Researching Robustly for a Post Takes A Long Time

Sorry about the lack of posting.But my twitter account got suspended.

I’ve been working on the twitter thing instead of some of my ideas for posts. But the twitter thing is taxing. Twitter automates the suspension of accounts but uses REAL people to resolve suspension disputes.

That means I’m probably in a giant queue of DMV-like doom. I couldn’t read basketball tweets about the NBA Finals, and I’ll probably miss the NBA draft too.

I could make a new account but I’m really tired. And angry. But mostly tired. Instead, I’m gonna build up some long range articles and wait for the twitter account to get unfrozen. Also, my RSS Reader (Reeder) got entirely messed up due to a Google Reader change. Linking to articles is 10x more difficult without RSS searching out the name for me. I’ll link more when it comes back online. You’ll just have to take my word, for now.

Here’s some articles I’m working on. Some of these are long term projects.

1. Durant and Harden. Who’s better?

This will actually be a positional comparison. Using the Wins Produced formula (See Research), I’ll assign a percentage value. For instance, Durant is great at scoring so what percentage of scoring efficiency affects Durant’s wp48 score.

Let’s say Durant averages 2x as good as Harden in scoring, and let’s say scoring is 20% of wins produced. And let’s say Durant hypothetically does everything else average. Harden scores 10 on avg while Durant scores 20. So 20 is 20% of 100.

Durant 20/80

Harden 10/40

He would have to be twice as good in rebounds over Durant to catch up. Etc Etc. Or a mixture thereof. I’m still fiddling with the math. Most stats have a limiter. Scoring Per Possession. Steals and Rebounds Over Personal Fouls (What % of Personal Fouls come from Defense? Probably a a majority. So research that coefficient or just do an average of the personal fouls collected by the top 30 ball thieves. I’m thinking taking charges or fouling on purpose is a minor part of a person’s game. Should not be a significant coefficient. .)

Hopefully by the end, I’ll have some thresholds. How much scoring per possession is enough to counteract being deficient in everything else? Technically, you don’t even need Harden and Durant for these questions/answers but the idea originated from that quandary. (Wagesofwin Podcast Link)

2. On The Different Statistical Models

Basketball Prospectus’ Kevin Pelton uses WARP.

Wagesofwins’ dberri uses Wins Produced.

Basketball Reference uses Win Shares.

What is the difference between these 3? Have they attacked their own models to see what their prediction rates are?

I was reading Ed Weiland the other day and he has a theory that high 2PP% and a high STL count should be the main factors in determining a PG draft prospect. I think Ian Levy did a thing where he implies that Scoring skills don’t translate well at all from the NCAA to the NBA. That fits with common perception but I’d like Levy to actually pump out the data. I’d LIKE to see his NBA Skill Transfer Analysis done on a per position basis. Maybe shooting guards have a higher percentage of their FT% or 3PP% transfer over to the NBA. My own small dataset shows that most good FT% shooters have good 3PP% in the NBA/NCAA. It’ll be wild if there was a correlation between NCAA FT% and NBA 3PP%. (But I’m not gonna say it because I’d like to get the code crunched through at least 12 years of Data (3 4-yr draft cycles. It’s important not to include the rookie year. Arturo did a data crunch showing that 9 out of 200 top NBAers have had a good rookie year. Or some ridiculously low number. Don’t quote me.) .

I really don’t think Ed Weiland has tested against his own theory robustly. I mean, he got Lin right but then again, so did Wins Produced. People sometimes get so in love with their idea they don’t want to do the hard work of breaking it down. Like PER. Oh god, I hate PER.

3. What are the biggest gambles in the draft?

I’d like to do this one first. Hopefully before the draft. Drummond, Eli Holman, Hummel etc.

The methodology would be to use Ian Levy’s (Hickory-high.com) Similarity Score method to see who these people are similar to. I will be taking fg% and all shooting % out of the equation. This is a pure leap of faith that shooting doesn’t translate at all in the NBA with the notable exception of FT%. This work has also been done by Ian Levy.

4. How good is your team’s Head Draft Guy?

This one is the furthest along as I’m only doing the Lottery Teams. What is the average win score or wins produced per draft pick they’ve chosen? I’ve already done some of this in the Research section. Note to all: Basketball-reference.com stats use winshares. The results are a bit wonky with winshares. I want to do one with the more familiar Wins Produced but that requires more work as I’d have to calc it myself.

The purpose would be to predict the player a GM would most likely pick. A GM who normally picks Bench-level players would probably pick another Bench-level player. The problem with this approach is that the sample sizes for many of these lottery teams are small. Neil Olshey has 0 draft history with the Blazers but he has a few with the Clippers. There’s a lot of wikipediaing for “Was Olshey responsible for 200X’s draft for the X team?” Hard to code for that kind of logic. Time intensive, which is why I plan to do it for just the Lottery teams. Will probably do a top tier team like the spurs and the thunder for control purposes.

Basically, the mission of this blog is to expand on other people’s work.

***P.S. How sick was that Heat/Thunder game!

***PPS How sick was Mark Cuban’s interview on First Take! I wonder if Thunder would have won if they played zone. Lebron’s outside the paint jumper was 16% or some such. Then again, Lebron barely took any of those. He either shot 3s or went to the hoop (rmdr: Link to Ken’s shot chart). Dude is a monster.

The FAD Kids of the 2012 Draft: Fresh Athletic Deadbets

I’ve actually had this in the back burner now because I didn’t want to scoop the sites I liked. I do this for a hobby. They get paid. I think?

Now that a sufficient amount of them have done their prep work. I’m going to release my own findings. Here is their work.

Ed Weiland

Wages of Wins

I’m not going to go over my complete list of busts. In fact, I dont know how to ‘define’ the term overrated or not. I’m just going on the mock draft analysis people are doing.  The spectacular Steve Von Horn did an entire chart on the consensus Mock Draft of several sites, and that’s what I’m going to use to determine which player is overrated.
.
.
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Andre Drummond
Harrison Barnes
Perry Jones
Terrence Jones
Meyers Leanord
Kendall Marshall
Terrence Ross
Dion Waiters
Jeremy Lamb
Made You Look!
Austin Rivers

Let’s go from the top of the list.On the side, i’ve listed the top overrated prospects. I’ve also ranked them according to their position. I’m going to bring out some boxscores now.

