web analytics

ASPM Prediction: Magic vs. Heat

February 3, 2011

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:

ASPM Prediction 2011-02-03 Magic Heat

Advanced Statistical Plus/Minus prediction for the 2/3/2011 Magic-Heat game

I estimated the minutes each player would play based upon the rotations of the last 5 games for each team. Redick is probable and Bass is out for the Magic; Haslem is of course out for the Heat. I used the Bayesian best estimate for the current true-talent ASPM as the projected plus/minus for each player. That hurts Anderson and Turkoglu (both have been playing really well recently). The Heat have tightened up their rotation recently (as I mentioned in my recap of the OKC-MIA game); they’ve basically dropped Arroyo from the lineup, which is good for them.

The resulting estimate has Orlando at a +6.2 efficiency differential and Miami at a +11.1. However, Orlando is at home. Both teams have the same rest situation. The result: Miami as a +9.9, Orlando as a +8.2 (compared to a league-average team with a league-average rest situation on a neutral floor.) The projected score is 94.6 to 92.8, or I’ll call it 95 to 93. Miami has a 59% chance of winning.

Tags: , , , ,

One Response to ASPM Prediction: Magic vs. Heat

  1. William on January 2, 2013 at 1:40 am

    Excellent work, thanks! Am playing around with this, wondering how you get the projected scores and the projected win%. I don’t understand what the 0.936 and 6.394 are or come from to be factored into the projected score.

    Then, you run a Normal Distribution on the efficiency differentials, plus a term derived from those numbers, divided by 9. I don’t understand why either.

    Would appreciate any comments, thanks so much.

Leave a Reply

Your email address will not be published. Required fields are marked *

Current day month ye@r *

DSMok1 on Twitter

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