## Visual Summary of Thunder-Lakers Series

Oklahoma City won game 5 last night and so won the series 4-1. Here’s a Synergy treemap breakdown of the series.

Click on “Sheet 1″ to see tabular data.
Treemap: Individual tile area is proportional to number of plays run of each type in terms of percentage. Color of each tile represents play efficiency (PPP), with red being more efficient (i.e. red hot), and blue less efficient (ice cold). Clicking on a tile with your mouse pops up the tile data.

### Definitions

• ISO isolation
• POST post-up
• BALL pick-and-roll ball handler shoots
• ROLL pick-and-roll rolling man shoots
• TRANS transition shot
• SPOT spot-up shot
• REB shot coming off offensive rebound
• SCREEN shot coming off screen
• CUT cut to the basket
• HAND shot coming after hand off
• OTHER miscellaneous shots not easily categorized

### Observations

• OKC killed it in transition shooting, both in terms of number of plays and efficiency. No surprise there.
• OKC was also the much better team in isolation with a very efficient 1.1 PPP compared to 0.7 PPP for LAL.
• LAL has a much better post-up offense (not surprisingly), but their spot-up shooting was a major weakness (not surprisingly).
• Spot-up shooting in general is one of the most efficient plays teams can run, which makes LAL’s 0.57 PPP all the more devastating. The Lakers need to address their lack of 3-pt shooting, if they want to even hope about competing in the future.

## A4PM Ratings for 2012 (Not Explicitly An MVP List!)

Got to have a disclaimer on things like this, so here it is:

The following ratings are for informational and/or entertainment purposes only. The creator of said ratings does not (necessarily) endorse using these ratings as sole criteria for MVP determination. Usage of these ratings in MVP discussions on the internet or twitter entails certain risks, including, but not limited to, people telling you to watch the games with your eyes, and other people calling you crazy for suggesting Matt Bonner or Vince Carter are more valuable than you might realize. People may unfollow you. These ratings are valid for 2012 only and are subject to change in future seasons of basketball.

Without further adieu, I’ve split the ratings into two sets. The first set is for players with >2500 possessions, and the second is for players having between 1000 and 2500 possessions. The reason for splitting it up this way is simply to acknowledge that we should have more confidence in the ratings for players with a larger sample size, especially in this shortened season. The ratings are sorted in descending order by the A4PM rating (make sure to read that article if you don’t know what A4PM means). The column VARP is Value Above Replacement Player, calculated as follows:

$VARP = (POSS/100)*(A4PM-2.0)$

The value 2.0 was used as the replacement level, since it represented approximately the value of the 15th %-ile of players with >1000 possessions. Continue reading

## The City’s 2012 Rookie Review

Kyrie Irving is the presumptive 2012 NBA ROY.

Kyrie Irving is going to win Rookie of the Year, and he would get my vote, even though as you’ll see it’s not quite that clear cut from an advanced stats perspective. Here, we’ll look at how this year’s freshman class performed in three of my homegrown statistical metrics: ezPM, A4PM, and PSAMS. Continue reading

## Defensive Player of The Year According to A4PM

The real DPOY?

Tyson Chandler was awarded the 2012 DPOY yesterday. Nobody was surprised by this, including myself. People did seem to be quite shocked and dismayed that Serge Ibaka got second place. If DPOY is stat-based, it’s likely only to the extent that players get above a certain threshold of blocks or steals. Of course, around these parts, we like to dig deeper and try to measure the true impact of a player on all parts of the game — those both seen and unseen. With that said, let’s see what the defensive half of A4PM (adjusted four factor +/-) has to say about DPOY. I’ve split the data into two sets, one for players who had >3000 possessions, and the other for players between 1500 and 3000 possessions. There’s not really much to say, except Andre Iguodala and Luol Deng probably should have got more votes. And, oh, Tyson Chandlerdoesn’t come anywhere near the top 5. Maybe those Ibaka nay-sayers are getting it wrong? Continue reading

## Similarities between 2012 NCAA Draft Class and Current NBA Players (A Rough Draft)

(No pun intended.)

So, once I get an interesting new idea in my head, I tend to obsess about it (perhaps, too much). Yesterday, I wrote about a way to compare players to each other using a “distance” measure of statistical similarity. Some time after I wrote that, I had a Eureka! moment and thought, hey, I should just put current NBA players into the model, and see who the current draft compares to. This is my first stab at it, using college stats from the last six draft classes (going back to 2006). I only used the basic pace-adjusted stats this time around, so I think there’s a lot of room for improvement. But I wanted to put something up, because I think the results are neat. There are definitely some head scratchers (Anthony Davis compared to Demar DeRozan?!). Oh, and in case you’re wondering, Jae Crowder is the next Jeremy Lin. Continue reading

## NBA Draft 2012: Playing Around with Player Similarities

This is my first post focused on the NCAA, but I’m excited about the prospect of the Warriors keeping their lottery pick after winning the epic coin toss against Toronto on Friday. (Yes, I am the first person in history to use “epic” and “coin toss” and “Toronto” in the same sentence.) I’ve been meaning to dip my toes in the draft analytics waters for a while now, so this seems as good a time as any. Continue reading

## Year-to-Year Correlation of A4PM and Most Increasingly Positive Player Award

One of the questions that often comes up when discussing player metrics involves year-to-year correlation (i.e. how consistent is it across years?). In fact, one of the main criticisms that is levied against adjusted +/- (APM or RAPM) is that it’s not “very” consistent. (The quotes are there because this is clearly a somewhat  subjective term.) This post is not going to be about that debate, as it’s been done elsewhere many times, and significantly better and more in-depth than I care to spend time on at the moment. But since the question is often asked, and has been raised about my new(ish) A4PM metric, I wanted to address it a bit. It’s also a good prelude to looking at “Most Improved Player”, or to be safer (by acknowledging that “Improvement” is subject to the validity of the metric), what I’m calling “Most Increasingly Positive” player (according to A4PM) — which is factually true, if nothing else. Continue reading