Name
Terrence
Jones
Dion
Waiters
Jeremy
Lamb
Terrence
Ross
Kendall
Marshall
Meyers
Leanord
Harrison
Barnes
Andre
Drummond
Perry
Jones III
Austin
Rivers
Name avg POS .0-Pos Rk. %avg Pts %avg FGA %avg FG% %avg 2PtA %avg 2P% %avg 3PtA %avg 3P% %avg FTA %avg FT% %avg Off %avg Def %avg Asts %avg Stls %avg Blks %avg PFs %avg PRAWS40 2YR AVG 2011 PONA7
. Terrence Jones 3.5 10 0.96 0.95 1.02 1.08 1.01 0.58 0.98 1.13 0.92 1.11 1.01 1.05 1.33 1.91 0.93 1.23 7.28 0.68
. Dion Waiters 1.5 10 1.21 1.20 1.07 1.34 1.06 1.00 1.11 1.07 0.97 0.81 0.85 0.93 1.69 1.81 1.17 1.36 7.45 0.45
. Jeremy Lamb 2 6 1.16 1.10 1.13 1.04 1.25 1.20 0.97 0.81 1.11 0.80 1.09 0.71 0.89 1.65 0.71 1.46 7.35 0.18
. Terrence Ross 2.5 15 1.24 1.29 1.04 1.15 1.08 1.53 1.07 0.73 1.05 1.17 1.40 0.73 1.22 2.00 1.20 1.39 7.06 0.06
. Kendall Marshall 1 9 0.60 0.60 1.10 0.63 1.13 0.55 1.03 0.51 0.91 0.27 0.91 2.10 0.86 1.14 0.76 1.40 5.68 -0.42
. Meyers Leonard 4.5 13 0.99 0.95 1.08 0.98 1.10 0.58 0.26 0.80 1.12 0.75 1.05 1.01 0.62 1.12 0.97 1.00 6.23 -0.77
. Harrison Barnes 3 22 1.44 1.47 0.96 1.63 0.92 1.14 1.14 1.61 1.01 1.21 0.86 0.66 1.10 0.60 0.80 0.91 5.42 -1.56
. Andre Drummond 5 8 0.94 1.12 1.03 1.14 1.02 0.32 0.00 0.68 0.45 1.29 0.91 0.50 1.27 1.35 0.75 1.16 5.38 -1.62
. Perry Jones III 4 40 0.99 1.11 0.96 1.16 0.96 0.79 0.98 0.70 1.04 1.03 0.90 0.98 0.90 0.46 0.82 0.89 5.32 -1.68
. Austin Rivers 2.5 10 1.10 1.06 0.98 0.98 0.99 1.20 1.05 1.35 0.90 0.45 0.71 1.01 0.91 0.00 0.90 0.62 3.16 -3.84

I don’t know if I am going to continue using wordpress…blogspot allows me to use javascript…

http://bballpants.blogspot.com/

You see those columns? With Javascript I can click on it and make it sortable.

Okay, back to analysis.

Harrison Barnes is bad. But look at Austin Rivers. -3.84 PONA7!

PONA7 is Points Over/Under NBA Average of  7. Translation: He’s 4 points below your Average NBA player. Just saying. That’s some giant growth he’s going to need. Like Magic.

I’m going to publish this little tidbit and then work on Harrison Barnes, PJ3, Meyers, and Marshall later. If you want go to the blogspot to see the better ranking Young FADs (Fresh Athletic Deadbets).

CAVEAT: This list only judges them based on their historic box scores. If they look sturdy, have long arms, are really tall, have good cheekbones or have good NBA DNA, that’s great for them. At the end of the day, box scores are more important to me. Do you want the valedictorian to operate on your loved one or do you want the guy with the longest fingers? This is a bad analogy, yes. I am expert bad analogist.

This article is a Work In Progress.

