Learning Human Sports Motion Feasibility via Quantile Regression on Tracking Data

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Computer Science > Artificial Intelligence arXiv:2610.36183 (cs) [Submitted on 26 Oct 2026] Title:Learning Human Sports Motion Feasibility via Quantile Regression on Tracking Data Authors:Marc Gutiérrez-Pérez, Antonio Agudo View a PDF of the paper titled Learning Human Sports Motion Feasibility via Quantile Regression on Tracking Data, by Marc Guti’errez-P’erez and Antonio Agudo View PDF HTML (experimental)

计算机科学 > 人工智能 arXiv:2610.36183 (cs) [提交于 2026 年 10 月 26 日] 标题：通过追踪数据的分位数回归学习人类运动的可行性作者：Marc Gutiérrez-Pérez, Antonio Agudo 查看论文 PDF，标题为《通过追踪数据的分位数回归学习人类运动的可行性》，作者：Marc Gutiérrez-Pérez 和 Antonio Agudo 查看 PDF HTML（实验性）

Abstract:We study time-to-arrival (TTA) in sports: the shortest plausible time a player needs to reach an arbitrary location on the pitch given their current kinematic state, estimated as the lower envelope of observed locomotion times. Rather than modeling space ownership through position density estimation or closed-form physics-based models, we cast TTA as a conditional quantile regression problem, estimating the lower quantile of locomotion time so the model learns a player’s lower-envelope arrival time.

摘要：我们研究了体育运动中的到达时间（TTA）：即在给定当前运动学状态的情况下，球员到达球场上任意位置所需的最短合理时间，该时间被估计为观测到的移动时间的下包络线。我们没有通过位置密度估计或封闭形式的物理模型来模拟空间占有率，而是将 TTA 视为一个条件分位数回归问题，通过估计移动时间的下分位数，使模型能够学习球员的下包络线到达时间。

We train the model on optical tracking data from all 64 matches of the PFF FC FIFA World Cup 2022 dataset, and evaluate a lightweight multilayer perceptron against six player-motion models from the literature, re-expressed as TTA predictors, and a learned reachable- hull baseline. Across our evaluation metrics, the MLP consistently outperforms every physics-based baseline.

我们使用 PFF FC 2022 年国际足联世界杯数据集全部 64 场比赛的光学追踪数据对模型进行了训练，并将一个轻量级多层感知机（MLP）与文献中的六种球员运动模型（重新表述为 TTA 预测器）以及一个学习到的可达外壳基准进行了评估。在我们的评估指标中，MLP 的表现始终优于所有基于物理的基准模型。

To probe the generality of the learned locomotion model, we further evaluate it on basketball tracking data and a single-game NFL tracking dataset, analyzing the transferability of player motion dynamics across sports. Our project is available at this https URL.

为了探究所学移动模型的通用性，我们进一步在篮球追踪数据和一场 NFL 追踪数据集上对其进行了评估，分析了球员运动动力学在不同体育项目间的可迁移性。我们的项目可在该 HTTPS 链接中获取。