Spatial modeling of shot conversion in soccer to single out goalscoring ability

Goals are results of pin-point shots and it is a pivotal decision in soccer when, how and where to shoot. The main contribution of this study is two-fold. First, we show that there exists high spatial correlation in the data of shots across games. Then, we introduce a spatial process in the error structure to model the probability of conversion from a shot depending on positional and situational covariates. The model is developed using a full Bayesian framework. Next, based on the proposed model, we define two new measures that can appropriately quantify the impact of an individual in soccer, by evaluating the positioning sense and shooting ability. As a practical application, the method is implemented on Major League Soccer data from 2016/17 season.