​​GNNs + ⚽ = 🏆

The NeurIPS deadline has passed and we are back to posting!

If you thought that sophisticated GNNs for modelling trajectories are only used for molecular dynamics and arcane quantum simulations, fear not! Here is a cool practical application with a very high potential outreach: Graph Imputer by DeepMind and FC Liverpool (YNWA and checkmate, Man City) predicts trajectories of football players (and the ball).

The graph consists of 23 nodes, gets updated with a standard message passing encoder and a special time-dependent LSTM. The dataset is quite novel, too - it consists of 105 English Premier League matches (avg 90 min each), all players and the ball were tracked at 25 fps, and the resulting training trajectory sequences encode about 9.6 seconds of gameplay.

The paper is easy to read and has numerous football illustrations, check it out! Sports tech is actively growing those days, and football analysts now could go even deeper in studying their competitors. Will EPL clubs compete for GNN and Graph ML researchers in the upcoming transfer windows? Time to create our own transfermarkt? 😉