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.
arXiv:2608. 05030v1 Announce Type: new Abstract: Football score forecasting combines a strong statistical core with a difficult contextual edge.
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.
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: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.
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.
The paper investigates how large language models (LLMs) used as judges in absolute scoring tasks exhibit systematic biases that compromise reliability. It shows that a judge’s task accuracy strongly predicts both its judging accuracy and its directional bias, yet more capable examinee models consistently receive more lenient judgments. To mitigate these biases, the authors propose a calibrated weighted majority voting (WMV) ensemble that estimates judges’ error rates from inter-judge agreement patterns, achieving near-oracle performance without labeled data and improving both accuracy and fairness.
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. We present WorldCupArena, a dynamic benchmark for language models and deep-research agents.
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: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.
arXiv:2608. 03416v1 Announce Type: new Abstract: Large language models (LLMs) are now regularly asked to forecast real-world events, but comparisons are often difficult because models receive different information, use different tools, and are evaluated under different rules.
arXiv:2606. 13221v2 Announce Type: replace Abstract: Evaluating new large language models typically requires costly human annotation campaigns at scale.
arXiv:2607. 17384v1 Announce Type: new Abstract: This paper provides an experimentally verified formal law for calculating the uplift that diversity of thought provides in Large Language Model (LLM) ensembles.