arXiv Machine Learning By Seongjin Choi

Training-Free Off-Screen Player Imputation for Broadcast-Based Spatial Football Analytics

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arXiv:2607. 11548v1 Announce Type: cross Abstract: Spatial football metrics such as pitch control assume access to the positions of all 22 players, yet the most widely available source of positional data -- the broadcast main camera -- shows only 10-16 of them at any moment.

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arXiv Machine Learning
Sep 11

How Much Velocity Does Off-Ball Space Value Need? A Broadcast-Viewport Benchmark

The paper investigates how different velocity regimes affect broadcast‑viewport basketball analytics. Using a calibrated off‑screen imputation protocol, it compares four velocity settings—none, viewport‑legal observed, true‑for‑visible, and true‑for‑all—against a velocity‑aware ground truth across three analytical layers. Results show that velocity is largely useless for imputation, modestly useful for the control surface, and minimally useful for final team verdicts, with the visible channel providing the most benefit.

By Seongjin Choi
arXiv AI
Jul 17

SportD: Can VLMs Physically Strategize?

arXiv:2607. 14616v1 Announce Type: new Abstract: Vision--language models have become increasingly capable of interpreting visual scenes, but it remains unclear whether they can use information to make strategically effective decisions.

By Jasin Cekinmez, Addison J. Wu, Haotian Xia, Akshaya Bharadhwaj, Anay Putty, Anirudh Ravishankar, Jaewoong Lee, Jinglin Xiao, Kyumin Andrew Shim, Mishika Ahuja, Nisarga Patil, Leo Liu, Zhuohan Liu, Weining Shen
arXiv Computation and Language
Aug 21

StreamSoccer: Event-Driven Memory for Streaming Soccer Commentary

arXiv:2608. 19723v1 Announce Type: cross Abstract: Streaming video understanding requires models to causally update state as video arrives and organize growing history into semantic units that can evolve, persist, and be recalled under bounded computation and memory.

By Chenxi Shao, Bozhong Wang, Jiaxin Huang, Zhao Liu, Sunwei Zhu, Tianxin Hang, Gaoqi He, Yang Li, Changbo Wang
arXiv AI
Aug 19

Validated Adaptation for Aerial Crowd Monitoring at Mass Gathering Scale: A Deployment Protocol, a Severity Law, and a Diagnostic for Label-Free Drone Crowd Counting, Toward the FIFA World Cup 2034 (Saudi Arabia)

The paper presents a validated protocol for adapting drone‑based crowd‑counting models to the extreme conditions expected at the 2034 FIFA World Cup in Saudi Arabia. Using 525 controlled runs and a full‑resolution corpus, the authors demonstrate that label‑free adaptation can recover 31‑49% of shift‑induced error across multiple corruptions and severities, achieving a 41.8 MAE improvement over a frozen source model. They also introduce a severity law, a stability budget, and a flux‑based risk module that detects real congestion episodes, culminating in a six‑point deployment protocol for safe aerial crowd monitoring.

By AlAnoud AllGhayth, AlJawharh AlOtaibi, Jude AlSubaie