Research on betting market efficiency has produced conflicting findings, with some studies identifying persistent inefficiencies and profitable strategies, while others suggest that these inefficiencies are temporary or context-dependent. Several studies have documented systematic pricing errors in sports betting markets, with models identifying opportunities for consistent profits across different sports (Dixon & Coles, 1997; Gray & Gray, 1997; Cortis et al., 2013; Sung & Johnson, 2010). However, other research indicates that such inefficiencies may not persist over time, as betting markets adjust and correct pricing errors, particularly in well-established leagues with high betting volumes (Winkelmann et al., 2020). One of the key factors influencing market efficiency is the volume of bets placed, with larger and more liquid markets exhibiting fewer pricing errors and non-optimizing behaviors (Busche & Walls, 2000).
Despite the overall efficiency of many betting markets, certain biases have been consistently observed, particularly in horse racing, where bettors tend to overbet longshots, leading to systematic underpricing of favorites (Asch et al., 1984). Similar inefficiencies have been identified in sports betting, especially in short-term tournaments where pricing models struggle to incorporate new information quickly (Cortis et al., 2013). While some studies report profitable betting strategies based on these inefficiencies, others caution that such strategies may be the result of random fluctuations rather than persistent market failures (Winkelmann et al., 2020). The debate over betting market efficiency underscores the need to consider market ecology, the robustness of statistical models, and the impact of betting volume on price formation when evaluating potential strategies (Sung & Johnson, 2010). These findings suggest that while inefficiencies can exist, they are often difficult to exploit consistently, particularly in high-liquidity markets where bookmakers and bettors continuously refine pricing mechanisms.
Several studies have documented systematic pricing errors in sports betting markets, with models identifying opportunities for consistent profits across different sports
Summary of: Dixon & Coles 1997; Gray & Gray 1997; Cortis et al 2023; Sung & Johnson 2010
Anecdote
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Articles Cited
- “P. Asch, B. Malkiel, R. Quandt (1984): Market Efficiency in Racetrack Betting, https://doi.org/10.1086/296257
- The paper examines the efficiency of the racetrack betting market and whether profitable betting strategies can be devised based on observed betting patterns, finding that while such strategies may exist, they may not be exploitable on a substantial scale, and that the betting market does not necessarily exhibit irrational behavior.”
- “David Winkelmann, Marius Oetting, C. Deutscher (2020): Betting Market Inefficiencies in European Football — Bookmakers’ Mispricing or Pure Chance?, https://doi.org/10.2139/ssrn.3672233
- The paper finds that while previous research has identified biased betting odds and market inefficiencies in European football, these biases do not persist systematically over time and across leagues, and the number of inefficient periods is not significantly more than what would be expected in an efficient market.”
- “M. Dixon, S. Coles (1997): Modelling Association Football Scores and Inefficiencies in the Football Betting Market, https://doi.org/10.1111/1467-9876.00065
- The authors developed a parametric model to predict scores in English league and cup football matches, and found that this model could be used to exploit inefficiencies in the football betting market.”
- “P. Gray, Stephen Gray (1997): Testing market efficiency: Evidence from the NFL sports betting market, https://doi.org/10.1111/J.1540-6261.1997.TB01129.X
- The paper examines the efficiency of the NFL betting market using a probit model and finds that probit-based betting strategies can generate statistically significant profits, though with some inconsistency in out-of-sample predictions.”
- “David Wikelmann, Marius Oetting, C. Deutscher (2020): Short and Long-Term Biases in European Football Betting Markets, https://doi.org/10.2139/ssrn.3672233
- The summary is that while previous research has found evidence of biased betting odds and market inefficiencies in European football betting markets, the authors’ analysis shows that these biases do not persist systematically over time or across leagues, and the number of inefficient periods is not significantly more than what would be expected in an efficient market.”
- “Dominic Cortis, Steven D. Hales, F. Bezzina (2013): Profiting on inefficiencies in betting derivative markets: The case of UEFA EURO 2012, https://doi.org/10.5750/JGBE.V7I1.597
- The authors describe a method using Monte Carlo simulation to determine odds for derivative betting markets, and when there is a mismatch between the model odds and the market odds, they propose a modified Kelly strategy to profit from the market inefficiencies, which they demonstrate by applying to the UEFA Euro 2012 tournament and generating a 12% profit.”
- “M. Sung, Johnnie E. V. Johnson (2010): Revealing Weak-Form Inefficiency in a Market for State Contingent Claims: The Importance of Market Ecology, Modelling Procedures and Investment Strategies, https://doi.org/10.1111/j.1468-0335.2008.00716.x
- The paper examines the degree of weak-form inefficiency in a market for state contingent claims by using conditional logit analysis to predict winning probabilities based on market prices, and then using various wagering strategies to yield substantial abnormal returns, suggesting the market is not weak-form efficient.”
- “K. Busche, W. Walls (2000): Decision Costs and Betting Market Efficiency, https://doi.org/10.1177/104346300012004006
- The paper examines the relationship between decision costs and market efficiency in racetrack betting markets, finding that metrics of non-optimizing behavior are inversely related to the volume of betting.”
Insufficient Detail?
At times it is difficult to answer the question as there are not enough relevant published journal articles to relate. It could be that the topic is niche, there’s a significant edge (and researchers prefer not to publish), there is no edge or simply no one has thought to investigate.
