNKEN Alberto (CHI)       LANGENUS Jean (BEL)   \n",
       "4     REGO Gilberto (BRA)      SAUCEDO Ulises (BOL)   \n",
       "\n",
       "                  Assistant 2  RoundID  MatchID    a    b  \n",
       "0         REGO Gilberto (BRA)    201.0   1096.0  FRA  MEX  \n",
       "1       WARNKEN Alberto (CHI)    201.0   1090.0  USA  BEL  \n",
       "2         BALWAY Thomas (FRA)    201.0   1093.0  YUG  BRA  \n",
       "3    MATEUCCI Francisco (URU)    201.0   1098.0  ROU  PER  \n",
       "4  RADULESCU Constantin (ROU)    201.0   1085.0  ARG  FRA  "
      ]
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>City</th>\n",
       "      <th>Home Team Name</th>\n",
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       "      <th>Half-time Away Goals</th>\n",
       "      <th>Referee</th>\n",
       "      <th>Assistant 1</th>\n",
       "      <th>Assistant 2</th>\n",
       "      <th>RoundID</th>\n",
       "      <th>MatchID</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>05 Jul 2014 - 17:00</td>\n",
       "      <td>Quarter-finals</td>\n",
       "      <td>Arena Fonte Nova</td>\n",
       "      <td>Salvador</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>Netherlands win on penalties (4 - 3)</td>\n",
       "      <td>51179.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Ravshan IRMATOV (UZB)</td>\n",
       "      <td>RASULOV Abduxamidullo (UZB)</td>\n",
       "      <td>KOCHKAROV Bakhadyr (KGZ)</td>\n",
       "      <td>255953.0</td>\n",
       "      <td>300186488.0</td>\n",
       "      <td>NED</td>\n",
       "      <td>CRC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>08 Jul 2014 - 17:00</td>\n",
       "      <td>Semi-finals</td>\n",
       "      <td>Estadio Mineirao</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td></td>\n",
       "      <td>58141.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>RODRIGUEZ Marco (MEX)</td>\n",
       "      <td>TORRENTERA Marvin (MEX)</td>\n",
       "      <td>QUINTERO Marcos (MEX)</td>\n",
       "      <td>255955.0</td>\n",
       "      <td>300186474.0</td>\n",
       "      <td>BRA</td>\n",
       "      <td>GER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>09 Jul 2014 - 17:00</td>\n",
       "      <td>Semi-finals</td>\n",
       "      <td>Arena de Sao Paulo</td>\n",
       "      <td>Sao Paulo</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentina win on penalties (2 - 4)</td>\n",
       "      <td>63267.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C�neyt �AKIR (TUR)</td>\n",
       "      <td>DURAN Bahattin (TUR)</td>\n",
       "      <td>ONGUN Tarik (TUR)</td>\n",
       "      <td>255955.0</td>\n",
       "      <td>300186490.0</td>\n",
       "      <td>NED</td>\n",
       "      <td>ARG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>12 Jul 2014 - 17:00</td>\n",
       "      <td>Play-off for third place</td>\n",
       "      <td>Estadio Nacional</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td></td>\n",
       "      <td>68034.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>HAIMOUDI Djamel (ALG)</td>\n",
       "      <td>ACHIK Redouane (MAR)</td>\n",
       "      <td>ETCHIALI Abdelhak (ALG)</td>\n",
       "      <td>255957.0</td>\n",
       "      <td>300186502.0</td>\n",
       "      <td>BRA</td>\n",
       "      <td>NED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>13 Jul 2014 - 16:00</td>\n",
       "      <td>Final</td>\n",
       "      <td>Estadio do Maracana</td>\n",
       "      <td>Rio De Janeiro</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Germany win after extra time</td>\n",
       "      <td>74738.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nicola RIZZOLI (ITA)</td>\n",
       "      <td>Renato FAVERANI (ITA)</td>\n",
       "      <td>Andrea STEFANI (ITA)</td>\n",
       "      <td>255959.0</td>\n",
       "      <td>300186501.0</td>\n",
       "      <td>GER</td>\n",
       "      <td>ARG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       Year              Datetime                     Stage  \\\n",
       "847  2014.0  05 Jul 2014 - 17:00             Quarter-finals   \n",
       "848  2014.0  08 Jul 2014 - 17:00                Semi-finals   \n",
       "849  2014.0  09 Jul 2014 - 17:00                Semi-finals   \n",
       "850  2014.0  12 Jul 2014 - 17:00   Play-off for third place   \n",
       "851  2014.0  13 Jul 2014 - 16:00                      Final   \n",
       "\n",
       "                 Stadium             City Home Team Name  Home Team Goals  \\\n",
       "847     Arena Fonte Nova        Salvador     Netherlands              0.0   \n",
       "848     Estadio Mineirao  Belo Horizonte          Brazil              1.0   \n",
       "849   Arena de Sao Paulo       Sao Paulo     Netherlands              0.0   \n",
       "850     Estadio Nacional        Brasilia          Brazil              0.0   \n",
       "851  Estadio do Maracana  Rio De Janeiro         Germany              1.0   \n",
       "\n",
       "     Away Team Goals Away Team Name                         Win conditions  \\\n",
       "847              0.0     Costa Rica  Netherlands win on penalties (4 - 3)    \n",
       "848              7.0        Germany                                          \n",
