2013 NBA Draft Extravaganza: The Worst Draft Ever!

(Author’s note: I royally screwed the pooch here. I will leave this up as evidence that I am totally fallible. The Correction is now up. Please ignore anything written here. Pretty Please.)

“The best lack all conviction, while the worst
Are full of passionate intensity.”

-William Butler Yeats

This is quite possibly the worst draft I have ever covered.

But not the worst draft ever. Oh no. I'm not gonna cry.

But not the worst draft ever. Oh no. I’m not gonna cry.

Granted, this was not readily apparent when Dave talked about the College Wins Produced numbers for 2012-13 or when Ari charted them yesterday. The problem lies in that Wins Produced is a comparative metric. Yes, there are players who were outstanding in the NCAA…in comparison to their peers.

Their peers were just utterly terrible.

An early round NIT game

An early round NIT game

The number to keep in mind is 12.2 Win Score per 40 minutes.

To put this in perspective, there were 44 Players from the 2010, 2011, and 2012 drafts that had a better Win Score per 40 minutes than every player who played more than 400 minutes in the NCAA in 2012-13. There were 16 players drafted in those three drafts who projected better than every single player likely in this draft but one.

Kawhi Leonard, Kyrie Irving, Derrick Favors, and Anthony Davis all had better numbers at 19 than every single player in the NCAA right now.

Really, this draft is so bad that it made me do some extra research. Granted, it doesn’t take that much to get me started. We will get to that in a bit. First let’s cover the basics.

Welcome to my fourth annual draft preview and rankings, where I take it upon myself to write, project, and speculate about the NBA draft using a surprisingly effective draft model to predict player performance using data publicly available on the internet.

I will admit that it may not be 100% unique

I will admit that it may not be 100% unique

Stunning I know. Using this data, I built two models to predict the future performance of NBA draft picks (go here for the model build parts 1 & part 2 ). In very general terms, the models use the available data to predict future performance for each player coming into the draft from the NCAA. Based on that prediction, a ranking is done and a draft recommendation is generated.

It has performed at a very high level. For the full history you can go to:

Without further ado, here’s the 2013 NBA Draft Rank. This year I’ve include all eligible NCAA Prospect in Draft Express Top 100 and all recommended draftees by the model outside of the Draft Express Top 100 (this includes some interesting names). First, the table sorted by the draft express rankings:

Draft Model 1

Now, let’s sort that by projected productivity:

Draft Model 2

That was a fun 72 hour build. Again, that’s the productivity projection for every eligible NCAA draft prospect who made it into Draft Express‘ Top 100 or was identified by the model as a possible NBA player. As always, my plan is to continue to monitor these projections in the future.

Let’s review the models real quick for any newcomers. I built two draft models and I called them Yogi and Booboo. They both use a series of publicly available factors (WS40, Age, Height, etc.) to project the player’s Wins Produced numbers for the duration of a player’s rookie contract in different ways. Yogi gives the go ahead for drafting at 0.090 projected WP48 and Boo Boo does the same at 0.067 WP48.

A simple test for the models is to look at the correlation between where the player was picked, where the models suggested picking him, and actual rank by draft in terms of production. Draft order vs production shows minimal correlation with an R-square of about 5% . It jumps to 25% for the predicted production rank.

For this year, I did a more complex and interesting test based on the age model:


That table tells me that if I want to draft a player who will be starter-calibre (>=.100 WP48) after his 4th year, I need to draft a player that averages .078 WP48 over his first four year (and before you ask in the comments, yes, the implication is that drafting younger is better). When I apply that test to different scenarios I get:

Hit Ratio for >.078 in first four Years (1996 to 2009)
All Picks 50.9%
Both Models 83.9%
Both Models Not Top 5 Pick 88.6%
Model 1 81.4%
Model 2 76.1%
Top 3 Pick 71.4%
Top 5 Pick 66.7%
Top 10 Pick 58.5%

The models perform better than a top 3, top 5, or top 10 pick.

So to review, using publicly available data we built a model that picks draft winners at a 80%+ rate, which is, in general, better than having a top 3 pick in the draft.

But you’re not really here for the science are you? Let’s give you the money shot.

Draft Model 3

That is the smallest list of draft recommendations that I have ever given (see table below).

Draft Top Tier Prospects Good Prospects Total
1995 3 2 5
1996 3 0 3
1997 2 3 5
1998 2 3 5
1999 9 3 12
2000 5 4 9
2001 4 2 6
2002 2 0 2
2003 7 0 7
2004 5 3 8
2005 6 3 9
2006 4 2 6
2007 4 3 7
2008 5 5 10
2009 4 3 7
2010 3 5 8
2011 6 0 6
2012 4 3 7
2013 1 3 4

Let’s do some takeaways shall we?

  • Victor Oladipo is the truth. He’s the only sure thing in this draft. After him, it’s all pure speculation.
  • The other prospects recommended by the model likely to be drafted are: C.J. McCollum, Kentavious Caldwell-Pope and Nate Wolters.
  • As for the likely undrafted prospects, all are really perfect for your D-League team, and I really would not be surprised to see them doing well next season in Europe (or with the Austin Toros).

As for the rest of the prospects? The best advice I can give is based on the misses we’ve had before (David Lee, Joakim Noah, Andre Drummond). In this kind of scenario, if you can’t trade out, you draft for potential, size, or hidden talent. Younger and taller players are better, as are players that play on deep and talented teams.

By that logic, I like Noel, Ben McLemore, Cody Zeller, and Alex Len (in that order) of the players not recommended by the model. Outside of that, my advice to a team would be to look to trade or look to Europe.

We will get to that tomorrow.


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