from collections import defaultdict
import collections
import time
import re
import csv
import matplotlib.pyplot as plt
import numpy as np
import random
import statGeneration as s
import estimation as e
playerRatingTrue, minutesPlayedTrue = s.generateTrueStrength()
teamRatingTrue = e.playerToTeamRating(playerRatingTrue, minutesPlayedTrue,
minutesPlayedTrue)
time.sleep(20)
K = [20, 100, 200, 500, 750, 1000]
MSE = np.zeros(6)
#playerRating,TrueStrength=e.estimate(15)
for j in range(10):
print("Iteration", j)
s.statsGenerate(playerRatingTrue, minutesPlayedTrue, 1000)
for i in range(6):
playerRating, minutesPlayedEstimate = e.estimate(K[i])
teamRatingTrue = e.playerToTeamRating(playerRatingTrue,
minutesPlayedTrue,
minutesPlayedTrue)
teamRating = e.playerToTeamRating1(playerRating, minutesPlayedEstimate,
minutesPlayedTrue)
MSE[i] = e.MeanSquareError(teamRating, teamRatingTrue)
print("K value", K[i], "MSE", MSE[i])
MSEfinal = np.zeros(6)
csv_file1 = open('MSEvalues.csv', 'w')
def estimate(gamma, eta, minutesPlayedTrue, playerRatingTrue):
#csv_file1 = open('MatchupGenerate.csv', 'r')
df1 = pandas.read_csv('MatchupGenerate.csv')
df2 = pandas.read_csv('playerstats.csv')
row1 = df1.values
totalGames = np.shape(row1)[0]
#reader1 = csv.reader(csv_file1)
i = 0
j = 0
noOfPredictions = 0
minutesPlayed = defaultdict(dict)
minutesPlayedEstimate = defaultdict(dict)
NumberMinutes = defaultdict(dict)
playerRating = defaultdict(dict)
check = defaultdict(dict)
TeamRating = defaultdict(dict)
HomeAdv = defaultdict(dict)
TrueStrength = defaultdict(dict)
teamRating = defaultdict(dict)
match = 0
avg = []
number = 0
flag = 0
MSE = []
for i in range(1, 17):
Team = "T" + str(i)
teamRating[Team] = 0
teamRating[Team] = 1000
#print("entered first file")
for i in range(totalGames):
plusminus1 = defaultdict(dict)
plusminus2 = defaultdict(dict)
#print("MATCH NUMBER:",i)
matchNumber = row1[i][1]
s = re.split('\W+', row1[i][0])
#print(s)
Team1 = "T" + s[0]
Team2 = "T" + s[3]
flag = 1
HomeAdv[Team1] = 0
HomeAdv[Team2] = 0
for fixedPlayer in minutesPlayed[Team1]:
minutesPlayed[Team1][fixedPlayer] = 0
plusminus1[fixedPlayer] = 0
for fixedPlayer in minutesPlayed[Team2]:
minutesPlayed[Team2][fixedPlayer] = 0
plusminus2[fixedPlayer] = 0
#print("opening player stats")
df3 = df2.loc[df2["matchnumber"] == matchNumber]
row2 = df3.loc[df3["Team"] == Team1].values
nop = np.shape(row2)[0]
for j in range(nop):
minutesPlayed[Team1][row2[j][1]] = float(row2[j][3]) / (float(
row1[i][5]))
plusminus1[row2[j][1]] = float(row2[j][4])
TrueStrength[row2[j][1]] = float(row2[j][6])
if check[row2[j][1]] == {}:
playerRating[row2[j][1]] = 1000
check[row2[j][1]] = 'Filled'
df3 = df2.loc[df2["matchnumber"] == matchNumber]
row2 = df3.loc[df3["Team"] == Team2].values
nop = np.shape(row2)[0]
for j in range(nop):
minutesPlayed[Team2][row2[j][1]] = float(row2[j][3]) / (float(
row1[i][6]))
plusminus2[row2[j][1]] = float(row2[j][4])
TrueStrength[row2[j][1]] = float(row2[j][6])
if check[row2[j][1]] == {}:
playerRating[row2[j][1]] = 1000
check[row2[j][1]] = 'Filled'
for fixedPlayer in minutesPlayed[Team1]:
if minutesPlayedEstimate[fixedPlayer] == {}:
#print(fixedPlayer)
minutesPlayedEstimate[fixedPlayer] = minutesPlayed[Team1][
fixedPlayer]
NumberMinutes[fixedPlayer] = 1
else:
minutesPlayedEstimate[fixedPlayer] = (
NumberMinutes[fixedPlayer] *
minutesPlayedEstimate[fixedPlayer] +
minutesPlayed[Team1][fixedPlayer]) / (
NumberMinutes[fixedPlayer] + 1)
NumberMinutes[fixedPlayer] = NumberMinutes[fixedPlayer] + 1
for fixedPlayer in minutesPlayed[Team2]:
if minutesPlayedEstimate[fixedPlayer] == {}:
minutesPlayedEstimate[fixedPlayer] = minutesPlayed[Team2][
fixedPlayer]
NumberMinutes[fixedPlayer] = 1
else:
minutesPlayedEstimate[fixedPlayer] = (
NumberMinutes[fixedPlayer] *
minutesPlayedEstimate[fixedPlayer] +
minutesPlayed[Team2][fixedPlayer]) / (
NumberMinutes[fixedPlayer] + 1)
NumberMinutes[fixedPlayer] = NumberMinutes[fixedPlayer] + 1
# rating update for the 2 teams
m1 = 0
m2 = 0
for players in minutesPlayed[Team1]:
m1 += playerRating[players] * minutesPlayedEstimate[players]
for players in minutesPlayed[Team2]:
m2 += playerRating[players] * minutesPlayedEstimate[players]
m1 += HomeAdv[Team1]
m2 += HomeAdv[Team2]
if m1 > m2:
outcome = 'W'
else:
outcome = 'L'
if outcome == row1[i][2]:
noOfPredictions = noOfPredictions + 1
W = 1
else:
W = 0
match = match + 1
#print(Team1, row1[2], Team2, noOfPredictions, match,"Prediction Rate",noOfPredictions/match)
playerRating = e.eloModified(playerRating, minutesPlayed, Team1, Team2,
float(row1[i][5]), float(row1[i][6]),
plusminus1, plusminus2, gamma, eta)
m1 = 0
m2 = 0
for players in minutesPlayed[Team1]:
m1 += playerRating[players] * minutesPlayedEstimate[players]
for players in minutesPlayed[Team2]:
m2 += playerRating[players] * minutesPlayedEstimate[players]
teamRating[Team1] = m1
teamRating[Team2] = m2
#teamRating = e.playerToTeamRating1(playerRating, minutesPlayedEstimate, minutesPlayedTrue)
teamRatingTrue = e.playerToTeamRating(playerRatingTrue,
minutesPlayedTrue,
minutesPlayedTrue)
x1 = e.dictToInt(teamRating)
x2 = e.dictToInt(teamRatingTrue)
MSE.append(mean_squared_error(x1, x2))
#print("ESTIMATED WITH THESIS")
#print("Number of correct predicitons:" ,noOfPredictions,"Total number of games:", match, "Prediction Rate", noOfPredictions / match)
return playerRating, minutesPlayedEstimate, MSE