       "849              0.0      Argentina    Argentina win on penalties (2 - 4)    \n",
       "850              3.0    Netherlands                                          \n",
       "851              0.0      Argentina          Germany win after extra time    \n",
       "\n",
       "     Attendance  Half-time Home Goals  Half-time Away Goals  \\\n",
       "847     51179.0                   0.0                   0.0   \n",
       "848     58141.0                   0.0                   5.0   \n",
       "849     63267.0                   0.0                   0.0   \n",
       "850     68034.0                   0.0                   2.0   \n",
       "851     74738.0                   0.0                   0.0   \n",
       "\n",
       "                   Referee                  Assistant 1  \\\n",
       "847  Ravshan IRMATOV (UZB)  RASULOV Abduxamidullo (UZB)   \n",
       "848  RODRIGUEZ Marco (MEX)      TORRENTERA Marvin (MEX)   \n",
       "849     C�neyt �AKIR (TUR)         DURAN Bahattin (TUR)   \n",
       "850  HAIMOUDI Djamel (ALG)         ACHIK Redouane (MAR)   \n",
       "851   Nicola RIZZOLI (ITA)        Renato FAVERANI (ITA)   \n",
       "\n",
       "                  Assistant 2   RoundID      MatchID    a    b  \n",
       "847  KOCHKAROV Bakhadyr (KGZ)  255953.0  300186488.0  NED  CRC  \n",
       "848     QUINTERO Marcos (MEX)  255955.0  300186474.0  BRA  GER  \n",
       "849         ONGUN Tarik (TUR)  255955.0  300186490.0  NED  ARG  \n",
       "850   ETCHIALI Abdelhak (ALG)  255957.0  300186502.0  BRA  NED  \n",
       "851      Andrea STEFANI (ITA)  255959.0  300186501.0  GER  ARG  "
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(850, 20)\n"
     ]
    }
   ],
   "source": [
    "past_matches = pd.read_csv('data/fifa-2018/fifa-world-cup/WorldCupMatches.csv')\n",
    "past_matches.dropna(inplace=True)\n",
    "\n",
    "past_matches.rename(columns={'Home Team Initials': 'a', 'Away Team Initials': 'b'}, inplace=True)\n",
    "\n",
    "display(past_matches.head())\n",
    "display(past_matches.tail())\n",
    "print(past_matches.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turns out the data is already in out so we don't need to parse to datetime to sort them.\n",
    "\n",
    "Now it's time to make all the countries and see who is the best today!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82\n",
      "['HAI', 'UAE', 'GHA', 'YUG', 'JPN', 'ISR', 'URU', 'NZL', 'DEN', 'CUB', 'KUW', 'BIH', 'NGA', 'TCH', 'RUS', 'ENG', 'TRI', 'MAR', 'WAL', 'SVK', 'SLV', 'SCO', 'CHN', 'GDR', 'TUR', 'AUS', 'GER', 'CMR', 'SEN', 'COL', 'TOG', 'CIV', 'ITA', 'CRO', 'RSA', 'ROU', 'KOR', 'PER', 'EGY', 'ZAI', 'FRA', 'URS', 'MEX', 'NIR', 'SVN', 'CAN', 'ARG', 'HON', 'CHI', 'PRK', 'NED', 'AUT', 'IRL', 'ANG', 'CZE', 'INH', 'ESP', 'IRQ', 'BUL', 'NOR', 'BEL', 'POL', 'GRE', 'JAM', 'SCG', 'SUI', 'USA', 'BRA', 'BOL', 'POR', 'CRC', 'SRB', 'ALG', 'ECU', 'FRG', 'IRN', 'SWE', 'PAR', 'TUN', 'HUN', 'UKR', 'KSA']\n"
     ]
    }
   ],
   "source": [
    "team_names = set(past_matches['a'].tolist() + past_matches['b'].tolist())\n",
    "team_names = list(team_names)\n",
    "\n",
    "print(len(team_names))\n",
    "print(team_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialise Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ALG': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'ANG': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'ARG': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'AUS': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'AUT': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'BEL': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'BIH': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'BOL': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'BRA': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'BUL': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'CAN': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'CHI': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'CHN': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'CIV': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'CMR': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'COL': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'CRC': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'CRO': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'CUB': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'CZE': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'DEN': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'ECU': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'EGY': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'ENG': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'ESP': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'FRA': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'FRG': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'GDR': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'GER': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'GHA': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'GRE': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'HAI': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'HON': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'HUN': