We develop a new approach to evaluating all types of soccer actions from play-by-play event data

2020

We develop a new approach to evaluating all types of soccer actions from play-by-play event data

2020

- The classifier is implemented with a neural network rather than CatBoost in due to the size of dataset
- We are indebted for helpful discussion and comments to Norm Ferns, Evin Keane, and Bahar Pourbabee from Sportlogiq
- We denote the number of times such a transition occurs as
- Where the \('\) indicates the successor triple. We freely use this notation for marginal counts as well, for instance
- Line uses the empirical estimate of the expected Q-value \(Q_{{ team}}(s',a')]\) given that player i acts computed from the maximum likelihood estimates of the transition probabilities: