Evan Zamir at The City recently unveiled Adjusted 4-Factors at the individual level, using a 2.5 year sample size and a Ridge Regression approach. The results are very interesting. Here, I’m simply converting his initial work into an interactive visualization. Powered by Tableau
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Tags: NBA, Player Ratings, RAPM, Statistics, Visualization
Posted in NBA Stats | 1 Comment »
As I prepare to release my first work based on the Adjusted Plus/Minus and derivative methods, I felt it would be wise to write a plain-English review of the state-of-the-art of Adjusted Plus/Minus and its derivatives, or at least what is known in the public domain. What is Plus/Minus? Plus/Minus, at its core, simply...
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Tags: Adjusted +/-, Player Ratings, RAPM, Stat Theory, Statistics, With-or-Without-You
Posted in NBA Stats | 10 Comments »
I’m working on a new metric to quickly measure a player’s single-game contribution (a slightly more complex “game score”). I’ve tabulated the results for every game played this year, and that lets me do this: Here are the brightest stars in the NBA, by dominant superstar performances this year. I’m giving 3 points for...
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Tags: Box Score Analysis, NBA, Player Ratings, Statistics, Visualization
Posted in NBA Stats | 6 Comments »
A collection of With-or-Without-You tables for playoff teams. All regressions were stabilized with about 30 games worth of “average for the team” performance. Not all regressions have the last game of the season included. Oklahoma City Thunder: WOWY equally weighted over season Eff Mar Off Eff Def Eff Games Equiv+/- Team 9.4 8.1 -1.3...
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Tags: Bayesian Analysis, Charts, NBA, NBA Playoffs, Player Ratings, Statistics, Team Ratings, Trade Analysis, With-or-Without-You
Posted in NBA Rankings, NBA Stats, WOWY | No Comments »
In continuing my series of With-or-Without-You (WOWY) analyses, I will next look at my hometown Oklahoma City Thunder, one of the hottest teams going into the playoffs. In the first two posts on WOWY, I looked at Oklahoma City in January(before the trades), and yesterday I looked at the Bulls and their strength...
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Tags: Bayesian Analysis, NBA, NBA Playoffs, Player Ratings, Statistics, Team Ratings, Trade Analysis, With-or-Without-You
Posted in NBA Adjusted Efficiencies, NBA Stats, WOWY | 4 Comments »
A couple of months ago, I introduced my method of With-or-Without-You(WOWY) for the NBA. This time, I'll revise and expand upon the method, and take a pre-playoff look at the hottest team going: the Chicago Bulls.
As Kevin Pelton chronicled, the Bulls have actually been quite healthy this year--only Joakim Noah and Carlos Boozer...
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Tags: Bayesian Analysis, NBA, NBA Playoffs, Player Ratings, Statistics, With-or-Without-You
Posted in NBA Adjusted Efficiencies, NBA Stats, WOWY | 5 Comments »
That’s right, golf. I’m taking up where Ken Pomeroy left off. A year or two ago, he developed a rating system for golfers–basically, he created a huge regression of all players and all specific rounds at tournaments. Each round was assigned a level of difficulty, and each player was assigned an overall rating. His...
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Tags: Bayesian Analysis, Golf, Major Championships, PGA, Player Ratings, Stat Theory, Statistics
Posted in Golf | 11 Comments »
Carmelo Anthony was FINALLY traded yesterday, in a mega 3-team deal. How did the teams make out? There are several good trade analyses around, but none of them are really focusing on the financial aspect. Kevin Pelton’s article is a good primer on the trade as a starting point, and Joe Treutlein at Hoopdata...
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Tags: Advanced SPM, NBA, Player Ratings, Projections, Salaries, Statistics, Trade Analysis, VORP
Posted in NBA Stats | 9 Comments »
Well, everyone else has an All-Star post up already: The actual All-Stars Mike G at APBR, with his eWins selections (also PER shown) (his thread inspired me to write this up) EvanZ, with his ezPM on the West and East All-Stars Kevin Pelton, with his WARP All-Stars John Hollinger’s look at ESPN Zach Lowe’s...
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Tags: Advanced SPM, NBA, Player Ratings, Statistics, VORP
Posted in Advanced SPM, NBA Stats | 9 Comments »
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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Tags: Google Motion Charts, NBA, Player Ratings, Stat Theory, Statistics
Posted in Advanced SPM, Google Motion Charts, K-Means Clustering, NBA Stats | 8 Comments »