2012 Draft Centers Palooza (Fixed)

. Name avg
POS
Rk.
. Kyle
O’Quinn
4.5 6
. Cody Zeller 5 2
. Eli Holman 4.5 5
. Tyler Zeller 5 1
. Jared
Sullinger
5 3
. John
Henson
4 12
. Ricardo
Ratliffe
4.5 2
. Miles
Plumlee
5 4
. Bernard
James
5 6
. Andrew
Nicholson
4.5 7
. Meyers
Leonard
4.5 13
. Andre
Drummond
5 8
. Henry Sims 4.5 25
. Name avg POS Rk. %avg Pts %avg FG %avg FGA %avg FG% %avg 2Pt %avg 2PtA %avg 2P% %avg 3Pt %avg 3PtA %avg 3P% %avg FTM %avg FTA %avg FT% %avg Off %avg Def %avg TOT %avg Asts %avg Stls %avg Blks %avg TOs %avg PFs %avg RAWS40 %avg PRAWS40 2011 PRAWS40 2YR AVG
. Kyle O’Quinn 4.5 6 1.18 1.13 1.06 1.06 1.15 1.03 1.11 0.81 1.59 0.55 1.33 1.28 1.06 1.06 1.31 1.21 1.07 0.94 1.59 1.29 1.11 1.31 1.39 8.63 8.95
. Cody Zeller 5 2 1.46 1.37 1.16 1.19 1.39 1.19 1.18 0.00 0.00 0.00 1.78 1.58 1.14 0.93 0.90 0.91 1.49 2.02 0.57 0.88 0.92 1.63 1.91 8.88 8.88
. Eli Holman 4.5 5 1.08 1.23 1.09 1.12 1.28 1.15 1.10 0.00 0.00 0.00 0.70 0.79 0.91 1.32 0.98 1.10 0.77 1.35 1.08 0.72 1.32 1.32 1.41 8.73 8.78
. Tyler Zeller 5 1 1.54 1.44 1.38 1.05 1.46 1.42 1.04 0.00 0.00 0.00 1.89 1.58 1.22 1.51 1.27 1.35 1.08 1.38 0.78 1.04 0.92 1.80 2.16 10.03 8.53
. Jared Sullinger 5 3 1.53 1.42 1.44 0.99 1.36 1.35 1.00 6.50 4.49 2.99 1.72 1.53 1.16 1.12 1.24 1.20 1.32 1.59 0.50 0.96 0.92 1.49 1.71 7.93 8.26
. John Henson 4 12 1.05 1.19 1.24 0.96 1.30 1.42 0.93 0.00 0.00 0.00 0.70 0.92 0.76 1.03 1.44 1.31 0.98 0.66 2.30 0.62 0.60 1.31 1.37 8.17 8.22
. Ricardo Ratliffe 4.5 2 1.24 1.43 1.10 1.28 1.49 1.17 1.26 0.00 0.00 0.00 0.70 0.67 1.05 1.24 0.94 1.05 0.54 0.94 0.70 0.91 1.05 1.50 1.63 10.13 8.15
. Miles Plumlee 5 4 0.86 0.90 0.78 1.16 0.91 0.80 1.15 0.00 0.00 0.00 0.78 0.83 0.96 1.56 1.27 1.38 0.83 1.06 0.64 0.92 1.09 1.47 1.69 7.83 7.83
. Bernard James 5 6 1.02 1.13 0.98 1.16 1.14 1.01 1.14 0.00 0.00 0.00 0.72 0.89 0.83 1.21 1.12 1.15 0.58 1.17 1.21 1.15 0.68 1.36 1.53 7.08 7.76
. Andrew Nicholson 4.5 7 1.43 1.41 1.34 1.05 1.32 1.23 1.08 4.06 3.18 1.26 1.28 1.11 1.18 0.91 1.10 1.03 0.83 0.94 1.22 1.25 0.97 1.30 1.38 8.58 6.55
. Meyers Leonard 4.5 13 0.99 1.04 0.95 1.08 1.07 0.98 1.10 0.00 0.58 0.26 0.90 0.80 1.12 0.75 1.05 0.95 1.01 0.62 1.12 1.02 0.97 1.00 1.00 6.23 6.23
. Andre Drummond 5 8 0.94 1.14 1.12 1.03 1.16 1.14 1.02 0.00 0.32 0.00 0.31 0.68 0.45 1.29 0.91 1.05 0.50 1.27 1.35 0.84 0.75 1.11 1.16 5.38 5.38
. Henry Sims 4.5 25 0.98 0.90 1.05 0.85 0.94 1.12 0.84 0.00 0.00 0.00 1.26 1.19 1.08 0.67 0.87 0.80 2.97 0.94 0.93 1.55 1.05 0.58 0.46 2.88 2.88

Very sorry about the mis-post. Word press is kindah wonky today. It doesn’t like it when I fiddle around with html tables.In any case. This is the last of the series of Paloozas.

This table does not need to be this big. In fact, I should make a Apple 4 quadrants map and post it up.
There’s the old and good: Tyler Zeller.
There’s the young and bad: Everyone else.
There’s the old and bad:
And there’s the young and good:

Jared Sullinger. Sophomore Except he’s 6’7 wth long arms. And he can’t block. Assists, Rebounds and Blocks transfer over to the NBA from the NCAA north of 85% of the time. God, i’m sick of stretch forwards.

There’s John Henson, a junior, 6′ 9. 2.3x blocks! But…everything is normy.

Miles Plumlee. He can rebound. That’s about it.

I’ve included Cody Zeller, and Holman but their not in the draft. I just like their stats. Also Holman is short, got shot in college once, and skipped out half the year due to personal issues—meaning he got kicked off the team for doing something bad.

Turns out, Holman is in the draft as a 2nd rounder!

If you can get past the fact that Holman is such a nutcase, he is 5th in my rankings for center/forwards, 13th for my Power Forward list. He gives you consistent production. He’s aggressive. Everything else is average except for his STEAL and OREBS. He’s 6’8 with a 7’4 wingspan. He  is a senior though. He played in a competitive area and he’s got a decent 8 PAWS. I mean, you could do worse.

What you want from your center is rebounding and blocks. If Sullinger develops blocking ability–well, he would have by now–he’s not gonna. Your best rebounding blocker is Zeller. Followed by Miles Plumlee, and Henson.

Once those three get taken, your left with Meyers and Fab Melo. Like coffee stains.

School’s out! Have fun with the stats!

Re: Ed Weiland’s on 2012 Draft Passing Point Guards

I was reading Ed Weiland’s excellent peice on Point Guards. In fact he is doing a 2012 Draft like me.

Unfortunately, hoopsanalyst.com, whom he writes for, has really bad tables and his hard work is hard to read.

Here’s the Ed Weiland article I will dissect.
And here’s the chart he made below.

. Player 2PP 3PP P40 A40 S40 RSB40
. Jason Kidd 0.545 0.362 19 10.3 3.6 11.8
. Rod Strickland 0.541 0.444 24.9 9.7 3.6 8.5
. Eric Snow 0.547 0.292 13.2 9.5 2.3 6.5
. Sherman Douglas 0.594 0.368 20.6 9.7 2.1 4.8
. Rafer Alston 0.513 0.337 13.9 9.1 2.7 5.8
. Brevin Knight 0.407 0.409 20.4 9.8 3.5 8.3
. JJ Barea 0.485 0.291 25.1 10 1.5 6.8
. Moochie Norris 0.483 0.424 24.5 9.3 3.1 9.1
. Jacque Vaughn 0.493 0.345 11.5 9.1 1.2 5.8
. Bobby Hurley 0.421 0.421 19.2 9.2 1.7 4.7
. TJ Ford 0.429 0.265 17.8 9.2 2.4 7.2
. Mateen Cleaves 0.472 0.292 15 9.3 2.3 4.5
. Jared Jordan 0.511 0.298 18.1 9.2 1.4 7.7
. Omar Cook 0.424 0.309 16.1 9.1 2.4 5.8
. Chris Herren 0.435 0.383 16.6 10.6 1.5 3.9
. Kendall Marshall 0.527 0.354 9.2 11 1.4 4.5
. Scott Machado 0.537 0.404 15 11 1.8 7.5
. Jesse Sanders 0.514 0.356 14.2 9.1 1.4 10.4

I don’t want to make it a habit of rewriting other people’s work so this will probably be the only POST on this. I will probably hit him up on twitter about making better HTML tables. There’s no point in posting up tables if they look like garbage.

