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Sunday, August 7, 2016

Effect of New Extra Point Rules on NFL Point Spreads

Last season's extra point rule change saw extra point accuracy decrease from 99.3% in 2014 to 94.2%. This drop is significant, and it did play a role in some games: most notably the AFC championship game.

My question though is whether it affected the public's perception of the expected margin of victory at the end of games, i.e. the gambling spread. Historically, spreads around a multiple of 7 or 3 are very sticky (toggle numbers around these multiples in SBR's half-point calculator and see the difference). In other words, once a spread is around, say, -7, moving to -6.5 or -7.5 is a much bigger jump (percentage-wise) than -5 moving to -4.5 or -5.5. It is much more likely a score finishes at exactly -7, which would greatly increase the chances of one side covering in this example at -6.5 or -7.5.

So did spreads move off of these multiples of 3 or 7 as the season progressed and the public (theoretically) adapted to the lower accuracy on extra point (XP) attempts? I first looked at the closing lines from last season that were on multiples of 7 only throughout the regular season. If XP accuracy decreasing had an effect, we would expect the frequency of spreads to decline throughout the season:


Likewise, we would expect the number of spreads within a half-point of 7 to increase throughout the season:



Neither frequency shows much of a linear trend from the beginning to the end of the season. So I expanded to multiples of both 3 and 7:

Thursday, June 23, 2016

Fading State in NCAAF/NCAAB

Awhile back during college football season, some friends of mine observed the fact that NC State seems to consistently blow it in football and basketball and seemingly "underpeforms". (Also note that I wrote about this overall trend in the ACC of "Clemsoning".) We have a saying to explain this behavior: "State = State", which illustrates how the circular universe of NC State athletics seems to collapse upon itself year-after-year under the weight of unfounded expectations.



One way to gauge this is to see if "fading" (i.e. betting against) State against the spread would be a profitable exercise. I used Prediction Machine's Trend Machine to pull NC State's games against Division 1 opponents (non-FCS) in both college football and basketball over the past 5 years. They have amassed the following records against the spread (ATS) in each sport:

Football: 25-32, 43.86%
Basketball: 87-69, 55.77%

The breakeven win percentage needed to make money ATS is 52.38%, so fading State in football would actually be profitable. However, they have posted a rather robust 55.77% win rate ATS in basketball in recent years. So it looks like only football is applicable in this "fading State" strategy.

Friday, May 20, 2016

Calibration of Horse Handicapping

The Preakness Stakes will be run this Saturday, representing the second leg of the Triple Crown and following the Kentucky Derby, the most popular (and most bet) horse race out there. In politics, Nate Silver and FiveThirtyEight have recently summarized the "calibration" of their polling models in predicting the winner of each state's primary/caucus this nomination cycle. How do these two topics intersect? I figured I would do the same thing for horse handicapping by calculating the calibration of the pre-race odds for both the Derby and the Preakness.

As Silver describes it, "Calibration works like this: Out of all events that you forecast to have (for example) a 10 percent chance of occurring, they should happen around 10 percent of the time — not much more often but also not much less often." With any betting odds, there is an associated "implied probability" after you take out the "juice" or vig (the house cut). I used this racetrack chart to get the house cut for both Churchill Downs and Pimlico, and then determined the implied probability each horse (that wasn't scratched) had at winning each race in the past 10 years.

This now allows me to calibrate each race for the past decade. Do long shots win more or less often than they're supposed to? What about the favorites?

Range# of HorsesE[X]Winners
[0, 0.05)1283.403
[0.05, 0.1)463.221
[0.1, 0.15)111.411
[0.15, 0.25)50.871
[0.25, 1]41.154

The < 5% long shots (i.e. 20-1 or higher) actually have won just about as often as expected. But what really stands out are the favorites: horses with > 25% have gone 4 for 4, and all in the last 4 years (and the favorite came in 2nd in 2012). Betting the favorite to win/place would have been very profitable in the past half decade.

The Derby has already happened though, so let's provide some insight looking forward. Here is the calibration for the Preakness over the same time frame:

Range# of HorsesE[X]Winners
[0, 0.05)561.751
[0.05, 0.1)281.841
[0.1, 0.15)91.090
[0.15, 0.25)81.553
[0.25, 0.5)72.542
[0.5, 1]21.242
Notice that the range of values is much larger, as the Preakness generally has less entrants, so the shortest odds generally are even shorter in this race than in the Derby. There isn't as much of an advantage for the heavy favorites, but horses with greater than a 50% shot against the entire rest of the field are 2 for 2. This year that honor goes to Nyquist at 3-5. Additionally, there appears to be value in the 15%-25% range. The only horse that fits this criteria in 2016 is Exaggerator at 3-1 (who finished 2nd to Nyquist in Louisville). Sometimes it pays to go chalk.

Friday, January 22, 2016

Incremental Effect of Win % Picking ATS

It's generally known that in order to break even picking games against the spread, you need to maintain a win percentage of 52.38% (with the standard -110 line). Also, it's incredibly hard to do: which is why even the best in the world only hit 54-55% of their bets:

My goal has always been 55%, which still means you're losing 4 out of every 9 bets you make. However, you should never try to stop improving: my current win rate sits at 54.9%, but I'm still chasing the holy grail: 60%.

How much of a difference is there between those 5 points? I wrote a quick simulator in Python to estimate the average ROI, when compounding your bankroll after each bet, for each 1 point increase from 53% to 60% (over 1,000 bets). This is pretty simplistic, but over 10,000 simulations the outliers should be smoothed out enough to get an idea of the big picture. And the results are (predictably) exponential:


Win %ROIx Over
52.5%2.90%0.04
53%13.2%0.20
54%36.5%0.55
55%66.2%1.00
56%99.2%1.50
57%243.6%3.68
58%294.4%4.45
59%356.0%5.38
60%429.2%6.48

60% is worth almost 6.5x MORE than 55% (over 1,000 bets). In graph form:

Never stop chasing 60.