用python基于2015-2016年的NBA常规赛及季后赛的统计数据分析

$ wget http://labfile.oss.aliyuncs.com/courses/782/prediction.py

$ sudo pip install pandas
$ sudo pip install scipy
$ sudo pip install sklearn

$ cd Code
$ mkdir cs_782 && cd cs_782# 获取数据文件$ wget http://labfile.oss.aliyuncs.com/courses/782/data.zip# 解压data压缩包并且删除该压缩包$ unzip data.zip 
$ rm -r data.zip

# -*- coding:utf-8 -*-import pandas as pdimport mathimport csvimport randomimport numpy as npfrom sklearn import cross_validation, linear_model

# 当每支队伍没有elo等级分时，赋予其基础elo等级分base_elo = 1600team_elos = {} 
team_stats = {}
X = []
y = []
folder = 'data' #存放数据的目录

# 根据每支队伍的Miscellaneous Opponent，Team统计数据csv文件进行初始化def initialize_data(Mstat, Ostat, Tstat):
    new_Mstat = Mstat.drop(['Rk', 'Arena'], axis=1)
    new_Ostat = Ostat.drop(['Rk', 'G', 'MP'], axis=1)
    new_Tstat = Tstat.drop(['Rk', 'G', 'MP'], axis=1)

    team_stats1 = pd.merge(new_Mstat, new_Ostat, how='left', on='Team')
    team_stats1 = pd.merge(team_stats1, new_Tstat, how='left', on='Team')    return team_stats1.set_index('Team', inplace=False, drop=True)

def get_elo(team):
    try:        return team_elos[team]    except:        # 当最初没有elo时，给每个队伍最初赋base_elo
        team_elos[team] = base_elo        return team_elos[team]

# 计算每个球队的elo值def calc_elo(win_team, lose_team):
    winner_rank = get_elo(win_team)
    loser_rank = get_elo(lose_team)

    rank_diff = winner_rank - loser_rank
    exp = (rank_diff  * -1) / 400
    odds = 1 / (1 + math.pow(10, exp))    # 根据rank级别修改K值
    if winner_rank < 2100:
        k = 32
    elif winner_rank >= 2100 and winner_rank < 2400:
        k = 24
    else:
        k = 16
    new_winner_rank = round(winner_rank + (k * (1 - odds)))
    new_rank_diff = new_winner_rank - winner_rank
    new_loser_rank = loser_rank - new_rank_diff    return new_winner_rank, new_loser_rank

def  build_dataSet(all_data):
    print("Building data set..")
    X = []
    skip = 0
    for index, row in all_data.iterrows():

        Wteam = row['WTeam']
        Lteam = row['LTeam']        #获取最初的elo或是每个队伍最初的elo值
        team1_elo = get_elo(Wteam)
        team2_elo = get_elo(Lteam)        # 给主场比赛的队伍加上100的elo值
        if row['WLoc'] == 'H':
            team1_elo += 100
        else:
            team2_elo += 100

        # 把elo当为评价每个队伍的第一个特征值
        team1_features = [team1_elo]
        team2_features = [team2_elo]        # 添加我们从basketball reference.com获得的每个队伍的统计信息
        for key, value in team_stats.loc[Wteam].iteritems():
            team1_features.append(value)        for key, value in team_stats.loc[Lteam].iteritems():
            team2_features.append(value)        # 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
        # 并将对应的0/1赋给y值
        if random.random() > 0.5:
            X.append(team1_features + team2_features)
            y.append(0)        else:
            X.append(team2_features + team1_features)
            y.append(1)        if skip == 0:            print X
            skip = 1

        # 根据这场比赛的数据更新队伍的elo值
        new_winner_rank, new_loser_rank = calc_elo(Wteam, Lteam)
        team_elos[Wteam] = new_winner_rank
        team_elos[Lteam] = new_loser_rank    return np.nan_to_num(X), y

if __name__ == '__main__':

    Mstat = pd.read_csv(folder + '/15-16Miscellaneous_Stat.csv')
    Ostat = pd.read_csv(folder + '/15-16Opponent_Per_Game_Stat.csv')
    Tstat = pd.read_csv(folder + '/15-16Team_Per_Game_Stat.csv')

    team_stats = initialize_data(Mstat, Ostat, Tstat)

    result_data = pd.read_csv(folder + '/2015-2016_result.csv')
    X, y = build_dataSet(result_data)    # 训练网络模型
    print("Fitting on %d game samples.." % len(X))

    model = linear_model.LogisticRegression()
    model.fit(X, y)    #利用10折交叉验证计算训练正确率
    print("Doing cross-validation..")
    print(cross_validation.cross_val_score(model, X, y, cv = 10, scoring='accuracy', n_jobs=-1).mean())

def predict_winner(team_1, team_2, model):
    features = []    # team 1，客场队伍
    features.append(get_elo(team_1))    for key, value in team_stats.loc[team_1].iteritems():
        features.append(value)    # team 2，主场队伍
    features.append(get_elo(team_2) + 100)    for key, value in team_stats.loc[team_2].iteritems():
        features.append(value)

    features = np.nan_to_num(features)    return model.predict_proba([features])

#利用训练好的model在16-17年的比赛中进行预测
    print('Predicting on new schedule..')
    schedule1617 = pd.read_csv(folder + '/16-17Schedule.csv')
    result = []    for index, row in schedule1617.iterrows():
        team1 = row['Vteam']
        team2 = row['Hteam']
        pred = predict_winner(team1, team2, model)
        prob = pred[0][0]        if prob > 0.5:
            winner = team1
            loser = team2
            result.append([winner, loser, prob])        else:
            winner = team2
            loser = team1
            result.append([winner, loser, 1 - prob])    with open('16-17Result.csv', 'wb') as f:
        writer = csv.writer(f)
        writer.writerow(['win', 'lose', 'probability'])
        writer.writerows(result)