论文标题
使用Hidden Markov模型预测国家橄榄球联盟的比赛电话
Predicting play calls in the National Football League using hidden Markov models
论文作者
论文摘要
近年来，数据驱动的方法已成为各种运动中的流行工具，例如分析对手的潜在策略。尽管在篮球和棒球等体育运动中逐场比赛或球员跟踪数据的可用性导致了体育分析研究的增加，但长期以来，国家橄榄球联盟（NFL）的等效数据集并非很长一段时间。在此贡献中，我们考虑了由www.kaggle.com提供的全面逐播NFL数据集，其中包括289,191个观察值，以使用隐藏的Markov模型预测NFL中的播放电话。 2018 NFL季节的样本外预测准确性为71.5％，与NFL中的Play Call Call预测相似的类似研究相比，这要高得多。
In recent years, data-driven approaches have become a popular tool in a variety of sports to gain an advantage by, e.g., analysing potential strategies of opponents. Whereas the availability of play-by-play or player tracking data in sports such as basketball and baseball has led to an increase of sports analytics studies, equivalent datasets for the National Football League (NFL) were not freely available for a long time. In this contribution, we consider a comprehensive play-by-play NFL dataset provided by www.kaggle.com, comprising 289,191 observations in total, to predict play calls in the NFL using hidden Markov models. The resulting out-of-sample prediction accuracy for the 2018 NFL season is 71.5%, which is substantially higher compared to similar studies on play call predictions in the NFL.
