APBRmetrics

DSMok1

I just put up a tremendously long post on my website discussing how to use Bayesian updating with NBA predictive efficiency ratings.

I'm sure there are some flaws (such as how to adjust for opponent), so let me know!

http://godismyjudgeok.com/DStats/2011/nba-rankings/on-bayesian-predictive-efficiency-rankings/
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back2newbelf · Joined: 21 Jun 2005 Posts: 274

I get an Error in query: User not signed in <a target="_blank" href="https://spreadsheets0.google.com/">Sign in</a>

and the last charts don't show up
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DSMok1 · Posted: Tue Feb 15, 2011 5:36 pm Post subject:

back2newbelf · Joined: 21 Jun 2005 Posts: 274

Yes it works now
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Ilardi · Joined: 15 May 2008 Posts: 265 Location: Lawrence, KS

DSM -

Intriguing work, as usual.

So, I'm curious to know if your Bayesian model does a reasonable job approximating the observed distribution of actual game results? In other words, if we took a largish historical sample of n games, and for each game computed your model's predicted game outcome (based on the Bayesian efficiency differential of each team's antecedent games), would the actual observed game outcomes be distributed more or less normally, with the predicted distribution means and sd's?

Steve

DSMok1 · Posted: Tue Feb 15, 2011 6:57 pm Post subject:

Crow · Joined: 20 Jan 2009 Posts: 821

Do you want to predict the playoffs with this tool and compare to Hollinger's and the others out there?

DSMok1 · Posted: Tue Feb 15, 2011 11:33 pm Post subject:

Jeff Fogle · Joined: 11 Jan 2011 Posts: 70

Not sure if you would consider this helpful or not DSM, but I know a programmer who used Bayesian priors for all boxscore stats in a predictive format two seasons ago (and had a methodology for ranking the importance of each stat based on its relationship to winning as the season played itself out). If I recall, he was surprised to find that the most predictive hunk of games was the prior five...rather than much longer term samples. He had expected larger sample sizes to make better predictions.

Ultimately he decided the approach was still inferior to the prediction markets reflected by the Vegas pointspreads. Those have the ability to react to on the fly information like injuries, magnified fatigue spots, guys coming back from injuries, etc...

Best of luck with your efforts...

bbstats · Joined: 25 Apr 2010 Posts: 46

You had me at "bayesian."

Great stuff.
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bbstats · Joined: 25 Apr 2010 Posts: 46

PS - Would I be correct in saying that the theory here is similar to Dean Oliver's Kalman Filter?
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EvanZ · Joined: 22 Nov 2010 Posts: 295

John Hollinger · Joined: 14 Feb 2005 Posts: 175

Fascinating stuff, and interesting that it supports my notion that Philly and Memphis are both a hell of a lot better than people realize and Utah is much worse. I suspect our big difference on Dallas is due to the timing of Dirks injury since I just weight last 10 rather than using a gradual function like you did....

DSMok1 · Posted: Wed Feb 16, 2011 11:23 am Post subject:

Jeff Fogle · Joined: 11 Jan 2011 Posts: 70

Regarding JH's notes on Philly/Memphis/Utah, confirmation from the markets...

*Philadelphia has topped expectations to the tune of 34-20-1 this season, including 29-14 the last 43 games.

*Memphis is 35-21-1, including 28-12 the last 40 games.

*Utah is 25-30-1 for the year, including 8-19 the last 27 games.

If one accepts the premise that market prices are a composite of "what people are thinking," we can probably move that from notion to confirmed fact I'd think.