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Posts Tagged ‘ Stat Theory ’

RAPM vs. MPG (A Look at Replacement Level)

October 30, 2012
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RAPM vs. MPG (A Look at Replacement Level)

One of the big ongoing questions in NBA stats and player valuation is where to set “replacement level”.  I believe an empirical approach to determining that level is best. Jeremias Engelmann has produced an outstanding resource in his 12 year average RAPM data.  Since so many years are used, it is quite stable; however,...
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Posted in NBA Stats | No Comments »

A Review of Adjusted Plus/Minus and Stabilization

May 20, 2011
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A Review of Adjusted Plus/Minus and Stabilization

As I prepare to release my first work based on the Adjusted Plus/Minus and derivative methods, I felt it would be wise to write a plain-English review of the state-of-the-art of Adjusted Plus/Minus and its derivatives, or at least what is known in the public domain. What is Plus/Minus? Plus/Minus, at its core, simply...
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Posted in NBA Stats | 28 Comments »

Bayesian Golf Ratings and Masters Preview

April 6, 2011
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That’s right, golf.  I’m taking up where Ken Pomeroy left off.  A year or two ago, he developed a rating system for golfers–basically, he created a huge regression of all players and all specific rounds at tournaments.  Each round was assigned a level of difficulty, and each player was assigned an overall rating.  His...
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Posted in Golf | 11 Comments »

NCAA Tourney Bayesian Ratings and Odds

March 16, 2011
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NCAA Tourney Bayesian Ratings and Odds

Okay, ready for the tournament? I’ve put together some adjustments based on the work I .  The theory behind the adjustments may be found on STATS @ MTSU and Dr. Winner @ Florida.  Basically, I’m adjusting for teams that raise their game to a higher level against good foes (or vise-versa).  For instance, Long...
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Posted in NCAA Basketball | 3 Comments »

Raising Their Game

March 15, 2011
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Raising Their Game

When we get to the NCAA tournament, it seems that inevitably, some teams will raise their game, matching up with the “better” teams, suddenly emerging as a top team.  Some teams play well when their opponent is better, and let the foot off the gas when playing East Popcorn St.  Those teams tend to...
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Posted in NCAA Basketball | No Comments »

How Does the Committee Seed? Introducing ExpSd

March 14, 2011
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The Bracket was revealed yesterday.  Quick thoughts and long ramblings below: I have two rankings systems: my Bayesian predictive power ratings, which tell how good the teams are, and my DSMRPI ratings, which tell how much they have accomplished.  I would put teams in and seed them based on DSMRPI, which looks purely at...
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Posted in NCAA Basketball | 2 Comments »

NCAA Bayesian Ratings, With Projection Prior

March 12, 2011
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In my , I took as the Bayesian prior the overall distribution of NCAA teams.  Now, we know more than that–we can create a pretty good projection of how good a team will be based on how good the team has been the previous few years.  So let’s do it! I compiled the Pomeroy...
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Posted in NCAA Basketball | No Comments »

NCAA Bayesian Analysis & DSMRPI

March 7, 2011
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NCAA Bayesian Analysis & DSMRPI

 I posted my NCAA Bayesian Ratings and methodology.  Today I thought I’d update the numbers quickly and add a new twist. What is the objective in basketball? To win the game! When doing a predictive rating system (like this Bayesian method) or even trying to tell how good teams are over this season (KenPom’s...
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Posted in NCAA Basketball | No Comments »

Bayesian Efficiency Ratings: NCAA Basketball

February 18, 2011
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A few days ago, I put up a massive post on . Nathan Walker (the Basketball Distribution) commented that I should apply the system to NCAA basketball, and so I have. Thanks to Ken Pomeroy’s incredible NCAA basketball database, the data was quite easy to obtain. Since he already compiles a fully adjusted efficiency...
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Posted in NBA Adjusted Efficiencies, NBA Rankings, NCAA Basketball | 1 Comment »

On Bayesian Predictive Efficiency Rankings

February 15, 2011
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Bayesian Update

It is fairly easy to construct a retrospective efficiency rating. Take the efficiencies for each game, correct for location and rest, and then solve using an OLS regression for each team’s true efficiency rating. Nice and neat. However, how should a predictive rating work? The best approach would be to adjust for what players...
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Posted in Google Motion Charts, NBA Adjusted Efficiencies, NBA Rankings, NBA Stats | 12 Comments »

DSMok1 on Twitter

To-Do List

  1. Salary and contract value discussions and charts
  2. Multi-year APM/RAPM with aging incorporated
  3. Revise ASPM based on multi-year RAPM with aging
  4. ASPM within-year stability/cross validation
  5. Historical ASPM Tableau visualizations
  6. Create Excel VBA recursive web scraping tutorial
  7. Comparison of residual exponents for rankings
  8. Comparison of various "value metrics" ability to "explain" wins
  9. Publication of spreadsheets used
  10. Work on using Bayesian priors in Adjusted +/-
  11. Work on K-Means clustering for player categorization
  12. Learn ridge regression
  13. Temporally locally-weighted rankings
  14. WOWY as validation of replacement level
  15. Revise ASPM with latest RAPM data
  16. Conversion of ASPM to" wins"
  17. Lineup Bayesian APM
  18. Lineup RAPM
  19. Learn SQL