What Do Bettors Want? Determinants of Pari‐Mutuel Betting Preference

A bstract .   This paper is an analysis of the demand for thoroughbred racetrack wagers, examining evidence that would support the existence of two types of bettors: the risk-averse informed bettor versus the uninformed bettor. Looking at 12 major racetracks over the fall of 2002, we undertake an empirical examination of the determinants of bettors' preferences for particular wagers on specific races. The goal is to try to determine what individual aspects of a race (conditions, surface, participants, etc.) will encourage increased wagering dollars. With the advent of simulcasting, the competition for the wagering dollar is fierce, as the bettor can choose from more than 100 races daily, each race offering numerous betting options. We find for most wagers that higher quality participants, larger and more competitive fields, and turf races increase betting volume while higher pari-mutuel takeout, poor track conditions, and other races run concurrently reduce volume. However, more competitive fields reduce betting volume in the show and trifecta pools. Optimal field size is determined to be between 10 and 12 betting interests. Overall, we find support for the existence of a significant share of risk-averse informed bettors.     

Additional evidence of heuristic-based inefficiency in season wins total betting markets: Major League Baseball

The large majority of sports betting papers have addressed questions of market efficiency based on the outcome of single game, such as spread (sides) or point totals wagers. This research examines the Major League Baseball (MLB) season wins total over/under betting market with respect to questions of market efficiency and profitability. Woodland and Woodland (2013, 2015) investigated the season wins total markets for the National Football League (NFL) and the National Basketball Association (NBA) and found significant inefficiencies. Betting rules tested in this paper parallel those proposed by Woodland and Woodland for the NFL and NBA. They aim to take advantage of the implications of the representativeness heuristic, that is, individuals expect results from a small number of games to generalize to the entire population. The MLB market is found to be inefficient, and provides opportunities for profitable wagering. We establish a tendency for bettors to overreact to a team’s performance in the previous season, particularly for teams with winning records. Results are consistent with the findings for the NFL and NBA season wins totals betting markets. This may be the consequence of monetary betting limits and a structure requiring the completion of a sport’s season before the bet outcome is determined, both of which could discourage some bettors from participating.     

Bettor preferences and market efficiency in football totals markets

The betting markets for totals in college football and arena football provide additional evidence of bettor preference for scoring. The results for college football and arena football markets are similar to those found in the professional football and professional basketball totals market. In all of these leagues, the overs are overbet. We suggest that there is a clear preference for bettors to bet the over and the extent of the bias depends upon the volume of uninformed bettors to informed bettors and limits placed on bets in these markets.     

Empirical evidence of error in pricing of favorites and longshots in greyhound racing

Sports bettors' success depends on the ability to accurately assess the true probability of outcomes. Successful racetrack bettors can realize returns better than the track take out. Historical empirical evidence shows the presence of favorite-longshot bias (FLB) in horse racing where bettors underbet favorites. Conversely, bettors overbet longshots. We tested for FLB bias in racing data from three greyhound racetracks. Our results show opposite behavior. We show bettors apparently underestimated for longshots, and overestimated for favorites, the true probability of winning. In 10 out of 14 grades bettors significantly overbet favorites, and underbet longshots in 8 out of 14.     

Efficiency of online football betting markets

This paper evaluates the efficiency of online betting markets for European (association) football leagues. The existing literature shows mixed empirical evidence regarding the degree to which betting markets are efficient. We propose a forecast-based approach for formally testing the efficiency of online betting markets. By considering the odds proposed by 41 bookmakers on 11 European major leagues over the last 11 years, we find evidence of differing degrees of efficiency among markets. We show that, if the best odds are selected across bookmakers, eight markets are efficient while three show inefficiencies that imply profit opportunities for bettors. In particular, our approach allows the estimation of the odds thresholds that could be used to set profitable betting strategies both ex post and ex ante.     

Relative pricing of binary options in live soccer betting markets

Live soccer betting markets differ from other binary options markets in that all fundamental information is observable, the options mature in less than two hours and the markets are highly liquid. This study presents a new method for the identification of hidden information in market prices. The method is based on two independent Poisson distributions and on a numerical algorithm for the aggregation of all market price information into one rational number. The method is applied to an empirical dataset of real time market prices in 29,413 soccer games. The results indicate that the method selects the most profitable markets and allows for a significant improvement in average investment returns.     

The wisdom of large and small crowds: Evidence from repeated natural experiments in sports betting

Prediction markets have proved excellent tools for forecasting, outperforming experts and polls in many settings. But do larger markets, with a wider participation, perform better than smaller markets? This paper analyses a series of repeated natural experiments in sports betting. The Queen’s Club Tennis Championships are held every year, but every other year the Championships clash with a major soccer tournament. We find that tennis betting prices become significantly less informative when participation rates are affected adversely by the clashing soccer tournament. This suggests that measures which increase prediction market participation may lead to a greater forecast accuracy.     

