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 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 of center is changing, morphing into something different than it once was. The presence of numerous “centers” that hang around on the perimeter shooting 3’s is an indicator of this phenomenon. Still, there is a defined way a center plays–and to define it, let’s turn to the lovely tool known as K-Means Clustering.
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…]
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 evaluation … [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…]
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 can handle it.