足球比赛预测：机器学习与泊松方法之比较
Match predictions in soccer: Machine learning vs. Poisson approaches
AI总结：
本文在5个欧洲顶级联赛数据上比较了神经网络、随机森林等机器学习模型与泊松模型在单场足球比赛预测中的表现，发现特征选择与模型选择对预测质量影响均较小，并探讨了可能的改进方向。
摘要：
预测足球比赛结果具有重大意义。这不仅源于该运动的普及性和私人“博彩竞猜”的乐趣，也源于庞大的体育博彩市场。以往依赖专家知识和直觉，如今则使用模型分析大量数据并据此进行预测。除泊松模型外，属于机器学习（ML）类别的方法正被越来越多地采用。例如，本文比较了神经网络或随机森林模型之间以及它们与泊松模型在单场比赛预测方面的表现。每种方法均以一个赛季的比赛结果作为数据基础。分析针对5个欧洲顶级联赛展开。统计分析表明，各支球队的表现在一个赛季内不会发生系统性变化。为了尽可能准确地刻画表现水平，除待预测的比赛外，所有比赛结果均可作为特征以相同权重使用。可以看出，特征的具体选择以及模型的选择对预测质量仅产生轻微影响。文中还讨论了改进比赛预测的可能途径。
英文摘要：
Predicting the results of soccer matches is of great interest. This is not only due to the popularity of the sport and the joy of private "betting rounds", but also due to the large sports betting market. Where previously expert knowledge and intuition were used, today there are models that analyze large amounts of data and make predictions based on them. In addition to Poisson models, approaches that belong to the machine learning (ML) category are increasingly being used. These include, for example, neural network or random forest models, which are compared in this article with each other as well as with Poisson models with regard to single-match prediction. In each case, the match results of a season are used as the data basis. The analysis is carried out for 5 European top leagues. A statistical analysis shows that the performance levels of the teams do not change systematically during a season. In order to characterize performance levels as accurately as possible, all match results, except from the match to be predicted, can be used as features with equal weighting. It can be seen that both, the exact choice of features and the choice of model, have only a minor influence on the prediction quality. Possible improvements in match prediction are discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