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'INH': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'IRL': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'IRN': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'IRQ': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'ISR': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'ITA': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'JAM': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'JPN': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'KOR': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'KSA': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'KUW': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'MAR': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'MEX': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'NED': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'NGA': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'NIR': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'NOR': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'NZL': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'PAR': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'PER': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'POL': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'POR': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'PRK': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'ROU': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'RSA': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'RUS': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'SCG': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'SCO': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'SEN': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'SLV': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'SRB': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'SUI': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'SVK': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'SVN': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'SWE': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'TCH': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'TOG': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'TRI': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'TUN': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'TUR': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'UAE': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'UKR': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'URS': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'URU': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'USA': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'WAL': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'YUG': trueskill.Rating(mu=25.000, sigma=8.333),\n",
      " 'ZAI': trueskill.Rating(mu=25.000, sigma=8.333)}\n"
     ]
    }
   ],
   "source": [
    "teams = {name: Rating() for name in team_names}\n",
    "pprint(teams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ALG': trueskill.Rating(mu=25.946, sigma=2.089),\n",
      " 'ANG': trueskill.Rating(mu=23.741, sigma=3.727),\n",
      " 'ARG': trueskill.Rating(mu=31.319, sigma=0.998),\n",
      " 'AUS': trueskill.Rating(mu=25.524, sigma=2.308),\n",
      " 'AUT': trueskill.Rating(mu=29.384, sigma=1.511),\n",
      " 'BEL': trueskill.Rating(mu=26.858, sigma=1.299),\n",
      " 'BIH': trueskill.Rating(mu=22.737, sigma=4.629),\n",
      " 'BOL': trueskill.Rating(mu=18.028, sigma=4.107),\n",
      " 'BRA': trueskill.Rating(mu=33.314, sigma=0.941),\n",
      " 'BUL': trueskill.Rating(mu=24.811, sigma=1.647),\n",
      " 'CAN': trueskill.Rating(mu=18.105, sigma=5.370),\n",
      " 'CHI': trueskill.Rating(mu=29.180, sigma=1.408),\n",
      " 'CHN': trueskill.Rating(mu=17.663, sigma=5.469),\n",
      " 'CIV': trueskill.Rating(mu=25.217, sigma=2.746),\n",
      " 'CMR': trueskill.Rating(mu=26.439, sigma=1.552),\n",
      " 'COL': trueskill.Rating(mu=27.894, sigma=1.834),\n",
      " 'CRC': trueskill.Rating(mu=28.331, sigma=1.810),\n",
      " 'CRO': trueskill.Rating(mu=28.022, sigma=1.995),\n",
      " 'CUB': trueskill.Rating(mu=24.897, sigma=4.662),\n",
      " 'CZE': trueskill.Rating(mu=23.701, sigma=4.919),\n",
      " 'DEN': trueskill.Rating(mu=30.004, sigma=2.006),\n",
      " 'ECU': trueskill.Rating(mu=28.355, sigma=2.471),\n",
      " 'EGY': trueskill.Rating(mu=26.160, sigma=3.654),\n",
      " 'ENG': trueskill.Rating(mu=30.498, sigma=1.043),\n",
      " 'ESP': trueskill.Rating(mu=29.848, sigma=1.109),\n",
      " 'FRA': trueskill.Rating(mu=30.436, sigma=1.081),\n",
      " 'FRG': trueskill.Rating(mu=32.862, sigma=1.087),\n",
      " 'GDR': trueskill.Rating(mu=29.520, sigma=2.940),\n",
      " 'GER': trueskill.Rating(mu=33.474, sigma=1.242),\n",
      " 'GHA': trueskill.Rating(mu=27.341, sigma=2.258),\n",
      " 'GRE': trueskill.Rating(mu=24.691, sigma=2.396),\n",
      " 'HAI': trueskill.Rating(mu=18.389, sigma=5.506),\n",
      " 'HON': trueskill.Rating(mu=23.216, sigma=2.436),\n",
      " 'HUN': trueskill.Rating(mu=28.187, sigma=1.605),\n",
      " 'INH': trueskill.Rating(mu=20.328, sigma=6.919),\n",
      " 'IRL': trueskill.Rating(mu=28.442, sigma=1.883),\n",
      " 'IRN': trueskill.Rating(mu=21.470, sigma=2.430),\n",
