arXiv:2603. 15212v2 Announce Type: replace Abstract: Evaluating football player transfers is challenging because player actions depend strongly on tactical systems, teammates, and match context.
By Miru Hong, Minho Lee, Geonhee Jo, Hyeokje Cho, Hyunsung Kim, Pascal Bauer, Sang-Ki Ko
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.
By Seongjin Choi
The paper audits the Duckworth‑Lewis‑Stern (DLS) method, the standard for revising cricket scores after rain, using 8,150 international matches to generate 233,550 synthetic interruption scenarios. It finds two structured biases: a 137‑run prediction error range across match‑state buckets and a gender‑differential bias in ODIs, with women’s scores over‑predicted by an average of +7.63 runs versus +1.51 runs for men. The authors benchmark DLS against five modern machine‑learning models and introduce DLS‑Cal, a lightweight calibration layer that reduces overall bias by 31% in ODIs and 19% in T20Is, and a gender‑aware variant that nearly eliminates the residual bias for women’s ODIs.
By Soumyadeep Roy
The study introduces a framework that reconciles minute‑resolution athlete monitoring data with injury labels that are only available at the session level. By creating fixed‑time landmarks (10, 20, 30, 40, 50, and 60 minutes) and generating a single representation per athlete‑session up to each landmark, the authors evaluate several machine‑learning models and data‑augmentation strategies on elite women’s football data. Results show that discrimination varies across landmarks, with TabPFN outperforming Logistic Regression at later landmarks but not consistently beating Random Forest, and that synthetic augmentation offers benefits only in specific conditions.
By Evangelos Chatzidimitriou, Konstantinos Tserpes
arXiv:2607. 26061v1 Announce Type: new Abstract: Pre-match tactical decision-making in professional football relies heavily on subjective expert analysis and identity-based scouting systems that cannot generalize to unseen teams.
By Mouad Zemzoumi, Amine Abouaomar
arXiv:2607. 18084v1 Announce Type: new Abstract: Predicting a football match before kickoff requires more than knowing past results: a model must use changing information and make a clear prediction before the answer is available.
By Zhaokai Wang, Tianlin Gui, Jiayuan Rao, Shangzhe Di, Yihong Tang, Dingli Liang