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Not feeling the buzz: Correction study of mispricing and inefficiency in online sportsbooks

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  • Clegg, Lawrence
  • Cartlidge, John

Abstract

We present a replication and correction of a recent article (Ramirez et al., 2023). RRS measure profile page views on Wikipedia to generate a “buzz factor” metric for tennis players and show that it can be used to form a profitable gambling strategy by predicting bookmaker mispricing. Here, we use the same dataset as RRS to reproduce their results exactly, which confirms the robustness of RRS’ mispricing claim. However, we discover that RRS’ published out-of-sample betting results are significantly affected by a single bet (the “Hercog” bet), which returns substantial outlier profits based on erroneously long odds. When this data quality issue is resolved, the majority of reported profits disappear and only one strategy, which bets on “competitive” matches, remains significantly profitable in the original out-of-sample period. While one profitable strategy offers weaker support than the original study, it still provides an indication that market inefficiencies may exist, as originally claimed by RRS. As an extension, we continue testing after 2020. The strategy generates no further profits and model coefficients estimated over this period are no longer reliable predictors of bookmaker mispricing.

Suggested Citation

  • Clegg, Lawrence & Cartlidge, John, 2025. "Not feeling the buzz: Correction study of mispricing and inefficiency in online sportsbooks," International Journal of Forecasting, Elsevier, vol. 41(2), pages 798-802.
  • Handle: RePEc:eee:intfor:v:41:y:2025:i:2:p:798-802
    DOI: 10.1016/j.ijforecast.2024.06.012
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    2. Wunderlich, Fabian & Memmert, Daniel, 2020. "Are betting returns a useful measure of accuracy in (sports) forecasting?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 713-722.
    3. Ramirez, Philip & Reade, J. James & Singleton, Carl, 2023. "Betting on a buzz: Mispricing and inefficiency in online sportsbooks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1413-1423.
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    5. Angelini, Giovanni & Candila, Vincenzo & De Angelis, Luca, 2022. "Weighted Elo rating for tennis match predictions," European Journal of Operational Research, Elsevier, vol. 297(1), pages 120-132.
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