      " 'IRQ': trueskill.Rating(mu=15.806, sigma=4.996),\n",
      " 'ISR': trueskill.Rating(mu=28.629, sigma=3.700),\n",
      " 'ITA': trueskill.Rating(mu=31.357, sigma=0.960),\n",
      " 'JAM': trueskill.Rating(mu=22.392, sigma=5.548),\n",
      " 'JPN': trueskill.Rating(mu=25.118, sigma=1.920),\n",
      " 'KOR': trueskill.Rating(mu=24.547, sigma=1.469),\n",
      " 'KSA': trueskill.Rating(mu=22.978, sigma=2.327),\n",
      " 'KUW': trueskill.Rating(mu=23.408, sigma=3.944),\n",
      " 'MAR': trueskill.Rating(mu=25.887, sigma=2.153),\n",
      " 'MEX': trueskill.Rating(mu=27.653, sigma=1.194),\n",
      " 'NED': trueskill.Rating(mu=32.172, sigma=1.096),\n",
      " 'NGA': trueskill.Rating(mu=24.234, sigma=1.893),\n",
      " 'NIR': trueskill.Rating(mu=28.502, sigma=2.046),\n",
      " 'NOR': trueskill.Rating(mu=28.448, sigma=2.554),\n",
      " 'NZL': trueskill.Rating(mu=25.322, sigma=3.232),\n",
      " 'PAR': trueskill.Rating(mu=26.314, sigma=1.527),\n",
      " 'PER': trueskill.Rating(mu=27.099, sigma=2.313),\n",
      " 'POL': trueskill.Rating(mu=30.024, sigma=1.455),\n",
      " 'POR': trueskill.Rating(mu=29.676, sigma=1.533),\n",
      " 'PRK': trueskill.Rating(mu=25.839, sigma=3.015),\n",
      " 'ROU': trueskill.Rating(mu=29.086, sigma=1.783),\n",
      " 'RSA': trueskill.Rating(mu=27.088, sigma=2.397),\n",
      " 'RUS': trueskill.Rating(mu=24.059, sigma=2.530),\n",
      " 'SCG': trueskill.Rating(mu=16.972, sigma=5.288),\n",
      " 'SCO': trueskill.Rating(mu=26.338, sigma=1.721),\n",
      " 'SEN': trueskill.Rating(mu=30.505, sigma=3.007),\n",
      " 'SLV': trueskill.Rating(mu=13.140, sigma=4.697),\n",
      " 'SRB': trueskill.Rating(mu=26.336, sigma=4.264),\n",
      " 'SUI': trueskill.Rating(mu=26.967, sigma=1.424),\n",
      " 'SVK': trueskill.Rating(mu=25.264, sigma=3.717),\n",
      " 'SVN': trueskill.Rating(mu=23.224, sigma=3.319),\n",
      " 'SWE': trueskill.Rating(mu=29.726, sigma=1.210),\n",
      " 'TCH': trueskill.Rating(mu=28.627, sigma=1.556),\n",
      " 'TOG': trueskill.Rating(mu=16.991, sigma=5.242),\n",
      " 'TRI': trueskill.Rating(mu=23.961, sigma=3.798),\n",
      " 'TUN': trueskill.Rating(mu=24.332, sigma=2.142),\n",
      " 'TUR': trueskill.Rating(mu=29.459, sigma=2.897),\n",
      " 'UAE': trueskill.Rating(mu=17.640, sigma=5.556),\n",
      " 'UKR': trueskill.Rating(mu=27.016, sigma=3.382),\n",
      " 'URS': trueskill.Rating(mu=29.532, sigma=1.475),\n",
      " 'URU': trueskill.Rating(mu=28.444, sigma=1.157),\n",
      " 'USA': trueskill.Rating(mu=25.130, sigma=1.460),\n",
      " 'WAL': trueskill.Rating(mu=29.285, sigma=3.009),\n",
      " 'YUG': trueskill.Rating(mu=29.247, sigma=1.410),\n",
      " 'ZAI': trueskill.Rating(mu=16.782, sigma=5.649)}\n",
      "CPU times: user 274 ms, sys: 1.51 ms, total: 276 ms\n",
      "Wall time: 272 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Update skill using bayesian stats\n",
    "for i, row in past_matches.iterrows():\n",
    "    a, b = row['a'], row['b']\n",
    "    \n",
    "    a_goals = row['Home Team Goals']\n",
    "    b_goals = row['Away Team Goals']\n",
    "    \n",
    "    if a_goals > b_goals:\n",
    "        # A Wins\n",
    "        winner_rating, loser_rating = rate_1vs1(teams[a], teams[b])\n",
    "        teams[a] = winner_rating\n",
    "        teams[b] = loser_rating\n",
    "        \n",
    "    elif a_goals < b_goals:\n",
    "        # B Wins\n",
    "        winner_rating, loser_rating = rate_1vs1(teams[b], teams[a])\n",
    "        teams[b] = winner_rating\n",
    "        teams[a] = loser_rating\n",
    "        \n",
    "    else:\n",
    "        # Draw\n",
    "        a_new_rating, b_new_rating = rate_1vs1(teams[a], teams[b], drawn=True)\n",
    "        teams[a] = a_new_rating\n",
    "        teams[b] = b_new_rating\n",
    "        \n",
    "pprint(teams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>skill</th>\n",
       "      <th>sd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>GER</td>\n",
       "      <td>33.474017</td>\n",
       "      <td>1.241809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>BRA</td>\n",
       "      <td>33.314170</td>\n",
       "      <td>0.940663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>FRG</td>\n",
       "      <td>32.862492</td>\n",
       "      <td>1.087475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>NED</td>\n",
       "      <td>32.172358</td>\n",
       "      <td>1.095652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>ITA</td>\n",
       "      <td>31.357034</td>\n",
       "      <td>0.960135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ARG</td>\n",
       "      <td>31.318842</td>\n",
       "      <td>0.998126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SEN</td>\n",
       "      <td>30.504668</td>\n",
       "      <td>3.006538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ENG</td>\n",
       "      <td>30.498369</td>\n",
       "      <td>1.042673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>FRA</td>\n",
       "      <td>30.435863</td>\n",
       "      <td>1.081231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>POL</td>\n",
       "      <td>30.024109</td>\n",
       "      <td>1.454822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DEN</td>\n",
