SportsNGEN: Sustained Generation of Multi-player Sports Gameplay
Lachlan Thorpe Lewis Bawden Karanjot Vendal John Bronskill Richard E. Turner
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.