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Tightened Rotations

April 16, 2011
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After a ton of math and fudging, I’ve come up with these estimates for how much a “tightened rotation” will help each team in the playoffs. I took close games between playoff teams in the past 1.5 months, figured out the top rotation (MPG/player) for each team when everyone’s healthy, and took the max of that and the average of all playoff teams’ average...
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With-or-Without-You Compilation

April 15, 2011
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A collection of With-or-Without-You tables for playoff teams.  All regressions were stabilized with about 30 games worth of “average for the team” performance.  Not all regressions have the last game of the season included. Oklahoma City Thunder: WOWY equally weighted over season Eff Mar Off Eff Def Eff Games Equiv+/- Team 9.4 8.1 -1.3 In Out MPG Tot O D Kevin Durant 3.4 3.3...
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Team Charts 2011

April 15, 2011
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Here are all of the Adjusted Efficiency Team Charts for the 2011 NBA regular season.  These are adjusted for opponent (average for their whole season), rest-day-situation, and location. Here are links to each image (so as to avoid flipping through the slideshow): Atlanta Hawks Boston Celtics Charlotte Bobcats Chicago Bulls Cleveland Cavaliers Dallas Mavericks Denver Nuggets Detroit Pistons Golden State Warriors Houston Rockets Indiana...
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NBA Final Regular Season Bayesian Ratings

April 15, 2011
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There is a !  I’m putting together an NBA Playoffs preview; this is the first of the data for that: Here is a table of playoff bound teams and their season-long adjusted efficiency ratings. Ratings are adjusted for location, pace (obviously, since these are per 100 possessions), opponent (recursively) and rest day situation. Previous is the rating of the team at the time of...
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With or Without You: OKC, Perk, and Nazr

April 13, 2011
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With or Without You: OKC, Perk, and Nazr

In continuing my series of With-or-Without-You (WOWY) analyses, I will next look at my hometown Oklahoma City Thunder, one of the hottest teams going into the playoffs. In the first two posts on WOWY, I looked at Oklahoma City in January(before the trades), and yesterday I looked at the Bulls and their strength going into the playoffs. In the Bulls post, I...
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With or Without You: The Bulls

April 12, 2011
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With or Without You: The Bulls

A couple of months ago, I introduced my method of With-or-Without-You(WOWY) for the NBA. This time, I'll revise and expand upon the method, and take a pre-playoff look at the hottest team going: the Chicago Bulls. As Kevin Pelton chronicled, the Bulls have actually been quite healthy this year--only Joakim Noah and Carlos Boozer have missed significant time, among the regulars in the rotation....
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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 numbers, prior to the PGA Championship in 2009, are on...
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Final 4 Update

April 2, 2011
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VCU and Butler have continued their run.  Before the tournament, I had them highly because they elevated their games in the regular season when playing good opponents.  They have both continued that and increased that trend. One confounding issue now with adding the “Raising their Game” (“Ag25″ in the table below) bonus is that for Butler and VCU, most of their games against good...
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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