Abstract
The expected possession value (EPV) of a soccer possession represents the likelihood of a team scoring or conceding the next goal at any time instance. In this work, we develop a comprehensive analysis framework for the EPV, providing soccer practitioners with the ability to evaluate the impact of observed and potential actions, both visually and analytically. The EPV expression is decomposed into a series of subcomponents that model the influence of passes, ball drives and shot actions on the expected outcome of a possession. We show we can learn from spatiotemporal tracking data and obtain calibrated models for all the components of the EPV. For the components related with passes, we produce visually-interpretable probability surfaces from a series of deep neural network architectures built on top of flexible representations of game states. Additionally, we present a series of novel practical applications providing coaches with an enriched interpretation of specific game situations. This is, to our knowledge, the first EPV approach in soccer that uses this decomposition and incorporates the dynamics of the 22 players and the ball through tracking data.
Highlights
Professional sports teams have started to gain a competitive advantage in recent decades by using advanced data analysis
We develop a series of deep learning architectures to estimate the expected possession value surface of potential passes, pass success probability, pass selection probability surfaces, and show these three networks provide both accurate and calibrated surface estimates
The definition of success varies from one event to another: a pass is successful if a player of the same team receives it, a ball drive is successful if the team does not lose control of the ball after the action occurs, and a shot is labeled as successful if a goal is scored from that shot
Summary
Professional sports teams have started to gain a competitive advantage in recent decades by using advanced data analysis. Handcrafted features based on the opinion of a committee of soccer experts have been used to quantify the likelihood of scoring in a continuous-time range during a match (Link et al 2016) Another approach uses a broad set of attributes to estimate individual actions’ value during the development of possessions (Decroos et al 2019). In Spearman (2018), a physics-based statistical model is designed to quantify the quality of players’ off-ball positioning based on the positional characteristics at the time of the action that precedes a goal-scoring opportunity All of these previous attempts on quantifying action value in soccer assume a series of constraints that reduce the scope and reach of the solution. We provide such a framework and go one step further estimating the added value of observed actions by providing an approach for estimating the expected value of the possession at any time instance
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