Oklahoma City won game 5 last night and so won the series 4-1. Here’s a Synergy treemap breakdown of the series.
View in Google Spreadsheet
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.
- 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
- 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.
Posted in Golden State
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
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
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
This post should be thought of more as a tool than a prediction, although I do give predictions at the end of each section. The tables and treemaps below show “expected scoring” for each series. Expected scoring is simply defined as the average rate (%-total plays) and efficiency (PPP) between the offense of one team vs. the defense of the other team (and vice-versa). The data comes from Synergy, of course. Continue reading
Some more stats to throw at you today using my new distance metric, which judges scoring based on both efficiency (measured by TS% ) and volume (measured by USG%).
Here are the rookies in 2012 with greater than 300 FGA attempted. Recall that 1.0 is the greatest DIST a player can have, 0 is what an average player would have, and -1 would be very bad. I’ve also standardized the rating according to how rookies peform. That’s given in the STD column. You can see that Kyrie Irving has been a very, very good scorer. He is 2.5 standard deviations above an average rookie. Klay Thompson (yes!) and Isaiah Thomas have also been quite good. Continue reading