You may already be familiar with my marginal scoring metric, PSAMS (if not, see here). The basic idea with that metric is that I try to take into account the volume and type of shots (inside, mid-range, 3-pt, and free throws) for each player and calculate an "adjusted" scoring metric. For example, I give more credit to players who generate a high volume of inside shots and debit players who don't. I take into account the fact that some players are responsible for taking more than their fair share of mid-range shots (which tend to be lower efficiency), while others take less, thus placing the burden of taking those bad shots on their teammates. And so on... Continue reading
This is going to be a data-intensive post. So strap in, buckle up, put on your helmets, and let's do some stats. By now, you're probably aware of my PSAMS scoring metric, which adjusts for shot volume and location (roughly speaking) according to position. In terms of shot locations, I keep track of four categories: INSIDE (dunk, layups, tips, and hooks), 3PT, FT (including And1), and MID-RANGE (all jumpers that are not classified INS or 3PT).
An interesting question to ask is how does a particular player's shot selection affect the team level shot distribution? (And maybe vice-versa, right?) To begin to address this issue, I've added a new wrinkle to my play-by-play code that enables me to do ON/OFF (also called "With or Without You" or WOWY) tracking of team-level stats. In other words, when Player X is on the floor, what is the distribution of INSIDE, 3-PT, MID-RANGE, and FT shooting? How does it change when he is off the floor? I can answer that now. Continue reading
I wanted to take a quick look at rookie scoring through my PSAMS metric. There are 18 rookies with at least 500 possessions, which was my cutoff. Unfortunately, I only have the first 510 possessions for MarShon Brooks, because there have been issues with the matchup files that I use the past several weeks. I contacted Aaron B. who runs basketball-value.com and is the source of all the play-by-play data that I use, and he explained that all the lineups containing Shawne and Shelden Williams in NJN are wreaking havoc. He has to go in manually and do the matchup files for those games, and it's going to take him a while to fix them all. So, for now, Brooks is at the top. I expect he's probably still somewhere up there, but just keep in mind, the data for him are limited to what he did the first few weeks of the season. Continue reading
I've updated the data in the PSAMS page. Click there to see the full set of stats, including rates and %'s for each shot type, in addition to the actual number made and attempted for each shot type (Hint: You could use those data to figure out who has dunked the most so far this season!). Here are the top 25 as of Jan. 12:
Top 25 PSAMS
Among players with minimum 300 possessions played. The column labeled MOD is the PSAMS rating regressed onto RAPM, as discussed here.
Conspicuous by his absence on this list is Kevin Martin who comes in at #54. His FT component is at 1.10, compared to a gaudy 4.14 last season. I've got to believe it's the new "rip through" rule taking effect. Job well done, NBA rules makers. Andrea Bargnani has surged into the top 5 with a great mid-range rating. Marcin Gortat is also breaking out with a combination of inside and mid-range scoring. The top 25 list is full of young players moving in (Greg Monroe the only viable scoring option on a bad Pistons team, Spencer Hawes playing out of his mind, Brandon Bass with a change of scenery, Byron Mullens(?!), Mario Chalmers literally on fire, rookies Irving and Morris, and James Harden who is becoming the second best scoring option for OKC). I expect this list to change quite a bit as hot players regress and other veterans make it back.
And as always, where there's a "Top 25", a "Bottom 25" list surely follows. Here it is:
Bottom 25 PSAMS
It's surprising to see Odom, Felton, and Wright (who's been mostly miserable this season) on the list, but the other names are the usual suspects, for the most part.
|211||Metta World Peace||LAL||3.23||472||0||-4.66||-2.84||-0.19||-0.69||-3.86||0.07|
One form of +/- that I didn't mention in my Advanced Stats Primer (but which will be included in a future update) is statistical +/- (SPM). I know, you're thinking, isn't +/- already "statistical"? Yes, but in the land of jargon, even jargon begets its own jargon. SPM essentially is a model created by regressing simple or advanced box score stats (see here and here for current examples) onto some form of adjusted +/- (APM or RAPM).
In the past I looked into the correlation between the offensive components of 3-yr RAPM and ezPM, and found that the results were statistically significant and fairly high (). Here, I took the individual components of my PSAMS (Position- and Shot-Adjusted Marginal Scoring) metric for 2011, and regressed those onto Jeremias Engelmann's 2011 ORAPM data set. Continue reading
Moving right along with this visualization kick I'm on, I came upon a very simple way to graphically represent a distance function using R. That sounds mathy, I know, but essentially what it means is that we can plot players on a two-dimensional space (x vs. y) with the distance between each player representing their similarity in terms of one or more metrics. Players that appear closer together are more similar, while those farther apart or more different. It's pretty intuitive once you see it on the plot. Continue reading