More NBA Combine Player Comparisons

Building from my post yesterday on comparing the body types among NBA players, today I’m throwing the athletic measurements taken at the pre-draft combine into the mix. Once again, I want to make note that all the data is being pulled from DraftExpress.com. Here is the dendrogram:

Let’s go through some of the comps.

Dunleavy/Singler

Two white guys from Duke. Who knew they were so much alike?

NAME YEAR HEIGHT WEIGHT WINGSPAN REACH NOSTEP MAX REPS AGILITY SPRINT
Mike Dunleavy 2002 80.00 230 81.00 106.00 24.5 29.0 11 11.55 3.30
Kyle Singler 2011 79.50 228 82.10 106.00 23.0 30.0 10 11.22 3.21

Randolph/Wright

As a Warriors fan, I’ve never heard this comp before. Shocking.

NAME YEAR HEIGHT WEIGHT WINGSPAN REACH NOSTEP MAX REPS AGILITY SPRINT
Brandan Wright 2007 80.75 200 87.75 108.50 30.5 35.5 2 11.76 3.31
Anthony Randolph 2008 81.00 197 87.00 109.00 29.0 35.0 7 11.86 3.26

McGee/Jordan

NAME YEAR HEIGHT WEIGHT WINGSPAN REACH NOSTEP MAX REPS AGILITY SPRINT
DeAndre Jordan 2008 81.75 250 90.00 113.50 26.0 30.5 8 12.30 3.27
JaVale McGee 2008 83.00 241 90.00 114.50 27.0 32.5 7 12.75 3.25

Paul/Walker

No, not that Paul Walker.

NAME YEAR HEIGHT WEIGHT WINGSPAN REACH NOSTEP MAX REPS AGILITY SPRINT
Chris Paul 2005 71.75 178 76.25 93.00 32.0 38.0 10 11.09 3.22
Kemba Walker 2011 71.50 184 75.50 91.50 32.0 39.5 7 10.87 3.16

Lawson/Nelson

NAME YEAR HEIGHT WEIGHT WINGSPAN REACH NOSTEP MAX REPS AGILITY SPRINT
Jameer Nelson 2004 71.00 199 74.50 95.00 28.5 33.5 15 10.95 3.14
Ty Lawson 2009 71.25 197 72.75 94.50 29.0 36.5 14 10.98 3.12

Cole/Thomas

I should point out on this one that Cole and Thomas were one branch removed from each other. McClinton (???) and Cole are descended from the same branch, though.

NAME YEAR HEIGHT WEIGHT WINGSPAN REACH NOSTEP MAX REPS AGILITY SPRINT
Jack McClinton 2009 71.75 185 74.50 96.00 30.0 36.5 13 10.44 3.21
Norris Cole 2011 72.25 174 74.25 95.50 29.5 38.5 11 10.07 3.22
Isaiah Thomas 2011 68.75 186 73.75 91.50 31.5 40.0 13 10.49 3.14

Landry/Griffin

This one genuinely surprised me. You too?

NAME YEAR HEIGHT WEIGHT WINGSPAN REACH NOSTEP MAX REPS AGILITY SPRINT
Carl Landry 2007 79.75 248 83.00 102.50 31.5 36.5 21 11.35 3.29
Blake Griffin 2009 80.50 248 83.25 105.00 32.0 35.5 22 10.95 3.28

Love/Beasley/Williams

If I recall, the knock on Love was his “lack of athleticism”. Funny that he would show up next to these other two guys.

NAME YEAR HEIGHT WEIGHT WINGSPAN REACH NOSTEP MAX REPS AGILITY SPRINT
Michael Beasley 2008 79.00 239 84.25 107.00 30.0 35.0 19 11.06 3.24
Kevin Love 2008 79.75 255 83.25 106.00 29.5 35.0 18 11.17 3.22
Derrick Williams 2011 79.25 248 85.50 108.00 29.0 34.5 19 11.03 3.23

Tyreke/Carmelo/Iguodala

An interesting three-way comp here.

NAME YEAR HEIGHT WEIGHT WINGSPAN REACH NOSTEP MAX REPS AGILITY SPRINT
Carmelo Anthony 2003 78.25 233 84.00 105.50 30.5 33.5 7 11.40 3.15
Andre Iguodala 2004 77.75 217 83.00 105.50 30.5 34.5 4 11.17 3.18
Tyreke Evans 2009 76.00 221 83.25 104.00 28.5 34.0 7 11.81 3.17

Allen/Harden

Another surprising result. Could you imagine combining these two players? Allen  Harden. Tony James. That player would wreak havoc.

NAME YEAR HEIGHT WEIGHT WINGSPAN REACH NOSTEP MAX REPS AGILITY SPRINT
Tony Allen 2004 75.50 214 81.00 102.00 31.5 36.5 17 10.70 3.19
James Harden 2009 76.00 222 82.75 103.50 31.5 37.0 17 11.10 3.13

Brewer/Hayward

NAME YEAR HEIGHT WEIGHT WINGSPAN REACH NOSTEP MAX REPS AGILITY SPRINT
Corey Brewer 2007 78.75 185 80.25 103.00 30.5 36.5 11 11.69 3.22
Gordon Hayward 2010 78.75 211 79.75 103.00 30.5 34.5 10 11.73 3.22

Battier/McGuire

(They don’t test basketball IQ?)

NAME YEAR HEIGHT WEIGHT WINGSPAN REACH NOSTEP MAX REPS AGILITY SPRINT
Shane Battier 2001 80.25 229 82.50 105.00 29.5 33.0 12 10.95 3.30
Dominic McGuire 2007 79.75 220 82.50 104.00 28.5 34.5 12 10.95 3.25

Webster/Deng

Oh, Martell. You could’ve been someone. You could’ve been a contender (literally).

NAME YEAR HEIGHT WEIGHT WINGSPAN REACH NOSTEP MAX REPS AGILITY SPRINT
Luol Deng 2004 79.00 220 84.50 108.50 27.5 31.5 5 11.46 3.34
Martell Webster 2005 78.00 230 83.00 106.00 28.0 30.5 7 11.39 3.39

That’s all for now. I’ll re-visit this after the 2012 combine.

NBA Player Body Type Similarities

I’m back with another dendrogram! This time I took all the pre-draft measurements for drafted players that are available on DraftExpress and used the height (w/o shoes), weight, standing reach, and wingspan to calculate a similarity metric (using R, of course). I needed to do this because I am planning to use both the physical measurements and college stats for this year’s draft class to improve the similarity analysis I did previously. Just waiting on the combine measurements in a couple weeks. Continue reading

Visual Summary of 2012 Miami Heat vs. Indiana Pacers Playoff Series

Miami beat the Pacers last night in Game 6, thus winning the series, and moving on to the winner of Game 7 of the 76ers-Celtics series. Below is a treemap showing the offensive Synergy stats of the Heat-Pacers series. Continue reading

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. Continue reading

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