The ASPM spreadsheet (download here) has a sheet for estimating a game’s results, based on team rest, location, and the rotation of players expected to play. For a quick example, I’ll look at the Magic vs. Heat game this evening. Here’s the sheet: I estimated the minutes each player would play based upon the rotations … [Read more...]
Yesterday’s Thunder-Heat game was a fun game to watch and a great game to investigate. Oklahoma City started out with a significant advantage in this game: OKC was at home (worth 3.24 pts/100 poss) and was playing on 1 day of rest (+1.94), while Miami was playing their 3rd game in 4 days, and on … [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...]
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...]
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 then … [Read more...]
There are many ways to rank NBA teams; some better than others. This method runs as follows…
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.