论文标题
投注系统：使用阵容来预测足球得分
Betting the system: Using lineups to predict football scores
论文作者
论文摘要
本文旨在通过使用我们开发的机器学习预测模型来分析阵容在最终分数中的作用来降低足球的随机性。足球俱乐部在阵容上投资数百万美元，并知道个人统计数据如何转化为更好的结果可以优化投资。此外，体育博彩正在成倍增长，并且能够预测未来是有利可图和可取的。我们使用来自英超联赛（2020-2022）的机器学习模型和历史播放器数据来预测分数并了解个人绩效如何改善比赛的结果。我们比较了不同的预测技术，以最大程度地找到有用模型的可能性。我们创建了启发式和机器学习模型，以预测足球得分以比较不同的技术。我们使用了不同的功能集，显示的守门员统计数据比攻击者统计数据更为重要，以预测得分的目标。我们应用了广泛的评估过程来评估模型在现实世界应用中的功效。在预测连续100场比赛之后，我们设法正确预测了所有降级团队。我们表明，支持向量回归优于预测最终分数的其他技术，并且阵容不能改善预测。最后，当使用现实世界赔率数据模拟博彩系统时，我们的模型是有利可图的（回报率为42％）。
This paper aims to reduce randomness in football by analysing the role of lineups in final scores using machine learning prediction models we have developed. Football clubs invest millions of dollars on lineups and knowing how individual statistics translate to better outcomes can optimise investments. Moreover, sports betting is growing exponentially and being able to predict the future is profitable and desirable. We use machine learning models and historical player data from English Premier League (2020-2022) to predict scores and to understand how individual performance can improve the outcome of a match. We compared different prediction techniques to maximise the possibility of finding useful models. We created heuristic and machine learning models predicting football scores to compare different techniques. We used different sets of features and shown goalkeepers stats are more important than attackers stats to predict goals scored. We applied a broad evaluation process to assess the efficacy of the models in real world applications. We managed to predict correctly all relegated teams after forecast 100 consecutive matches. We show that Support Vector Regression outperformed other techniques predicting final scores and that lineups do not improve predictions. Finally, our model was profitable (42% return) when emulating a betting system using real world odds data.
