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Posts Tagged ‘ NBA ’

Center Comparison Chart (and K-Means Clustering)

January 19, 2011
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Center Comparison Chart (and K-Means Clustering)

Last week, I unveiled a Google Motion Chart that included a large number of advanced stats comparing point guards. This week, we'll start at the other end: centers. I actually am including players classified as either C or PF/C by BasketballValue, where I got the position information. Most people feel that the position...
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Posted in Advanced SPM, Google Motion Charts, K-Means Clustering, NBA Stats | 8 Comments »

NBA Adjusted Efficiency Rankings 1-13-2011

January 14, 2011
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NBA Adjusted Efficiency Rankings 1-13-2011

Last week, I unveiled my . This week, I’ll explore some of the decisions I made with that system, per the request of . The first question is how much the ranking are effected by using rest-days adjustments.  See my original research on APBRmetrics for where this comes from.  Here’s a table comparing the...
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Posted in Google Motion Charts, NBA Adjusted Efficiencies, NBA Rankings, NBA Stats | 4 Comments »

Point Guard Comparison Chart

January 12, 2011
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So, what else can Google Motion Charts be used to visualize? Well, this application doesn’t actually *move*, but it does visualize a ton of point guard advanced statistics at once. That’s quite a few advanced stats in one place!  Play around with the chart and see what can be revealed.  I have 4 player...
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Posted in Advanced SPM, Google Motion Charts, NBA Stats | 6 Comments »

NBA Adjusted Efficiencies 1-11-2011

January 11, 2011
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NBA Adjusted Efficiencies 1-11-2011

Last week, I unveiled the first iteration of my . This week, I’ll revise and expand on it. The main thing I didn’t like about the chart was the 5-game moving averages. The games dropping off the far end of the moving average add just as much movement as the newest game adds. The...
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Posted in Google Motion Charts, NBA Adjusted Efficiencies, NBA Stats | 3 Comments »

With or Without You: OKC and Nick Collison

January 7, 2011
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With or Without You: OKC and Nick Collison

The concept of With-or-Without-You is very basic. If you are playing, is our team better or worse? If the team is worse with you available, then that’s a really bad sign! It’s the core concept behind such basketball metrics as +/-, Statistical Plus/Minus and Advanced Plus/Minus. In baseball, Tom Tango and MGL work with...
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Posted in NBA Adjusted Efficiencies, NBA Stats, WOWY | 4 Comments »

ASPM Box Score: Spurs-Celtics

January 6, 2011
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ASPM Box Score: Spurs-Celtics

Recently, I developed a method for analyzing single games through the Advanced Statistical Plus/Minus (ASPM) lens. Basically, in order to keep the data in the range where the weights for the stats makes sense (some of the weights are nonlinear), I add several games worth of average stats to the player’s stat line. I...
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Posted in Advanced SPM, ASPM Box Score, NBA Stats | 3 Comments »

NBA Adjusted Efficiency Rankings 1-5-2011

January 6, 2011
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NBA Adjusted Efficiency Rankings 1-5-2011

There are many ways to rank NBA teams; some better than others. This method runs as follows...
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Posted in NBA Adjusted Efficiencies, NBA Rankings, NBA Stats | 6 Comments »

NBA Adjusted Efficiencies 1-4-2011

January 5, 2011
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NBA Adjusted Efficiencies 1-4-2011

Hi, I’m Daniel (known as DSMok1 elsewhere), and this will be my first attempt at some fancy Google Visualizations. On my fancy new website. This viz plots the 5-game TRAILING moving averages for each team in the NBA up through January 4th. It’s a bunch of data; we’ll see if the Google API...
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Posted in Google Motion Charts, NBA Adjusted Efficiencies, NBA Stats | 5 Comments »

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