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:
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.
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 “Defensive Player of The Year According to A4PM”
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 “Similarities between 2012 NCAA Draft Class and Current NBA Players (A Rough Draft)”
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 “Year-to-Year Correlation of A4PM and Most Increasingly Positive Player Award”
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 “Advanced NBA Playoff Previews and Predictions: Round 1”
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 “Top Scoring Rookies in the 3-Point Era”