Earlier this week, the Supreme Court struck down a 1992 federal law that effectively prohibited sports betting in the United States, save for a handful of state-specific exceptions. In a 6-3 decision, the court ruled that the Professional and Amateur Sports Protection Act (PASPA) violated the 10th Amendment, which limits the federal government from controlling state policy.
The decision paves the way for states to decide whether to offer legal sports betting. ESPN’s David Purdum reports that New Jersey, which brought the case, Mississippi, New York, Pennsylvania, and West Virginia could be among the first to do so. The Associated Press reports that as many as 14 states could act within the next two years, with another 18 states to follow.
So what does the decision mean for you? For starters, it’s likely to bring much of the estimated $150 billion-dollar-a-year black market in sports gambling above board. You’ll conceivably be able to place a bet on your phone, at a local sportsbook, or even in an arena. Casinos should see a boost. And fans will have more reason to engage, which supports broadcasters, franchises, and leagues.
Most importantly, however, the Supreme Court’s decision on sports betting means that you will now be able to lose your money legally – which, either right away or over time, you are exceedingly likely to do.
At the start of last NFL season, I walked through some of the success rates and financial dynamics of the most popular sports wager in the United States – the spread bet. I specifically looked at the against-the-spread performance of 60 individuals and 60 prediction models during the 2016 NFL season. I’ve since updated those statistics to include the 2017 NFL season.
To the unfamiliar, here’s an example of how a spread bet works. Assume the Dallas Cowboys are 4.5-point favorites at home against the New York Giants. If you pick Dallas to win “against the spread,” they need to win by five points or more for you to win the bet (“Dallas -4.5”). If you pick New York, you win if the Giants win the game outright or lose by four points or less (“Giants +4.5”).
In a typical spread bet, you risk 10% more money than you would win (-110), known as a 10% “vig.” Bet $110 and win, and you get $100. Bet $110 and lose, and you lose all $110. Historically, sportsbooks would set and adjust point spreads to attract and maintain equal action on both teams. Doing so guarantees the books a profit on those bets, equal to 4.5% of the total amount wagered.
The chart below shows the results of a random group of individual bettors (60 in 2016 and 53 in 2017) and prediction models (60 in 2016 and 57 in 2017) “against the spread” over the course of the last two NFL regular seasons. As expected, both groups had an average success rate right around 50%. And while the sample size is still small, you can see a typical bell curve forming.
Half of the bettors won more games than they lost, and half lost more games than they won. But when losses cost $110 and wins only earn $100 – thanks to that 10% vig – things get pretty ugly pretty fast. Had everyone wagered real money on every game in equal amounts, only 30 out of 113 individuals (27%) would have netted a single-season profit. Nearly three-quarters of bettors would have lost money.
Now consider this. Certain professional sports leagues have been lobbying to receive an “integrity fee” for all wagers placed on their respective games, theoretically compensating them for “[creating] the source of the activity” and “[bearing] the majority of the integrity risk.” Fee estimates range from 0.25% to 1.0% of money wagered, or 2.5% of profits, with other wrinkles attached.
Sports betting operators will also have to pay taxes – potentially as high as 12.5% of gross sports wagering revenue in one version of an Illinois bill. Against this backdrop, rumors began to circulate last fall that sportsbooks might consider covering these taxes and fees by raising the standard vig from 10% (-110, risk $110 to win $100) to 20% (-120, risk $120 to win $100).
If that happens, your chances of making money get even dimmer. With a 10% vig in the example above, we saw how roughly three out of four individual bettors would have lost money – already pretty tough sledding. With a 20% vig in the same example, nearly seven out of eight would have lost money on a single-season basis – worse still, 95% of the individuals who were part of the sample in both years would have lost money over the course of the two seasons combined.
Let's take a closer look at just how devastating the vig is. With a 10% vig, only three out of 113 individuals (2.7%) would have netted a single-season return-on-dollars-wagered of 10% or more. Meanwhile, 22 people (19.5%) would have had a 10%+ loss. Only 18 of the individuals would have earned a 3%+ profit, while 61 bettors would have lost at least that much. And the 20 worst performers would have lost over 2.0x the money that the 20 best performers gained.
Now let’s repeat those sentences assuming a 20% vig. With a 20% vig, only one out of 113 individuals (0.9%) would have netted a single-season return-on-dollars-wagered of 10% of more. Meanwhile, 50 people!!! (44.2%) would have had a 10%+ loss. Only four of the individuals would have earned a 3%+ profit, while 83 bettors would have lost at least that much. And the 20 worst performers would have lost almost 8.0x the money that the 20 best performers gained.
And again, those are single-season returns. It's even harder to stay in the black year over year. With a 10% vig, bettors in this example would have had to finish in the 90th percentile in 2017 just to offset the damage of finishing in 50th percentile (i.e., being exactly average) the year before. (Remember that half of the bettors did even worse!)
With a 20% vig, they'd have had to finish in the 97th percentile in 2017 to recoup the money they lost by finishing in the 65th percentile (above average!) the year before. In other words, the reward for being good-but-not-great is a financial penalty and a requirement that you be exceptional next year to break even. You can deduce from the chart how ugly it gets when you're actually below average, like half of everybody is.
Despite all that, most folks kinda pretend the vig isn’t there, casually chalking it up as the cost of doing business. Ask your buddy how much he has on a game, and he’ll likely say “$100” or $50,” not “$110” or “$55,” which is really what he’d lose. If he wins two bets and loses two others, he’ll probably tell you that he went two and two, not that he lost money.
Of course, not everyone puts money on every game. Bettors often target select games. But even then, one person's "best bet" is another's "trap of the week," and over a long enough period, the vast majority of casual bettors will lose money. There’s nothing special about the math. Sometimes we just need to see it all on paper.
 Market estimates vary wildly, from $67 billion to $380 billion. For context, Nevada sportsbooks handled $4.8 billion in sports wagers in 2017.
 Evidence suggests that sportsbooks have grown increasingly comfortable straying from that 50-50, guaranteed-profit balance. When they do so – that is, when they set lines that attract significantly more money on one team – they’re effectively betting on the other team. In early 2017, certain books allowed big imbalances in nine of 10 NFL playoff games. The books lost all of them against the spread.
 In this particular sample, that's true both on a single-season basis and combined over the course of the two seasons. There were 60 individuals in 2016 and 53 individuals in 2017. Forty-two of them were the same across seasons. On a single-season basis, 83 out of 113 (73%) would have lost money with a 10% vig. On a combined basis over the two seasons, 31 out of 42 (74%) would have lost money with a 10% vig.
 Again, there were 60 individuals in 2016 and 53 individuals in 2017. Forty-two of them were the same across seasons. On a single-season basis, 97 out of 113 (86%) would have lost money with a 20% vig. On a combined basis over the two seasons, 40 out of 42 (95%) would have lost money with a 20% vig.
Portions of this story were updated and adapted from one of my previous posts.
Data was compiled and analyzed by ELDORADO. All charts and graphics herein were created by ELDORADO.