The determinants of wagering behavior

The total volume of wagering at a racetrack is important since it affects both racetrack and state revenues. This paper studies the factors that determine the volume of wagering. One of these factors is the sire stakes, a major form of government subsidy. We conclude that this subsidy has little or no economic impact on the volume of wagers. Unless the state recoups nearly all of the subsidy in other ways, such as sales taxes on racehorses, the sire stakes amount to a direct transfer from taxpayers to the breeding industry. We also develop information useful to racetrack managers.     

Wage against the machine: A generalized deep-learning market test of dataset value

How can you tell whether a particular sports dataset really adds value, particularly with regard to betting effectiveness? The method introduced in this paper provides a way for any analyst in almost any sport to attempt to determine the additional value of almost any dataset. It relies on the use of deep learning, comprehensive historical box score statistics, and the existence of betting markets. When the method is applied as an illustration to a novel dataset for the NBA, it is shown to provide more information than regular box score statistics alone, and appears to generate above-breakeven wagering profits.     

Forecasting football results and exploiting betting markets: The case of “both teams to score”

The continuous growth of available football data presents unprecedented research opportunities for a better understanding of football dynamics. While many research works focus on predicting which team will win a match, other interesting questions, such as whether both teams will score in a game, are still unexplored and have gained momentum with the rise of betting markets. With this in mind, we investigate the following research questions in this paper: “How difficult is the ‘both teams to score’ (BTTS) prediction problem?”, “Are machine learning classifiers capable of predicting BTTS better than bookmakers?”, and “Are machine learning classifiers useful for devising profitable betting strategies in the BTTS market?”. We collected historical football data, extracted groups of features to represent the teams’ strengths, and fed these to state-of-the-art classification models. We performed a comprehensive set of experiments and showed that, although hard to predict, in some scenarios it is possible to outperform bookmakers, which are robust baselines per se. More importantly, in some cases it is possible to beat the market and devise profitable strategies based on machine learning algorithms. The results are encouraging and, besides shedding light on the problem, may provide novel insights for all kinds of football stakeholders.     

Sportsbook pricing and the behavioral biases of bettors in the NHL

The betting market for the NHL is investigated using actual betting percentages on favorites and underdogs from real sportsbooks. Sportsbooks do not appear to attempt to price to balance the book as betting percentages are not proportional to set odds. As in the NFL and NBA, bettors are shown to have a strong preference for favorites and road favorites in particular. Simple strategies of betting against significant imbalances toward the favorite are shown to generate positive returns. Although not pricing to balance the book, sportsbooks do not appear to price to exploit known bettor biases in all cases. Clear bettor behavioral biases for road favorites are not priced into the odds as the prices set in these cases appear to be a forecast of game outcomes. Pricing as a forecast may ensure long-run viability for the sportsbook as it discourages entry into this market by informed traders and still allows the sportsbook to capture its commission on losing bets over time.     

Betting on a buzz: Mispricing and inefficiency in online sportsbooks

Bookmakers sell claims to bettors that depend on the outcomes of professional sports events. Like other financial assets, the wisdom of crowds could help sellers to price these claims more efficiently. We use the Wikipedia profile page views of professional tennis players involved in over 10,000 singles matches to construct a buzz factor. This measures the difference between players in their pre-match page views relative to the usual number of views they received over the previous year. The buzz factor significantly predicts mispricing by bookmakers. Using this fact to forecast match outcomes, we demonstrate that a strategy of betting on players who received more pre-match buzz than their opponents can generate substantial profits. These results imply that sportsbooks could price outcomes more efficiently by listening to the buzz.     

The behaviour of betting and currency markets on the night of the EU referendum

We study the behaviours of the Betfair betting market and the sterling/dollar exchange rate (futures price) during 24 June 2016, the night of the EU referendum. We investigate how the two markets responded to the announcement of the voting results by employing a Bayesian updating methodology to update prior opinion about the likelihood of the final outcome of the vote. We then relate the voting model to the real-time evolution of the market-determined prices as the results were announced. We find that, although both markets appear to be inefficient in absorbing the new information contained in the vote outcomes, the betting market seems less inefficient than the FX market. The different rates of convergence to the fundamental value between the two markets lead to highly profitable arbitrage opportunities.     

