We present a transformer decoder based model, SportsNGEN, that is trained on sports player and ball tracking sequences that is capable of generating realistic and sustained gameplay. We train and evaluate SportsNGEN on a large database of professional tennis tracking data and demonstrate that by combining the generated simulations with a shot classifier and logic to start and end rallies, the system is capable of simulating an entire tennis match. In addition, a generic version of SportsNGEN can be customized to a specific player by fine-tuning on match data that includes that player. We show that our model is well calibrated and can be used to derive insights for coaches and broadcasters by evaluating counterfactual or what if options. Finally, we show qualitative results indicating the same approach works for football.

我们提出了一种基于Transformer解码器的模型SportsNGEN，通过训练运动员和球的追踪序列，能够生成逼真且持续的游戏场景。我们使用大量职业网球追踪数据来训练和评估SportsNGEN，并证明通过将生成的模拟结果与射击分类器和逻辑以开始和结束比赛，该系统能够模拟整个网球比赛。此外，可以通过在包括该选手的比赛数据上进行微调，将通用版本的SportsNGEN定制为特定选手。我们展示了我们的模型经过校准，并且可以通过评估反事实或“假设是”选项来为教练和广播人员提供见解。最后，我们展示了类似的方法也适用于足球。

SportsNGEN: 多人体育游戏持续生成