Stop Guessing: Optimizing Goalkeeper Policies for Soccer Penalty Kicks

TL;DR

This paper introduces a simulation framework to optimize goalkeeper strategies in soccer penalties, accounting for player interactions and individual skills, based on extensive annotated data.

Contribution

It develops a player-agnostic simulation model that evaluates and optimizes goalkeeper policies considering opponent behavior and skill levels.

Findings

Framework effectively evaluates goalkeeper strategies.

Incorporates rich choice sets and skill information.

Optimizes policies for real-world penalty scenarios.

Abstract

Penalties are fraught and game-changing moments in soccer games that teams explicitly prepare for. Consequently, there has been substantial interest in analyzing them in order to provide advice to practitioners. From a data science perspective, such analyses suffer from a significant limitation: they make the unrealistic simplifying assumption that goalkeepers and takers select their action -- where to dive and where to the place the kick -- independently of each other. In reality, the choices that some goalkeepers make depend on the taker's movements and vice-versa. This adds substantial complexity to the problem because not all players have the same action capacities, that is, only some players are capable of basing their decisions on their opponent's movements. However, the small sample sizes on the player level mean that one may have limited insights into a specific opponent's capacities. We address these challenges by developing a player-agnostic simulation framework that can evaluate the efficacy of different goalkeeper strategies. It considers a rich set of choices and incorporates information about a goalkeeper's skills. Our work is grounded in a large dataset of penalties that were annotated by penalty experts and include aspects of both kicker and goalkeeper strategies. We show how our framework can be used to optimize goalkeeper policies in real-world situations.