A context-aware framework to evaluate passing decisions in soccer

Evaluating passing decision by comparing the value of a pass to the value of realistic alternative passes.

Table 1

Resource	Date	Link
Talk presented at the 2026 Sports Analytics Workshop (SAW)	2026-05-05	Presentation (SAW 26)

Note

This project is an extension of the project: Expected Pass Value (xPV): A holistic framework for evaluating passing situations in soccer.

Overview

To evaluate passes, modern passing metrics, rely on machine learning models to estimate a value for each pass. While this is a reasonable approach, it does not account for the distribution of alternatives available to the passer in a given situation. As a result, a safe backwards pass under pressure might be rated negatively even if it was the best reasonable options. Conversely, a short progressive pass might obtain a positive value even though much better options were available. In this work, we address this issue and evaluate the decision behind a pass, taking into account the situational context. To do so, we compare the value of a pass, as estimated by an expected pass value (xPV) model, to the average value of alternative passes weighted by their feasibility and likelihood in the given situation. We obtain this context-dependent baseline for each situation by combining a conditional density surface, which capturing where the ball is likely to end up in a given situation, with a feasibility surface, which capturing where passes are structurally feasible given the game situation. The feasibility surface is obtained by estimating a neural inhomogeneous Poisson process (NIPP), while we use mixture density networks (MDNs) to obtain a full conditional density estimate for the likelihood of a pass. We apply our framework to tracking data from the 2022 FIFA World Cup, enabling the evaluation of player decision- making ability that goes beyond simply averaging pass values, rewarding players who consistently identify and exploit the best available options.