3.19.2009

NCAA Hoops Projections

I'm not any sort of basketball expert. For that I defer to my colleagues at Basketball Prospectus.

What I can do, however, is adulterate the hard work of others.

Specifically, what I can do is the following:

1. Take any of several power ratings. This requires nothing other than a search engine. The four power rankings that I like, in rough order of preference, are:

a) The Pomeroy RPI ratings.
b) The Sagarin ratings ('Predictor' version)
c) The Greenfield Predictive Power ratings;
d) And the Massey ratings.

In the interest of time, I'm not going to go into great length about the differences between these various systems. They're far more similar than they are different, with the partial exception of the Massey ratings, which do not account for margin of victory in their calculations and are probably inferior for that reason, but can be useful in providing something of a hedge against the more aggressive systems.

2. Translate the power ratings into winning percentages. For instance, if Louisville has a rating of X, and Ohio State has a rating of Y, I want to know how often Louisville will beat Ohio State. In fact, we want to develop a whole matrix, knowing how often any team in the tournament field is expected to beat any other team. The Pomeroy ratings, fortunately, are specifically designed for this purpose. For the other systems, I first 'map' them to the Pomeroy ratings by running a regression, and then proceed.

3. Use a Markov prcess to chain together the probabilities and play out the tournament an infinite number of times based on these head-to-head matchups.

This sounds fancy, but it really isn't; all I'm doing is taking the power ratings and translating them into probabilities, through a process that ultimately requires nothing more than addition, subtraction, multiplication and division. To the extent there's much value-add, it's not so much in picking the winners, but in adapting the system to the peculiar scoring rules that your tourney pool might have. In one pool I'm in, for example, which heavily rewards picking lower seeds, I have a final four of Memphis (a #2 seed), Gonzaga (#4), West Virginia and UCLA (both #6s). In another, which doesn't reward picking upsets at all, I play it much safer: Memphis and then the three number one seeds: North Carolina, Louisville and Pittsburgh (which happens to match the president's bracket).

So without further ado -- and since the tourney is about to tip off in 5 minutes -- here is what my numbers show this year. The first chart is the probability of each team reaching the Final Four from any of the four regions. Separate derivations are provided for each of the four power rating systems that I evaluated, as well as a composite rating that averages the four.



And here are the probabilities of winning it all. Get your money in now on Tennessee-Chattanooga, which is a mere 114,853,003-to-1 underdog.


17 comments

Dale said...

One of the few things I can agree with Nate and POTUS Obama - my Final Four matches theirs.

I've got UNC winning it, bad foot and all.

Plannerama.net said...

I ran a remarkably similar process and arrived at somewhat different conclusions. Notably, Pitt was the favorite to take it all against Memphis, with UNC and UConn also making it to the Final Four.

BillyPilgrim said...

It seems to me like this predicts too high a chance of upsets. For example, an average of 1.7 #1 seeds have made the final four in the last 20 years. This model predicts that 1.3 will. Conversely, an average of 0.2 teams seeded lower than 6 have made the final four, while this model predicts that 0.33 of just the four 6 seeds will make the final four (I imagine the total probability if you added up all the seeds 6 and lower would be 0.5-0.7, but I'm not going to add them all up).

BillyPilgrim said...

Sorry, where I said "Conversely, an average of 0.2 teams seeded lower than 6" it should say "6 or lower"

OneWingedAngel said...

Nate, check out http://www.ncaanerdoff.com/

It's probably too late to enter now, but maybe your celebrity status will get you a waiver.

Troy said...

The results in the midwest seem rather bumpy, as there's four teams that are less likely to win the region than the team immediately below them.

Any idea if that's some artifact of the scheduling, or just that several teams are seaded higher than they really should be?

Sarah Palindrome said...

What program do you use to run the Markov-chain process? I came up with different numbers using Pomeroy's ratings, but my process was a little simpler. I found the probability a team would make a round and multiplied that by the probability (a team would play each of the possible opponents X they would beat each opponent). Nobody came out above a 10% shot to win it all, even before I nudged UNC's rating down.

This system also spit out a 1.6% chance BYU would win it all, making them the biggest underbet/contrarian pick on the board, and that is not looking good at all at the moment.

matador said...

I am a soccer fan.
(I also played it in my far youth...I mean,professionaly).

so:
#1-I admire your graphic Sir Nate,but it is a sort of "Palinese" for me.

#Do Obama loves soccer ???
...let me know.

bye.
:)

matador said...

matador said...
Do Obama loves soccer ???

**************
oh Lord...
before Juris cames with the red pencil,I meant:

DOES Obama love soccer....

:P

Clark said...

The LRMC rankings have been a much better statistical indicator of the Final recently. 30/36 in the last nine years. That's a lot better than the RPI. Perhaps if you add them to your composite rankings you'll get the final four right on the nose.

Skeptic said...

Nate

Did you use these rankings for your brackets on my pool on Yahoo?

mcc said...

Okay, Nate, here's a question: Do your picks at all favor swing states?

jack said...

This is a nice little process, but I think the thing that's missing is that you don't want to optimize for E(Score), but P(winning). Intuitively, I'd think, the larger the pool, the more unique your bracket should be. Picking upsets probably leads to more uniqueness, so there may be some inherent incentive to pick the upsets. Obviously, you have the standard game theoretic problems that it depends on the play of your opponents; I'd probably use the ESPN data of how popular each pick is to randomly create opponent.

Doing this way does make exploring the state space harder, though, since you lose the independence of selection across games.

Mike G said...

Nate, thanks for including the Team Rankings (Greenfield) system in this. How did you calculate our probabilities for teams making each round? We publish probabilities using our predictive ratings, and they are fairly different from what you have here.

Thanks

Mike Greenfield, Team Rankings

Nigel said...

BillyPilgrim is so right. Similarly, I just rolled a 6-sided die 12 times and got the value 5 a total of three times. I have seen models that suggest the probability of rolling a 5 to be one in six, which is ridiculous since my data shows it's obviously one in four.

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