We present a strict replication and correction of results published in a
recent article (Ramirez, P., Reade, J.J., Singleton, C., Betting on a buzz:
Mispricing and inefficiency in online sportsbooks, International Journal of
Forecasting, 2022, doi:10.1016/j.ijforecast.2022.07.011). RRS introduced a
novel "buzz factor" metric for tennis players, calculated as the log difference
between the number of Wikipedia profile page views a player receives the day
before a tennis match and the player's median number of daily profile views.
The authors claim that their buzz factor metric is able to predict mispricing
by bookmakers and they demonstrate that it can be used to form a profitable
strategy for betting on tennis match outcomes. Here, we use the same dataset as
RRS to reproduce their results exactly. However, we discover that the published
results are significantly affected by a single bet (the "Hercog" bet) that
returns substantial outlier profits; and these profits are generated by taking
advantage of erroneously long odds in the out-of-sample test data. Once this
data quality issue is addressed, we show that the strategy of RRS is no longer
profitable in "practical" scenarios. Using an extended and cleaned dataset, we
then perform further exploration of the models and show that the "impractical"
betting strategy that uses best odds in the market remains profitable (in
theory). However, evidence suggests that the vast majority of returns are
generated by exploiting individual bookmaker's mispricing of odds relative to
the market, and the novel buzz factor metric has negligible contribution to
profits. We make all code and data available online.Comment: 26 pages, 2 figures. This paper is a replication study. Problems in
the original study are discovered and corrected. Replication code and data
are available online: https://github.com/Faxulous/notFeelingTheBuz