Institute of Training and Computer Science in Sport, German Sport University Cologne, Cologne, Germany.
PLoS One. 2018 Jun 5;13(6):e0198668. doi: 10.1371/journal.pone.0198668. eCollection 2018.
Betting odds are frequently found to outperform mathematical models in sports related forecasting tasks, however the factors contributing to betting odds are not fully traceable and in contrast to rating-based forecasts no straightforward measure of team-specific quality is deducible from the betting odds. The present study investigates the approach of combining the methods of mathematical models and the information included in betting odds. A soccer forecasting model based on the well-known ELO rating system and taking advantage of betting odds as a source of information is presented. Data from almost 15.000 soccer matches (seasons 2007/2008 until 2016/2017) are used, including both domestic matches (English Premier League, German Bundesliga, Spanish Primera Division and Italian Serie A) and international matches (UEFA Champions League, UEFA Europe League). The novel betting odds based ELO model is shown to outperform classic ELO models, thus demonstrating that betting odds prior to a match contain more relevant information than the result of the match itself. It is shown how the novel model can help to gain valuable insights into the quality of soccer teams and its development over time, thus having a practical benefit in performance analysis. Moreover, it is argued that network based approaches might help in further improving rating and forecasting methods.
赔率在体育相关的预测任务中经常被发现优于数学模型，然而，促成赔率的因素并不能完全追踪，与基于评级的预测不同，从赔率中推导出团队特定质量的直接衡量标准是不可得的。本研究探讨了结合数学模型方法和赔率中包含的信息的方法。提出了一种基于著名的 ELO 评级系统的足球预测模型，并利用赔率作为信息来源。该模型使用了近 15000 场足球比赛（2007/2008 赛季至 2016/2017 赛季）的数据，包括国内比赛（英超、德甲、西甲和意甲）和国际比赛（欧冠、欧联）。结果表明，基于赔率的新型 ELO 模型优于经典 ELO 模型，这表明比赛前的赔率包含比比赛结果更多的相关信息。研究还展示了如何利用这种新模型深入了解足球队伍的质量及其随时间的发展，从而在绩效分析中具有实际的益处。此外，还认为基于网络的方法可能有助于进一步改进评级和预测方法。
