PDF Is the NBA betting market efficient?

We analyze point spread changes and betting volume for NCAA Division IA men’s basketball games using survival analysis techniques. Sports books do not appear to change point spreads to induce equal volumes of bets on either side of propositions; the “balanced book” model of sports book behavior does not describe observed point spread changes in this market. Market efficiency in betting refers to how accurately odds reflect the true probability of an event. Over time, as money flows in and information spreads, odds get sharper and harder to exploit. Invariably, most bettors will find that their models don’t outperform the market consistently.

Proof of Market Efficiency: Backtesting 11,848 Matches

Sports betting, like any other market, becomes more efficient as more participants enter the fray. The greater the volume of money and opinions involved, the more accurate and reliable the odds become. 🔹 Focusing on Low-Liquidity Markets – Smaller leagues and niche markets tend to be less efficient, creating more opportunities. ✔ Timing – The closer a market gets to an event starting, the more efficient it becomes as new information is priced in.

• Simply put, a market is considered efficient when all available information is already factored into the odds, making it difficult to find a consistent edge.
• This phenomenon is known as price discovery, which I explain in my post on the Wisdom of Crowds Theory.
• The concept helps explain why beating the bookmaker is so hard, but also where opportunities still exist.
• Our statistical tests detect two specific biases in the NFL market and an unspecified bias in the college market.

🧠 What is the Efficient Market in Betting?

Market efficiency matters because it shapes the odds you see and the bets you place. If you want to move from casual punter to smart bettor, you need to understand when the market is efficient — and where the inefficiencies hide. One way to capitalise on slow-moving odds is to get in early, before the market fully forms. This is a tactic frequently used by successful horse racing tipsters, especially in sports like horse racing, where early odds can often offer significant value.

As a bettor aiming for profitability, it’s crucial to understand how market efficiency works and how it varies across different betting markets. In particular, you must recognise the pros and cons of targeting both popular and obscure sports betting markets and understand what conditions make them easier or harder to beat. Our statistical tests are stronger than those in previous studies, and we examine both NFL and college data over a sample period of fifteen years.

Furthermore, Shin’s model provides unbiased estimates of the winning probability, while basic normalization does not account for the favourite-longshot bias (Koning and Boot 2020). According to Cain et al. (2003), there is no direct effect of the presence of insiders when using Shin’s model within soccer. If the bookmakers create a bias in their odds because of the presence of insiders, this would suggest that there is no favourite-longshot bias when the Shin (1993) model is used. However, Cain et al. (2003) suggest that further research for soccer is required due to the possible indirect effect of the presence of insiders and the possibility of more than two outcomes.

That means you might spot odds errors, late team news, or unique angles faster than the market. If you’re interested in finding these opportunities, I recommend reading my Review of the Best Value Bet Finders, which can help you identify these inefficiencies. ✔ Information Gaps – Sudden changes (injuries, weather, lineup adjustments) can create inefficiencies before the market corrects itself. We tested that as well, and the outcome was even more striking—losses almost exactly matching the 2% commission. The confirmation bias and halo effect strategies show you how to ignore hype and stick to facts.

Understanding bet types is key to building a solid betting foundation, and the APWin dafabet app Academy breaks them down in a way that’s easy to apply. You’ll start with the basics, like moneyline bets where you simply back a team or player to win. At APWin, we deliver step-by-step guides to help you understand how online betting works, from the basics of odds to advanced tactics. Our Betting Academy is your one-stop learning hub, offering detailed advice to help you bet smarter, whether it’s football or any other sport.

Today’s Matches!

Our statistical tests detect two specific biases in the NFL market and an unspecified bias in the college market. We examine the year-to-year consistency and magnitudes of the biases and find that the NFL bias against home teams has been nearly eliminated, while the bias against underdogs has increased. In the analysis of this section we use data from eighteen seasons, and it is possible that the relation between implied probabilities and outcomes has changed over time. This could be due to changes in regulation, the profile of internet bettors, or the advent of betting exchanges.

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. Understanding market efficiency in sports betting is crucial for anyone aiming to make a profit.

From there, you’ll discover flat betting, a low-risk system where you stake the same amount on every bet, which is ideal for maintaining discipline during losing streaks. We want our member to have confidence in our platform, and that’s why we offer a Credit Back Guarantee to all tips without profits. This program comes into effect on the 01 July 2015 (“Effective Date”) and is offered to ensure that member who made tips purchase of any tips that ended with either lose, draw, cancelled or postponed game.

Deschamps and Gergaud (2007) explore the favourite-longshot bias in English soccer data and show that this bias depends on the odds status. For the odds status home win and away win, the authors find a clear favourite-longshot bias, however, there is a reversed favourite-longshot bias for draw odds. Cain et al. (2000) also indicate the presence of favourite-longshot bias in betting odds of soccer results. Cain et al. (2000) note that the existing literature is unclear whether the bias only exists in the low and in the high probability cases and how these extreme cases extend. Relevant recent contributions to this literature that concern a similar betting market to ours are Angelini and De Angelis (2019) and Elaad et al. (2020). In both cases, implied probabilities are derived using the method of basic normalization.

In sports trading, this concept holds especially true in high-liquidity markets like Premier League Match Odds or Champions League Over/Under. Odds here are influenced by millions of pounds and are shaped by bookmakers, professional traders, and even AI-based models. While betting exchanges can be tough to beat, many traditional soft bookmakers fail to quickly align their odds with the more accurate prices available on exchanges or from sharper bookmakers like Pinnacle.