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NCAA Advanced Statistical Plus/Minus Released

February 20, 2013
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NCAA Advanced Statistical Plus/Minus Released

Advanced Statistical Plus/Minus (ASPM) is now available for NCAA Division 1 Men’s Basketball! ASPM was originally derived for the NBA, but the weights found for each event, as well as the methods for adjusting for opposition, should be equally valid for college basketball. The full Tableau visualization will be permanently located at http://godismyjudgeok.com/DStats/aspm-and-vorp/ncaa-advanced-statistical-plus-minus-and-vorp/, under the “ASPM and VORP” menu above. I will reproduce it...
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NFL: Adjusted Points per Possession

January 4, 2013
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NFL: Adjusted Points per Possession

As the NFL playoffs approach, I was looking around for some good, adjusted performance data on the teams. In the NBA, we would immediately look at adjusted efficiencies–Points per 100 Possessions, adjusted for opponent. That data isn’t available in the NFL; in fact, it is hard to locate data on possession-based stats at all. Many stats are stated per game, which bothers me when...
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ASPM Notes and Viz Update

December 6, 2012
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ASPM Notes and Viz Update

I have added another pane to my 2012-13 ASPM/VORP Tableau Visualization, allowing a better look at the distribution of talent across positions in the NBA. Powered by Tableau The new position vs. ASPM chart is quite interesting: there is an amazing lack of talent/performance right now at the 2,3, and 4 positions outside of a few elite players. There’s Kobe Bryant, Jason Kidd (who’s...
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2012-2013 ASPM & VORP Released

November 14, 2012
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A quick note: 2012-2013 ASPM & VORP have now been released! See the new 2013 ASPM page. A few early notes: The cream is already rising to the top after 4 to 8 games for each team. LeBron James is #1 in total VORP, with an ASPM almost exactly the same as last year. The only big outliers right now are the Knicks, buoyed...
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Overrated and Underrated via RAPM (Part 2)

November 10, 2012
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Overrated and Underrated via RAPM (Part 2)

A week ago, I discussed overrated and underrated players over the past 12 years, evaluated by comparing popular box-score stats with Jeremias Engelmann’s 12 year average Regularized Adjusted Plus/Minus (RAPM) dataset. For a primer on the virtues of RAPM (in large samples), see my article reviewing the state-of-the-art of adjusted plus/minus and stabilization. This time, I am looking at offensive and defensive RAPM over...
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Underrated and Overrated Via RAPM

November 2, 2012
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Underrated and Overrated Via RAPM

In several of my recent articles, I have been using Jeremias Engelmann’s 12 year average RAPM dataset. Here, I will look at what players may have been underrated or overrated by even advanced stats over the course of the last 12 years. I decided to use PER and WS/48 as my advanced stats to compare to, and I am limiting this to players with...
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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, one simply gets an “average” view of the players. It...
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2013 NBA ASPM Team Projections

October 29, 2012
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Here are my 2013 NBA Team Projections, based on my ASPM player projections. For playing time, I roughly used ESPN’s Fantasy Projections, tweaked so each team totaled 475% of the minutes (I assumed the rest of the minutes would go to replacement level (-3.25) players). Here are my regular season projections: I have updated these to deal with some issues with the ESPN Fantasy...
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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