Nichols and Dimes

Innovative Basketball Research

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:

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.


April 8, 2009 Posted by | Box Score Prediction System | Leave a comment

Explanation of the Box Score Prediction System (BSPS)

One of the hardest things to project is a college player’s performance in the NBA.  So many factors come into play, many of which can’t be measured.  However, I have attempted to solve this problem.  Using regression analysis, I have developed the Box Score Prediction System (BSPS).

The system works on the basis of a player’s statistical performance at the college level.  It also takes into account a player’s height, weight, and NCAA experience.

With BSPS, I can input the various data about a player and it will shoot out the projected NBA numbers.  Each NBA box score statistic is calculated based on some combination of the above variables and different coefficients.  Certain NBA stats, such as rebounds, can be predicted using many different variables.

The adjusted R^2 values (go to for an explanation) for the NBA stats I project are as follows:

Points: 0.4557

Field Goal Attempts: 0.4666

Field Goal Percentage: 0.5879

Three Point Attempts: 0.6765

Three Point Percentage: 0.7972

Free Throw Attempts: 0.3874

Free Throw Percentage: 0.8052

Rebounds: 0.8927

Assists: 0.887

Steals: 0.5904

Blocks: 0.9314

Turnovers: 0.5639

Personal Fouls: 0.5629

As you can see, we can project with the most certainty blocks, rebounds, assists, free throw percentage, and three-point percentage.  Predictions regarding free throw attempts, points, field goal attempts, turnovers, and fouls are the most questionable.

At this time I’m not willing to give out the exact formulas I use, but in the future I will be predicting various college players’ future performances in the NBA using their college stats.

BSPS has the following limitations:

  • Player statistics are not adjusted for strength of schedule.  Just like with any other statistic, you have to keep context in mind when you look at the results.  A guy who has lit up poor competition may project to be better than he actually will do.
  • Most of the R^2 values are not extremely high, which just confirms common sense that a lot more goes into NBA success besides college success.  Athleticism, game IQ, work ethic, etc. all have an effect.
  • The study only includes NBA players that have “made it.”  Players that fizzled out or were never good enough to get drafted in the first place aren’t included.  Therefore, these numbers are more useful when projecting the guys who are likely to have an NBA future.

March 20, 2009 Posted by | Box Score Prediction System | Leave a comment

How Do NCAA Statistics Translate to the NBA?

As March Madness begins and the NBA Draft approaches, I often wonder how close the college game is to the professional one.  It’s clear who the stars in the college game are.  But are they just “built” for that style of play, or are they true stars who excel at any level (including the NBA)?

I have attempted to solve this problem by seeing how college stats correlate to NBA stats.  To do this, I first took a large sample size of current NBA players’ career statistics and compared it with those sample players’ college stats.  Everything was calculated on a per-minute basis.  Once I had the stats, I ran a series of simple regressions to see how well the NBA numbers correlated with the college ones.

Below I have posted the R^2 values of the different correlations.  R^2 basically says how well future outcomes are likely to be predicted by the model and can be thought of as a percentage.  For example, if the R^2 of the correlation between college points per game and NBA points per game is 0.3405, then we can say that about 34.05% of NBA players’ PPG can be explained by their college PPG.  The higher the R^2, the better.

Below are the R^2’s for the different correlations:

Points per minute: 0.3405

Field goal attempts per minute: 0.3522

Field goal percentage: 0.3436

Three-point attempts per minute: 0.6391

Three-point percentage: 0.7941

Free throw attempts per minute: 0.286

Free throw percentage: 0.7615

Rebounds per minute: 0.8312

Assists per minute: 0.8823

Steals per minute: 0.5981

Blocks per minute: 0.9327

Turnovers per minute: 0.4535

Personal fouls per minute: 0.4447

Those numbers are all higher than I expected before I began the study.  Specifically, we can predict with pretty good certainty an NBA player’s blocks, assists, rebounds, three-point percentage, and free throw percentage based on their equivalent college statistics.  Free throw attempts, points per game, field goal percentage, and field goal attempts are the weakest.

This all comes with one big caveat.  The sample only includes guys that have made it in the NBA.  The college stars that fizzled out at the pro level or the guys who NBA teams knew had no chance at the highest level before the draft were not included in this study.  In other words, just because a guy is great in college doesn’t mean he will be great in the NBA.  However, if he does make the NBA, we can somewhat predict how he’ll end up doing based on his college stats.