       "      <td>30.003865</td>\n",
       "      <td>2.005684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>ESP</td>\n",
       "      <td>29.848298</td>\n",
       "      <td>1.108826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>SWE</td>\n",
       "      <td>29.726163</td>\n",
       "      <td>1.209612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>POR</td>\n",
       "      <td>29.676339</td>\n",
       "      <td>1.533403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>URS</td>\n",
       "      <td>29.532158</td>\n",
       "      <td>1.474863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>GDR</td>\n",
       "      <td>29.520064</td>\n",
       "      <td>2.940471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>TUR</td>\n",
       "      <td>29.459393</td>\n",
       "      <td>2.896960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>AUT</td>\n",
       "      <td>29.383545</td>\n",
       "      <td>1.511200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>WAL</td>\n",
       "      <td>29.285143</td>\n",
       "      <td>3.008784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YUG</td>\n",
       "      <td>29.247440</td>\n",
       "      <td>1.409533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>CHI</td>\n",
       "      <td>29.179607</td>\n",
       "      <td>1.408258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ROU</td>\n",
       "      <td>29.085880</td>\n",
       "      <td>1.783132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ISR</td>\n",
       "      <td>28.629500</td>\n",
       "      <td>3.699766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>TCH</td>\n",
       "      <td>28.626864</td>\n",
       "      <td>1.555829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>NIR</td>\n",
       "      <td>28.502422</td>\n",
       "      <td>2.045768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>NOR</td>\n",
       "      <td>28.448013</td>\n",
       "      <td>2.553517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>URU</td>\n",
       "      <td>28.444171</td>\n",
       "      <td>1.157076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>IRL</td>\n",
       "      <td>28.442241</td>\n",
       "      <td>1.882991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>ECU</td>\n",
       "      <td>28.354944</td>\n",
       "      <td>2.470908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>CRC</td>\n",
       "      <td>28.331497</td>\n",
       "      <td>1.809707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>USA</td>\n",
       "      <td>25.129562</td>\n",
       "      <td>1.459717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JPN</td>\n",
       "      <td>25.118354</td>\n",
       "      <td>1.920348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CUB</td>\n",
       "      <td>24.896930</td>\n",
       "      <td>4.661853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>BUL</td>\n",
       "      <td>24.811221</td>\n",
       "      <td>1.646624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>GRE</td>\n",
       "      <td>24.690994</td>\n",
       "      <td>2.395969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>KOR</td>\n",
       "      <td>24.546716</td>\n",
       "      <td>1.468574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>TUN</td>\n",
       "      <td>24.331920</td>\n",
       "      <td>2.142096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NGA</td>\n",
       "      <td>24.233583</td>\n",
       "      <td>1.893264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RUS</td>\n",
       "      <td>24.058980</td>\n",
       "      <td>2.529906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TRI</td>\n",
       "      <td>23.960664</td>\n",
       "      <td>3.797794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>ANG</td>\n",
       "      <td>23.741039</td>\n",
       "      <td>3.727014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>CZE</td>\n",
       "      <td>23.701345</td>\n",
       "      <td>4.919358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KUW</td>\n",
       "      <td>23.407868</td>\n",
       "      <td>3.944490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>SVN</td>\n",
       "      <td>23.224251</td>\n",
       "      <td>3.318507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>HON</td>\n",
       "      <td>23.216256</td>\n",
       "      <td>2.436153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>KSA</td>\n",
       "      <td>22.978363</td>\n",
       "      <td>2.327004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BIH</td>\n",
       "      <td>22.737497</td>\n",
       "      <td>4.628545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>JAM</td>\n",
       "      <td>22.391843</td>\n",
       "      <td>5.547805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>IRN</td>\n",
       "      <td>21.469765</td>\n",
       "      <td>2.430009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>INH</td>\n",
       "      <td>20.328498</td>\n",
       "      <td>6.919006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HAI</td>\n",
       "      <td>18.388769</td>\n",
