Betting Market Research Articles

How quickly do markets incorporate new information? Evidence from the NCAA men’s basketball betting market

ABSTRACT Gambling markets for sporting events provide opportunities to examine factors that influence the forecast accuracy of prediction markets in a broader sense. In this study, we use information from the betting market on NCAA men’s basketball contests to analyse the rate at which markets incorporate changing information into pricing. Using data from the 2009 to 2022 seasons, we investigate how turnover in player personnel, coaching, and conference affiliation between seasons impacts the accuracy of betting lines. We find some evidence that coaching changes and, to a lesser extent increased player turnover, lead to decreased accuracy and a greater amount of movement in market lines, but that these effects are generally only evident for the first few weeks of the season.

Sales Volume, Noise Traders, and Efficiency of the Japanese Racetrack Betting Market

This study examines 3,270 races of the Japanese thoroughbred racetrack betting market. It finds that market efficiency improves with the sales volume and deteriorates with the number of horses on a race and an increase in less-informed bettors.

A statistical theory of optimal decision-making in sports betting.

The recent legalization of sports wagering in many regions of North America has renewed attention on the practice of sports betting. Although considerable effort has been previously devoted to the analysis of sportsbook odds setting and public betting trends, the principles governing optimal wagering have received less focus. Here the key decisions facing the sports bettor are cast in terms of the probability distribution of the outcome variable and the sportsbook's proposition. Knowledge of the median outcome is shown to be a sufficient condition for optimal prediction in a given match, but additional quantiles are necessary to optimally select the subset of matches to wager on (i.e., those in which one of the outcomes yields a positive expected profit). Upper and lower bounds on wagering accuracy are derived, and the conditions required for statistical estimators to attain the upper bound are provided. To relate the theory to a real-world betting market, an empirical analysis of over 5000 matches from the National Football League is conducted. It is found that the point spreads and totals proposed by sportsbooks capture 86% and 79% of the variability in the median outcome, respectively. The data suggests that, in most cases, a sportsbook bias of only a single point from the true median is sufficient to permit a positive expected profit. Collectively, these findings provide a statistical framework that may be utilized by the betting public to guide decision-making.

Momentum trading in the NFL gambling market

We explore the impact of momentum trading in the NFL betting market at the aggregate weekly level. Bettors prefer to bet on momentum rather than mean reversion in the totals market. The effect is strongest when they bet that the total points scored by both teams will be under the betting line set by bookmakers, especially when the under bet won 60% or more of the games in the previous 1, 2, and 3 weeks. Momentum trading by betting the score will be over the betting line loses money, while trading in the under makes a profit.

Bettors’ reaction to match dynamics: Evidence from in-game betting

It is still largely unclear to what extent bettors update their prior assumptions about the strength and form of competing teams considering the dynamics during the match. This is of interest not only from the psychological perspective, but also as the pricing of live odds ideally should be driven both by the (objective) outcome probabilities and also the bettors’ behaviour. Using state-space models (SSMs) to account for the dynamically evolving latent sentiment of the betting market, we analyse a unique high-frequency data set on stakes placed during the match. We find that stakes in the live-betting market are driven both by perceived pre-game strength and by in-game strength, the latter as measured by the Valuing Actions by Estimating Probabilities (VAEP) approach. Both effects vary over the course of the match.

Estimating the Implied Probabilities in the Tennis Betting Market: A New Normalization Procedure: Comment

Abstract: In this note, we comment on a recent paper in the journal that proposes a new normalization procedure when converting tennis betting odds to the implied probabilities of each player winning. The new procedure is especially germane for matches in which there is a heavy favorite and where there is concern that traditional conversion methods understate the true probability of the favorite winning. However, in this comment, we argue that the new adjustment, while an interesting contribution, suffers from at least three limitations that make the procedure relatively costly while not materially improving predictions of match outcomes.

