Investigating the efficiency of the Asian handicap football betting market with ratings and Bayesian networks

Despite the massive popularity of the Asian Handicap (AH) football (soccer) betting market, its efficiency has not been adequately studied by the relevant literature. This paper combines rating systems with Bayesian networks and presents the first published model specifically developed for prediction and assessment of the efficiency of the AH betting market. The results are based on 13 English Premier League seasons and are compared to the traditional market, where the bets are for win, lose or draw. Different betting situations have been examined including a) both average and maximum (best available) market odds, b) all possible betting decision thresholds between predicted and published odds, c) optimisations for both return-on-investment and profit, and d) simple stake adjustments to investigate how the variance of returns changes when targeting equivalent profit in both traditional and AH markets. While the AH market is found to share the inefficiencies of the traditional market, the findings reveal both interesting differences as well as similarities between the two.

Case Report: A gambling-related suicide in rural Malawi

Background As in many other countries across sub-Saharan Africa, Malawi’s commercial gambling sector has grown considerably in recent years. Driven by the widespread availability of internet through mobile devices, the industry has penetrated both urban and rural settings. In Malawi the model commonly implemented by gambling companies is similar to that used by mobile phone operators. Agents equipped with cellular devices connect to providers’ servers to place wagers for customers and print receipts using simple printers attached to their devices. This has produced lucrative returns for providers. While increasing attention is being paid to this trend, most research focusses on sports betting and there is a deficit of papers that document gambling-related harms. Methods Here we present a narrative case report of a 16-year-old boy, ‘Wati’ (pseudonym), who lived in rural Malawi and took his own life after gambling and losing money that did not belong to him. As his community is part of a demographic surveillance site, a verbal autopsy was conducted, later supplemented with interviews with Wati’s close friend and uncle, to whom his mother referred us. We triangulated data from these three sources to create a narrative case report of Wati’s suicide and its relationship to his gambling practices. Results We found that the gambling harms leading up to Wati’s suicide were recurrent, that his gambling practices were diverse (lottery, football betting, digital games and cards) and that signs of distress were apparent before his suicide. Conclusions From this case report, we learn that underage individuals participate in gambling in Malawi, can develop harmful habits and that their gambling is not confined to sports betting. We also learn that there is a lack of accessible services for people who develop harmful gambling practices. Wati could have benefited from such services and they may have saved his life.

Market efficiency and the Greek fixed-odds betting market

PurposeThe purpose of this study is to empirically examine the efficiency of Greek fixed-odds (offline) betting market as offered by OPAP for the period 2016–2019.Design/methodology/approachUsing a four-year data sample of OPAP's opening and closing odds for football matches from all over the world and applying linear probability and probit models, the market efficiency is examined and the existence of possible anomalies is investigated.FindingsThe main findings of research suggest that although the odds are dominated primarily by favorite-longshot bias and secondarily by draw bias, this mispricing cannot prove profitable. However, the opening odds, the margin levels and the market structure provide information that is not fully captured by the closing odds, giving bettors profit opportunities. Thus, findings show that the semi-strong market efficiency is questionable. Finally, competition reduces commissions leading to more efficient odds.Practical implicationsThe conclusions of this study are useful for football betting market and, particularly, for government authorities, bookmakers and bettors. Findings can be extended in future research to prediction tasks.Originality/valueTo the best of the authors’ knowledge, this is the first study about the Greek football betting market. The contribution to the literature lies on the one hand in the examination of a monopolistic land-based betting market, which is being squeezed and threatened by the more competitive online betting market, and on the other hand in the simultaneous examination of the opening and closing odds.

Efficiency Testing of Prediction Markets: Martingale Approach, Likelihood Ratio and Bayes Factor Analysis

This paper studies efficient market hypothesis in prediction markets and the results are illustrated for the in-play football betting market using the quoted odds for the English Premier League. Our analysis is based on the martingale property, where the last quoted probability should be the best predictor of the outcome and all previous quotes should be statistically insignificant. We use regression analysis to test for the significance of the previous quotes in both the time setup and the spatial setup based on stopping times, when the quoted probabilities reach certain bounds. The main contribution of this paper is to show how a potentially different distributional opinion based on the violation of the market efficiency can be monetized by optimal trading, where the agent maximizes logarithmic utility function. In particular, the trader can realize a trading profit that corresponds to the likelihood ratio in the situation of one market maker and one market taker, or the Bayes factor in the situation of two or more market takers.

Investigating inefficiencies of bookmaker odds in football using machine learning

The efficient-market hypothesis states that it is impossible to beat the market, as the price reflects all available information. Applied to bookmaker odds for football games, there should not be a systematic way of winning money on the long run.However, we show that by using simple machine learning models we can systematically outperform the markets belief manifested through the bookmakers odds. The effect of this inefficiency is diminishing over time, which indicates that the knowledge that has been derived from and the pure amount of the data is also reflected in the odds in recent times.We give some insights how this effect differs across major football leagues in Europe, which algorithms are performing best and statistics on the ROI using machine learning in football betting. Additionally, we share how the simulation study has been designed in more detail.

Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics

In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team.