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 a … [Read more…]
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 In … [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.
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 numbers, … [Read more…]
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 has … [Read more…]
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 take … [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.
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…]
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…]