The big thing is the 2PP percentage. He hasn’t shown his data on his theory that >.500 college 2pt% translates into NBAness but it does SEEM true.
*Not to self: Create a 10 year list of the Top 30 point guards in the league comparing their current WP48(w/ scoring and assist efficiency) and their college WS40. I’m quite certain he’s right. But data has to show it.
RSB40 > 7 is another indicator. In many ways that fits the WS40 theory. After all, Rebounds  and steals make up 25% of the WS40 score. An Average PG scores 5 on WS40. (Keep in mind that PAWS40 and WS40 are different things)
*Again, where is the hard data for this?
I still think my percentage over average approach is better than these numbers. You get a feel of how low usage or high usage someone is. Having a good 2pt efficiency is useless if your 2pt attempts are 50% below average.
In conclusion, PGs and Centers may be the easiest positions to isolate stats for.
  • Poing guards need good RSBs. What they lack in blocks must be made up for in steals and rebounds. I would actually do RSB/PF (Personal Fouls) as every defensive stat is an opportunity for a Foul.
  • Centers need great RSBs as well, with emphasis more on Rebounds and Blocks.
I normally disregard FG% in my analysis because shooting ability never translates well into the NBA. But being high usage and having a high 2pt% may indicate many layups. And layups are a skill that translates well.
(A message to Ed: You can save a spreadsheet in html form in Google Docs. Copy the html from Google Docs and paste directly to the CMS HoopsAnalyst uses. You may have to fiddle with the html directly to make it visually appealing but the code is practically in english.)

2012 Draft Power Forward Palooza

Because power forwards are so important in team play, i’ve included the entire gamut of stats. A little primer might be needed to understand them. This will be a page in the Glossary.

It is your normal set of box scores with a twist. Those numbers below are all percentages. Let’s take Anthony Davis. He made 98% of the average Field Goals a PF should make. It means he is 2% below Average. You can quickly scan and mark things down into trios.

Field Goals Made (FG), Field Goals Attempted (FGA), FG% are what I like to call ‘Points’

I’ve carved up the box scores into Three Sections

Scoring Stat Section: Points, Paint, Perimeter, Freebies. (with Respect to Turnovers and Assists.)

– A high scoring, high usage player with high turnovers and low assists is a danger flag.

People like Kevin Jones may get a high win score because he’s such a beast in the paint but I don’t like it. You take away his one weapon and he’s left with nothing to fall back on.

Defensive Stat Section: Rebounding, Blocks/PFouls, Steals/PFouls

– Defense is a gamble. Every steal or block is an opportunity for a foul.

Star Section: Assists/TOs

– Because point forwards are so rare and valuable, i’m almost willing to forgoe all the other sections if a player is spectacular in this.

*I run these sections thru my head for every pick. For Guards, the scoring stat section is more important. For Forwards, the defensive stat section is.

TWO Things to Note About This Chart

1. It includes 2011’s PRAWS40 at the end. It also includes the average between the last two seasons PRAWS40 contribution called 2YR PRAWS40. A bigger data set should more accurately reflect reality.

IF 2011 PRAWS40 > 2YR PRAWS40 then there was a DECLINE in productivity, year to year.

IF 2YR PRWS40 > 2011 PRAWS40 then that means GROWTH.

IF the two are EQUAL, then that means the player is relatively young or has only seen starter minutes recently.