A profitable model for predicting the over/under market in football

The over/under 2.5 goals betting market allows gamblers to bet on whether the total number of goals in a football match will exceed 2.5. In this paper, a set of ratings, named ‘Generalised Attacking Performance’ (GAP) ratings, are defined which measure the attacking and defensive performance of each team in a league. GAP ratings are used to forecast matches in ten European football leagues and their profitability is tested in the over/under market using two value betting strategies. GAP ratings with match statistics such as shots and shots on target as inputs are shown to yield better predictive value than the number of goals. An average profit of around 0.8 percent per bet taken is demonstrated over twelve years when using only shots and corners (and not goals) as inputs. The betting strategy is shown to be robust by comparing it to a random betting strategy.     

Platform competition: Betfair and the UK market for sports betting

We examine two episodes of strategic interaction in the UK betting industry: (a) Betfair (an entrant multisided platform, or MSP) versus Flutter (also an MSP), and (b) Betfair versus traditional bookmakers. We find that although Betfair was an underfunded second mover in the betting exchange space, it was able to attract punters at a much faster rate than the better‐funded first mover, Flutter. Moreover, while Betfair and traditional bookmakers competed aggressively for market share, they also developed a highly complementary relationship that favored all parties. We discuss implications for research in the economics and management of MSPs. Specifically, we argue that the literature would benefit from work that endogenizes platform design and that considers the possible competitive and cooperative interactions between the business models of traditional incumbents and those of potential innovative MSP entrants.     

A Markov chain model for forecasting results of mixed martial arts contests

In this paper, we present a new methodology for forecasting the results of mixed martial arts contests. Our approach utilises data scraped from freely available websites to estimate fighters’ skills in various key aspects of the sport. With these skill estimates, we simulate the contest as an actual fight using Markov chains, rather than predicting a binary outcome. We compare the model’s accuracy to that of the bookmakers using their historical odds and show that the model can be used as the basis of a successful betting strategy.     

Are betting returns a useful measure of accuracy in (sports) forecasting?

In an economic context, forecasting models are judged in terms not only of accuracy, but also of profitability. The present paper analyses the counterintuitive relationship between accuracy and profitability in probabilistic (sports) forecasts in relation to betting markets. By making use of theoretical considerations, a simulation model, and real-world datasets from three different sports, we demonstrate the possibility of systematically or randomly generating positive betting returns in the absence of a superior model accuracy. The results have methodological implications for sports forecasting and other domains related to betting markets. Betting returns should not be treated as a valid measure of model accuracy, even though they can be regarded as an adequate measure of profitability. Hence, an improved predictive performance might be achieved by carefully considering the roles of both accuracy and profitability when designing models, or, more specifically, when assessing the in-sample fit of data and evaluating out-of-sample forecasting performances.     

Regional information and market efficiency: the case of spread betting in United States college football

Using game results over a seven year span (1999–2006), we find that United States college football teams in arid regions “win” against the spread in 56.64% of games in which they host a team from a humid region. This result provides statistically significant evidence for both weak and strong form inefficiency in the spread betting markets of such games. By examining other cases of intraregional and interregional competition within the sport, we conclude that this inefficiency does not arise from the effects of travel or home field advantage. Rather, the result indicates that climate aridity is an observed characteristic for which college football betting markets do not accurately control. It is quite rare to find strong form market inefficiency arise from a single variable rather than from an elaborate, multivariable betting strategy. Therefore, the effect of climate aridity upon college football spread betting market efficiency can be characterized as dramatic. It is conjectured that remote market participants may need to “experience” certain types of relevant regional information, such as climate, to act in a market efficient manner.     

On determining probability forecasts from betting odds

We show that the probabilities determined from betting odds using Shin’s model are more accurate forecasts than those determined using basic normalization or regression models. We also provide empirical evidence that some bookmakers are significantly different sources of probabilities in terms of forecasting accuracy, and that betting exchange odds are not always the best source, especially in smaller markets. The advantage of using Shin probabilities and the differences between bookmakers decrease with an increasing market size.     

Informational efficiency and behaviour within in-play prediction markets

Studies of financial market informational efficiency have proven burdensome in practice, because it is difficult to pinpoint when news breaks and is known by some or all the participants. We overcome this by designing a framework to detect mispricing, test informational efficiency and evaluate the behavioural biases within high-frequency prediction markets. We demonstrate this using betting exchange data for association football, exploiting the moment when the first goal is scored in a match as major news that breaks cleanly. There are pre-match and in-play mispricing and inefficiency in these markets, explained by reverse favourite-longshot bias (favourite bias). The mispricing tends to increase when the major news is a surprise, such as a goal scored by a longshot team late in a match, with the market underestimating their chances of going on to win These results suggest that, even in prediction markets with large crowds of participants trading state-contingent claims, significant informational inefficiency and behavioural biases can be reflected in prices.