Sports Forecasting

A great amount of effort is spent in forecasting the outcome of sporting events, but few papers have focused exclusively on the characteristics of sports forecasts. Rather, many papers have been written about the efficiency of sports betting markets. As it turns out, it is possible to derive considerable information about the forecasts and the forecasting process from the studies that tested the markets for economic efficiency. Moreover, the huge number of observations provided by betting markets makes it possible to obtain robust tests of various forecasting hypotheses. This paper is concerned with a number of forecasting topics in horse racing and several team sports. The first topic involves the type of forecast that is made: picking a winner or predicting whether a particular team beats the point spread. Different evaluation procedures will be examined and alternative forecasting methods (models, experts, and the market) will be compared. The paper also examines the evidence about the existence of biases in the forecasts and concludes with the applicability of these results to forecasting in general.Sports forecasting, gambling markets, prediction markets

Inter-market Arbitrage in Sports Betting

Unlike the existing literature on sports betting, which concentrates on arbitrage within a single market, this paper examines inter-market arbitrage by searching for arbitrage opportunities through combining bets at the bookmaker and the exchange market. Using the posted odds of eight different bookmakers and the corresponding odds traded at a well-known bet exchange for 5,478 football matches played in the top-five European leagues during three seasons, we find (only) ten intra-market arbitrage opportunities. However, we find 1,450 cases in which a combined bet at the bookmaker as well as at the exchange yields a guaranteed positive return. Further analyses reveal that inter-market arbitrage emerges from different levels of informational efficiency between the two markets.sports betting, inter-market arbitrage

Issues in Sports Forecasting

A great amount of effort is spent in forecasting the outcome of sporting events, but few papers have focused exclusively on the characteristics of sports forecasts. Rather, many papers have been written about the efficiency of sports betting markets. As it turns out, it is possible to derive considerable information about the forecasts and the forecasting process from the studies that tested the markets for economic efficiency. Moreover, the huge number of observations provided by betting markets makes it possible to obtain robust tests of various forecasting hypotheses. This paper is concerned with a number of forecasting topics in horse racing and several team sports. The first topic involves the type of forecast that is made: picking a winner or predicting whether a particular team beats the point spread. Different evaluation procedures will be examined and alternative forecasting methods (models, experts, and the market) will be compared. The paper also examines the evidence about the existence of biases in the forecasts and concludes with the applicability of these results to forecasting in general.sports forecasting; betting markets; efficiency; bias; sports models

When do betting odds best represent the actual outcomes? Predicting NHL results based on moneyline odds movement

The sports betting market has grown quickly over the past the few decades mostly due to the digitalization of the business. The bookmakers have moved online from the physical locations. The market has thus globalized, and the competition has increased, forcing the bookmakers to produce more accurate estimates about the events to keep up with the competition. This study investigates the accuracy of NHL moneyline betting odds, in predicting the actual outcomes of games throughout the time that the betting events are open. The dataset covers three full seasons from 2015 to 2018. The odds are collected at 5 different time points for each game and the differences in the predictive power of time points is analyzed. Then, the odds and their movement is investigated further to see if there are profitable betting strategies to be found based solely on information about odds movement. The tests related to the prediction accuracy of the odds reveal that there are no statistically significant differences between the prediction accuracy for different time points. Aggregate results are rather consistent though in showing that before the day of the game, the estimates implied by the odds aren’t quite as accurate as they are during the game day. The regression tests further indicate that when the implied probability of a selection grows, the objective probability grows at a higher rate, meaning that there’s a Favorite-Longshot Bias in the market. This means that betting on a more likely outcome yields better returns on average. The tests for finding profitable betting strategies further enforce the notion of Favorite-Longshot Bias and subsequently all of the consistently profitable betting strategies, that are found, involve only betting on favorites. The data about the odds movement between time points and splitting the teams to favorites and underdogs reveals that betting on favorite teams who have had their odds rising in a given time point interval, yields a profit for 80% of the intervals. The margins are so small that none of the returns for profitable strategies are significantly larger than zero in a statistical sense but the difference to the average bookmaker margin is more significant. According to the analysis about different staking strategies, for this kind of betting system where no estimate is calculated individually for each game, simple staking strategies of betting a fixed amount or to win fixed amount, yielded the best balance of capital growth and risk. The study concludes that there is little difference in predictive power of NHL moneyline betting odds at different time points throughout the life cycle of betting events. Based on the results it’s clear that the bookmaker margin isn’t allocated evenly between favorites and underdogs and there’s an apparent Favorite-Longshot Bias in the market, which is in contrast with previous research about NHL moneyline odds. The bias logically leads to favorites being the side that offers better returns and betting on favorites with rising odds offers returns that consistently beat the bookmaker margin and are also marginally profitable