       "      <td>5.505845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>CAN</td>\n",
       "      <td>18.104559</td>\n",
       "      <td>5.369542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>BOL</td>\n",
       "      <td>18.027578</td>\n",
       "      <td>4.107244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CHN</td>\n",
       "      <td>17.662908</td>\n",
       "      <td>5.468638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UAE</td>\n",
       "      <td>17.639553</td>\n",
       "      <td>5.556096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>TOG</td>\n",
       "      <td>16.990756</td>\n",
       "      <td>5.241945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>SCG</td>\n",
       "      <td>16.972302</td>\n",
       "      <td>5.288386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>ZAI</td>\n",
       "      <td>16.781612</td>\n",
       "      <td>5.648625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>IRQ</td>\n",
       "      <td>15.806358</td>\n",
       "      <td>4.996435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SLV</td>\n",
       "      <td>13.140289</td>\n",
       "      <td>4.696748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   name      skill        sd\n",
       "26  GER  33.474017  1.241809\n",
       "67  BRA  33.314170  0.940663\n",
       "74  FRG  32.862492  1.087475\n",
       "50  NED  32.172358  1.095652\n",
       "32  ITA  31.357034  0.960135\n",
       "46  ARG  31.318842  0.998126\n",
       "28  SEN  30.504668  3.006538\n",
       "15  ENG  30.498369  1.042673\n",
       "40  FRA  30.435863  1.081231\n",
       "61  POL  30.024109  1.454822\n",
       "8   DEN  30.003865  2.005684\n",
       "56  ESP  29.848298  1.108826\n",
       "76  SWE  29.726163  1.209612\n",
       "69  POR  29.676339  1.533403\n",
       "41  URS  29.532158  1.474863\n",
       "23  GDR  29.520064  2.940471\n",
       "24  TUR  29.459393  2.896960\n",
       "51  AUT  29.383545  1.511200\n",
       "18  WAL  29.285143  3.008784\n",
       "3   YUG  29.247440  1.409533\n",
       "48  CHI  29.179607  1.408258\n",
       "35  ROU  29.085880  1.783132\n",
       "5   ISR  28.629500  3.699766\n",
       "13  TCH  28.626864  1.555829\n",
       "43  NIR  28.502422  2.045768\n",
       "59  NOR  28.448013  2.553517\n",
       "6   URU  28.444171  1.157076\n",
       "52  IRL  28.442241  1.882991\n",
       "73  ECU  28.354944  2.470908\n",
       "70  CRC  28.331497  1.809707\n",
       "..  ...        ...       ...\n",
       "66  USA  25.129562  1.459717\n",
       "4   JPN  25.118354  1.920348\n",
       "9   CUB  24.896930  4.661853\n",
       "58  BUL  24.811221  1.646624\n",
       "62  GRE  24.690994  2.395969\n",
       "36  KOR  24.546716  1.468574\n",
       "78  TUN  24.331920  2.142096\n",
       "12  NGA  24.233583  1.893264\n",
       "14  RUS  24.058980  2.529906\n",
       "16  TRI  23.960664  3.797794\n",
       "53  ANG  23.741039  3.727014\n",
       "54  CZE  23.701345  4.919358\n",
       "10  KUW  23.407868  3.944490\n",
       "44  SVN  23.224251  3.318507\n",
       "47  HON  23.216256  2.436153\n",
       "81  KSA  22.978363  2.327004\n",
       "11  BIH  22.737497  4.628545\n",
       "63  JAM  22.391843  5.547805\n",
       "75  IRN  21.469765  2.430009\n",
       "55  INH  20.328498  6.919006\n",
       "0   HAI  18.388769  5.505845\n",
       "45  CAN  18.104559  5.369542\n",
       "68  BOL  18.027578  4.107244\n",
       "22  CHN  17.662908  5.468638\n",
       "1   UAE  17.639553  5.556096\n",
       "30  TOG  16.990756  5.241945\n",
       "64  SCG  16.972302  5.288386\n",
       "39  ZAI  16.781612  5.648625\n",
       "57  IRQ  15.806358  4.996435\n",
       "20  SLV  13.140289  4.696748\n",
       "\n",
       "[82 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings = pd.DataFrame([(k, v.mu, v.sigma) for k, v in teams.items()], columns=['name', 'skill', 'sd'])\n",
    "\n",
    "display(ratings.sort_values(by='skill', ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Current Updates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Match Quality: 86.7%\n",
      "P(CRO beats FRA): 35.1%\n",
      "P(FRA beats CRO): 64.9%\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaEAAAEGCAYAAADMsSqUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvFvnyVgAAHw5JREFUeJzt3X+QVOW95/H3BwgNK2rUmMgFE/E6UUl0x6uAP1BTxigmLnoTTUDdmIuJMRUrJu6tulpJieGaXZPU5kfVcjfGG1a9GjFRk526S9Z4VcoYNIEogcDI7QEpHHFlIwKXFZoA3/2jz0yapnvmDNPdp7vn86qamj7nPE+f7+nTM99+nvP0cxQRmJmZZWFU1gGYmdnI5SRkZmaZcRIyM7PMOAmZmVlmnITMzCwzY7IOYPv27R6eZ2bW5o488khVWu+WkJmZZcZJyMzMMjMik1A+n886hGFr9WNw/Nlq9fih9Y/B8ReNyCRkZmbNwUnIzMwyk/noODOzdhcR7Ny5k/379/evGzduHNu3b88wquEpj3/UqFFMmDABqeIguKqchMzM6mznzp3kcjnGjh3bvy6XyzFu3LgMoxqe8vj37NnDzp07Ofzww4f0PO6OMzOrs/379x+QgNrR2LFjD2jppeUkZGZmmXESMjMbId544w3mzZtHZ2cnM2bM4Oqrr6anp4fjjjuOmTNnMmPGDD7/+c/zpz/9qb/O888/z0UXXcS0adOYNm0a9913X01j8jUhMzvI+Pk3ArDr6z/MOJL29KGuLeyP/YxSbdoBS2e/e9AyEcF1113H3LlzWbRoEQCrVq1iy5YtTJkyheeee459+/Zx5ZVX8rOf/YxPfvKTvPHGG3zuc5/jwQcfpLOzkzfffJOPf/zjTJw4kQsvvLAmsbslZGYH6EtA1l6effZZxowZw7x58/rXnX766UyaNKl/efTo0Zx55pm8/vrrANx7773MnTuXzs5OAI455hgWLFjA9773vZrF5SRkZoMaP/9GJ6cW193d3Z9Mqtm9ezcrVqzgwx/+MAAvv/zyQXU6Ozvp7u6uWVxOQmZmI9wrr7zCzJkzOfHEE5k8eTIf