Peer-reviewed studies, preprints and classic methods papers on pricing, efficiency, risk and research practice. Each entry is our summary, with the original link and a plain statement of what the study does not show.
Summaries are original paraphrases of published abstracts and records; no paper text is reproduced. Bibliographic details were checked against publisher, index and author listings on 25 October 2026. Where a paper was later corrected, the correction is shown. Preprints are labelled as not peer reviewed.
7 sources
Apparent inefficiencies appear in individual seasons but do not persist systematically across seasons or leagues, and simulations show that significant single-season results are likely even in a fully efficient market.
Does not show: That no bettor can ever find an edge; it shows that single-season evidence is weak.
Much of the original out-of-sample profit depended on a single outlier bet recorded at erroneously long odds; after correction only one strategy remained significantly profitable in the original period, returns came mainly from individual bookmakers' mispricing rather than the buzz signal, and there were no further profits in data after 2020.
Does not show: That attention-based signals never contain information; it shows this published profit was not robust.
Selecting models by calibration produced better simulated betting outcomes than selecting by accuracy. After the 2025 corrigendum, average returns were negative for both selection rules (about −9.8% for calibration versus −26.8% for accuracy), while the best single models reached about +23.1% and +10.9%.
Does not show: That calibrated models are profitable; the corrected average returns were negative.
Correction: Corrigendum, Machine Learning with Applications 19 (2025), 100627 (doi:10.1016/j.mlwa.2025.100627). The original 2024 return figures were withdrawn; use the corrected values.
Tests based on normalised probabilities behave well; tests based on raw inverse odds do not, and are biased against finding a favourite–longshot bias.
Does not show: Any profitable strategy; it shows how test design can change conclusions.
Introduces what is now called the Brier score, which rewards forecasts that are both well calibrated and sharp.
Does not show: Whether a forecast can make money against a market.
With enough trials, an impressive in-sample backtest is likely even when no strategy has skill, and overfit strategies tend to perform worse out of sample.
Does not show: That every strong backtest is overfit; it shows why the number of trials must be reported.
Reported that attention-related signals were associated with mispricing and profitable strategies; later corrected by Clegg & Cartlidge.
Does not show: A durable profitable strategy, once the outlier bet is corrected.