. Name avg POS Rank
. Anthony Davis 4 1
. Marshawn Powell 4 2
. Arsalan Kazemi 4 3
. William Mosley 4.5 4
. Ricardo Ratliffe 4.5 5
. Doug McDermott 4 6
. Jack Cooley 4.5 7
. Trevor Mbakwe 4.5 8
. Andre Roberson 4 9
. Robert Covington 4 10
. Kevin Jones 4 11
. Julian Boyd 4 12
. Eli Holman 4.5 13
. Drew Gordon 4 14
. Kyle O’Quinn 4.5 15
. Andrew Nicholson 4.5 16
. Gregory Echenique 4 17
. Mike Glover 4 18
. Mike Muscala 4.5 19
. John Henson 4 20
. Eric Griffin 4 21
. Dennis Tinnon 4.5 22
. Mike Scott 4 23
. Thomas Robinson 4 24
. Mike Moser 4 25
. Javon McCrea 4 26
. Arnett Moultrie 4.5 27
. Quincy Acy 4 28
. De’Mon Brooks 4 29
. Draymond Green 4 30
. Mitchell Watt 4 31
. Gorgui Dieng 4 32
. Mason Plumlee 4.5 33
. Eric Moreland 4.5 34
. Greg Mangano 4 35
. C.J. Aiken 4 36
. Aaron White 4 37
. Meyers Leonard 4.5 38
. Name avg POS Rank
. Name avg POS Rank %avg Pts %avg FG %avg FGA %avg FG% %avg 2Pt %avg 2PtA %avg 2P% %avg 3Pt %avg 3PtA %avg 3P% %avg FTM %avg FTA %avg FT% %avg Off %avg Def %avg TOT %avg Asts %avg Stls %avg Blks %avg TOs %avg PFs %avg RAWS40 %avg PRAWS40 2011 PRAWS40 2YR PRAWS40
. Anthony Davis 4 1 0.99 0.98 0.81 1.20 1.06 0.87 1.21 0.17 0.37 0.49 1.17 1.10 1.06 1.09 1.30 1.24 0.92 1.39 3.34 0.45 0.65 2.12 2.36 14.02 14.02
. Marshawn Powell 4 2 2.03 2.09 1.50 1.38 2.28 1.56 1.46 0.00 1.16 0.00 2.19 2.10 1.03 1.38 0.92 1.07 1.61 1.56 1.61 2.55 1.53 1.84 2.01 11.97 8.55
. Arsalan Kazemi 4 3 0.92 0.83 0.73 1.15 0.91 0.83 1.10 0.00 0.00 0.00 1.35 1.32 1.02 1.03 1.48 1.33 1.67 2.29 0.75 0.93 0.95 1.73 1.88 11.17 8.53
. William Mosley 4.5 4 0.92 0.95 0.84 1.12 0.99 0.89 1.11 0.00 0.00 0.00 0.87 1.25 0.70 1.19 1.18 1.18 0.83 1.98 2.57 0.64 0.92 1.59 1.75 10.88 10.20
. Ricardo Ratliffe 4.5 5 1.24 1.43 1.10 1.28 1.49 1.17 1.26 0.00 0.00 0.00 0.70 0.67 1.05 1.24 0.94 1.05 0.54 0.94 0.70 0.91 1.05 1.50 1.63 10.13 6.55
. Doug McDermott 4 6 1.61 1.64 1.41 1.16 1.48 1.26 1.17 3.30 2.44 1.58 1.25 1.04 1.19 0.88 1.03 0.98 0.75 0.25 0.06 0.97 0.60 1.55 1.67 9.92 10.23
. Jack Cooley 4.5 7 1.02 1.04 0.90 1.15 1.08 0.95 1.13 0.00 0.00 0.00 0.99 0.98 1.03 1.42 1.00 1.15 0.65 0.83 1.03 0.72 0.97 1.45 1.57 9.78 7.95
. Trevor Mbakwe 4.5 8 1.13 0.90 0.80 1.11 0.94 0.85 1.10 0.00 0.00 0.00 1.94 1.75 1.11 1.40 1.05 1.17 1.19 1.67 1.12 1.67 0.84 1.44 1.56 9.68 7.83
. Andre Roberson 4 9 0.86 0.82 0.83 0.98 0.78 0.79 0.99 1.21 1.10 1.23 0.99 1.06 0.92 1.21 1.51 1.41 0.87 1.39 1.44 0.83 0.85 1.51 1.62 9.62 7.35
. Robert Covington 4 10 1.28 1.22 1.21 1.02 0.93 0.88 1.06 4.34 3.41 1.45 1.02 0.87 1.16 1.09 0.90 0.96 0.98 1.64 1.04 1.04 1.04 1.46 1.55 9.22 10.51
. Kevin Jones 4 11 1.17 1.22 1.25 0.98 1.16 1.07 1.09 1.91 2.44 0.86 0.86 0.73 1.16 1.32 0.97 1.09 0.75 0.57 0.63 0.45 0.35 1.45 1.54 9.17 7.68
. Julian Boyd 4 12 1.44 1.37 1.28 1.07 1.35 1.26 1.07 1.56 1.34 1.36 1.64 1.48 1.10 1.06 1.44 1.32 0.69 0.57 0.52 1.07 1.17 1.40 1.48 8.82 8.50
. Eli Holman 4.5 13 1.08 1.23 1.09 1.12 1.28 1.15 1.10 0.00 0.00 0.00 0.70 0.79 0.91 1.32 0.98 1.10 0.77 1.35 1.08 0.72 1.32 1.32 1.41 8.73 9.03
. Drew Gordon 4 14 1.00 1.04 1.00 1.05 1.12 1.13 1.00 0.17 0.06 3.24 0.99 0.89 1.11 1.18 1.47 1.37 0.92 1.15 0.75 1.04 0.79 1.39 1.46 8.72 7.55
. Kyle O’Quinn 4.5 15 1.18 1.13 1.06 1.06 1.15 1.03 1.11 0.81 1.59 0.55 1.33 1.28 1.06 1.06 1.31 1.21 1.07 0.94 1.59 1.29 1.11 1.31 1.39 8.63 6.62
. Andrew Nicholson 4.5 16 1.43 1.41 1.34 1.05 1.32 1.23 1.08 4.06 3.18 1.26 1.28 1.11 1.18 0.91 1.10 1.03 0.83 0.94 1.22 1.25 0.97 1.30 1.38 8.58 7.76
. Gregory Echenique 4 17 0.91 0.92 0.79 1.17 1.01 0.91 1.12 0.00 0.00 0.00 1.02 1.01 1.00 1.35 1.09 1.16 0.63 0.49 1.55 0.90 1.01 1.34 1.41 8.37 6.38
. Mike Glover 4 18 1.22 1.28 1.04 1.23 1.40 1.19 1.18 0.00 0.00 0.00 1.17 1.22 0.97 1.12 0.97 1.02 0.69 0.98 0.81 1.10 0.95 1.34 1.41 8.37 6.10
. Mike Muscala 4.5 19 1.32 1.15 1.23 0.93 1.15 1.23 0.93 1.22 1.15 1.02 1.89 1.46 1.30 0.96 1.21 1.12 1.43 0.62 1.08 1.06 0.87 1.27 1.34 8.33 5.33
. John Henson 4 20 1.05 1.19 1.24 0.96 1.30 1.42 0.93 0.00 0.00 0.00 0.70 0.92 0.76 1.03 1.44 1.31 0.98 0.66 2.30 0.62 0.60 1.31 1.37 8.17 8.74
. Eric Griffin 4 21 1.16 1.12 0.96 1.18 1.16 0.98 1.18 0.87 0.79 1.19 1.35 1.58 0.85 1.03 1.11 1.09 1.15 0.98 1.78 1.24 1.17 1.28 1.34 7.97 8.37
. Dennis Tinnon 4.5 22 0.81 0.87 0.85 1.02 0.89 0.89 1.00 0.41 0.14 2.91 0.65 0.59 1.10 1.42 1.15 1.25 0.77 0.94 0.19 0.68 1.00 1.21 1.27 7.88 6.58
. Mike Scott 4 23 1.29 1.28 1.18 1.09 1.37 1.28 1.07 0.35 0.49 0.97 1.46 1.20 1.21 0.82 1.10 1.01 0.87 0.66 0.35 0.93 0.60 1.25 1.31 7.77 6.43
. Thomas Robinson 4 24 1.25 1.25 1.29 0.97 1.34 1.43 0.94 0.35 0.30 1.62 1.38 1.34 1.02 1.06 1.59 1.42 1.33 1.15 0.63 1.17 0.95 1.25 1.30 7.72 8.22
. Mike Moser 4 25 1.00 1.00 1.15 0.87 0.83 0.89 0.94 2.78 2.93 1.07 0.73 0.63 1.16 1.00 1.42 1.28 1.67 2.05 0.75 1.28 0.95 1.23 1.27 7.57 6.44
. Javon McCrea 4 26 1.31 1.45 1.30 1.10 1.58 1.49 1.06 0.00 0.06 0.00 1.02 1.20 0.83 1.32 0.92 1.05 1.73 1.31 1.09 1.21 1.34 1.23 1.27 7.57 7.28
. Arnett Moultrie 4.5 27 1.06 1.04 1.02 1.01 1.03 1.03 1.01 1.22 1.01 1.29 1.11 0.95 1.19 1.06 1.08 1.08 0.77 0.94 0.42 1.02 0.66 1.15 1.20 7.43 8.28
. Quincy Acy 4 28 0.90 0.83 0.75 1.11 0.90 0.84 1.07 0.17 0.12 1.94 1.25 1.08 1.17 1.03 0.92 0.95 0.75 0.98 1.44 0.86 1.12 1.20 1.24 7.37 7.57
. De’Mon Brooks 4 29 1.55 1.59 1.57 1.02 1.56 1.52 1.04 1.91 1.83 1.19 1.28 1.20 1.08 1.26 0.95 1.05 0.81 1.48 0.52 1.04 1.55 1.20 1.24 7.37 6.61
. Draymond Green 4 30 1.10 1.03 1.19 0.87 0.85 0.97 0.88 2.95 2.68 1.26 1.07 0.97 1.08 0.76 1.45 1.22 2.65 1.48 0.63 1.24 0.93 1.18 1.22 7.27 7.74
. Mitchell Watt 4 31 1.21 1.21 1.13 1.07 1.24 1.16 1.07 0.87 0.91 1.05 1.30 1.16 1.11 0.82 1.00 0.94 1.79 0.66 1.67 1.17 1.04 1.18 1.22 7.27 6.03
. Gorgui Dieng 4 32 0.62 0.66 0.65 1.01 0.72 0.74 0.97 0.00 0.06 1.62 0.57 0.57 1.01 1.24 0.97 1.06 0.75 1.15 2.24 0.83 1.09 1.16 1.19 7.07 7.57
. Mason Plumlee 4.5 33 0.91 0.92 0.87 1.05 0.95 0.92 1.04 0.00 0.00 0.00 0.94 1.17 0.81 1.06 1.25 1.19 1.37 1.14 1.08 1.17 0.95 1.10 1.12 6.98 5.88
. Eric Moreland 4.5 34 0.59 0.56 0.59 0.94 0.57 0.60 0.94 0.41 0.29 0.97 0.73 0.96 0.76 1.06 1.31 1.22 1.31 1.56 1.73 1.06 1.29 1.03 1.04 6.43 7.45
. Greg Mangano 4 35 1.27 1.27 1.38 0.91 1.11 1.15 0.97 2.78 2.99 1.08 1.07 1.08 0.98 1.15 1.14 1.14 0.35 0.41 1.61 0.83 0.93 1.07 1.08 6.42 6.23
. C.J. Aiken 4 36 0.78 0.79 0.76 1.03 0.68 0.57 1.23 1.91 2.19 0.99 0.57 0.54 1.07 0.35 0.75 0.62 0.87 0.49 2.59 0.48 0.49 1.06 1.07 6.37 7.04
. Aaron White 4 37 1.05 0.97 1.00 0.97 0.93 0.88 1.05 1.39 1.77 0.90 1.25 1.20 1.04 1.03 0.85 0.91 0.87 1.23 0.63 0.83 0.95 1.04 1.05 6.27 5.33
. Meyers Leonard 4.5 38 0.99 1.04 0.95 1.08 1.07 0.98 1.10 0.00 0.58 0.26 0.90 0.80 1.12 0.75 1.05 0.95 1.01 0.62 1.12 1.02 0.97 1.00 1.00 6.23 5.35
. Name avg POS Rank %avg Pts %avg FG %avg FGA %avg FG% %avg 2Pt %avg 2PtA %avg 2P% %avg 3Pt %avg 3PtA %avg 3P% %avg FTM %avg FTA %avg FT% %avg Off %avg Def %avg TOT %avg Asts %avg Stls %avg Blks %avg TOs %avg PFs %avg RAWS40 %avg PRAWS40 2011 PRAWS40 2YR PRAWS40

