Understanding the Efficient Market in Betting and Sports Trading
In this paper, the efficiency of basic normalization and Shin’s method is compared by analyzing whether these implied winning probabilities are unbiased indeed for the actual probability of outcomes. The most common adjustment is basic normalization, where the inverse odds are divided by their sum (Štrumbelj 2014). However, scaled probabilities do not lead to unbiased estimates of the true winning probabilities (Deschamps and Gergaud 2007; Koning and Boot 2020). The biased estimators of the winning probabilities tend to be too low for favourites and too high for underdogs, which is called the favourite-longshot bias. The concept of favourite-longshot bias was first documented by Griffith (1949) for horse racing and the existence of this bias has been found in the betting odds of several other sports including soccer (Cain et al. 2000).
While popular betting markets offer high liquidity and accurate odds, they are also highly competitive and difficult to beat. In contrast, obscure markets may present opportunities, but they come with the challenge of limited information and mispriced odds. Market efficiency in sports betting refers to how accurately the odds reflect the true probabilities of an event occurring. The more efficient a market, the harder it is for bettors to identify value and gain an edge. In efficient markets, the odds are shaped by a large number of opinions and are constantly adjusted to reflect the latest information. In contrast, inefficient markets lack sufficient data or liquidity, which can create opportunities for bettors who can spot discrepancies before they are corrected.
Popular betting markets involve major sports and events, where millions of bettors contribute their opinions, such as top football leagues or iconic matchups like El Clasico (Real Madrid vs Barcelona). These markets are characterised by high liquidity, with a large volume of bets and opinions shaping the odds. The odds in these markets are more efficient because they reflect a wide range of information, including data, news, and public sentiment. Obscure betting markets refer to events that attract only a small group of bettors, often because they involve niche sports, lower-profile leagues, or events with limited media attention.
Is the NBA betting market efficient?
By detecting these slow-moving or soft prices, you can gain an edge in the market. This is one of the best approaches for new bettors looking to make profits in value betting. Yes, it’s true… sports trading markets are not always perfectly efficient—and sometimes opportunities can arise. A perfectly efficient betting market would mean that every piece of public information, statistical model, and betting activity is already incorporated into the odds, making it difficult for traders to find value. In this article, we’ll break down what market efficiency is, how it applies to sports trading, and whether it’s possible to beat an efficient market. Additional bet types like Asian handicap, draw no bet, each way, and BTTS or both teams to score help you change your approach.
- The evidence also suggests that, contrary to previous studies, gamblers that do bet the long shots are acting as rational individuals attempting to maximize their return for a given level of risk.
- Yes, it’s true… sports trading markets are not always perfectly efficient—and sometimes opportunities can arise.
- These bookmakers often provide “soft” odds, which are inaccurate and offer profitable betting opportunities.
- We analyze point spread changes and betting volume for NCAA Division IA men’s basketball games using survival analysis techniques.
In this paper we suggest that, in fact, betting exchanges have brought about significant efficiency gains by lowering transaction costs for consumers. We test this hypothesis using matched data on UK horse racing from betting exchanges and from traditional betting media. In contrast to traditional betting media, we find that betting exchanges exhibit both weak and strong form market efficiency. Further, we find evidence that an information based model explains the well documented favourite-longshot bias more convincingly than traditional explanations based on risk preferences.
Elaad et al. (2020) document odds to be unbiased in general, both in terms of the favourite-longshot bias or outcome type. In some cases, implied probabilities (or measures derived from those) are used as covariates to model, for example, match attendance or television demand. It is important that such probabilities are good estimates of the actual probability of outcome. Moreover, the analysis of the informational efficiency of the sports betting market has a broader relevance. If it can be shown that the sports betting market is efficient, then it is more likely that similar markets also process information efficiently. Similar to previous research, basic normalization and Shin’s model are used to transform betting odds into winning probabilities (Clarke et al. 2017; Koning and Boot 2020; Štrumbelj 2014).
