修正足球表现评分PlayeRank的训练漏洞，提升比赛预测准确性。
Revisiting PlayeRank
- 修复了训练时误包含进球事件导致的评分偏差
- 新权重使94.13%比赛中强队胜或平局，符合预期
- 支持实时计算，教练可每5分钟获取比赛态势洞察
本文重新审视2019年由Pappalardo等人提出的足球表现评分系统PlayeRank。首先，分析解决“哪类事件对赢球影响更大”分类问题的线性支持向量机（SVM）权重，发现此前结果在训练阶段错误地包含了进球事件，导致不一致。我们修正该问题，提出新的权重，可有效解决该分类任务。基于“强队应始终获胜”的直觉，将球队质量定义为参与比赛的平均球员数。结果显示，使用原始PlayeRank时，在94.13%的比赛中，实力较强的队伍要么获胜，要么在比分接近时打成平局。最后，我们提出一种在线计算PlayeRank的方法，利用改进的自由分析工具，实现每5分钟对一场比赛进行一次实时分析。通过专家与教练评估，证实该信息能提供赛中前所未有的决策支持。
原文摘要 · Abstract (English)
In this article we revise the football's performance score called PlayeRank, designed and evaluated by Pappalardo et al.\ in 2019. First, we analyze the weights extracted from the Linear Support Vector Machine (SVM) that solves the classification problem of "which set of events has a higher impact on the chances of winning a match". Here, we notice that the previously published results include the Goal-Scored event during the training phase, which produces inconsistencies. We fix these inconsistencies, and show new weights capable of solving the same problem. Following the intuition that the best team should always win a match, we define the team's quality as the average number of players involved in the game. We show that, using the original PlayeRank, in 94.13\% of the matches either the superior team beats the inferior team or the teams end tied if the scores are similar. Finally, we present a way to use PlayeRank in an online fashion using modified free analysis tools. Calculating this modified version of PlayeRank, we performed an online analysis of a real football match every five minutes of game. Here, we evaluate the usefulness of that information with experts and managers, and conclude that the obtained data indeed provides useful information that was not previously available to the manager during the match.
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