Chance or Ability? The Efficiency of the Football Betting Market Revisited

Chance or Ability? The Efficiency of the Football Betting Market Revisited

Aggregate earnings and market expectations in United States presidential election prediction markets

This study uses election futures market data to provide the first empirical evidence that aggregate earnings conveys timely “election-relevant” information effecting betting market participants' expectations about the likely outcomes of United States presidential election campaigns. I document that aggregate earnings news is associated with multiple facets of U.S. economic health affecting voter utility. I then use high-frequency data from the Iowa Electronic Political Prediction Market (IEM) to document that aggregate earnings news, including cash flow news, is significantly related to changes in the expected outcomes of U.S. presidential elections and incremental of other measures of economic health.

Risk management in the shadow economy: Evidence from the sport betting market

I use the market for college football point spread gambling as a laboratory for assessing risk management activity among illegal firms. Offshore bookmakers employ pricing policies that reflect a book balancing objective whereas legal bookmakers seldom do so. Pricing policies that prioritize book balance lower the variance of bookmakers’ expected cash flow, but at the cost of lower profit. Due to the legal and moral hazards involved in operating an illegal enterprise, these firms rely on internal financing. Consequently, illegal bookmakers behave as mean–variance optimizers as opposed to legal bookmakers who appear to behave as profit maximizers.

Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball

Several recent studies suggest that the home advantage, that is, the benefit competitors accrue from performing in familiar surroundings, was—at least temporarily—reduced in games played without spectators due to the COVID-19 Pandemic. These games played without fans during the Pandemic have been dubbed ‘ghost games’. However, the majority of the research to date focus on soccer and no contributions have been provided for indoor sports, where the effect of the support of the fans might have a stronger impact than in outdoor arenas. In this paper, we try to fill this gap by investigating the effect of ghost games in basketball with a special focus on the possible reduction of the home advantage due to the absence of spectators inside the arena. In particular, we test (i) for the reduction of the home advantage in basketball, (ii) whether such reduction tends to disappear over time, (iii) if the bookmakers promptly adapt to such structural change or whether mispricing was created on the betting market. The results from a large data set covering all seasons since 2004 for the ten most popular and followed basketball leagues in Europe show, on the one hand, an overall significant reduction of the home advantage of around 5% and no evidence that suggests that this effect has been reduced at as teams became more accustomed to playing without fans; on the other hand, bookmakers appear to have anticipated such effect and priced home win in basketball matches accordingly, thus avoiding creating mispricing on betting markets.

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.

On sticky bookmaking as a learning device in horse-racing betting markets

We present a stochastic dynamic model of the adjustment of betting odds by bookmakers in a horse-racing betting market. We use optimal stopping theory in a two-horse benchmark model with both informed and noise punters. A costly learning process discloses what information the informed traders possess and a risk-neutral bookmaker selects a stopping time at which the betting odds for each horse in a race are adjusted. Our main finding shows that an increased fraction of informed punters has a non-monotonic effect on the loss per trade to the bookmaker. We also find that as the fraction of noise traders goes up, the learning process is less informative, so that the decision to change the prices for each horse is taken sooner.

Female jockeys - what are the odds?

Under-representation of women persists in many industries and represents an important area of concern for society. We use a revealed preference approach to test for bias against females in an underexplored environment. Whilst much use has been made of the financial industry to examine how market prices reveal implicit views on the relative productivity of men and women, our setting offers advantages through both volume of data and unambiguity of outcome. Over a 20-year period, the effect of jockey gender on fixed price betting odds was examined in National Hunt racing. Employing censored regression to account for non-finishers we find female jockeys to be underestimated by the UK betting market. Results indicate an increasing trend for underestimation in recent years, despite growing representation and rising performance levels of female jockeys. We conclude that mistake-based discrimination and confirmation bias may be impacting efficiency in the betting market. The market might recognise some improvement in female performance but may be failing to adapt at the speed with which female jockeys are professionalising.

