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import pandas as pd
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import math
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import csv
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import random
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import numpy as np
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from sklearn import linear_model
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from sklearn.model_selection import cross_val_score
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# 当每支队伍没有elo等级分时，赋予其基础elo等级分
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base_elo = 1600
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team_elos = {}
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team_stats = {}
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X = []
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y = []
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# 存放数据的目录
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folder = r'C:\Users\lenovo\Desktop\data'
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# 根据每支队伍的Miscellaneous Opponent，Team统计数据csv文件进行初始化
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def initialize_data(Mstat, Ostat, Tstat):
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new_Mstat = Mstat.drop(['Rk', 'Arena'], axis=1)
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new_Ostat = Ostat.drop(['Rk', 'G', 'MP'], axis=1)
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new_Tstat = Tstat.drop(['Rk', 'G', 'MP'], axis=1)
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team_stats1 = pd.merge(new_Mstat, new_Ostat, how='left', on='Team')
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team_stats1 = pd.merge(team_stats1, new_Tstat, how='left', on='Team')
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return team_stats1.set_index('Team', inplace=False, drop=True)
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def get_elo(team):
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try:
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return team_elos[team]
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except:
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# 当最初没有elo时，给每个队伍最初赋base_elo
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team_elos[team] = base_elo
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return team_elos[team]
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# 计算每个球队的elo值
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def calc_elo(win_team, lose_team):
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winner_rank = get_elo(win_team)
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loser_rank = get_elo(lose_team)
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rank_diff = winner_rank - loser_rank
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exp = (rank_diff * -1) / 400
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odds = 1 / (1 + math.pow(10, exp))
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# 根据rank级别修改K值
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if winner_rank < 2100:
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k = 32
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elif winner_rank >= 2100 and winner_rank < 2400:
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k = 24
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else:
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k = 16
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# 更新 rank 数值
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new_winner_rank = round(winner_rank + (k * (1 - odds)))
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new_loser_rank = round(loser_rank + (k * (0 - odds)))
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return new_winner_rank, new_loser_rank
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def build_dataSet(all_data):
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print("Building data set..")
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X = []
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skip = 0
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for index, row in all_data.iterrows():
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Wteam = row['WTeam']
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Lteam = row['LTeam']
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#获取最初的elo或是每个队伍最初的elo值
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team1_elo = get_elo(Wteam)
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team2_elo = get_elo(Lteam)
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# 给主场比赛的队伍加上100的elo值
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if row['WLoc'] == 'H':
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team1_elo += 100
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else:
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team2_elo += 100
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# 把elo当为评价每个队伍的第一个特征值
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team1_features = [team1_elo]
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team2_features = [team2_elo]
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# 添加我们从basketball reference.com获得的每个队伍的统计信息
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for key, value in team_stats.loc[Wteam].iteritems():
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team1_features.append(value)
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for key, value in team_stats.loc[Lteam].iteritems():
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team2_features.append(value)
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# 将两支队伍的特征值随机的分配在每场比赛数据的左右两侧
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# 并将对应的0/1赋给y值
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if random.random() > 0.5:
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X.append(team1_features + team2_features)
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y.append(0)
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else:
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X.append(team2_features + team1_features)
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y.append(1)
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if skip == 0:
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print('X',X)
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skip = 1
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# 根据这场比赛的数据更新队伍的elo值
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new_winner_rank, new_loser_rank = calc_elo(Wteam, Lteam)
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team_elos[Wteam] = new_winner_rank
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team_elos[Lteam] = new_loser_rank
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return np.nan_to_num(X), y
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if __name__ == '__main__':
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Mstat = pd.read_csv(folder + '/15-16Miscellaneous_Stat.csv')
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Ostat = pd.read_csv(folder + '/15-16Opponent_Per_Game_Stat.csv')
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Tstat = pd.read_csv(folder + '/15-16Team_Per_Game_Stat.csv')
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team_stats = initialize_data(Mstat, Ostat, Tstat)
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result_data = pd.read_csv(folder + '/2015-2016_result.csv')
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X, y = build_dataSet(result_data)
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# 训练网络模型
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print("Fitting on %d game samples.." % len(X))
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model = linear_model.LogisticRegression()
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model.fit(X, y)
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# 利用10折交叉验证计算训练正确率
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print("Doing cross-validation..")
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print(cross_val_score(model, X, y, cv = 10, scoring='accuracy', n_jobs=-1).mean())
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def predict_winner(team_1, team_2, model):
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features = []
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# team 1，客场队伍
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features.append(get_elo(team_1))
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for key, value in team_stats.loc[team_1].iteritems():
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features.append(value)
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# team 2，主场队伍
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features.append(get_elo(team_2) + 100)
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for key, value in team_stats.loc[team_2].iteritems():
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features.append(value)
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features = np.nan_to_num(features)
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return model.predict_proba([features])
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# 利用训练好的model在16-17年的比赛中进行预测
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print('Predicting on new schedule..')
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schedule1617 = pd.read_csv(folder + '/16-17Schedule.csv')
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result = []
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for index, row in schedule1617.iterrows():
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team1 = row['Vteam']
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team2 = row['Hteam']
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pred = predict_winner(team1, team2, model)
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prob = pred[0][0]
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if prob > 0.5:
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winner = team1
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loser = team2
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result.append([winner, loser, prob])
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else:
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winner = team2
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loser = team1
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result.append([winner, loser, 1 - prob])
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with open('16-17Result.csv', 'w') as f:
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writer = csv.writer(f)
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writer.writerow(['win', 'lose', 'probability'])
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writer.writerows(result)
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print('done.')
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pd.read_csv('16-17Result.csv', header=0)
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pd.read_csv('16-17Result.csv',header=0)
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