TTA-Quantile: A Quantile Regression Model for Predicting Sports Player Arrival Times

Summary:The TTA-Quantile model, developed by mguti97, predicts the shortest plausible time (Time-to-Arrival, TTA) for a player to reach any location on the pitch given their current kinematic state. Using conditional quantile regression, the model learns the lower-envelope arrival time by training on optical tracking data from the 2022 PFF FC FIFA World Cup. Evaluations show that TTA-Quantile outperforms physics-based baselines across multiple metrics and demonstrates strong transferability across sport

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Technical Mechanism Analysis

The core idea of the TTA-Quantile model is to cast the problem of predicting the shortest plausible time for an athlete to reach a target location as a conditional quantile regression problem. Specifically, the model estimates the lower envelope of the locomotion time given the current kinematic state of the player, thereby learning the lower-bound arrival time. This approach avoids the complexities of traditional physics-based modeling, such as intricate dynamic equations and spatial occupancy modeling.

The model is trained on optical tracking data from all 64 matches of the 2022 PFF FC FIFA World Cup, which provides detailed movement trajectories and positional information of players during the games. TTA-Quantile employs a lightweight multilayer perceptron (MLP) architecture to ensure efficient training and inference.

Engineering Trade-offs and Performance

Compared to physics-based baseline models, TTA-Quantile demonstrates superior performance across multiple evaluation metrics, particularly in handling complex movement scenarios and dynamic changes. Furthermore, TTA-Quantile shows strong transferability across different sports, as evidenced by its performance on basketball and NFL datasets.

However, TTA-Quantile also has some limitations. For instance, the model is highly data-dependent and requires large amounts of high-quality training data to achieve optimal performance. Additionally, the MLP architecture may have limitations in capturing long-range dependencies, and future work could explore more complex model architectures, such as Transformer-based models, to further enhance performance.

Developer Deployment and Implementation Recommendations

For developers in the field of sports analytics, TTA-Quantile offers a new approach and tool that can be applied to scenarios such as athlete movement trajectory prediction, game strategy analysis, and athlete performance evaluation. Developers can refer to the following recommendations for implementation and deployment:

1. Data Preparation: Ensure sufficient high-quality movement tracking data and perform appropriate preprocessing and cleaning.
2. Model Training: Adjust model hyperparameters according to the specific application scenario and conduct thorough training and validation.
3. Model Evaluation: Use multiple evaluation metrics to assess the model and perform cross-domain generalization tests.
4. Deployment Optimization: Optimize and compress the model according to actual application requirements to improve inference speed and reduce computational costs.

Conclusion

The release of the TTA-Quantile model provides a new technical tool for the field of sports analytics. Its quantile regression-based approach and cross-sport generalization capabilities give it a significant advantage in predicting athlete arrival times. As data quality and model architecture continue to improve, TTA-Quantile is expected to play an even greater role in more application scenarios.

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Source: ArXiv AI (cs.AI) (2026-10-28)

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Tags: #Sports Analytics Follow #Quantile Regression Follow #Machine Learning Follow #Sports Technology Follow #Open Source Model Follow