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2013 ASPM

All of the derivation of Advanced Statistical Plus/Minus (ASPM) and Value over Replacement Player (VORP) may be found on my ASPM and VORP page.

To see the full underlying spreadsheet:

9 Responses to 2013 ASPM

  1. ASPM Notes and Viz Update | DStats on December 6, 2012 at 1:11 pm

    [...] 2013 ASPM [...]

  2. [...] basketball-reference.com (for PER and Win Shares), 82games.com (for opponent PER and Net +/-), and godismyjudgeok.com (for ASPM and VORP). Today, Izzy starts us out with the results from the Eastern [...]

  3. [...] basketball-reference.com (for PER and Win Shares), 82games.com (for opponent PER and Net +/-), and godismyjudgeok.com (for ASPM and [...]

  4. [...] *RSPM is Regularized Statistical Plus-Minus **ASPM is Advanced Statistical Plus-Minus [...]

  5. […] overpaid a bit for Splitter and Ginobili, the production numbers tend to show that is not the case. According to the great work of Daniel Myers, a combination of advanced statistical plus-minus (ASPM) and value-over-replacement-player (VORP) […]

  6. […] – noticeably higher than his team’s already poor defensive rating of 109.2. Finally, D-ASPM — a metric derived from exclusively box score data — gave Lillard a putrid -1.80 […]

  7. One More Projection Post: A Different Method on October 28, 2013 at 11:24 pm

    […] things. I blended a number of plus-minus based advanced stats: xRAPM, RAPM, ezPM, Estimated Impact, ASPM, and IPV, then I added an aging curve. I also added Evan Zamir’s work on home court advantage […]

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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