Here are all of the Adjusted Efficiency Team Charts for the 2011 NBA regular season. These are adjusted for opponent (average for their whole season), rest-day-situation, and location. Here are links to each image (so as to avoid flipping through the slideshow): Atlanta Hawks Boston Celtics Charlotte Bobcats Chicago Bulls Cleveland Cavaliers Dallas Mavericks Denver … [Read more…]
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 going into the playoffs. In the Bulls post, I also revised and expanded this method with regression to the mean and Bayesian weighting for best future prediction.
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 have missed significant time, among the regulars in the rotation. Kurt Thomas has missed time, also, but he has also been sat by Tom Thibodeau when healthy–complicating any analysis of WOWY. For this analysis, I’ll focus on Noah and Boozer.
A few days ago, I put up a massive post on . Nathan Walker (the Basketball Distribution) commented that I should apply the system to NCAA basketball, and so I have. Thanks to Ken Pomeroy’s incredible NCAA basketball database, the data was quite easy to obtain. Since he already compiles a fully adjusted efficiency rating … [Read more…]
It is fairly easy to construct a retrospective efficiency rating. Take the efficiencies for each game, correct for location and rest, and then solve using an OLS regression for each team’s true efficiency rating. Nice and neat. However, how should a predictive rating work? The best approach would be to adjust for what players are … [Read more…]
It’s been a while since I last put together the fully adjusted NBA efficiency rankings. Here are the latest ratings (prev is 1-13-2011, the last time I updated): If you will recall, I last time was puzzling over what exponent to use when minimizing |residuals|^n. I tried out a number of different exponents in out-of-sample … [Read more…]
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 effect … [Read more…]
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 solution? … [Read more…]
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 it … [Read more…]
There are many ways to rank NBA teams; some better than others. This method runs as follows…