Basketball Reference.com 數據源地址
http://labfile.oss.aliyuncs.com/courses/782/data.zip數據下載地址
用到的三張表
Team Per Game Stats
Opponent Per Game Stats
Miscellaneous Stats
整體思路,根據輸贏按公式賦分,贏了且是主場加分
第一個函數,首先初始化一下數據,去除一些無關數據並將這三個表格通過Team
屬性列進行連接
第二個函數,獲取每支隊伍的Elo Score
等級分,最開始給定一個初始值
第三個函數,計算每支球隊的Elo等級分,分輸贏隊伍對每個隊伍進行等級賦分
這里借鑒了國際象棋的等級划分制度,Elo 等級划分制度
A 和 B 的當前等級分為 RA RB, A 對 B 的勝率期望值為EA,B 對A 的勝率期望值為EB
k值根據不同的分數會發射變化
- 大於等於2400,K=16
- 2100~2400 分,K=24
- 小於等於2100,K=32
第四個函數,調用上面的相關函數,得到最后的elo分值,進行回歸分析
此處的iterrows()是對dataframe進行遍歷
最后就是預測模型了
import pandas as pd import math import csv import random import numpy as np from sklearn import linear_model from sklearn.model_selection import cross_val_score base_elo = 1600 team_elos = {} team_stats = {} X = [] y = [] #初始化數據,從T,O,M表格中讀取數據,取出一些無關數據並將這三個表格通過team樹形列進行連接: #根據每個隊伍的Miscellaneous Opponent,Team統計數據csv文件進行初始化 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: team_elos[team] = base_elo return team_elos[team] def calc_elo(win_team,lose_team): winner_rank = get_elo(win_team) loser_rank = get_elo(lose_team) #根據Logistic Distribution計算 PK 雙方(A和B)對各自的勝率期望值計算公式 rank_diff = winner_rank - loser_rank exp = (rank_diff *-1)/400 odds = 1/(1+math.pow(10,exp)) #根據rank界別修改k值 if winner_rank < 2100: k = 32 elif winner_rank >=2100 and winner_rank <2400: k = 24 else: k=16 #更新rank數值 new_winner_rank = round(winner_rank+(k*(1-odds))) new_loser_rank = round(loser_rank+(k*(0-odds))) return new_winner_rank,new_loser_rank #基於統計好的數據,給每只隊伍的eloscore計算結果,建立對應15-16年數據集,我們認為主場作戰的隊伍更有優勢,因此會給主場隊伍加上100分 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'] #獲取最初的elo或者每個隊伍最初的elo值 team1_elo = get_elo(Wteam) team2_elo = get_elo(Lteam) #給主場比賽隊伍加上100的elo值 if row['WLoc'] == 'H': team1_elo += 100 else: team2_elo += 100 #把elo當成評價每個隊伍的第一個特征值 team1_features = [team1_elo] team2_features = [team2_elo] # 添加我們從basketball reference.com獲得的每個隊伍的統計信息 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) # 將兩支隊伍的特征值隨機的分配在每場比賽數據的左右兩側 # 並將對應的0/1賦給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',X) skip = 1 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 #最終利用訓練好的模型在 16~17 年的常規賽數據中進行預測 def predict_winner(team_1, team_2, model): features = [] # team 1,客場隊伍 features.append(get_elo(team_1)) for key, value in team_stats.loc[team_1].iteritems(): features.append(value) # team 2,主場隊伍 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]) #最終在 main 函數中調用這些數據處理函數,使用 sklearn 的Logistic Regression方法建立回歸模型 if __name__=='__main__': folder = 'data' 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) #訓練網絡模型 print("Fitting on %d game samples.." % len(X)) model = linear_model.LogisticRegression() model.fit(X,y) print("Doing cross-validation..") cross_val_score(model,X,y,cv = 10,scoring='accuracy',n_jobs=-1).mean() print(model) 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', 'w') as f: writer = csv.writer(f) writer.writerow(['win', 'lose', 'probability']) writer.writerows(result) print('done.')
代碼部分轉載自https://blog.csdn.net/u010824946/java/article/details/89441145