Let’s start.

1. Anthony Davis

He’s so sickeningly the first choice that I don’t even want to talk about how well he finishes around the rim. FT needs work…who am i kidding. The clear first pick. For comparison, Tim Duncan was a 10 PRAWS40 College Senior. A quick glance at the 4 primary stats show that Duncan was at least a 12 during his sophomore year.

2. 2013 Draftees.

Marshawn Powell, Kazemi, McDermott, Jack Cooley, Covington, Roberson

It is frightening how many of the top bigs are up for grabs next year. Powell is a monster. High turnovers, but high usage scoring machine. Low defensive rebounds but an offensive rebounding fiend. Improving upwards nicely.

I know I shouldn’t talk about next year’s prospects but Covington and McDermott need to be spoken about.  Covington shoots 45% better than average PF McDermott shoots 60% better! In my list of 3 shooters who are better than average NCAA players, McDermott is number 1 at 48.6%. On average, he makes 2 out of 4 treys per game. And he still rebounds. Baller. Covington is in 6th place.

Aside from Andrew Nicholson, McDermott and Covington are the only Power Forwards in the top 10 NCAA trey shooters.

3. Mbawke (junior), Ratcliffe, Kevin Jones

They went from 7s to 9s. Not bad. KJ is a favorite of wagesofwins. Offensively, both high usage and offensively efficient. FT% great. Offensive Rebounds, great. Average DREBs. Below average assist/blocks/steals. I am not as gungho about Kevin Jones as other people are. But he is a 9 PRAWS40. With good Offensive Rebounds. Trending upward. Not a bad choice if you need some offense in your roster (cough sixers). He makes 2x as much treys but shots 3x as much. His 3pt efficiency is 14% less than average. I just can’t recommend him despite him being a 9. He has ONE trick. It’s a very good trick but it’s only one trick.

