import sqlite3
from RankingAlgorithms import EloForTeams
import numpy as np
ELO_DEFAULT_RATING = 1200
ELO_DEFAULT_MAX = 1400
ELO_DEFAULT_MIN = 1000
elo_logistic = EloForTeams(x=16.359300036046655, y=397.8279040482779)
elo_normal = EloForTeams(distribution="normal", sigma=270.7020217152946)
dataset = sqlite3.connect('database.sqlite')
query = 'SELECT * FROM Match ORDER BY date ASC'
players_fetch = dataset.execute('SELECT player_api_id, overall_rating from Player_Attributes ORDER BY date ASC')
players_logistic = {}
players_normal = {}
overall_rating = {}
for row in players_fetch:
if row[0] is not None and row[1] is not None:
overall_rating[row[0]] = row[1]
r_max = max(overall_rating.values())
r_min = min(overall_rating.values())
for p,r in overall_rating.items():
rating = ((r-r_min)/(r_max-r_min))*(ELO_DEFAULT_MAX-ELO_DEFAULT_MIN)+ELO_DEFAULT_MIN
players_logistic[p] = rating
players_normal[p] = rating
for i in range(0, 2):
matches_fetch = dataset.execute(query)
import sqlite3
from RankingAlgorithms import EloForTeams
import numpy as np
ELO_DEFAULT_RATING = 1200
elo_logistic = EloForTeams()
elo_normal = EloForTeams(distribution="normal")
dataset = sqlite3.connect('database.sqlite')
players_logistic = {}
players_normal = {}
query = 'SELECT * FROM Match ORDER BY date ASC'
for i in range(0, 2):
matches_fetch = dataset.execute(query)
total_logistic = 0
total_normal = 0
predicted_logistic = 0
predicted_normal = 0
log_likelihood_logistic = 0
log_likelihood_normal = 0
for row in matches_fetch.fetchall():
team1_logistic, team2_logistic = {}, {}
team1_normal, team2_normal = {}, {}
for index in range(55, 66):
id = row[index]
if id in players_logistic:
team1_logistic[id] = players_logistic[id]
else:
def get_ratings(self):
return self.ratings
def add_rating(self, date, rating):
self.ratings[date] = rating
def last_rating(self):
return self.ratings[next(reversed(self.ratings))]
ELO_DEFAULT_RATING = 1200
dataset = sqlite3.connect('database.sqlite')
matches_fetch = dataset.execute(
'SELECT * FROM Match WHERE country_id=1729 ORDER BY date ASC')
elo = EloForTeams()
# dictionary of players with database's player_api_id as key and a Player object as value
players = {}
for row in matches_fetch.fetchall():
team1, team2 = {}, {}
for index in range(55, 66):
id = row[index]
if id in players:
team1[id] = players[id].last_rating()
else:
if id is not None:
team1[id] = ELO_DEFAULT_RATING
import sqlite3
from RankingAlgorithms import EloForTeams
import math
ELO_DEFAULT_RATING = 1200
ELO_DEFAULT_MIN = 1000
ELO_DEFAULT_MAX = 1400
dataset = sqlite3.connect('database.sqlite')
query = "SELECT * FROM Match ORDER BY date ASC"
elo = EloForTeams()
# assign players a default rating based on the overall_rating column
players_fetch = dataset.execute(
'SELECT player_api_id, overall_rating from Player_Attributes')
players_o = {}
overall_rating = {}
for row in players_fetch:
if row[0] is not None and row[1] is not None:
overall_rating[row[0]] = row[1]
r_max = max(overall_rating.values())
r_min = min(overall_rating.values())
for p, r in overall_rating.items():
players_o[p] = ((r - r_min) /
(r_max - r_min)) * (ELO_DEFAULT_MAX -
ELO_DEFAULT_MIN) + ELO_DEFAULT_MIN
# calculate the percentage of draws in all matches
matches_fetch = dataset.execute(query)
import sqlite3
from RankingAlgorithms import EloForTeams
import pandas as pd
import matplotlib.pyplot as plt
elo = EloForTeams()
ELO_DEFAULT_RATING = 1200
dataset = sqlite3.connect('database.sqlite')
players = {}
df = pd.DataFrame(columns=['iteration', 'n_games'])
for i in range(1, 101):
matches_fetch = dataset.execute('SELECT * FROM Match ORDER BY date ASC')
total = 0
well_predicted = 0
for row in matches_fetch.fetchall():
team1, team2 = {}, {}
for index in range(55, 66):
id = row[index]
if id in players:
team1[id] = players[id]
else:
if id is not None:
team1[id] = ELO_DEFAULT_RATING
for index in range(66, 77):
id = row[index]
if id in players:
import sqlite3
from RankingAlgorithms import EloForTeams
import numpy as np
ELO_DEFAULT_RATING = 1200
elo_logistic = EloForTeams()
elo_normal = EloForTeams(distribution="normal")
dataset = sqlite3.connect('database.sqlite')
query = 'SELECT * FROM Match ORDER BY date ASC'
players_logistic_SA = {}
players_normal_SA = {}
players_logistic_CEM = {}
players_normal_CEM = {}
training_set_logistic_SA = []
training_set_normal_SA = []
training_set_logistic_CEM = []
training_set_normal_CEM = []
matches_fetch = dataset.execute(query)
for row in matches_fetch.fetchall():
team1_logistic_SA, team2_logistic_SA = {}, {}
team1_normal_SA, team2_normal_SA = {}, {}
team1_logistic_CEM, team2_logistic_CEM = {}, {}
team1_normal_CEM, team2_normal_CEM = {}, {}
for index in range(55, 66):
id = row[index]
if id in players_logistic_SA:
team1_logistic_SA[id] = players_logistic_SA[id]
else:
players = {}
overall_rating = {}
for row in players_fetch:
if row[0] is not None and row[1] is not None:
overall_rating[row[0]] = row[1]
r_max = max(overall_rating.values())
r_min = min(overall_rating.values())
for p,r in overall_rating.items():
players[p] = ((r-r_min)/(r_max-r_min))*(ELO_DEFAULT_MAX-ELO_DEFAULT_MIN)+ELO_DEFAULT_MIN
matches_query = dataset.execute('SELECT * FROM Match WHERE country_id=1729 ORDER BY date ASC')
elo = EloForTeams()
matches_fetch = matches_query.fetchall()
matches_count = len(matches_fetch)
training_set_border = math.ceil(matches_count*TRANING_SET_SIZE)
training_set = []
matches_total = 0
draws = 0
for i in range(training_set_border):
team1, team2 = {}, {}
row = matches_fetch[i]
for index in range(55, 66):