# SoccerMap: A Deep Learning Architecture for Visually-Interpretable Analysis in Soccer

arXiv: https://arxiv.org/abs/2010.10202 · HTML: https://mlsanity.com/paper/2010.10202/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 17 of 17)

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- [Better Prevent than Tackle: Valuing Defense in Soccer Based on Graph Neural Networks](https://mlsanity.com/paper/2512.10355/) (2025)
- [Towards optimized actions in critical situations of soccer games with deep reinforcement learning](https://mlsanity.com/paper/2109.06625/) (2021)
- [Event Detection in Football using Graph Convolutional Networks](https://mlsanity.com/paper/2301.10052/) (2023)
- [Passing Heatmap Prediction Based on Transformer Model and Tracking Data](https://mlsanity.com/paper/2309.01526/) (2023)
- [HydraNet: Momentum-Driven State Space Duality for Multi-Granularity Tennis Tournaments Analysis](https://mlsanity.com/paper/2505.21882/) (2025)
- [Wide Open Gazes: Quantifying Visual Exploratory Behavior in Soccer with Pose Enhanced Positional Data](https://mlsanity.com/paper/2602.18519/) (2026)
- [TemPose-TF-ASF: Two-Stage Bidirectional Stroke Context Fusion for Badminton Stroke Classification](https://mlsanity.com/paper/2605.02558/) (2026)
- [Evaluating passing decision-making in professional football: An enhanced MPNN approach to Receiver Selection](https://mlsanity.com/paper/2605.25696/) (2026)
- [Hierarchical Possession-Aware Graph Pointer Network for Pass Receiver Selection](https://mlsanity.com/paper/2609.04803/) (2026)
- [Graph-to-Grid (G2G): Continuous-Coordinate Feature Painting for Soccer Pass Surfaces](https://mlsanity.com/paper/2609.24040/) (2026)
- [SambaGraph: Action-Reaction Spatio-Temporal Graphs for Soccer Tactical Response Modeling](https://mlsanity.com/paper/2609.25569/) (2026)

## Abstract (arXiv)

We present a fully convolutional neural network architecture that is capable of estimating full probability surfaces of potential passes in soccer, derived from high-frequency spatiotemporal data. The network receives layers of low-level inputs and learns a feature hierarchy that produces predictions at different sampling levels, capturing both coarse and fine spatial details. By merging these predictions, we can produce visually-rich probability surfaces for any game situation that allows coaches to develop a fine-grained analysis of players' positioning and decision-making, an as-yet little-explored area in sports. We show the network can perform remarkably well in the estimation of pass success probability, and present how it can be adapted easily to approach two other challenging problems: the estimation of pass-selection likelihood and the prediction of the expected value of a pass. Our approach provides a novel solution for learning a full prediction surface when there is only a single-pixel correspondence between ground-truth outcomes and the predicted probability map. The flexibility of this architecture allows its adaptation to a great variety of practical problems in soccer. We also present a set of practical applications, including the evaluation of passing risk at a player level, the identification of the best potential passing options, and the differentiation of passing tendencies between teams.