Ratcliffe went from a 8 to a 10. Very similar to Jones but with a spike in points. He scores 25% more than the average 4. Everything else is average. Defensive stats are below average. Rebounds are at a respectable 20%. He is the better KJ.

Mbawke. A mixed bag. 20% more assists but with 67% more turnovers. But good steals/blocks and 40% better OREBs. Decent FT rate. The guy gets to the foul line and makes it 2x more than his peers. A brawler.

If I had to choose, I’d pick Mbawke. The problems with turnovers can be alleviated with good passing guards. He’s younger by a year as well.

I will always lean toward the Pick with the better Defensive Stat Section

4.  Julain Boyd -Poor kid had a heart problem last year.

5. Eli Holman – 6’7… but with a 7’4 wingspan.

Ranks 13 on my PF list. 35% more steals but 32% more turnovers. 12% fg efficiency but only 8% better point scoring. Other stuff is average sauce. 30+% better OREBs. eh. Not an impressive year. But still quite a good player. Seems like he uses a lot of effort to run around in circles. a Constant 8-9 score is still 3 points above average NBA players. I really expected better blocks. Only 8% better.

6. The Crazy 8s.

John Henson, Drew Gordon, Kyle O’Quinn, Andrew Nicholson, Eric Griffin

Out of this whole group, the one with the best Defensive Stat Section is Drew Gordon, his steal percentage and offensive rebounds making up for his lack of blocks. His scoring is economical and efficient. He sports a 11% Above Average FT% as well. The %PRAWS indicate that he is the best buy as well.

Kyle O’Quinn is better defensively than Nicholson by a good margin but at the expense of being 14% more foul prone than Nicholson. But 20% more rebs and 20% more blocks more than cover it. It’s really a toss up on who’s better scoring paint points. Both are average with Kyle being more aggressive. Nicholson is a better shooter by far as he hits 1 out ever 2.2 treys per game. His ft% rate is better as is the general case of good perimeter players. But Kyle gets to the line more often. I give the edge to Kyle.

Henson and Griffin were much better last year which made me google them for injury. Henson suffered from a sprained wrist.. Griffin has only been playing basketball for 6 years. If we look at there Def. Section, Henson is the clear winner. In fact, he is the clear leader of the entire group in blocked shots. Offensively, Eric Gordon is 20% better than Henson both in the paint and at the perimeter. But I see that as a wash since he has 15% more Pfouls than avg. Henson’s offense this year has been underwhelming. But because of the injury, I think Henson is a better bet. He also commits 40% less personal fouls than average.

I think Henson is the best choice out of this series while Gordon is the safest.

7. Thomas Robinson, Javon McCrea.

Oh this is fun. Both are high usage players. 20% Above Average PF in scoring but Javon is 10% more efficent than average and 12% better at the paint than Robinson. Javon is a horrible free throw shooter while Robinson is average. If we look at the defensive side, Javon’s got high orebs but due to high usage, he might just be grabbing his own misses. Defensively i conisder both a wash. Javon is technically better but has 30+% more fouls than average.

On the turnover side, TR is 17% above avg, while Javon is 21%. But Javon assists 73% more to TRob’s paltry 33.

I’d vote for Javon.

8. Meyers Leanord.

I just wanted to include Meyers in the analysis because of all the draft talk about him. As you can see above, he is completely average to below average. His two saving graces is that he shoots 12% above ft% average and he blocks 12% better than average. Which, in the great scheme of things, still makes him below average.

His %PRAWS is exactly 1.00

He is exactly Average. Whoever drafts him, probably already has someone on the bench exactly like him.

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Oh my god, this list was long.

If I can’t have Anthony Davis, it would have to be Mbawke William Mosley with Drew Gordon as my safety pick.

Javon McCrea is the only one amongst this group with a Star Section.

If I wanted to pick a breakout star, fully well knowing that stars are barely 7% of the market, I might pick Javon.

#Whoops. He plans to stay in college and graduate!

Update: Whoops. Mbawke has a torn ACL and has to prove himself in college again. Can’t Believe I didn’t write about William Mosley. I forgot that I had already written about him. Check him out here.

Any Questions?

feel free to hit me up on twitter.

@twitter

In Defense of Rebounding Part 2

The rebounding experiment went like this: 10 basketball players, 10 coaches and 10 sportswriters, plus a group of complete basketball novices, watched video clips of a player attempting a free throw. (You can watch the videos here.) Not surprisingly, the professional athletes were far better at predicting whether or not the shot would go in. While they got it right more than two-thirds of the time, the non-playing experts (i.e., the coaches and writers) only got it right about 40 percent of the time. The athletes were also far quicker with their guesses, and were able to make accurate predictions about where the ball would end up before it was even airborne. (This suggests that the players were tracking the body movements of the shooter, and not simply making judgments based on the arc of the ball.) The coaches and writers, meanwhile, could only predict a make or miss after the shot, which required an additional 300 milliseconds.

What allowed the players to make such speedy judgments? By monitoring the brains and bodies of subjects as they watched free throws, the scientists were able to reveal something interesting about the best rebounders. It turned out that elite athletes, but not coaches and journalists, showed a sharp increase in activity in the motor cortex and their hand muscles in the crucial milliseconds before the ball was released. The scientists argue that this extra activity was due to a “covert simulation of the action,” as the athletes made a complicated series of calculations about the trajectory of the ball based on the form of the shooter. (Every NBA player, apparently, excels at unconscious trigonometry.) But here’s where things get fascinating: This increase in activity only occurred for missed shots. If the shot was going in, then their brains failed to get excited. Of course, this makes perfect sense: Why try to anticipate the bounce of a ball that can’t be rebounded? That’s a waste of mental energy.

The larger point is that even a simple skill like rebounding reflects an astonishing amount of cognitive labor. The reason we don’t notice this labor is because it happens so fast, in the fraction of a fraction of a second before the ball is released. And so we assume that rebounding is an uninteresting task, a physical act in a physical game. But it’s not, which is why the best rebounders aren’t just taller or more physical or better at boxing out – they’re also faster thinkers. This is what separates the Kevin Loves and Kevin Garnetts from everyone else on the court: They know where the ball will end up first.

