TTA-Quantile: A Sports Time-to-Arrival Prediction Model via Quantile Regression
Summary:The TTA-Quantile model, developed by mguti97, predicts the shortest plausible time for a player to reach any location on the pitch given their current kinematic state. Using conditional quantile regression, the model, implemented as a lightweight multilayer perceptron (MLP), is trained on optical tracking data from the FIFA World Cup 2022 dataset. Evaluations show that TTA-Quantile outperforms traditional physics-based baselines across multiple metrics and demonstrates strong transferability acr
1. Technical Mechanism Analysis
The core of the TTA-Quantile model lies in transforming the problem of predicting a player's time-to-arrival (TTA) into a conditional quantile regression problem. Specifically, the model uses a multilayer perceptron (MLP) to capture the player's current kinematic state (such as position, velocity, acceleration, etc.) and predicts the lower bound of the time required to reach the target location, i.e., the 'shortest plausible time' to arrival. Unlike traditional physics-based models, TTA-Quantile does not rely on closed-form equations of motion but learns the patterns of movement directly from the data, thus more accurately reflecting the complexity of the real world.
2. Engineering Trade-offs and Performance
The model was trained on optical tracking data from 64 matches of the 2022 FIFA World Cup and optimized for the following aspects:
- Data Efficiency: Through quantile regression, the model can effectively utilize sparse data, avoiding overfitting and improving generalization.
- Computational Efficiency: The MLP architecture is relatively simple and has low computational overhead, making it suitable for real-time applications.
- Transferability: Tests on basketball and American football datasets show that the model has strong transferability across different sports.
Experimental results show that TTA-Quantile outperforms traditional physics-based models across multiple benchmarks. For example, on the FIFA dataset, the average prediction error of TTA-Quantile is 15% lower than traditional methods, and it performs particularly well in complex scenarios (such as multi-person confrontations and high-speed movements).
3. Developer Implementation and Deployment Recommendations
The open-source code of the TTA-Quantile model has been released on GitHub, allowing developers to easily integrate it into existing sports analytics platforms. Here are some deployment recommendations:
- Real-time Applications: Due to the high computational efficiency of the MLP, TTA-Quantile is suitable for real-time prediction and tactical decision support.
- Multi-sport Support: Developers can fine-tune the model according to the data characteristics of different sports to improve prediction accuracy.
- Data Preprocessing: To achieve the best performance, it is recommended to standardize and normalize the input data and ensure data quality.
4. Future Outlook
The release of TTA-Quantile brings new possibilities to the field of sports analytics. In the future, researchers can further explore the following directions:
- Multimodal Data Fusion: Combining video, sensor, and physiological data to improve the model's prediction accuracy and robustness.
- Personalized Prediction: Providing personalized time-to-arrival predictions based on the characteristics and styles of different players.
- Real-time Tactical Optimization: Integrating TTA-Quantile with real-time tactical planning systems to achieve more intelligent tactical decision support.
— END —Source: ArXiv AI (cs.AI) (2026-10-29)
Tags: #TTA-Quantile #Quantile Regression #Sports Analytics #Multilayer Perceptron #Movement Prediction
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