论文标题
使用GPS跟踪数据的NFL防御通过干扰预测的基于ML的方法
ML-Based Approach for NFL Defensive Pass Interference Prediction Using GPS Tracking Data
论文作者
论文摘要
防守通行干扰（DPI）是NFL中最有影响力的处罚之一。 DPI是一个犯规的地方，首先是自动犯规的。有了对游戏的影响，裁判没有错误的空间。这也是一个非常罕见的事件，每100次通行证尝试发生1-2次。随着技术的改进，将许多物联网可穿戴设备放在运动员那里以收集有价值的数据，因此有一个扎实的应用机器学习（ML）技术来改善游戏的各个方面。这里介绍的工作是使用播放器跟踪GPS数据预测DPI的首次尝试。在整个2018年常规赛中，NFL的下一个Gen Stats收集了我们使用的数据。我们提出了高度不平衡时间序列分类的ML模型：LSTM，GRU，ANN和多元LSTM-FCN。结果表明，使用GPS跟踪数据预测DPI的成功有限。最佳性能模型的召回率很高，因此导致许多假阳性示例的分类。仔细查看数据证实，没有足够的信息来确定是否犯规。这项研究可能是用于视频序列分类的多步管道的过滤器，这可以解决此问题。
Defensive Pass Interference (DPI) is one of the most impactful penalties in the NFL. DPI is a spot foul, yielding an automatic first down to the team in possession. With such an influence on the game, referees have no room for a mistake. It is also a very rare event, which happens 1-2 times per 100 pass attempts. With technology improving and many IoT wearables being put on the athletes to collect valuable data, there is a solid ground for applying machine learning (ML) techniques to improve every aspect of the game. The work presented here is the first attempt in predicting DPI using player tracking GPS data. The data we used was collected by NFL's Next Gen Stats throughout the 2018 regular season. We present ML models for highly imbalanced time-series binary classification: LSTM, GRU, ANN, and Multivariate LSTM-FCN. Results showed that using GPS tracking data to predict DPI has limited success. The best performing models had high recall with low precision which resulted in the classification of many false positive examples. Looking closely at the data confirmed that there is just not enough information to determine whether a foul was committed. This study might serve as a filter for multi-step pipeline for video sequence classification which could be able to solve this problem.