This is an quote from wired.

Here is the study just in case you suspect the controls of the study.

http://brainvitge.org/papers/Aglioti%20et%20al%20%20%20Nature2008.pdf

Pay attention to the Figure 1, Figure 2, and Figure 3.

They haven’t given me permission to use their charts but in general, it took the expert players a much shorter span of time to ascertain whether or not a shot was going to miss than even expert watchers such as coaches and analysts. Expert novices were only slightly better at it than ‘pure’ novices! ***

It does not fully disprove the thesis that the rebounding stat belongs in the category of defense which is a team attribute—but it does highly indicate that rebounding is an individual skill. Well worth north of .5 win score multiplier on DREBS and 1.0 WS multiplier on OREB accreditation to the rebounder. As for how much, I think that’s for the future when AI equipped cameras replace refereeing.

Yes I am stalwartly defending win score and by extension wins produced.

But what I am truly defending are the robust principles behind wins score and wins produced, namely running the regression from wins—scratch that. Working backwards from wins to box scores to derive a sensible equation is currently our BEST method and model for predicting wins. Wins Produced and Win Score are both tested against real world data and critiqued by the community—so far I haven’t seen anything that dissuades me from its efficacy.

Data isn’t always going to agree with common sense. Useful data always disagrees with commons sense. That’s what makes it useful. Heh.

————-

I’d like to thank Unnamed for most of the impetus behind this work

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To Read Part One of “In Defense of Rebounding” Click Here

In Defense of Rebounding

This is a response to Steve Van Horn’s excellent arcticle in which he lays out the inadequacy of some box scores in determining wins. Defensive rebounds are a team activity and cannot reliably be credited in any percentage to the rebounder. The article can be read here.

The full back and forth conversation can be read here. (Wwert is me.)

I agree. Rebounds are the result to the successful completion of a good defense. Where I disagree is the area of rebounding as an individual skill.  Rebounds are consistent through time. If a good rebounder is switched to the bench, that bench’s lineup is going to get more rebounds. If he switches teams, that team will have more rebounds. Anything consistent through time is generally attributed to the individual skill of the rebounder. While the consecutive actions of teammates setting screens, the resulting defensive posture, the hedging, the skip pass, the iso distraction, the back screens are not recorded and  go uncredited, the fact that rebounds are consistently factored to a player(even when he leaves a team) makes me consider that rebounds to be a individual skill that is not as affected by other teammates as one might think.

according to wagesofwins.com

  • Rebounds are one of the most consistent statistics in team sports.  Players who rebounded well in the past tend to do in this in the future.  Players who are not good at rebounding in the past tend to be poor at this aspect of the game in the future.
  • Rebounds also vary across teams.  And teams that rebound well tend to employ better rebounders (or at least, avoid employing really bad rebounders).
  • Diminishing returns does exist.  This is seen with respect to Wins Produced in general and also with respect to defensive rebounds (but not, apparently, with respect to offensive rebounds).  The effect, though, is small. This is seen when we estimate the size of the diminishing returns effect.  It is also seen when we re-estimate player productivity with different value for defensive rebounds.
  • Although rebounds do have a substantial impact on wins, it is shooting efficiency – not rebounding – that is the most important determinant of a player’s Wins Produced.

(Sidenote completely unscientific: I play pick-up like most hobby sport statisticians. My shot is pretty bad. But I’ve always been a good rebounder. To me, rebounding is a skill. The percentages of the left side rolling to the right. The 50/50 ball. The particular English on the ball as it travels through the your enemies’ fingers. Over-thinking things is probably one of the reasons my shot is so bad/slow.)

From the stats, Wins Produced explains 90+% of the wins. Win Score per Minute has a 95% correlation with Wins Produced. So yeah, the question of that formula being overly sympathetic to rebounds is moot to me since it basically works 95% of the time. If you treat players like gambling bets, and if a formula comes along that has those odds, you don’t worry about that formula.

The new formula accredits only 50% of defensive rebounds to  the Win Score. It does not affect most of the positions too much. The PFs and Centers move down 3 spots maybe. In general, that .50% accreditation affects a very minuscule portion of the rankings. To sum it up, my rankings are 90% correlated with Wins Produced which is explains wins 95% of the time. Even if Wins Produced had 75% Explanatory Power, the gambler in me would take it. Wouldn’t you?

50% is erring in the side of caution. If we made it 25%, i have no doubt that the Win Score to WP correlation would suffer but it would still be quite a tool since the WP48 formula is so successful.

While I see the thesis that defensive rebounds are a team activity, I think situations where team rebounds are a team activity are far fewer. I think that players, actively thinking, and predicting and guiding the ball generate rebounding opportunities for themselves. It is hard to coordinate 4 man box out so that your tallguy can grab the DREB. It is much easier to be at the spot where the ball bounces to faster than your enemy.

This is just a matter of practicality for me.

If 25% credit to defensive rebounds gives me a greater correlation to Wins Produced, then i’m perfectly willing to do that. As such, I’m happy to use the 50% credit if only for the fact that some of the college kids do try to feed their seniors more rebounds and stats because they know the NBA Combines are coming up.

Yes, i am sidestepping the notion of whether or not rebounds can be used to gauge a player’s contributions to wins by totally relying on the efficacy of Wins Produced.

In conclusion, I don’t care if it’s a red panda or white cat, if it catches mice, then it’s a good cat.

(Cultural joke: Panda and Red Cat in Chinese are homonyms.)

I just want to predict future NBA stars. Maybe, in the future, we have AI who can analyze each game to give a variable defensive rebound coefficient per play but it is in my opinion that there are more situations where a rebounder is actively using his own skills to overpower his enemy and grabbing that sweet rebound, be it offensive or defensive. Whether or not their are more opportunities available to the rebounder because the team employs great man on man defense to call misses, should not be that statistically significant in the course of a season.

Additional very dry reading material which I did not want to messy this post with:

http://wagesofwins.com/2006/11/09/do-we-overvalue-rebounds/

http://sportsillustrated.cnn.com/vault/article/magazine/MAG1179946/2/index.htm

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To Read Part 2 of “In Defense of Rebounding” Click Here