/OAHgWIXXqXv/Qz1u0ADcRIyM8CtnXZ36qmnsnLlyorb+q4Jvfjii6xYsYIlS5b013nppZcOKLty5UpOOeWUmsXlJGQ2wg2UfJyY2scFF1zAnj17uP/++/vXvfjii7z66qv9y8cddxzz58/nu9/9LgCf/exnefjhh1m1ahUAW7du5c477+RLX/pSzeJyEjIzGwEk8eCDD/LMM8/Q2dnJ2Wefzd13383EiRMPKHf55Zfz9ttvs2zZMo477jjuuecebrnlFqZNm8Yll1zCddddx2WXXVazuDxE28yswZbOfje7d+9u+LQ9EydOrPg9n+eff77/sSR+/etf9y+fd955PPPMM3WLKVVLSNIsSesk9Ui6rcL2myStlrRS0nOSpibrT5C0K1m/UtIPan0AZmbWugZtCUkaDSwEPgL0AssldUXE2pJiP46IHyTlZwPfAWYl29ZHxMDjAs3MbERK0xKaDvRExIaI2AMsBq4oLRARO0oWDwOidiGamVm7SnNNaBLwaslyLzCjvJCkLwK3AmOBi0o2TZH0ErAD+FpE/Krajhp5u9tWv7UutP4xOP5s9cV/cqEw5DrNotniqWbcuHHkcrmD1u/evTuDaGqnPP4dO3awZcuW/uWOjo5BnyNNEqr0raSDWjoRsRBYKOka4GvA9cDrwHsj4k1JZwI/l/SBspbTkAKuhXw+37B91UurH4Pjz1Y+n+f0B79dXKjwz7GaZjrmVjoH27dvP2gQQhYDE2qpUvxHHHEExx9//JCeJ013XC9Q+qyTgc0DlF8MXAkQEYWIeDN5/DtgPfD+IUVoZk3D3xuyWkvTEloOdEiaArwGzAGuKS0gqSMi+trFHwPyyfpjga0RsU/SiUAHsKFWwZuZWTpHH300U6dO7V9+6KGH2LRpE9deey3ve9/72L17N5deeil33XXXAfXmzp3LH//4R5588sm6xDVoEoqIvZJuBp4ARgOLImKNpAXAiojoAm6WdDHwJ+Atil1xABcACyTtBfYBN0XE1nociJlZqxg//0Zy+4NRo2ozB1uaW26MHz+e55577oB1mzZt4pxzzuGRRx5h165dXHDBBVx++eWcffbZAGzbto1Vq1Zx2GGHsXHjRk444YSaxFsq1ZdVI2IJsKRs3R0lj2+pUu8x4LHhBGhmtTV+/o3FwQhDuBZk7W/8+PGcdtpp/bdxAOjq6mLWrFkce+yxPP7449x6660136+n7TEzGwF27drFzJkzmTlzJtdee+1B27dt28b69es599xz+9c99thjfOITn+Cqq67i0UcfrUtcnrbHzIZs/PwbfdfVFlOpOw6KU/ace+659PT08OUvf5n3vOc9AGzZsoUNGzZwzjnnIIkxY8awdu3aA64r1YJbQmZ2SDxSrj2cc845LFu2jGXLlrFo0aL+GbMff/xxtm/fzumnn85pp53Gpk2bePzxx2u+fychMzPjpJNO4itf+Qrf//73gWJX3KOPPsrq1atZvXo1S5cu5bHHan+J30nIbARxy8UGMm/ePJYtW8bGjRvp7e1l2rRp/dtOOOEEDj/8cFasWFHTffqakJlZg+36+g8bPmPCa6+9dtC6888/n/PPP79/efz48XR3dwP0/y717LPP1jwut4TMzCwzTkJmZpYZJyEzM8uMk5CZmWXGScjMrM5GjRrFnj17sg6jrvbs2cOoUUNPKR4dZ2bD0jfs2zMoVDdhwgR27tzJrl27+tft2LGDI444IsOohqc8/lGjRjFhwoQhP4+TkJlZnUni8MMPP2Ddli1bhnwDuGZSq/jdHWdmZplxEjIzs8w4CZmZWWZ8TchsBPCccdas3BIyM7PMOAmZmVlmUiUhSbMkrZPUI+m2CttvkrRa0kpJz0maWrLt9qTeOkmX1jJ4MzNrbYMmIUmjgYXAZcBUYG5pkkn8OCJOi4hO4FvAd5K6U4E5wAeAWcA/JM9nZmaWqiU0HeiJiA0RsQdYDFxRWiAidpQsHgZE8vgKYHFEFCLiFaAneT4zM7NUo+MmAa+WLPcCM8oLSfoicCswFriopO4LZXUnVdtRPp9PEU5tNHJf9dLqx+D46+/kH90FQKHCtkKh0tpDl8Xr0QrnYCDtHn9HR8egz5EmCanCujhoRcRCYKGka4CvAdenrdsnTcC1kM/nG7avemn1Y3D8jZHL5SquLxQKVbcdqka/Hq1yDqpx/EVpuuN6gdIJgiYDmwcovxi48hDrmpnZCJImCS0HOiRNkTSW4kCDrtICkkrT4ceAvjZaFzBHUk7SFKAD+O3wwzYzs3YwaHdcROyVdDPwBDAaWBQRayQtAFZERBdws6SLgT8Bb1HsiiMp9xNgLbAX+GJE7KvTsZiZWYtJNW1PRCwBlpStu6Pk8S0D1P0G8I1DDdDMWoPvK2SHwjMmmJlZZpyEzMwsM05CZmaWGSchMzPLjJOQmZllxje1M2tDWd7EzqPkbCjcEjIzs8w4CZmZWWachMzMLDNOQmZmlhknITMzy4yTkJmZZcZJyMzMMuMkZGZ1MX7+jZl+X8lag5OQmZllxjMmmLURtzys1bglZGZmmXESMjOzzDgJmZlZZlIlIUmzJK2T1CPptgrbb5W0VtIqSU9Jel/Jtn2SViY/XbUM3szMWtugAxMkjQYWAh8BeoHlkroiYm1JsZeAsyLibUlfAL4FfCrZtisiOmsct5mZtYE0LaHpQE9EbIiIPcBi4IrSAhHxTES8nSy+AEyubZhmZtaO0gzRngS8WrLcC8wYoPwNwC9KlsdJWgHsBe6OiJ9Xq5jP51OEUxuN3Fe9tPoxOP7aO7lQSF22MISyw1HP16kZz8FQtHv8HR0dgz5HmiSkCuuiYkHpOuAs4MKS1e+NiM2STgSelrQ6ItZXqp8m4FrI5/MN21e9tPoxOP76yOVyqcoVCoXUZYerXq9Ts56DtBx/UZruuF7g+JLlycDm8kKSLga+CsyOiP6PWBGxOfm9AVgKnDGMeM3MrI2kSULLgQ5JUySNBeYAB4xyk3QGcA/FBLSlZP1RknLJ43cB5wGlAxrMrEaadbaEZo3LmsOg3XERsVfSzcATwGhgUUSskbQAWBERXcC3gQnATyUBbIqI2cCpwD2S9lNMeHeXjaozM7MRLNXccRGxBFhStu6OkscXV6m3DDhtOAGamVn78owJZmaWGSchMzPLjJOQmZllxknIzMwy4yRkZmaZcRIyM7PMOAmZWd2Nn3+jv7RqFaX6npCZNS//c7dW5paQmZllxknIzMwy4yRkZmaZcRIyM7PMOAmZmVlmnITMrGE8VNvKOQmZmVlmnITMzCwzTkJmZpYZJyEzM8uMk5CZmWUmVRKSNEvSOkk9km6rsP1WSWslrZL0lKT3lWy7XlI++bm+lsGbmVlrGzQJSRoNLAQuA6YCcyVNLSv2EnBWRJwOPAp8K6l7NDAfmAFMB+ZLOqp24ZuZWStL0xKaDvRExIaI2AMsBq4oLRARz0TE28niC8Dk5PGlwJMRsTUi3gKeBGbVJnQzM2t1aW7lMAl4tWS5l2LLppobgF8MUHdStYr5fD5FOLXRyH3VS6sfQ7vF/+mVuf7HD3QW6r7/k390FwCHuqdCof4xVlOrc99u76FWM1j8HR0dgz5HmiSkCuuiYkHpOuAs4MKh1oV0AddCPp9v2L7qpdWPoR3i/1z3kQesy/05B/G57nFV6y6d/e6axJAr3eEQFQqFYdUfrlqc+3Z4Dzn+dEmoFzi+ZHkysLm8kKSLga8CF0ZEoaTuh8rqLj2UQM3axYe6tvQ/rlVCMmtVaa4JLQc6JE2RNBaYA3SVFpB0BnAPMDsitpRsegK4RNJRyYCES5J1ZmZmg7eEImKvpJspJo/RwKKIWCNpAbAiIrqAbwMTgJ9KAtgUEbMjYqukv6eYyAAWRMTWuhyJWQP0tWIKhRwZ9maZtY003XFExBJgSdm6O0oeXzxA3UXAokMN0MzM2leqJGRmjTFSrhf13c5h19d/mHEkljVP22NmZplxS8hsAKUtk1Z8frNm5yRk1mJ8Z1JrJ05CZmWapXVSHkc7XyOykcvXhMzMLDNOQmZmlhknITMzy4yTkJmZZcYDE8xaRN9AhQe27eXkd/pP19qDW0JmZpYZf5wyo3mGZZuNNG4JmZlZZtwSshGp1Vs+67bt7X/cyteHPJGpte6712yEeeCp27IOwazm3B1nZmaZcRIyM7PMuDvOrMWVXh+C1r5GZCOPW0JmZpaZVElI0ixJ6yT1SDro6qikCyS9KGmvpKvKtu2TtDL56apV4GZm1voGbbdLGg0sBD4C9ALLJXVFxNqSYpuAzwB/W+EpdkVEZw1iNTOzNpOmJTQd6ImIDRGxB1gMXFFaICI2RsQqYH8dYjSzNue7xY5caa5gTgJeLVnuBWYMYR/jJK0A9gJ3R8TPqxXM5/NDeNrhaeS+6qXVj6HR8X96Za6mz1co7K7p8w0mIt1nvEKhUNNyjXIo7wf/DWRrsPg7OjoGfY40SUgV1kWKen3eGxGbJZ0IPC1pdUSsr1QwTcC1kM/nG7avemn1Y8gi/lx37WZJKBR2k8uNq9nzpSGlG0eUyw3+Z10oFMjlapuUh2uo7wf/DWSrVvGnSUK9wPEly5OBzWl3EBGbk98bJC0FzgAqJiGzWmr1qXlKebYEa1dpPlotBzokTZE0FpgDpBrlJukoSbnk8buA84C1A9cys+FYt21v/49Zsxs0CUXEXuBm4AmgG/hJRKyRtEDSbABJ0yT1AlcD90hak1Q/FVgh6ffAMxSvCTkJmZkZkHLGhIhYAiwpW3dHyePlFLvpyustA04bZoxmZtamPGOCmZllxknIzMwy45kOzdpYK01u6hvcjUzN+440OwTtNCzbbCRwd5yZmWXGScjMzDLjJGRmZpnxNSGzJubpeqzdOQmZjSB9o+UixCnNNX+pjVBOQtbSPBrOrLX5mpCZmWXGScjMmsr4+Tf6TqsjiLvjzEao0tkUmnkmBWtvbgmZmVlmnITMzCwzTkJmZpYZJyEzM8uMr0ZayxkJ3w3yTAk2UrglZGZNyUO1R4ZUSUjSLEnrJPVIOugjmqQLJL0oaa+kq8q2XS8pn/xcX6vAzcys9Q2ahCSNBhYClwFTgbmSppYV2wR8BvhxWd2jgfnADGA6MF/SUcMP28zM2kGaa0LTgZ6I2AAgaTFwBbC2r0BEbEy27S+reynwZERsTbY/CcwCHh525GZWM610G3BrL2neaZOAV0uWeym2bNKoVHdStcL5fD7l0w5fI/dVL61+DIcaf6HQHNM/Fwq76/bcEeWf5xq7j0KhUPf