NhlElo.py

import pandas as pd
import numpy as np
from tkinter import *
from tkintertable import TableCanvas
import matplotlib.pyplot as plt

k_factor = 10


def update_elo(w_elo, l_elo):
    exp_win = expected_result(w_elo, l_elo)
    change = k_factor * (1-exp_win)
    w_elo = w_elo + change
    l_elo = l_elo - change
    return w_elo, l_elo


def expected_result(elo_a, elo_b):
    expect = 1.0/(1+10**((elo_b - elo_a)/400))
    return expect


df_lists = pd.DataFrame()
start_year = 2018
end_year = 2020

# scrapes data from hockeyreference.com
for year in range(start_year, end_year + 1):
    k = 1

    # 2005 was the lockout so there is no data to be scraped
    if year == 2005:
        print("2005 was the lockout")

    else:
        url = r'https://www.hockey-reference.com/leagues/NHL_' + str(year) + r'_games.html'
        df_temp_reg = pd.DataFrame(pd.read_html(url)[0])
        df_temp_reg['season'] = year

    # use commented out code if playoff data is desired

        try:
            df_temp_post = pd.DataFrame(pd.read_html(url)[1])
            df_temp_post['season'] = year

        except IndexError as e:
            k = 0
            print('no playoffs available yet')

        print(str(year) + " scraped")

    df_lists = df_lists.append(df_temp_reg)

    if k == 1:
        df_lists.append(df_temp_post)


df_lists.rename(columns={'G': 'VisitingGoals',
                         'G.1': 'HomeGoals',
                         'Unnamed: 5': 'OTSO',
                         'season': 'Season'},
                inplace=True)

df_lists.drop(['Att.', 'LOG', 'Notes'], axis=1, inplace=True)

df_lists.loc[:, 'Date'] = pd.to_datetime(df_lists['Date'])

replace_dict = {'Home': {'Atlanta Thrashers': 'Winnipeg Jets',
                         'Mighty Ducks of Anaheim': 'Anaheim Ducks',
                         'Phoenix Coyotes': 'Arizona Coyotes'},
                'Visitor': {'Atlanta Thrashers': 'Winnipeg Jets',
                            'Mighty Ducks of Anaheim': 'Anaheim Ducks',
                            'Phoenix Coyotes': 'Arizona Coyotes'}}

df_lists.replace(replace_dict, inplace=True)

ind = df_lists['OTSO'].isna()
df_lists.loc[ind, 'OTSO'] = 'REG'

# how come this doesnt work??? teams = df_lists['Home'].unique().sort()
teams = df_lists['Home'].unique()
teams.sort()

games = df_lists.reset_index()
games.drop('index', axis=1, inplace=True)


class TeamElos(dict):
    def __init__(self, teams):
        super().__init__(self)
        for team in teams:
            self[team] = 1500
        self.history = HistoryList(teams)

    def plot_history(self, *args):      
        fig = plt.figure()
        ax = fig.add_subplot(111)
        ax.set_ylim([1350,1700])
        plt.xticks(rotation=45)
    
        for team in args:
            cax = plt.plot(
                self.history[team]['Date'],
                self.history[team]['Elo Rating'],
                label = team)
        
        plt.title(f'{args} Elo History')
        plt.legend()
        plt.show()

    def update(self, game_tuple):
        if game_tuple.VisitingGoals > game_tuple.HomeGoals:
            winning_team = game_tuple.Visitor
            losing_team = game_tuple.Home
        else:
            winning_team = game_tuple.Home
            losing_team = game_tuple.Visitor
        self[winning_team], self[losing_team] = update_elo(
            self[winning_team],
            self[losing_team])
        self.history.update(
            winning_team = winning_team,
            losing_team = losing_team,
            win_elo = self[winning_team],
            lose_elo = self[losing_team],
            date = game_tuple.Date
            )


class HistoryList(dict):
    def __init__(self, teams):
        super().__init__(self)
        for team in teams:
            self[team] = pd.DataFrame(columns = ['Date', 'Elo Rating'])

    def update(self, winning_team, losing_team, win_elo, lose_elo, date):
        self[winning_team] = self[winning_team].append(
            {'Date': date,'Elo Rating': win_elo},
            ignore_index = True)
        self[losing_team]= self[losing_team].append(
            {'Date': date,'Elo Rating': lose_elo},
            ignore_index = True)


elos = TeamElos(teams)
for row in games.itertuples():
    if row.Date.date() < pd.Timestamp.today().date():
        elos.update(row)

elos.plot_history('Toronto Maple Leafs', 'Montreal Canadiens')

elos = pd.DataFrame(
    elos,
    index=['Elo Rating']).T.sort_values(by='Elo Rating', ascending=False)

ind = games['Date'] > pd.Timestamp('today')

games_future = games.loc[ind, :].reset_index()
games_future.drop('index', axis=1, inplace=True)

games_prediction = games_future.loc[0:10, ['Date', 'Visitor', 'Home']]

games_prediction = pd.DataFrame.merge(
    games_prediction,
    elos,
    left_on='Visitor',
    right_index=True)

games_prediction.rename(
    columns={'Elo Rating': 'Visitor Elo Rating'},
    inplace=True)

games_prediction = pd.DataFrame.merge(
    games_prediction,
    elos,
    left_on='Home',
    right_index=True)

games_prediction.rename(
    columns={'Elo Rating': 'Home Elo Rating'},
    inplace=True)

games_prediction['Home Win%'] = expected_result(
    games_prediction['Home Elo Rating'],
    games_prediction['Visitor Elo Rating'])

games_prediction['Visitor Win%'] = 1-games_prediction['Home Win%']

games_prediction.drop(
    ['Visitor Elo Rating', 'Home Elo Rating'],
    axis=1,
    inplace=True)

games_prediction = games_prediction[['Date', 'Visitor', 'Visitor Win%', 'Home', 'Home Win%']]

elos.to_csv('elos.csv', index=True)
games_prediction.to_csv('prediction.csv', index=False)


root = Tk()
root.title('NHL Elo Predictions')
root.geometry('800x600')
one = Label(root, text='NHL Elo Rankings', bg='blue', fg='white')
one.pack()
tframe = Frame(root)
tframe.pack()
table = TableCanvas(tframe, rowheaderwidth=0,
                    cellwidth=100, editable=False, width=325)
table.importCSV('elos.csv')
table.sortTable(columnName='Elo Rating', reverse=1)
colIndex = 1
table.show()
two = Label(root, text='Games Today', bg='blue', fg='white')
two.pack()
tframe2 = Frame(root)
tframe2.pack()
table2 = TableCanvas(tframe2, rowheaderwidth=0, editable=False, width=725)
table2.importCSV('prediction.csv')
table2.show()

root.mainloop()