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Posts Tagged ‘ Statistics ’

Adjusted 4-Factors Visualization

February 22, 2012
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Adjusted 4-Factors Visualization

Evan Zamir at The City recently unveiled Adjusted 4-Factors at the individual level, using a 2.5 year sample size and a Ridge Regression approach. The results are very interesting. Here, I’m simply converting his initial work into an interactive visualization. Powered by Tableau
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Posted in NBA Stats | 1 Comment »

WOWY: NBA Finals Edition

May 31, 2011
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It’s time for the With-or-Without-You analysis of the NBA Finals teams: Dallas and Miami. For this review, I will consider a player to not have played (i.e. “Without You”) if the player played 20% or less of their average MPG. This allows us to look into the coach’s rotation decisions a bit more, while...
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Posted in NBA Stats | 1 Comment »

Chart: With or Without Inefficient Scorers

May 23, 2011
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Chart: With or Without Inefficient Scorers

This is just a quick chart dump, based upon Neil Paine’s research at Basketball Reference:
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Posted in NBA Stats | 1 Comment »

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 | 10 Comments »

Round 2 Predictions

April 30, 2011
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Quickly (Updated WOWY Bayesian Ratings, roughly done due to lack of time:) CHI ATL MIA BOS OKC MEM LAL DAL 98.4% 1.6% 69.9% 30.1% 67.1% 32.9% 52.3% 47.7% CHI vs ATL MIA vs BOS OKC vs MEM LAL vs DAL 4 to 0 45.3% 4 to 0 10.3% 4 to 0 9.1% 4 to...
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Posted in NBA Stats | No Comments »

Visualization: The Brightest Stars in the NBA

April 27, 2011
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Visualization: The Brightest Stars in the NBA

I’m working on a new metric to quickly measure a player’s single-game contribution (a slightly more complex “game score”). I’ve tabulated the results for every game played this year, and that lets me do this: Here are the brightest stars in the NBA, by dominant superstar performances this year. I’m giving 3 points for...
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Posted in NBA Stats | 6 Comments »

Playoff Odds 2011

April 16, 2011
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Here are my Bayesian WOWY + Tightened Rotations NBA projections for 2011! (Note: I’d still bump the Lakers up, personally… and I’m going to pick OKC>DEN for matchup and homerism reasons) Seed Tm WOWY Shortened Combined ReStdev Win 1st Win 2nd Win Conf Win Title 1 CHI 10.40 0.16 10.56 11.9 96.85% 67.32% 44.34%...
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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...
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Posted in NBA Rankings, NBA Stats, WOWY | No Comments »

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...
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Posted in NBA Adjusted Efficiencies, NBA Rankings, NBA Stats | 2 Comments »

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...
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Posted in NBA Rankings, NBA Stats | 1 Comment »

DSMok1 on Twitter

To-Do List

  1. Google Motion Charts for each position, including salary and contract value
  2. Discussion of salary/contract value
  3. Aging curves for individual components (ORB%, Blk%, etc.)
  4. Comparison of residual exponents for rankings
  5. Comparison of various "value metrics" ability to "explain" wins
  6. Publication of spreadsheets used
  7. Work on using Bayesian priors in Adjusted +/-
  8. Work on K-Means clustering for player categorization
  9. Learn ridge regression
  10. Temporally locally-weighted rankings
  11. WOWY as validation of replacement level
  12. Revise ASPM with latest RAPM data
  13. Conversion of ASPM to" wins"
  14. Recursive WOWY Team Ratings
  15. Lineup Bayesian APM
  16. Lineup RAPM