The Online Betting Behavior of Sport Lottery Consumers

The sports lottery industry is growing globally, especially in the online betting market. This study focuses on online sports lottery behavior and tries to propose a framework applied in a virtual environment. Based on the Technology Acceptance Model, four antecedes (subject norm, computer anxiety, computer self-efficacy, and personal innovativeness) are added to this framework. The subjects of this study, from whom 268 valid questionnaires were collected, were betting members of sports lottery venders in the Taichung district. Findings show that subject norm is a good antecede in this framework, but computer anxiety does not reach statistical significance for explanation in this study. Both computer self-efficacy and personal innovativeness positively influence perceived usefulness. The results demonstrate the value of the expanded TAM and use of the expanded model indicates a high degree of acceptance for online betting systems among sports lottery consumers. The TAM expansion proved relevant in gathering applied information on the behavior of sports lottery consumers.

An Effective Approach for Exploiting the Inefficiencies of the Italian Football Betting Market

In this paper, the author analyses the main inefficiencies of the Italian Serie-A betting market: in particular, using previous findings on the favourite-long shot bias, the team scoring home advantage and the league winner scoring advantage, a strategy aimed to outperform the market will be presented. By testing the betting scheme over the past 19 seasons, it was possible to obtain an average return per bet of 9.31%, with 16 of the 19 years resulting in positive financial returns. In order to verify the computations, different betting odds databases were used. The results obtained are particularly significant for two reasons: firstly all computations were performed on market average coefficients, leaving on the table an additional 3-4% of profit, which could be obtained by using best coefficients among bookmakers and secondly compared to market benchmarks and other betting strategies, the net profit is considerably higher. In particular other three strategies were used as a benchmark: the first one uses the favourite-long shot bias, the second one the Home factor and the third one the league favourite team advantage. All these betting schemes performed more poorly, with the second best strategy scoring on average 6% worse. After analysing possible future improvements, in the final section the author describes how the findings of this paper may be applied to other betting markets, such as different football leagues, basketball, hockey and tennis.

New Entry and Strategic Group Emergence in the Soccer Betting Market

The arrival of online trading in European football betting markets has brought significant structural change to the sector in recent years, with the emergence of two strategic groups of bookmakers, characterised by distinctive operating behaviours. We examine the impact on market efficiency of interactions between these two groups, by comparing 51,000 individual odds offered by leading bookmakers at nine separate points on game outcomes, across 2,132 games in six European leagues. This longitudinal analysis reveals that interactions between these groups enable information to be transmitted from informed bettors to market prices, thereby increasing market efficiency.

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.

Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

Developing a Simulated Microcosm of a Contemporary Betting Exchange for the Discovery of Transferable and Profitable Algorithmic Betting Strategies

Since the dotcom boom of the early 2000s the betting industry has been transformed by the rise of betting exchanges, from a market dominated solely by traditional bookmakers, to one in which punters have a variety of wagering options. These exchanges bring together populations of bettors with varying and opposing views in the same way as traditional exchanges operating within the world's major financial markets. As a result the rise of algorithmic trading within the financial markets has been similarly observed within betting markets. The design and implementation of new financial trading algorithms is an arduous task due to the difficulty in obtaining high-quality data that can be used to evaluate a strategies' performance. As such Synthetic Data Generators (SGDs), such as Cliff's Bristol Stock Exchange (2012), have been developed to provide a test-bed in which researchers can develop trading algorithms and investigate market dynamics. This thesis therefore describes the design, implementation, and subsequent evaluation, of a novel asynchronous implementation of the Bristol Betting Exchange (BBE), the rationale and high-level design of which can be found in Cliff 2021. BBE provides an agent-based simulation model of a contemporary sports-betting exchange which will enable both researchers and industrial users to develop algorithmic betting agents and investigate previously unexplored betting market dynamics.

Behavioral biases in the NFL gambling market: Overreaction to news and the recency bias

This paper examines the recency bias and overreaction in the NFL betting market from 2003 to 2017. Consistent with the recency bias, bettors are more likely to bet on teams who have won previous outcomes. We add to the literature and find that the magnitude of prior wins and losses in the previous weeks’ plays a greater importance than the sole outcome of wins and losses in betting behavior. Additionally, our results show that bettors wager 2.1% less on the home team when their first-string quarterback does not play, and 3.1% more on the home team when the visitor’s first-string quarterback does not play, which is consistent with overreaction. Finally, our results show that bookmakers earn “over the odds” thanks to bettors’ quasi-rational behavior as they commit the recency bias.