Finally, topics like matched betting, rollover strategy, and avoiding loss chasing prepare you for sustainable long-term success. Traders with discipline and logic can take advantage of temporary mispricing caused by this emotional behavior. For example, our own analysis flagged that english team are overrated in European competitions. You can use tools like CGMBet to track unique insights like goal minutes, early momentum, or league-specific scoring patterns. Lower-tier competitions like Romanian Liga 2, Swedish Division 1, or Portuguese Segunda have less liquidity and media coverage.
How to Beat an Efficient Market
In other words, bettors overbet underdogs more than expected given their low winning frequency, while favourites are underbet given how often they win. Hence, bettors who bet systematically on underdogs receive lower returns than bettors who bet on favourites. This paper tests the efficiency of the football betting market and develops a model of the determinants of the closing spread. The empirical results suggest that the football betting market is not perfectly efficient. The evidence also suggests that, contrary to previous studies, gamblers that do bet the long shots are acting as rational individuals attempting to maximize their return for a given level of risk. Finally, at least six variables were found to be statistically significant factors in determining closing spreads in the football betting market.
Alternatively, Quandt (1986) explains that this bias arises since bettors are risk-loving. In other words, bettors are willing to give up some expected return in exchange for the additional risk. Additionally, Franke (2020) suggests that bettors bias odds due to misperception of the probabilities independently of the number of possible outcomes. To succeed, you need to either outsmart the market by leveraging specialist knowledge or act quickly to capitalise on slow-moving prices. Whether you choose to dive into the world of early betting opportunities or find inefficiencies in bookmaker odds, the key is to adopt a strategic approach and continually refine your methods. The winning probabilities determined from betting odds using basic normalization are referred to as scaled probabilities.
Another example of this approach to aggregate information based on many agents is to let the probability of outcome depend on the activity on social media (see for example Brown et al. 2018; Ramirez et al. 2021). We contribute to the literature on money line betting markets by investigating the relationships between the various methods used to derive subjective win probabilities from money lines. We also show that among the three distinct estimates, one is biased when money lines suggest a very heavy favorite in a particular sporting event. Thus, it is important to consider the assumptions for each method when deciding which to use in a particular context. Two empirical examples demonstrate how a market inefficiency, such as a favorite-longshot bias, should influence the choice of methodology. Established gambling operators have argued that person-to-person wagering on Internet ‘betting exchanges’ represents unfair competition.
This phenomenon is known as price discovery, which I explain in my post on the Wisdom of Crowds Theory. To put market efficiency to the test, we conducted a backtest on 11,848 matches from the top seven European leagues—England, Spain, Germany, Italy, France, Belgium, and dafabet app Portugal—over the past few seasons. Our results confirm weak form efficiency, while rejecting semi-strong efficiency. However, in the latter case, we are able to show the existence of profitable betting opportunities only in few of the scenarios.
Under-estimation of high probability events and over-estimation of low probability evens has also been documented in non-sports contexts (see for example Kip Viscusi 1998). The extent of market efficiency induced by rational behaviour of market participants is central for economic research. Many economists have already examined sports-betting markets as a laboratory to better understand trading behaviour and efficiency of stock prices while avoiding to jointly test the hypothesis of a correct capital market model. In view of existing market distortions as taxes, switching costs of changing betting providers and limitation in competition, the results o…
If you’re confident about a team and the market hasn’t yet reacted—there’s your value. To succeed in this approach, you’ll need to be highly methodical and dedicated. Developing your own pricing model requires constant adaptation and a deep understanding of both the sport and market dynamics. ✔ Sharp Bettors & Syndicates – Markets with professional bettors influence prices faster, making it harder to find edges.
One of the defining characteristics of a sports match is that the outcome is uncertain when the match is started. Betting odds offered by bookmakers are a good predictor of the probability of a certain outcome in a sport match (Stekler et al. 2010; Štrumbelj 2014). An example of the latter approach would be predictions based on a betting exchange such as Betfair, where market participants can both offer bets and accept bets (see for instance Croxson and Reade 2013; Dobson and Goddard 2017).