# Actions Speak Louder Than Goals: Valuing Player Actions in Soccer

arXiv: https://arxiv.org/abs/1802.07127 · HTML: https://mlsanity.com/paper/1802.07127/

## Key facts

- Cited by 90 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2018.

## Built on by (most cited first, 30 of 90)

- [Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting](https://mlsanity.com/paper/2104.09333/) (2021)
- [VARS: Video Assistant Referee System for Automated Soccer Decision Making from Multiple Views](https://mlsanity.com/paper/2304.04617/) (2023)
- [Hulk: A Universal Knowledge Translator for Human-Centric Tasks](https://mlsanity.com/paper/2312.01697/) (2023)
- [ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton](https://mlsanity.com/paper/2112.01044/) (2021)
- [Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction](https://mlsanity.com/paper/2206.01899/) (2022)
- [Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study](https://mlsanity.com/paper/2103.09627/) (2021)
- [ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis](https://mlsanity.com/paper/2306.04948/) (2023)
- [Towards Active Learning for Action Spotting in Association Football Videos](https://mlsanity.com/paper/2304.04220/) (2023)
- [Estimating counterfactual treatment outcomes over time in complex multiagent scenarios](https://mlsanity.com/paper/2206.01900/) (2022)
- [Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM](https://mlsanity.com/paper/2306.08206/) (2023)
- [Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis](https://mlsanity.com/paper/2302.09276/) (2023)
- [TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos](https://mlsanity.com/paper/2404.13868/) (2024)
- [TranSPORTmer: A Holistic Approach to Trajectory Understanding in Multi-Agent Sports](https://mlsanity.com/paper/2410.17785/) (2024)
- [What Happened Next? Using Deep Learning to Value Defensive Actions in Football Event-Data](https://mlsanity.com/paper/2106.01786/) (2021)
- [Exploring the Long Short-Term Dependencies to Infer Shot Influence in Badminton Matches](https://mlsanity.com/paper/2109.06431/) (2021)
- [Automatic event detection in football using tracking data](https://mlsanity.com/paper/2202.00804/) (2022)
- [Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning](https://mlsanity.com/paper/2305.17886/) (2023)
- [A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges](https://mlsanity.com/paper/2409.07569/) (2024)
- [Location analysis of players in UEFA EURO 2020 and 2022 using generalized valuation of defense by estimating probabilities](https://mlsanity.com/paper/2212.00021/) (2022)
- [Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset](https://mlsanity.com/paper/2306.15664/) (2023)
- [F3Set: Towards Analyzing Fast, Frequent, and Fine-grained Events from Videos](https://mlsanity.com/paper/2504.08222/) (2025)
- [Graph Neural Networks to Predict Sports Outcomes](https://mlsanity.com/paper/2207.14124/) (2022)
- [A Machine Learning Approach for Player and Position Adjusted Expected Goals in Football (Soccer)](https://mlsanity.com/paper/2301.13052/) (2023)
- [A Strategic Framework for Optimal Decisions in Football 1-vs-1 Shot-Taking Situations: An Integrated Approach of Machine Learning, Theory-Based Modeling, and Game Theory](https://mlsanity.com/paper/2307.14732/) (2023)
- [Foul prediction with estimated poses from soccer broadcast video](https://mlsanity.com/paper/2402.09650/) (2024)
- [OpenSTARLab: Open Approach for Spatio-Temporal Agent Data Analysis in Soccer](https://mlsanity.com/paper/2502.02785/) (2025)
- [Data-driven Analysis for Understanding Team Sports Behaviors](https://mlsanity.com/paper/2102.07545/) (2021)
- [How does AI play football? An analysis of RL and real-world football strategies](https://mlsanity.com/paper/2111.12340/) (2021)
- [ESTA: An Esports Trajectory and Action Dataset](https://mlsanity.com/paper/2209.09861/) (2022)
- [VREN: Volleyball Rally Dataset with Expression Notation Language](https://mlsanity.com/paper/2209.13846/) (2022)

## Abstract (arXiv)

Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process. Unfortunately, most traditional metrics fall short in addressing this task as they either focus on rare actions like shots and goals alone or fail to account for the context in which the actions occurred. This paper introduces (1) a new language for describing individual player actions on the pitch and (2) a framework for valuing any type of player action based on its impact on the game outcome while accounting for the context in which the action happened. By aggregating soccer players' action values, their total offensive and defensive contributions to their team can be quantified. We show how our approach considers relevant contextual information that traditional player evaluation metrics ignore and present a number of use cases related to scouting and playing style characterization in the 2016/2017 and 2017/2018 seasons in Europe's top competitions.