Dynamic Learning and Market Making in Spread Betting Markets with Informed Bettors

How Bookies Can Outwit Sophisticated Bettors Sports-betting markets are based entirely on predictions. A bettor has to pick a winning contestant, and a market maker―a bookie―bets on the opponent. As bookies have to take the other side of every bet, it is of great value to understand the market making problem, that is, how to set the spread lines as “prices” for the bookies. Nevertheless, understanding of this problem is limited. Specifically, sophisticated bettors exist in the market, and a bookie can be manipulated by skillful bettors because of information asymmetry. In “Dynamic Learning and Market Making in Spread Betting Markets with Informed Bettors,” Birge, Feng, Keskin, and Schultz study the market-making problem under information asymmetry and market manipulation. They show that, although many popular learning and pricing algorithms, such as Bayesian policies, are effective in learning, they are vulnerable to strategic manipulations. The authors propose a dynamic learning and pricing algorithm, called the inertial policy, that collects information from the market effectively but also protects the bookie from strategic manipulations.

Sports Betting and The Black-Litterman Model: A New Portfolio-Management Perspective

This paper transfers and adapts the Black-Litterman portfolio management model and its subsequent generalizations to the characteristics and specificities of assets quoted on sports betting markets. The results show that these assets are suitable for the application of portfolio management models with the possible inclusion of investors’ opinions. Information based on the variability of market prices and the attention received by NBA teams in Google Trends is successfully used to simulate the opinions expressed by a hypothetical portfolio manager. Furthermore, the assets are suitable for inclusion in portfolios in which managers are seeking returns uncorrelated with other assets.

Taking a Gamble

Gambling is an ancient human activity. We indulge ourselves by allowing ourselves to experience the dangers and thrills of chance in a somewhat controlled way. The history of lotteries and related games is explored. The chances of drawing various poker hands are laid out. The role of probability in horse racing is described, and how the odds quoted are not strictly statements of probability, but terms on which business is to be done. Political prediction betting markets give us a further interpretation of probability as a way of expressing strength of opinion in a quantifiable, albeit flawed way. Wagers encourage boasters to put their money where their mouth is, and so to quantify their degree of belief.

Baseball Home Field Advantage Without Fans in the Stands

Home field advantage is universally accepted across most major sports and levels of competition. However, exact causes of home field advantage have been difficult to disentangle. The COVID-19 pandemic offers a unique, natural experiment to isolate elements related to home field advantage since all 2020 regular season Major League Baseball games were played without fans. Results provide no statistically significant evidence of a difference in home field advantage between the 2019 and 2020 seasons, evidence that home crowd support is not a driver of home field advantage. There does appear to be a statistical advantage by the home team batting second in the inning. Travel fatigue seems to have no impact on home field advantage, and while home field advantage seems to increase throughout the 2020 season, we chalk that up to small sample noise. Despite lacking historical precedence, betting markets seemingly respond efficiently to the new home conditions. Keywords: home field advantage, market efficiency, baseball, ghost games

Are Sports Betting Markets Semi-Strong Efficient? Evidence From the COVID-19 Pandemic

This paper examines whether sports betting markets are semi-strong form efficient—i.e., whether new information is rapidly and completely incorporated into betting prices. We use news on ghost games in the top European football leagues due to the COVID-19 pandemic as a clean arrival of new public information. Because spectators are absent during ghost games, the home advantage is reduced, and we test whether this information is fully reflected in betting prices. Our results show that bookmakers and betting exchanges systematically overestimated a home team’s winning probability during the first period of the ghost games, which suggests that betting markets are, at least temporally, not semi-strong form efficient. Examining different leagues, we find that our main results are driven by the German Bundesliga, which was the first league to resume operations. We exploit a betting strategy that yields a positive net payoff over more than one month.

The concept of manipulation in sports competitions

Knowing the negative effect of sports manipulation for the sport and society, as well as the problems that is facing the Macolin Convention in its ratification, I decided to do this research in order investigate the phenomenon of match-fixing, controversial issues about the online gambling and potential effect of ratifycation of the convention and its entry into force. I was totally surprised when I found out so many International business law issues are covered in this topic. The sports betting markets are similar to the stock market, where odds movement de- pends on the performance of the teams and players. The insider trading is similar to the match-fixing, where a pre-match information for the result is used in order to achieve a great profit. Memorandums of understanding between betting companies, sports governing bodies and public authorities are used to cover the lack of regulation in this area. In the same time there is a need of acceleration of negotiations for the ratification of the convention due to the spreading of the phenomenon of sports manipulation. Key words: major manipulations, harmful aspects, existing problems, legalizing sports gambling, ethics of sports

Sports prediction and betting models in the machine learning age: The case of tennis

Machine learning and its numerous variants have meanwhile become established tools in many areas of society. Several attempts have been made to apply machine learning to the prediction of the outcome of professional sports events and to exploit “inefficiencies” in the corresponding betting markets. On the example of tennis, this paper extends previous research by conducting one of the most extensive studies of its kind and applying a wide range of machine learning techniques to male and female professional singles matches. The paper shows that the average prediction accuracy cannot be increased to more than about 70%. Irrespective of the used model, most of the relevant information is embedded in the betting markets, and adding other match- and player-specific data does not lead to any significant improvement. Returns from applying predictions to the sports betting market are subject to high volatility and mainly negative over the longer term. This conclusion holds across most tested models, various money management strategies, and for backing the match favorites or outsiders. The use of model ensembles that combine the predictions from multiple approaches proves to be the most promising choice.

Are Sports Bettors Biased toward Longshots, Favorites, or Both? A Literature Review

A large body of literature on the favorite–longshot bias finds that sports bettors in a variety of markets appear to have irrational biases toward either longshots (which offer a small chance of winning a large amount of money) or favorites (which offer a high chance of winning a small amount of money). While early studies in horse racing led to an impression that longshot bias is dominant, favorite bias has also now been found in a variety of sports betting markets. This review proposes that the evidence is consistent with both biases being present in the average sports bettor. Sports betting markets with only two potential outcomes, where the favorite therefore has a probability >0.5 of happening, often produce favorite bias. Sports betting markets with multiple outcomes, where the favorite’s probability is usually <0.5, appear more consistent with longshot bias. The presence of restricted odds ranges within any given betting market provides an explanation for why single studies support, at most, one bias. This literature review highlights how individual sports bettors might possess biases toward both highly likely, and highly unlikely, events, a contradictory view that has not been summarized in detail before.