# A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions

arXiv: https://arxiv.org/abs/2011.09426 · HTML: https://mlsanity.com/paper/2011.09426/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 24 of 24)

- [Explainable expected goal models for performance analysis in football analytics](https://mlsanity.com/paper/2206.07212/) (2022)
- [Glocal Explanations of Expected Goal Models in Soccer](https://mlsanity.com/paper/2308.15559/) (2023)
- [Estimating Player Performance in Different Contexts Using Fine-tuned Large Events Models](https://mlsanity.com/paper/2402.06815/) (2024)
- [A Foundation Model for Soccer](https://mlsanity.com/paper/2407.14558/) (2024)
- [Presenting Multiagent Challenges in Team Sports Analytics](https://mlsanity.com/paper/2303.13660/) (2023)
- [Action Valuation in Sports: A Survey](https://mlsanity.com/paper/2504.06163/) (2025)
- [Better Prevent than Tackle: Valuing Defense in Soccer Based on Graph Neural Networks](https://mlsanity.com/paper/2512.10355/) (2025)
- [Development of an expected possession value model to analyse team attacking performances in rugby league](https://mlsanity.com/paper/2105.05303/) (2021)
- [Is it worth the effort? Understanding and contextualizing physical metrics in soccer](https://mlsanity.com/paper/2204.02313/) (2022)
- [Valuing Players Over Time](https://mlsanity.com/paper/2209.03882/) (2022)
- [Revisiting Expected Possession Value in Football: Introducing a Benchmark, U-Net Architecture, and Reward and Risk for Passes](https://mlsanity.com/paper/2502.02565/) (2025)
- [The path to a goal: Understanding soccer possessions via path signatures](https://mlsanity.com/paper/2508.12930/) (2025)
- [A Machine Learning Framework for Off Ball Defensive Role and Performance Evaluation in Football](https://mlsanity.com/paper/2601.00748/) (2026)
- [Modeling Matches as Language: A Generative Transformer Approach for Counterfactual Player Valuation in Football](https://mlsanity.com/paper/2603.15212/) (2026)
- [Space-Creating versus Dead Possession: An Off-Ball Possession-Quality Index for Broadcast Football](https://mlsanity.com/paper/2608.09887/) (2026)
- [SoccerTrack v2: A Full-Pitch Panoramic Video Dataset for Game State Reconstruction and Ball Action Spotting](https://mlsanity.com/paper/2508.01802/) (2025)
- [Large-Scale In-Game Outcome Forecasting for Match, Team and Players in Football using an Axial Transformer Neural Network](https://mlsanity.com/paper/2511.18730/) (2025)
- [Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer](https://mlsanity.com/paper/2512.00203/) (2025)
- [Structural Pass Analysis in Football: Learning Pass Archetypes and Tactical Impact from Spatio-Temporal Tracking Data](https://mlsanity.com/paper/2603.28916/) (2026)
- [Maximising the Set-Piece Return: Optimising Football Corner Tactics with Graph Reinforcement Learning](https://mlsanity.com/paper/2606.06353/) (2026)
- [Monte Carlo Pass Search: Using Trajectory Generation for 3D Counterfactual Pass Evaluation in Football](https://mlsanity.com/paper/2606.11120/) (2026)
- [SportD: How do VLMs physically strategize?](https://mlsanity.com/paper/2607.14616/) (2026)
- [How Much Velocity Does Off-Ball Space Value Need? A Broadcast-Viewport Benchmark](https://mlsanity.com/paper/2609.10801/) (2026)
- [Graph-to-Grid (G2G): Continuous-Coordinate Feature Painting for Soccer Pass Surfaces](https://mlsanity.com/paper/2609.24040/) (2026)

## Abstract (arXiv)

The expected possession value (EPV) of a soccer possession represents the likelihood of a team scoring or receiving the next goal at any time instance. By decomposing the EPV into a series of subcomponents that are estimated separately, we develop a comprehensive analysis framework providing soccer practitioners with the ability to evaluate the impact of both observed and potential actions. We show we can obtain calibrated models for all the components of EPV, including a set of yet-unexplored problems in soccer. We produce visually-interpretable probability surfaces for potential passes from a series of deep neural network architectures that learn from low-level spatiotemporal data. Additionally, we present a series of novel practical applications providing coaches with an enriched interpretation of specific game situations.