# Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction

arXiv: https://arxiv.org/abs/2206.01899 · HTML: https://mlsanity.com/paper/2206.01899/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 54 references that are in the map.
- First posted on arXiv 4 Jun 2022.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [A Recurrent Latent Variable Model for Sequential Data](https://mlsanity.com/paper/1506.02216/) (2015)
- [Neural Relational Inference for Interacting Systems](https://mlsanity.com/paper/1802.04687/) (2018)
- [Google Research Football: A Novel Reinforcement Learning Environment](https://mlsanity.com/paper/1907.11180/) (2019)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [Sequential Neural Models with Stochastic Layers](https://mlsanity.com/paper/1605.07571/) (2016)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [Coordinated Multi-Agent Imitation Learning](https://mlsanity.com/paper/1703.03121/) (2017)
- [Z-Forcing: Training Stochastic Recurrent Networks](https://mlsanity.com/paper/1711.05411/) (2017)
- [Generating Multi-Agent Trajectories using Programmatic Weak Supervision](https://mlsanity.com/paper/1803.07612/) (2018)
- [Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation](https://mlsanity.com/paper/1805.11088/) (2018)
- [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)
- [Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling](https://mlsanity.com/paper/2007.03155/) (2020)
- [The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA](https://mlsanity.com/paper/1803.02940/) (2018)
- [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)
- [Measuring Football Players' On-the-ball Contributions From Passes During Games](https://mlsanity.com/paper/1810.02252/) (2018)

## Built on by (most cited first, 20 of 20)

- [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)
- [Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis](https://mlsanity.com/paper/2302.09276/) (2023)
- [Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning](https://mlsanity.com/paper/2305.17886/) (2023)
- [Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations](https://mlsanity.com/paper/2305.13030/) (2023)
- [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)
- [FootBots: A Transformer-based Architecture for Motion Prediction in Soccer](https://mlsanity.com/paper/2406.19852/) (2024)
- [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)
- [Mathematical models for off-ball scoring prediction in basketball](https://mlsanity.com/paper/2406.08749/) (2024)
- [Space evaluation based on pitch control using drone video in Ultimate](https://mlsanity.com/paper/2409.14588/) (2024)
- [TrackID3x3: A Dataset and Algorithm for Multi-Player Tracking with Identification and Pose Estimation in 3x3 Basketball Full-court Videos](https://mlsanity.com/paper/2503.18282/) (2025)
- [Space evaluation at the starting point of soccer transitions](https://mlsanity.com/paper/2505.14711/) (2025)
- [Evaluating Movement Initiation Timing in Ultimate Frisbee via Temporal Counterfactuals](https://mlsanity.com/paper/2508.17611/) (2025)
- [Expandable Decision-Making States for Multi-Agent Deep Reinforcement Learning in Soccer Tactical Analysis](https://mlsanity.com/paper/2510.00480/) (2025)
- [Passing Heatmap Prediction Based on Transformer Model and Tracking Data](https://mlsanity.com/paper/2309.01526/) (2023)
- [SportsNGEN: Sustained Generation of Realistic Multi-player Sports Gameplay](https://mlsanity.com/paper/2403.12977/) (2024)
- [Wide Open Gazes: Quantifying Visual Exploratory Behavior in Soccer with Pose Enhanced Positional Data](https://mlsanity.com/paper/2602.18519/) (2026)
- [Right Move, Right Time: Multi-Sport Space Evaluation Platform for Ultimate Frisbee, Basketball, and Soccer](https://mlsanity.com/paper/2603.06585/) (2026)
- [Monte Carlo Pass Search: Using Trajectory Generation for 3D Counterfactual Pass Evaluation in Football](https://mlsanity.com/paper/2606.11120/) (2026)
- [Strategic Multi-Agent Learning for Interpretable Action Valuation of All Players in Football](https://mlsanity.com/paper/2610.05961/) (2026)

## Abstract (arXiv)

Evaluating the individual movements for teammates in soccer players is crucial for assessing teamwork, scouting, and fan engagement. It has been said that players in a 90-min game do not have the ball for about 87 minutes on average. However, it has remained difficult to evaluate an attacking player without receiving the ball, and to reveal how movement contributes to the creation of scoring opportunities for teammates. In this paper, we evaluate players who create off-ball scoring opportunities by comparing actual movements with the reference movements generated via trajectory prediction. First, we predict the trajectories of players using a graph variational recurrent neural network that can accurately model the relationship between players and predict the long-term trajectory. Next, based on the difference in the modified off-ball evaluation index between the actual and the predicted trajectory as a reference, we evaluate how the actual movement contributes to scoring opportunity compared to the predicted movement. For verification, we examined the relationship with the annual salary, the goals, and the rating in the game by experts for all games of a team in a professional soccer league in a year. The results show that the annual salary and the proposed indicator correlated significantly, which could not be explained by the existing indicators and goals. Our results suggest the effectiveness of the proposed method as an indicator for a player without the ball to create a scoring chance for teammates.