If you think about it, a sportsbook isn’t so different from a financial market.
For simplicity, compare it to a stock market. You invest some money in a position, and at some future time you get a payoff (which may be zero). Prices of the positions (or odds on bets) change as new information becomes available. When demand is high and lots of people want a certain stock or side of a bet, prices rise. (In sports betting, the price is reflected by the odds, which pay less as a bet becomes more likely to win.) When everyone’s selling, or betting the other side, the price falls.
The Efficient Market Hypothesis
There’s an essential lesson to be gleaned from this comparison, and shockingly, it’s one that many casual sports bettors never get. It’s the notion of market efficiency, a concept that (unfortunately) makes it a lot harder to win consistently. But it’s important to understand, because it makes it clear exactly what is necessary in order to win.
The main point of the Efficient Market Hypothesis is this: All public information, as soon as it becomes available, is immediately reflected in the price of an asset. Information travels so quickly, and trading systems are so sophisticated, that as soon as fresh news comes out regarding a company, buyers bid up the price of a stock or sellers bid it down. Key here is that it happens instantaneously.
The conclusion: No public information, whether from a chart of historical prices or from an up-to-the-minute newswire, is useful in helping to predict the future price of a stock. By the time you can react to the public news that a startup tech company just got a huge government contract, the market will have already driven up the price to where it should be. (Note: Private information is a different story, and that’s why Martha Stewart got in trouble.)
Not everyone agrees that world financial markets are completely efficient. But there’s a lot of evidence that, at least to some extent, they are.
Are Sportsbooks Efficient?
However efficient financial markets may be, you can bet that sports betting markets are less so—good news, if you’re trying to beat them.
Why? Because they’re less liquid. While there are millions of participants in financial markets, a small sportsbook may be catering to a few dozen players.
Let’s say a bookie has gotten a few bets on the Saints for Monday Night Football tonight, but nothing on the 49ers. The goal of a smart bookmaker is to balance the book: He wants equal money on both sides of the game, so that he wins regardless of the outcome, by paying the winners slightly less than even money. For him, it’s about limiting or eliminating risk, not gambling.
So he hopes to get a few bets on the underdog 49ers today to balance the book, and as incentive for players to bet on the ‘9ers, offers to lay 7 points instead of the 6 that everyone else is laying. But what happens when news comes out that Saints QB Drew Brees is only 80% for tonight’s game, due to an ankle injury that flared up at the end of the week? (Note: This didn’t happen, so don’t use it to bet!)
Now the bookie is in a tough position. Given this news, he should probably move the spread back to Saints-minus-6 or even minus-5 or 4, but he also has to think about his unbalanced book. It’s very likely that he won’t move his line until some money comes in on the 49ers, or at least that he won’t move it enough. And if this happens, you’ll have a chance to get good line on the 49ers before he does.
In this case, the lack of participants in the market has made it illiquid, and therefore inefficient. The bookmaker can’t fully incorporate the information about the injury into his line because of concerns about his own risk, so there’s opportunity to get a favorable bet down if you can quickly adjust whatever predictive model you’re using to account for the new information.
Winning in an Efficient Betting Market
Of course, most larger sportsbooks won’t have this problem. They won’t be overly concerned with balancing their books if they have the bankroll to handle the risk associated with leaving a book unbalanced in order to incorporate all available information into their line.
In other words, it won’t always be this easy.
So let’s assume that sports betting markets aren’t completely efficient but are fairly close—in other words, it’s possible to win, but you have to be really good—which seems like an accurate assumption to me. In this case, “obvious” information is not going to help you, as it’s almost certainly already incorporated in the odds.
Example: When two high-powered offenses are playing, someone on sports-talk radio inevitably says, “You’d have to be an idiot to take the under in that game!” Well, it turns out he’s actually the idiot. True, the teams are likely to score a lot of points. But, of course, the over/under line is set to reflect this information.
Same goes for any other information that’s widely known. It simply will not help you.
In an efficient market, betting with information everyone knows is the same as betting with no information at all. You’ll win half the time, like everyone else. And in the long run, the vig will wipe you out.
If you’re going to make a long-term profit from sports betting, it has to come from one of two sources.
- You have information nobody else has; or
- You can process information better than anyone else
As someone with a math and statistics background (and without any inside sources), I’m interested in the second option.
Building Sports Betting Models
Processing information can be as simple as using your “feel” for the game, the teams, the matchups, and the like. But I don’t think you can win this way, unless you are truly something special and live and breathe the sport you’re betting on.
Instead, I prefer to build mathematical models to process the ample numbers that are now available for any sporting event. While everyone has access to these statistics, my feeling is that the mounds of data contain patterns of information that human beings are unable to recognize due to limited processing power in our brains.
With this in mind, in my next post I’m going to introduce the model I built for predicting outcomes of football games. It’s one that I bet with for an entire NFL season, and picked well above the 55% rate needed to beat the house edge.
So why am I not rich? Well, I didn’t make money that season. My system for sizing bets was flawed (or maybe, the victim of bad luck) so I lost enough large bets that the system didn’t come out on top. More importantly, manual accounting for injuries and other hard-to-automate information required far more hours than I had to give, so I eventually abandoned the system.
And that’s why I’m going to share the model here. Not for the purpose of selling picks, but because it’s interesting and I believe that making it public is the best way to make it better. (What if I could convince 32 passionate people to each handle the injuries for a single team that they follow anyway?)
Then again, people like picks. You didn’t think I could resist posting those, did you?
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