Bad Beats: Getting Back on the Bowl Prediction Horse

Last year I used the Sagarin pure points method and found the games with the biggest discrepancy between the spread Sagarin predicted and the actual spread. I charted this for all of last year's bowls and finished around .500.

This year, I'm using The Prediction Tracker, which aggregate predictions from a number of systems. Treat the picks as independent and assume they fall in a normal distribution (warning: this is probably a terrible set of assumptions, but roll with it). Then, we can see how many standard deviations each spread is from the mean predictions. The ones that are furthest away are the best values.

Let's run through the Chick-Fil-A Bowl (Duke-TAMU) as an example. There are 48 picks with a mean of TAMU -6.54 and a standard deviation of 4.2. The current spread at the LVH is TAMU -12.5, approximately 1.42 standard deviations away from the mean prediction. The high Z-score demonstrates that there's value in this line, and that TAMU is overvalued. And that's why I picked Duke.

For reference, I put all the scores in a Google Document so you can bet against them as you will.

There haven't been many updates here lately, but that doesn't mean I haven't been working. Expect another update soon with a bunch of news.

Optimizing Conference Scheduling for Tournament Selection: Part I, College Football

This is Part 1 of a two-part post about conference scheduling in college sports. I submitted a version of this for inclusion in this year's Evolution of Sport competition at the Sloan Sports Analytics Conference in March. Since they didn't accept it, I decided to post it here. Part 2 is due Monday.

In 2008, the Boise State Broncos of the Western Athletic Conference were ranked 9th in the final BCS standings. That same year, the TCU Horned Frogs of the Mountain West Conference were ranked 11th. Now, in part because neither school was in one of the power conferences like the SEC, both teams were passed over for the most prestigious bowls, and met in the Poinsettia Bowl. Both teams earned a payout of $750,000.

The next season, Boise State finished 6th in the BCS standings, and TCU finished 4th. This time, they met in the Fiesta Bowl, one of the four games in the Bowl Championship Series, and earned a payout of $18 million each. Same teams, very similar regular seasons, 24 times more money. 24 times! That's the difference between a filet mignon with crab meat on top at Smith and Wollensky, and two cheeseburgers – no fries – at McDonald's.

And that's just the monetary benefits. That doesn't even count the national exposure for recruiting, or the increase in freshman applications that typically follows athletic program success.

So, naturally, if you work for a school like Boise State or a conference like the Mountain West, you want to know, "What can I do to improve my chances to get into the biggest bowl games and get that BCS money?" My talk will describe how conferences can improve their members' chances by stacking their conference strength of schedule.

Bad Beats: Sagarin's Predictor Rankings and the Bowl Games

I promised more updates, but that obviously hasn't happened. There are three reasons behind this: first, the million distractions of the holidays got in the way, as tends to happen; second, I've been working on my Evolution of Sport proposal for the upcoming Sloan Sports Analytics Conference, and third, it's harder than you'd think to type with these broken thumbs.

Maybe I should explain.

A coworker of mine is in a weekly football pool: for each NFL game, guess which team will cover the spread to win fame and fortune, etc. The pool keeps going into the playoffs, but since there aren't that many playoff games, he's also required to pick any six college bowl games against the spread. Last year, he told me, he used some good guessing and coin flipping to go 3-3. This year, he wanted to do better.

Red Eyes, Luggage Carts, Can't Lose

Just something quick: the Big East released its 2013 conference schedule yesterday. It's an interesting exercise in realignment madness. I computed the distance between each school using this tool. This first table provided the total miles each team travels to get to its road games, along with its longest trip.

A Richly-Deserved Beating: CFB ATS Update

At the beginning of the college football season, I asked whether you could use a team's recent against-the-spread (ATS) history to predict how they would do against the spread this season. I came to the conclusion that

[T]he perpetually underperforming teams (like Tulane) and perpetually overperforming teams (like Boise State) are a function of luck* rather than some underlying market inefficiency.

Well, the season's over (except for the bowl games). Was I right?

The Big East and the Big Easy

News of the Big East's latest expansions burned up my little corner of the Internet yesterday. Being a Tulane fan, I was happy to hear my alma mater would be movin' on up to the Big East beginning in 2014.

Everyone else, of course, reacted with something between bewilderment and disappointment. To them, the Big East was the desperate drunk guy looking for someone to go home with at The Boot's last call, and Tulane...

GO HOME, BIG EAST. YOU ARE DRUNK.

— Pod Katt (@valleyshook) November 27, 2012

...hey, don't be mean. Tulane is cute and really fun, you guys.

Can't wait for the Tulane-Providence game at the Dunk. What the hell happened to Dave Gavitt's Big East?

— Fran Fraschilla (@franfraschilla) November 27, 2012

Aw, come on, Fran. Well, I'm from Rhode Island, so I'm excited.*

* - Seems like a strange matchup to pick, doesn't it? After all, Providence was IN Dave Gavitt's Big East. They named the floor at the Dunk after Gavitt.

Unusual homerism aside, let's take a look at some possible reasons why Tulane was added, essentially replacing Louisville (who left today for the ACC) and Rutgers (who left recently for the Big 10).

Bad Beats: The Predictive Power of Past Years' ATS Record

Last week, I used this space to complain about losing money to my friend Dave by betting against Tulane football.

Faaascinating, I know. But it gives me a chance to make an Important Point about the predictive value of statistics.

My confidence in my bet was based on the fact that, from 2003 to 2011, Tulane covered just under 40% of their games against the spread. Winning 60 percent of your bets would make the average professional bettor salivate, so I was happy to bet based on this big trend.

There were two things I ignored: first, that one game is the smallest of sample sizes, and second, that past results are no guarantee of future performance. The second point is the interesting one, so let's focus on that: if a team has done better/worse than average against the spread in the past, does that tell us anything about its performance against the spread in the future?

Bad Beats: Rutgers at Tulane, Sep. 1, 2012

I owe Tulane football an apology. It seems I underestimated this year's team.

When the opening line for the season opener against Rutgers was listed at 17, I immediately jumped on Twitter.

At work today: "Tulane is 17.5-pt underdogs in their home opener vs Rutgers!" "That's it? Take Rutgers."
— Bryan Cole (@Doctor_Bryan) August 9, 2012

Even when confronted with the one other Tulane football fan on Twitter, I refused to back down.

“@jbr1657: Rutgers didn't even score 17 at home against Tulane in 2010” // Forgot about this. Prediction: Rutgers 14, Tulane -6 #rollwave
— Bryan Cole (@Doctor_Bryan) August 9, 2012

And it seemed Vegas agreed with me, kind of: by the week of the game, the line had moved to 20, though the over/under still suggested Tulane's score would be a natural number.