Using a Case-Based Reasoning Approach for Trading in Sports Betting Markets

The sports betting market has emerged as one of the most lucrative markets in recent years. Trading in sports betting markets entails predicting odd movements in order to bet on an outcome, whilst also betting on the opposite outcome, at different odds in order to make a profit, regardless of the final result. These markets are mainly composed by humans, which take decisions according to their past experience in these markets. However, human rational reasoning is limited when taking quick decisions, being influenced by emotional factors and offering limited calibration capabilities for estimating probabilities. In this paper, we show how artificial techniques could be applied to this field and demonstrate that they can outperform even the bevahior of high-experienced humans. 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Do bookmakers possess superior skills to bettors in predicting outcomes?

In this paper we test the hypothesis that bookmakers display superior skills to bettors in predicting the outcome of sporting events by using matched data from traditional bookmaking and person-to-person exchanges. Employing a conditional logistic regression model on horse racing data from the UK we find that, in high liquidity betting markets, betting exchange odds have more predictive value than the corresponding bookmaker odds. To control for potential spillovers between the two markets, we repeat the analysis for cases where prices diverge significantly. Once again, exchange odds yield more valuable information concerning race outcomes than the bookmaker equivalents

Information and predictability: bookmakers, prediction markets and tipsters as forecasters

The more information is available, and the more predictable are events, the better forecasts ought to be. In this paper forecasts by bookmakers, prediction markets and tipsters are evaluated for a range of events with varying degrees of predictability and information availability. All three types of forecast represent different structures of information processing and as such would be expected to perform differently. By and large, events that are more predictable, and for which more information is available, do tend to be forecast better

How Do Markets Function? An Empirical Analysis of Gambling on the National Football League

The market for sports gambling is structured very differently than the typical financial market. In sports betting, bookmakers announce a price, after which adjustments are small and infrequent. As a consequence, bookmakers do not play the traditional role of market makers whose primary function is to match buyers and sellers, but rather, take large positions with respect to the outcome of game. Using a unique data set that includes both prices and quantities of bets placed over the course of an NFL season, I demonstrate that this peculiar price-setting mechanism allows bookmakers to achieve substantially higher profits than would be possible if they played the role of the typical market maker. Bookmakers are more skilled at predicting the outcomes of games than bettors and are able to systematically exploit bettor biases by choosing prices that deviate from the market clearing price. While this strategy exposes the bookmaker to risk on any particular game, in aggregate the risk borne is minimal. Bookmaker profit maximization provides a simple explanation for heretofore puzzling deviations from market efficiency that were observed in past empirical work. I find little evidence that there exist bettors who are systematically able to beat the bookmaker, even given the distorted prices that bookmakers set. The results concerning whether aggregating across bettor preferences improves the ability to forecast outcomes are inconclusive.

Behavioral complexity of British gambling advertising

Background: The scale and complexity of British gambling advertising has increased in recent years. ‘Live-odds’ TV gambling adverts broadcast the odds on very specific, complex, gambles during sporting events (e.g. in soccer, ‘Wayne Rooney to score the first goal, 5-to-1,’ or, ‘Chelsea to win 2-1, 10-to-1’). These gambles were analyzed from a behavioral scientific perspective (the intersection of economics and psychology).  Method: A mixed methods design combining observational and experimental data. A content analysis showed that live-odds adverts from two months of televised English Premier League matches were biased towards complex, rather than simple, gambles. Complex gambles were also associated with high bookmaker profit margins. A series of experiments then quantified the rationality of participants’ forecasts across key gambles from the content analysis (TotalN = 1467 participants across five Experiments).  Results: Soccer fans rarely formed rational probability judgments for the complex events dominating gambling advertising, but were much better at estimating simple events.  Conclusions: British gambling advertising is concentrated on the complex products that mislead consumers the most. Behavioral scientific findings are relevant to the active public debate about gambling

The Distribution of Information in Speculative Markets: A Natural Experiment

We use a unique natural experiment to shed light on the distribution of information in speculative markets. In June 2011, Betfair – a UK betting exchange – levied a tax of up to 60% on all future profits accrued by the top 0.1% of profitable traders. Such a move appears to have driven at least some of these traders off the exchange, taking their information with them. We investigate the effect of the new tax on the forecasting capacity of the exchange (our measure of the market's incorporation of information into the price). We find that there was scant decline in the forecasting capacity of the exchange – relative to a control market – suggesting that the bulk of information had hitherto been held by the majority of traders, rather than the select few affected by the rule change. This result is robust to the choice of forecasting measure, the choice of forecasting interval, and the choice of race type. This provides evidence that more than a few traders are typically involved in the price discovery process in speculative markets