基于分位数回归与追踪数据学习人类运动员运动可达性（TTA）
Learning Human Sports Motion Feasibility via Quantile Regression on Tracking Data
AI 导读
一项 arXiv 研究将运动员到达场上任意位置的最短可行时间建模为条件分位数回归问题，估计移动时间的下分位数。模型在 PFF FC FIFA World Cup 2022 全部 64 场比赛的光学追踪数据上训练，其轻量级 MLP 在各项评估指标上均优于六个文献中的物理运动模型基线和可学习 reachable-hull 基线。作者还在篮球追踪数据和单场 NFL 数据集上评估了运动模型跨项目的迁移性。
正文 · 原文
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. 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. 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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36183 [cs.AI] |
| (or arXiv:2610.36183v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36183 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Marc Gutiérrez-Pérez [view email]
[v1]
Mon, 26 Oct 2026 19:17:19 UTC (3,533 KB)
来源：arXiv cs.AI · arxiv.org