目录
一、背景
二、数据输入
2.1 赔率示意图
2.2 代码
三、数据处理
3.1计算各种组合可能性
3.2 修正概率
四、输出结果
一、背景
本文以世界杯体彩“混合过关”4场串胜平负为的赔率进行编码
其他类型如比分 、总进球数可以参考代码进行相应修改
需要的库：numpy与pandas
二、数据输入
2.1 赔率示意图
2.2 代码
采用字典保存各比赛对应的胜平负的赔率
import pandas as pdimport numpy as npvs1 = {"胜":2.38,"平":2.93,"负":2.65} #厄瓜多尔-塞内加尔vs2 = {"胜":13.0,"平":6.20,"负":1.11} #卡塔尔-荷兰vs3 = {"胜":3.58,"平":3.16,"负":1.84} #伊朗-美国vs4 = {"胜":7.35,"平":4.16,"负":1.31} #威尔士-英格兰三、数据处理
3.1计算各种组合可能性
计算采用的公式主要为：（图中10%为抽水率,仅为假设）
其中0.9913为初步计算得到的体彩抽水率，实际不准确，该数值仅供初步计算，之后需要根据计算所得的概率进行相应修正。
count = 1probList = []probListIndex = []probVs = []timesList = []vsCode1 = []vsCode2 = []vsCode3 = []vsCode4 = []for key1,each1 in vs1.items(): for key2,each2 in vs2.items(): for key3,each3 in vs3.items(): for key4,each4 in vs4.items():# print(count,key1,key2,key3,each1*each2*each3) prob = 99.13 / (each1*each2*each3*each4) #print(count,key1,key2,key3,key4,prob) probList.append(prob) probListIndex.append(count) probVs.append(key1+key2+key3+key4) timesList.append(each1*each2*each3*each4) vsCode1.append(key1) vsCode2.append(key2) vsCode3.append(key3) vsCode4.append(key4) count += 1data = pd.DataFrame(probList,index = probListIndex,columns=["prob"])data_temp = pd.DataFrame(probVs,index = probListIndex,columns=["vs"])data_times = pd.DataFrame(timesList,index = probListIndex,columns=["times"])data_vs1 = pd.DataFrame(vsCode1,index = probListIndex,columns=["vs1"])data_vs2 = pd.DataFrame(vsCode2,index = probListIndex,columns=["vs2"])data_vs3 = pd.DataFrame(vsCode3,index = probListIndex,columns=["vs3"])data_vs4 = pd.DataFrame(vsCode4,index = probListIndex,columns=["vs4"])# data = data.add(data_temp,fill_value=False)data["vs"] = data_temp["vs"]data["vs1"] = data_vs1["vs1"]data["vs2"] = data_vs2["vs2"]data["vs3"] = data_vs3["vs3"]data["vs4"] = data_vs4["vs4"]data["times"] = data_times["times"]data = data.sort_values(by="prob",ascending=False)data["total_prob"] = 0sum_prob = 0for each in data.index:# print(each) sum_prob += data["prob"].loc[each] data["total_prob"].loc[each] = sum_prob 3.2 修正概率
该段代码无实际含义，仅为修正由于采用估计抽水率计算所得的概率偏差
主要思路是采用数据标准化后并将数据映射到合理的区间，并对部分概率进行转换
total_prob_min = data["total_prob"].min()data["total_prob"] = (data["total_prob"]-data["total_prob"].min())/(data["total_prob"].max()-data["total_prob"].min())*(100-total_prob_min)+total_prob_mindata["total_prob"].iloc[0] = (data["total_prob"].iloc[1]*data["total_prob"].iloc[0])/(data["prob"].iloc[1]+data["total_prob"].iloc[0])temp = data["total_prob"] - data["total_prob"].shift(1)temp[0] = data["total_prob"].iloc[0]data["prob"] = tempdata["prob"].iloc[0] = data["total_prob"].iloc[0]data.to_csv(r"C:\Users\kkkk\Desktop\世界杯1129.csv")四、输出结果
prob该组合可能性，total_prob为累计可能性，times为赔率，VS1~4为该组合对应的胜平负
以11.29日赛程为参考，卡塔尔与威尔士大概率负，因此采用Excel筛选出相关组合，在所列组合中选取赔率较高的组合。
五、代码
import pandas as pdimport numpy as npvs1 = {"胜":2.38,"平":2.93,"负":2.65}vs2 = {"胜":13.0,"平":6.20,"负":1.11}vs3 = {"胜":3.58,"平":3.16,"负":1.84}vs4 = {"胜":7.35,"平":4.16,"负":1.31}count = 1probList = []probListIndex = []probVs = []timesList = []vsCode1 = []vsCode2 = []vsCode3 = []vsCode4 = []for key1,each1 in vs1.items(): for key2,each2 in vs2.items(): for key3,each3 in vs3.items(): for key4,each4 in vs4.items():# print(count,key1,key2,key3,each1*each2*each3) prob = 99.13 / (each1*each2*each3*each4) print(count,key1,key2,key3,key4,prob) probList.append(prob) probListIndex.append(count) probVs.append(key1+key2+key3+key4) timesList.append(each1*each2*each3*each4) vsCode1.append(key1) vsCode2.append(key2) vsCode3.append(key3) vsCode4.append(key4) count += 1data = pd.DataFrame(probList,index = probListIndex,columns=["prob"])data_temp = pd.DataFrame(probVs,index = probListIndex,columns=["vs"])data_times = pd.DataFrame(timesList,index = probListIndex,columns=["times"])data_vs1 = pd.DataFrame(vsCode1,index = probListIndex,columns=["vs1"])data_vs2 = pd.DataFrame(vsCode2,index = probListIndex,columns=["vs2"])data_vs3 = pd.DataFrame(vsCode3,index = probListIndex,columns=["vs3"])data_vs4 = pd.DataFrame(vsCode4,index = probListIndex,columns=["vs4"])# data = data.add(data_temp,fill_value=False)data["vs"] = data_temp["vs"]data["vs1"] = data_vs1["vs1"]data["vs2"] = data_vs2["vs2"]data["vs3"] = data_vs3["vs3"]data["vs4"] = data_vs4["vs4"]data["times"] = data_times["times"]data = data.sort_values(by="prob",ascending=False)data["total_prob"] = 0sum_prob = 0for each in data.index:# print(each) sum_prob += data["prob"].loc[each] data["total_prob"].loc[each] = sum_prob total_prob_min = data["total_prob"].min()data["total_prob"] = (data["total_prob"]-data["total_prob"].min())/(data["total_prob"].max()-data["total_prob"].min())*(100-total_prob_min)+total_prob_mindata["total_prob"].iloc[0] = (data["total_prob"].iloc[1]*data["total_prob"].iloc[0])/(data["prob"].iloc[1]+data["total_prob"].iloc[0])temp = data["total_prob"] - data["total_prob"].shift(1)temp[0] = data["total_prob"].iloc[0]data["prob"] = tempdata["prob"].iloc[0] = data["total_prob"].iloc[0]data.to_csv(r"C:\Users\kkkk\Desktop\世界杯1129.csv")