目录
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 pd
-
import numpy
as np
-
-
vs1 = {
"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 =
1
-
probList = []
-
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 +=
1
-
-
data = 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"] =
0
-
-
sum_prob =
0
-
for 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_min
-
data[
"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"] = temp
-
data[
"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 pd
-
import numpy
as np
-
-
vs1 = {
"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 =
1
-
probList = []
-
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 +=
1
-
-
data = 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"] =
0
-
-
sum_prob =
0
-
for 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_min
-
data[
"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"] = temp
-
data[
"prob"].iloc[
0] = data[
"total_prob"].iloc[
0]
-
-
data.to_csv(
r"C:\Users\kkkk\Desktop\世界杯1129.csv")
-
转载：https://blog.csdn.net/zsllsz2022/article/details/128106774
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