Euroleague Now Included in the Box Score Prediction System

Recently I explored the correlation between Euroleague and NBA stats.  My next step has been to incorporate Euroleague stats into my Box Score Prediction System.

I used the same process with Euroleague as I did with the NCAA.  I developed a formula that projects each NBA stat using Euroleague data and other variables such as height, weight, and experience.  These formulas were developed using multiple linear regressions.  The adjusted R^2 (http://en.wikipedia.org/wiki/Coefficient_of_determination) values for the different NBA stats are as follows:

FGA: 0.2971

FG%: 0.3004

3PA: 0.6834

3P%: 0.7729

FTA: 0.4111

FT%: 0.6887

REB: 0.8609

AST: 0.7202

STL: 0.5606

BLK: 0.7964

TO: 0.4538

PF: 0.5432

In the next few weeks, I will include projections of European prospects to go along with my projections of college players.  I have slightly less confidence about the Euro projections because the data is more unreliable, but the projections will still be useful.

All Euroleague stats were obtained from www.keyhoops.com.  NBA stats were obtained from www.basketball-reference.com.

How Do Euroleague Statistics Translate to the NBA?

A few weeks ago I explored how well NCAA statistics correlate with NBA numbers.  In other words, I wanted to see how much we could predict about a player’s professional career solely using his college performance.  Later, using a more complex form of these ideas, I developed my Box Score Prediction System.  However, not every NBA player comes from America.  Some of the game’s greats are international players that have already gained a lot of experience playing in tough leagues around the world.

At the MIT Sloan Sports Conference in early March, Mike Zarren of the Boston Celtics talked about how one of the things teams haven’t figured out is translating European statistics to the NBA.  If a foreign player dominates other foreign players, does that really matter?

Today is the first step in my process of answering that question.  I have gathered data about current and former NBA players that previously played in the Euroleague, arguably the second toughest league in the world behind the NBA (although you can argue for NCAA Division I as well).

The Euroleague features teams from all over Europe and some from the Middle East.  A lot of times these teams are champions of their respective countries, although this is not always the case.  There is a regular season, and then a few rounds of playoffs.  Many of the current international players in the NBA have experience in the Euroleague, making it a great league to examine.  Ricky Rubio, one of the top prospects in this year’s draft (or next year’s if he doesn’t declare), has Euroleague experience.

For each player, I calculated their per minute box score stats in both NBA and Euroleague play.  I ran simple regressions to find out the correlations between the NBA and Euroleague stats.  I have expressed the R^2 values below:

(For an explanation of what R^2 is, go to: http://en.wikipedia.org/wiki/Coefficient_of_determination.)

FGA: 0.2207

FG%: 0.2659

3PA: 0.6913

3P%: 0.7173

FTA: 0.354

FT%: 0.633

REB: 0.7448

AST: 0.6949

STL: 0.4654

BLK: 0.5949

TO: 0.4228

PF: 0.2654

As you can see, NBA field goal attempts, field goal percentage, free throw attempts, and fouls are the least predictable based on a player’s Euroleague stats.  Three point attempts, three point percentage, rebounds, assists, and blocks are the most predictable.  Compared to college correlations, the Euroleague correlations are all slightly lower.  However, they are comparable.

The next step I will take is to implement Euroleague stats into my Box Score Prediction System.  This involves more complex multiple regressions, so the R^2 values will be higher.  In other words, BSPS will do a better job of predicting a foreign player’s performance in the NBA than the simple correlations above.  Look for that in the next few days.