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.
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.
arXiv:2608. 12926v1 Announce Type: cross Abstract: Traditional player evaluation in professional handball relies on basic box-score metrics or heuristic indices, which fail to credit the multi-player build-up chain.
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.
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.
arXiv:2607. 17765v1 Announce Type: cross Abstract: We introduce WC2026-Agents, a benchmark and dataset for evaluating large language models (LLMs) as autonomous forecasting agents on real, future events.
arXiv:2606. 09289v1 Announce Type: new Abstract: Understanding tactical organisation of association football, hereafter referred to as football, requires identifying distinct match phases.
The paper introduces Graph-to-Grid (G2G), a method that paints continuous‑coordinate player features onto a grid using bilinear interpolation, enabling end‑to‑end training of per‑player encoders for pass surface prediction. On 53,628 World Cup passes, this painting approach improves selection likelihood by about a quarter of a nat compared to raster‑only inputs, with further gains from learned encoders and message passing. The study also shows that painting benefits existing models like SoccerMap and U‑Net, while other variants such as offset channels or raster‑free decoders do not provide similar improvements.
The paper investigates the reliability of Expected Goals (xG) as a measure of finishing skill in soccer, arguing that the common practice of comparing cumulative xG to actual goals is flawed. It presents three hypotheses: high variance and small sample sizes make the deviation metric inadequate, including all shot types can mask true finishing ability, and inherent biases in xG models reduce the apparent gap between expected and actual goals for top finishers. Using an AI‑fairness technique to calibrate xG across player subgroups, the authors demonstrate that standard models underestimate Messi’s goal‑adjusted xG (GAX) by 17% and that his GAX is 27% higher than that of typical elite high‑shot‑volume attackers, revealing him as an even more exceptional finisher than previously thought.
arXiv:2608. 05030v1 Announce Type: new Abstract: Football score forecasting combines a strong statistical core with a difficult contextual edge.
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.
arXiv:2607. 24573v1 Announce Type: new Abstract: Large language models (LLMs) increasingly support decisions about uncertain future events, yet evaluating their ability to forecast real-world outcomes remains difficult.
Football score forecasting combines a strong statistical core with a difficult contextual edge. Dynamic Poisson-family models estimate team strength, expected goals, and coherent score probabilities, but do not directly understand roles, tactical matchups, motivation, or how a first goal changes behaviour.