arXiv Machine Learning By Seongjin Choi

Space-Creating versus Dead Possession: An Off-Ball Possession-Quality Index for Broadcast Football

Read the original on arXiv Machine Learning →

arXiv:2608. 09887v1 Announce Type: cross Abstract: Ball possession is the most-cited and most-misleading number in football: 60% recycled in one's own half is not 60% spent pinning the opponent back.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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 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