9pzXQ+2Sk/g00i8Hi7+joGPQ50iQhVVgXKeoNuW6agGshn883bF/10urHMNT4S0fE5ZogBxUKu8nlxtX8eftHxam+Y4Yi9qMB9pHLNU9LqNr7ZKT9DTSbWsWf5p3eCxxfsjwZ2Jzy+YdT18zM2lyajzvLgQ5JU4DXgDnANSmf/wngP5cMRrgEuH3IUdqIMxK+C2RmKVpCEbEXuJliQukGfhIRayQtkDQbQNI0Sb3A1cA9ktYkdbcCf08xkS0HFvQNUjAzM0vV8RsRS4AlZevuKHm8nGJXW6W6i4BFw4jRzBrMt3mwRvGMCWZmlhknITNrap6+p705CZmZWWbc2WtmA/JsClZPfjeZNQHfusFGKichaxr+bpDZyONrQmZmlhknIbOMuSsuHY+Qa09OQmZmlhlfEzKzIfFsClZLbgmZmVlm/DHGMuPRcGbmlpCZmWXGScjMWobnkWs/7o4zs0PmKX1suPyOsYbquw5UKOTI5TIOxswy5yRklhF/SdXM14TMzCxDTkJmZpaZVN1xkmYB3wdGA/8YEXeXbc8BDwBnAm8Cn4qIjZJOALqBdUnRFyLiptqEbq3C3wcys2oGTUKSRgMLgY8AvcBySV0Rsbak2A3AWxFxkqQ5wDeBTyXb1kdEZ43jNrMm1KgpfcbPv5GTCwX2331/3fZhjZHmXTId6ImIDQCSFgNXAKVJ6ArgzuTxo8B/k6QaxmnWNjwgwezP0iShScCrJcu9wIxqZSJir6TtwDHJtimSXgJ2AF+LiF9V21E+n08b97A1cl/10irHUChUHotdKOxucCS1dajxR+yvcSSHpt5xvPzWnv7HU/5d1GUfrfI3UE27x9/R0THoc6RJQpVaNOXvqGplXgfeGxFvSjoT+LmkD0TEjko7ShNwLeTz+Ybtq15a6Rhy3QdfEyoUdpPLjcsgmtoYTvxS9uOBIvY3NI5crvZdc4VCoWX+Bipppb/hSmoVf5p3YS9wfMnyZGBztTKSxgBHAlsjohARbwJExO+A9cD7hxu0mRl4Gp92kObjyXKgQ9IU4DVgDnBNWZku4HrgeeAq4OmICEnHUkxG+ySdCHQAG2oWvTUlj4arzNeCzA42aBJKrvHcDDxBcYj2oohYI2kBsCIiuoAfAf8kqQfYSjFRAVwALJC0F9gH3BQRW+txIGZm1npSddRGxBJgSdm6O0oe7waurlDvMeCxYcZoLcCtHxuIJzq1avxOMKszd8PVX991oV1f/2HGkdhQOQnZIXHLx8xqIfuxomZmNmI5CZlZw63btrf/p5Y8XLv1OAmZmVlmfE3IUvN1oKHxgASzwTkJmVmmPHx7ZHN3nJm1FU/l01r8kcOsDtwVd+gadU8iaw4+w1aVrwGZWb05CZlZ0xpOq8izKLQGJyE7gFs/w+NuOLOhcRIyqwEnn/orH0V3wviMArGa8ug4M2trHi3X3NwSMnfBDYNbQNl55W2hXcXWUZrrRb5G1JychEYgJx0zaxZOQiOEE09tLf7VHUjuzW4WQ5l1Yfz8G90aaiJOQm3KSad+HnjqNiLrIMzahJOQmbWdgW4RcfI7x/j6UBNJlYQkzQK+D4wG/jEi7i7bngMeAM4E3gQ+FREbk223AzcA+4AvRcQTNYve3OJpAA8+aF/lo+aclBpv0CQkaTSwEPgI0Assl9QVEWtLit0AvBURJ0maA3wT+JSkqcAc4APAXwD/Iun9EbGv1gfSziolmkIhR67bCagenHTa24A30rtlHgCf/vDdLJ397gZFNLIpYuDebUnnAHdGxKXJ8u0AEfFfSso8kZR5XtIY4P8AxwK3lZYtLddXd/v27e5eNzNrc0ceeaQqrU8zvGcS8GrJcm+yrmKZiNgLbAeOSVnXzMxGqDRJqFL2Km+9VCuTpq6ZmY1QaQYm9ALHlyxPBjZXKdObdMcdCWxNU7daE83MzNpfmpbQcqBD0hRJYykONOgqK9MFXJ88vgp4OooXm7qAOZJykqYAHcBvaxO6mZm1ukGTUHKN52bgCaAb+ElErJG0QNLspNiPgGMk9QC38ucBCWuAnwBrgf8NfLGeI+MkHS/pGUndktZIuiVZf7SkJyXlk99HVal/fVImL+n6SmXqaYD4vy3pZUmrJP1M0jur1N8oabWklZJWNDb6/hiqHcOdkl5LYlsp6aNV6s+StE5Sj6SGD1MbIP5HSmLfKGlllfqZngNJ4yT9VtLvk/i/nqyfIuk3yXv7keQDZaX6tyev/TpJlzY2+gHjfyiJ6Q+SFkl6R5X6+0rOU/mH5bobIP77JL1SEltnlfqZ/g9KYqh2DL8qiX+zpJ9XqT+0cxARbfMDTAT+Knl8OPCvwFTgW8BtyfrbgG9WqHs0sCH5fVTy+Kgmif8SYEyy/puV4k+2bQTe1aTn4E7gbwepOxpYD5wIjAV+D0xthvjLyvxX4I5mPAcUr8NOSB6/A/gNcDbFD4NzkvU/AL5Qoe7U5DXPAVOSczG6SeL/aLJNwMOV4k/q7MzqtR8k/vuAqwapm/n/oIGOoazMY8Cna3EO2mryq4h4PSJeTB7/G8WW2yTgCuD+pNj9wJUVql8KPBkRWyPiLeBJYFb9o/6zavFHxC+j2CIFeIHitbWmNMA5SGM60BMRGyJiD7CY4rlrmMHilyTgkxT/ETadKNqZLL4j+QngIuDRZH21v4ErgMURUYiIV4AeiuekYarFHxFLkm1BsUu/Kf8GBnj908j8fxAMfgySDqf4fqrYEhqqtkpCpSSdAJxBMYu/JyJeh+I/GaDSt9Caajh5Wfyl5gG/qFItgF9K+p2kzG+gUuEYbk66FBdV6RJthXNwPvBGROSrVMv8HEganXQXbqH4j2w9sK3kg0y117UpXv/y+CPiNyXb3gH8R4rd+5WMk7RC0guSKiXauhsg/m8k7//vqjjLTLmmeP1h4HMA/DXwVETsqFJ9SOegLZOQpAkUm4tfHuCFOqhahXWZDCevFr+krwJ7gYeqVD0vIv4KuAz4oqQL6h5sFRWO4b8Dfwl0Aq9T7NI6qFqFdU11DoC5DNwKyvwcRMS+iOik2FqYDpxaqViFdU3x+pfHL+mDJZv/AXg2In5Vpfp7I+Is4Brge5L+ss7hHqRK/LcDpwDTKHa3/V2Fqk3x+sOg52Cwv4EhnYO2S0LJJ6XHgIci4vFk9RuSJibbJ1LM7uXSDEWvuyrxk1ykvBy4NumSOEhEbE5+bwF+RoO7UvpUOoaIeCN5Y+8H7q0SW7OfgzHAx4FHqtVtlnOQxLANWErxmsQ7k/ih+uvaFK9/n5L4ZwFImk9xJpZbB6jT9/pvSOqeUe84B4ilP/6kmzciogD8D5r4/V+qwjk4hmLs/2uAOkM6B22VhJL++h8B3RHxnZJNpUPIrwf+Z4XqTwCXSDoq6Sq6JFnXMNXiV3EC2b8DZkfE21XqHpb01SLpMIrx/6H+UR8UR7VjmFhS7K+rxJbm6wB1NcB7COBi4OWI6K1SN/NzIOlYJaMnJY2nGHM38AzFr09A9b+BzL9SUSX+lyV9luI1k7nJB5lKdY/q6+aS9C7gPIojcxtmgPj7PgSL4vW4Su+LzP8HQfVjSDZfDfxzROyuUnfo52Aooxia/QeYSbH5ugpYmfx8lOIUQk8B+eT30Un5syjOCt5Xfx7Fi7E9wN80Ufw9FPuK+9b9ICn/F8CS5PGJFEc2/R5YA3y1yc7BPwGrk/VdwMTyY0iWP0pxRNr6LI6hWvzJtvuAm8rKN9U5AE4HXkri/wPJKL4ktt8m76WfArlk/WxgQUn9ryav/TrgsiaKf28SV9856Vvf/zcMnJu8x36f/L6hieJ/OonpD8CD/Hn0WVP9DxroGJJtSym27ErLD+scDDqBqZmZWb20VXecmZm1FichMzPLjJOQmZllxknIzMwy4yRkZmaZcRIyqyNJn5H0XJVt10r6ZclySDopeXyfpLsaFadZVpyEzGpA0kxJyyRtl7RV0q8lTRuoTkQ8FBGXNCpGs2aU5s6qZjYASUcA/wx8geItE8ZSnOi0kGVcZq3ALSGz4Xs/QEQ8HMX58XZF8fYbq8oLqniDwuckHTlQV53ZSOEkZDZ8/wrsk3S/pMsq3aZC0ihJ91KcEuWSiNje8CjNmpCTkNkwRfFWD31zzt0L/F9JXZLekxR5B8Wp748G/kNUmYTWbCTyNSGzGoiIbuAzAJJOoThJ5fcozoJ8EvDvgelRvGOsmSXcEjKrsYh4meKM2303AusG/gb4haSTs4rLrBm5JWQ2TEnL52PAIxHRK+l4ineffKGvTEQ8nNwj6V8kfSgi1mcUrllTcUvIbPj+DZgB/EbS/6OYfP4A/KfSQhFxP7AAeFrSCQ2O0awp+X5CZmaWGbeEzMwsM05CZmaWGSchMzPLjJOQmZllxknIzMwy4yRkZmaZcRIyM7PMOAmZmVlmnITMzCwz/x8nQUUE8dNIuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a, b = 'CRO', 'FRA'\n",
    "\n",
    "print(f'Match Quality: {quality_1vs1(teams[a], teams[b]):.1%}')\n",
    "\n",
    "n_trials = int(1e5)\n",
    "a_rvs = stats.norm(teams[a].mu, teams[a].sigma).rvs(n_trials)\n",
    "b_rvs = stats.norm(teams[b].mu, teams[b].sigma).rvs(n_trials)\n",
    "\n",
    "print(f'P({a} beats {b}): {win_prob(teams[a], teams[b]):0.1%}')\n",
    "print(f'P({b} beats {a}): {1-win_prob(teams[a], teams[b]):0.1%}')\n",
    "\n",
    "graph.hist(a_rvs, alpha=0.8, bins=100, normed=True, label=a)\n",
    "graph.hist(b_rvs, alpha=0.8, bins=100, normed=True, label=b)\n",
    "graph.xlabel('Skill')\n",
    "graph.legend()\n",
    "graph.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Stephen Anthony Rose_"
   ]
  }
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