

A Stochastic Model for Picking Winners in the NCAA Tournament - jsomers
http://rwwb.wordpress.com/2010/03/17/a-stochastic-model-for-picking-winners-in-the-ncaa-tournament/

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xel02
For people interested in this kind of analysis I recommend Mathletics by Wayne
Winston. He's written a blog and a book about similar subjects.

The book itself is approachable with a basic knowledge of mathematics,
probability, and statistics. It provides a good introduction to things like
Sabermetrics (for baseball), and applications to the NBA and the NFL.

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jerf
"In real life the probability of one team beating another team is independent
of what happened in the past."

This sentence seems under-specified, but no matter how I slice it, it's not
true. In sports, past performance _is_ indicative of future results. Even in
college you'll have correlations brought on by relatively stable (across
decades in many cases) coaching staffs. The correlation isn't anywhere near
perfect, but it isn't 0 either.

(In your statistics courses, everything is independent for your computational
convenience. In the real world rather a lot of things are _not_ independent.)

Consider what it would mean for past performance to say nothing about future
results. It doesn't match reality.

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imp
I've wasted a lot of time on this kind of analysis for picking brackets. I
estimated that the odds of a person picking a perfect bracket are 1 in 924
trillion: [http://www.codeswimming.com/blog/2008/03/the-perfect-ncaa-
br...](http://www.codeswimming.com/blog/2008/03/the-perfect-ncaa-bracket/)

If you want a shortcut to estimate win probability instead of looking it up in
a chart:

Prob Team 1 winning = Rank Team 2 / (Rank Team 1 + Rank Team 2)

That formula only breaks down at 1 vs. 16 and 1 vs. 2.

It's fun to postulate, but it won't really help in picking your bracket. What
I do is look for teams highly rated by Sagarin or kenpom, but underrated by
seeding. I have Wisconsin winning the whole thing in my bracket. You've gotta
maximize the probability of occurance while also minimizing redundant picks
with the rest of the pool. However, I've never done especially well in
practice and regularly get beat by people who choose by jersey color. It's
still fun though.

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Locke
Some of the stats are kind of self-explanatory.

For example, "#8 seeds often upset higher ranked teams. In the four match-ups
against #2 teams, #8 teams won twice. They have also beaten #4 and #5 seeds
more often than they lost to them."

For an 8 to play a 2 (or 4, or 5) they'd have to have already beaten a 1.
That's a pretty good 8. The typical 8 will have already lost to a 9 or 1.

Personally, I love Ken Pomeroy's rankings / predictive model (kenpom.com). It
breaks games down into possessions and then builds a statistical model of each
team from that. The predictions seem pretty accurate by the time February
rolls around.

Of course, the biggest caveat of this kind of model is that it doesn't take
into account extraordinary events. For example, a player gets injured or comes
back from a suspension.

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zackattack
Does Pomeroy's beat Vegas odds? Has any model beaten vegas odds?

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seattlejet
Not just critiquing this particular model, but at least a couple things that
need to be improved upon in game prediction:

1\. Need to take into account playing styles of the teams along with their
seed. (eg. how do teams that rely on 3pt shots fare vs. teams that play very
physical defense, and all other permutations)

2\. As Locke pointed out, extraordinary events play a large part in games.
There was an article in the NY Times on stat analysis where a team official
(may have been Houston Rockets' Daryl Morey) lamented a lucky shot-clock-
beating 3-pointer by the opposing team, saying those kind of instances
significantly alter the game. It'd be interesting to see if some teams are
better at creating these lucky instances, or if they're strictly random

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jsm386
That would be this great magazine piece
[http://www.nytimes.com/2009/02/15/magazine/15Battier-t.html?...](http://www.nytimes.com/2009/02/15/magazine/15Battier-t.html?pagewanted=all)
written by Michael Lewis.

If you're looking for the quote about the three it is at the end of the
article, and is made more interesting by the context of the 'lucky' shot.
Briefly - Kobe misses 86.3% of long 3 pointers heaved up in the final seconds
of games. The Rockets knew this. Quoting now: 'It was a shot Battier could
live with, even if it turned out to be good. Battier looked back to see the
ball drop through the basket and hit the floor. In that brief moment he was
the picture of detachment, less a party to a traffic accident than a curious
passer-by. And then he laughed. The process had gone just as he hoped. The
outcome he never could control.'

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luckyland
Absolutely great article.

