How Contrarian Was College Football Season?

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The research project I mentioned on Saturday night is a quantitative assessment of the contrarian-ness* of the college football season. There are some huge caveats here. One is, of course, sample size. The second is the binning of Wagerline percentages. Different ways of binning will yield different results.

I think this last caveat is most important. While people like myself, Vegaswatch, Jonny, and Moneyline are attempting to make sports wagering a quantitative exercise, quite obviously there is a lot of subjectivity involved. What makes me place a bet on one five-point dog receiving 38% of the action at Wagerline versus another? Subjectivity. One of those teams might be Ole Miss the other might be Fresno State. I feel like Ole Miss is an underrated football squad, so there is value in taking their side. The opposite is true of Fresno.

In the figure below, "Wagerline %" is the percent of Wagerline users who selected the FAVORITE. Therefore, a value of 39 indicates there is a public dog and a value of 75 indicates the favorite is extremely public. The record and winning percentage are again for the FAVORITES. Here are the results for all 684 Division 1 NCAA Football games that statistics were available for on Wagerline.



Here is a philosophical question. If we agree that contrarians generally bet on anti-public underdogs successfully, and that gambling markets are efficient, then shouldn't there be a way to exploit favorites? The answer could be yes or no. One possibility is that since we know that gamblers tend to bet on favorites, we need to relax our definition of an anti-public favorite. Perhaps 54% on a favorite constitutes being "anti-public," however we define it? The other option is that favorites cover slightly more across all "public" bins.

With the obvious small sample size caveat, I think that, in general, favorites getting less than 55% of the action at Wagerline are worth looking at. It also appears that contrarians using only Wagerline numbers did not do well until the percentages were greater than 68%.

Another factor that I consider when placing a bet is home field advantage. I think it is just anecdotal over the years, but I prefer to be on a home team than a road team. In the figures below, I've separated out the sides for home favorites and road favorites.

HOME FAVORITES


ROAD FAVORITES


EDIT: When you upload more than one image at a time to blogger, they come out in reverse order of the way you uploaded them. Also, proofread your posts before you put them out there for the world to see. Idiot.

In this case, "anti-public" home favorites are even better than an ordinary favorite. Likewise, "anti-public" home dogs (bottom image) are better than an ordinary underdog. While certainly not exhaustive, I think these statistics do show some evidence for home field advantage in football wagering.

Lastly, I looked to see if the percentages changed much given a larger spread. The following four tables show how winning percentages change among consensus numbers for spreads less than 3, between 3.5 and 7, between 7.5 and 14, and greater than 14.5

FAVORITE GIVING 3 OR LESS



FAVORITE GIVING BETWEEN 3.5 AND 7


FAVORITE GIVING BETWEEN 7.5 AND 14


FAVORITE GIVING MORE THAN 14


Because sample sizes start getting really small here, it is difficult to gain a lot of information, but I hypothesize that short "anti-public" favorites and moderate "anti-public" dogs were the most profitable bets to make.

EDIT: It would help if I had uploaded the images correctly. Long (>7 pts) "anti-public" favorites appear to be profitable. The part about moderate "anti-public" dogs still stands, though you can probably add short dogs to the list as well (<14 pts).

Feel free to draw your own conclusions from the data and put them in the comments below.

*I can make new words out of contrarian, too.

14 comments:

JP said...

Nice work. The home field thing goes along with one of three case studies you posted. I think there might be something to that.

With more data we would definitely be able to draw more conclusions. The only question is whether previous years data is tainted or not due to the market changing a bit. I guess there is always the intelligent way of figuring that out.

am19psu said...

There was a lot of work that went into this as was. I didn't feel like looking up the t-test for proportions and figuring out which of those were statistically significant. It's an easy exercise, though.

Also, if anyone has the 2007 data (I don't), you could test the difference between two proportions.

Either way, hopefully some discussion gets sparked here or at one of the other blogs if anyone takes an interest in it.

moneyline said...

"One possibility is that since we know that gamblers tend to bet on favorites, we need to relax our definition of an anti-public favorite."

Considering that favorites are guaranteed roughly at least 14% of the action at Wagerline regardless of teams/ line, I think this is reasonable.

jwsherjr said...

It might be helpful to standardize spreads from across all sports. That would increase your sample size, and would probably be pretty interesting.

am19psu said...

It might be helpful to standardize spreads from across all sports. That would increase your sample size, and would probably be pretty interesting.

It actually wouldn't be hard to do in theory. All you would need is the mean and stdev of point spreads in each sport. Then, provided that point spreads are normally distributed (may not be a good assumption), you can compare apples-to-apples.

I have the 2008 NCAAF Wagerline dataset. Do any other ones exist? As far as I know, this is the first time somebody has done something like this, though I would like to be wrong about that.

moneyline said...

"I have the 2008 NCAAF Wagerline dataset. Do any other ones exist? As far as I know, this is the first time somebody has done something like this, though I would like to be wrong about that."

I am working on all of them, but my work is totally scattered right now. You will have access to the data once I am finished though.

jwsherjr said...

Doesn't Wagerline keep their numbers up from several years back?

Mjolnir said...

Awesome work. I had been wanting to do something just like this, but didn't have the time this year. Let me know if you want some help with it for next year.

As for the normal distribution amongst spread numbers, I doubt you'll get that in football. I think a lot of games will be clustered between 2.5-4.5 and 6.5-7.5. That doesn't mean you couldn't do the comparison, it just wouldn't be perfect.

Another way to do the comparison could be to compare the ML odds between the two sports. For example, a 3 point dog in basketball might average paying +200, while in football +200 would be the average for a 6 point dog.

Mjolnir said...

Oh, and obviously those numbers aren't exactly accurate, just pulled them out of my ass. The half point calculator at SBR may be of some assistance as well.

am19psu said...

Doesn't Wagerline keep their numbers up from several years back?

Well, fuck. How did I miss that their data goes back to 2005-6? Now all I need to do is find the time to do the analyses.

As for the normal distribution amongst spread numbers, I doubt you'll get that in football. I think a lot of games will be clustered between 2.5-4.5 and 6.5-7.5.

Eyeballing it makes me think that a higher proportion of NFL lines land on 2.5-3.5 and 6.5-7.5 than college. I'm hoping at least for college lines that I'll be able transform the data normal.

Anyone feel like talking to my fiancee and telling her I need about 20 more hours a week to be a degenerate?

ontilt said...

I found it most interesting that the range of 51-55% Wagerline favorites had a great winning record. I always assumed(from instinct) that this part of the pool could represent more like 35%-40% public money, because people with actual money on the game will be more afraid to lay that chalk than a wagerline free pick.

And, of course, the split action games are always the toughest to decipher which way and if the "real" betting public is leaning one side.

ontilt said...

Just amend the last comment. Not to assume ALL of the 51-54% games are slightly anti-public, really depends on the team and situation. But I would say there are probably a lot that fall into the anti-public category.

am19psu said...

Not to assume ALL of the 51-54% games are slightly anti-public, really depends on the team and situation. But I would say there are probably a lot that fall into the anti-public category.

I tentatively agree with this statement. I think more research needs to be done on the topic, but I am thinking that a good starting point to start narrowing down teams is dogs <35% and chalk <55% at Wagerline.

I'm quite interested to see if the same hypothesis holds for the NFL.

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