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Betting on Spreads

Randomness of Spreads: expected values vs. outcomes

One of the "a-ha moments" for me was realizing how much error betting lines have, relative to results. You should pretty much expect Spreads to be wrong by 10 points absolute. And that's just on average, whereas the error typically spans +/-30ish. That is a wide difference between expectations and possible outcomes. It effectively means you need to believe in more than a 1-point difference, to make a worthwhile bet.

Here (left-side) is the histogram of Vegas spread errors:

r/sportsbetting - Subvertadown Spread Picks for Week 14, and some discussion

The right-hand side compares against my mown model for comparison, while also highlighting how narrow even a +/-1 point change looks.

The whole curve represents "100%" of possible results, so the right half (or left half) represents winning (or losing) a bet on the spread. You can see that a small region like +/-0.5% is almost negligible in helping to secure a win. This makes me skeptical about betting towards key numbers (more on that another time). It also feeds into my approach: Prioritize betting when my models indicate significant point differences.

Betting line tendencies

One point I want to make is that the wide error range (discussed above) makes it hard to win a bet by pursuing marginal differences to get "key" numbers. I'm referring to the common suggestion of taking the +3.5 bet or -2.5 bet, because "3" is a common win margin. I'm going to support the counter-opinion that this probably helps very little, because changing the line by 0.5 points such a small part of the wide +/-20 point error distributions.

The distributions of spread outcomes cluster around "common numbers". Do the betting lines' spreads have a similar pattern? Yes. As many of you may know, they tend to cluster similarly around those same numbers:

That might seem normal to you..., but I want to say that it must be the result of bettors pushing towards those numbers, because a straightforward mathematical model would predict something more continuous— like the green curve in the above.

Is it worth making bets to get those "key numbers" (3,7,10)? I'm skeptical. In going from the continuous curve (green) to the bumpy curve (orange), the average point change is small-- between 0.25 - 0.5 points. That is a tiny amount, next to the 50ish-point error-range shown earlier. No doubt there is some small benefit, but too slight to reach 52.5% return. I suggest more effective gains come from expecting larger differences in spread. So take the +3.5, sure, but only if you think +2 (or less) is a more likely indication of win probability.

Spreads calculation

The question here is: "what do you think goes into calculating betting lines?"

Here's my awareness of some relevant variables:

  • Home vs. Away game,

  • historical team points,

  • historical TD rate,

  • historical points-allowed per play,

  • historical total yards gained,

  • historical field goals,

  • QB interceptions,

  • red zone efficiency,

  • and allowed rushing yards.

My opinion about best approach

First summarizing all the above, I think it's best to: (a) calculate the expected spread, based on historical stats and developing a statistical model, (b) look for which of those calculations differ significantly from current lines (more than 1 point difference), (c) mostly ignore the key numbers (like 3 and 7) unless you're betting in that direction anyway (taking +3.5 take or giving -2.5), and (d) diversify instead of focusing wagers.