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

## TL;DR

This paper introduces a Bayesian spatial model for shot conversion in soccer, accounting for spatial correlation and evaluating individual players' positioning and shooting skills, demonstrated on MLS data.

## Contribution

It develops a novel spatial Bayesian model for shot conversion and proposes new metrics to quantify players' positioning and shooting abilities.

## Key findings

- High spatial correlation in shot data identified
- Model effectively predicts shot conversion probabilities
- New metrics successfully evaluate individual player skills

## Abstract

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. At first, after showing that there exists high spatial correlation in the data of shots across games, 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. Secondly, 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 senses and shooting abilities of the players. As a practical application, the method is implemented on Major League Soccer data from 2016/17 season.