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Posts Tagged ‘ Game Prediction ’

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 »

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...
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Posted in NCAA Basketball | No Comments »

Sweet 16 Update

March 23, 2011
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Well, the first weekend of the NCAA Tournament is in the books.  I don’t have much time to post, so I’ll make it quick: Predictions did fairly well. In the 8 closest 1st-round games by my pregame predictions (averaging a 52.72% favorite) my stats went only 25%–otherwise, everything matched up really well.  The other...
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Posted in NCAA Basketball | No Comments »

ASPM Prediction: Magic vs. Heat

February 3, 2011
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ASPM Prediction: Magic vs. Heat

The ASPM spreadsheet (download here) has a sheet for estimating a game’s results, based on team rest, location, and the rotation of players expected to play. For a quick example, I’ll look at the Magic vs. Heat game this evening. Here’s the sheet: I estimated the minutes each player would play based upon the...
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Posted in Advanced SPM, ASPM Prediction, NBA Stats | 1 Comment »

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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