This is just the beginning of my study, though.  I have developed a model for predicting a player’s NBA stats using multiple variables at a time.  As it turns out, even things like NBA assists can be predicted using more than just college assist numbers.  In the next few days I will be revealing my system and using it to project some of the stars you’ll be watching in March.

March 18, 2009 Posted by | Box Score Prediction System | Leave a comment

Allen Iverson In His Later Years

With all the talk about Allen Iverson’s recent struggles and the Pistons’ success without him in the lineup, I decided to take a look at Allen Iverson’s Composite Score numbers since the 2003 season (as far back as my data goes).  We know he hasn’t been a great fit in Detroit, but how has his game been progressing over the last five years?

Iverson struggled in the 2003-04 season with the 76ers.  He was above average in terms of both Offensive Composite Score and Defensive Composite Score, but he was certainly not living up to the reputation he had built in the past.  He quickly turned it around for the 2004-05 season, starring on a decent Philadelphia team.  His offense improved dramatically while his defense got better as well.  This, of course, was his first season with Andre Iguodala, who would eventually prove to be his successor.  The following season his offense was among the league’s very best.  However, his defense slipped to around average.  According to Composite Score, he was the league’s 32nd best player that season.

The following season, 2006-07, Iverson experienced a relapse.  Amid many controversies in Philly, he was dealt to Denver.  It was immediately evident that playing with new teammate Carmelo Anthony would be a work in progress.  His numbers slipped back to their 2003-04 level, although his defense was as good as ever.  In 2007-08 (last season), Iverson put things together quite nicely, compiling the highest Composite Score he’s ever had.  His defense continued to be solid in Denver and his offense was extraordinary again.  His balance on both areas of the floor led to him being one of the league’s 20 best players again.  While Anthony sputtered, AI flourished.

It didn’t last long, however.  Denver decided to move him for Chauncey Billups (an excellent player in his own right).  As it has been discussed recently, Iverson has not fit in Detroit at all.  The slow pace and strong scoring balance is simply not what he’s made for.  Iverson needs to either play on a fast-paced team or one without many natural scorers.  Detroit is neither of those.  Iverson is now below average on both offense and defense, and his Value Rating is towards the very bottom of the league.  The sad thing is that this once great player is now just a very large expiring contract for a team looking to rebuild.

Is AI done?  The numbers think that’s a little premature.  Remember, as recently as last season he’s shown that he is among the league’s best.  In addition, he’s also shown that he needs time to adjust to new teams (although he’ll probably never get that chance in Detroit).  Until he puts together two bad seasons in a row, I wouldn’t call him finished.  He’s still got talent and would be a useful pickup for a team lacking scoring.  As long as he finds a good fit and is given time to adjust to his surroundings, he could still be one heck of a player.

March 4, 2009 Posted by | Commentary | Leave a comment

A Sign of the Times in the NBA

As I’ve been reading the great trade deadline analysis by John Hollinger of and Kevin Pelton of Basketball Prospectus (check their work out, it’s extremely well done), I’ve been struck by how many of the deadline deals were motivated purely by financial reasons.  Those two writers clearly understand the intricacies of the cap and the financial motivations behind many teams.

Whether they were positioning for free agency in 2009 or 2010, limiting luxury tax payments now or in the future, or simply saving a few bucks, many teams made some very astute moves.  This might be a source of complaint for many people (my biggest gripe would have been the Tyson Chandler trade that almost was, but even then I can understand), but I’m impressed and fascinated at the same time by these moves.  Teams clearly have a great understanding of every little loophole within the Collective Bargaining Agreement and are using them to their advantage.

A couple of minor deals prove this point very well.  At first glance, Portland’s trade of Ike Diogu for Michael Ruffin makes little sense when you look at the skills and contracts of the two players.  Both have expiring contracts, yet Diogu is younger and more talented.  However, he was very expendable for the Blazers and the trade accomplishes two things.  It lowers their luxury tax payments ever so slightly, and it gives them a $3 million trade exception that can be used within the next year (props to Pelton for explaining this well).  Kevin Pritchard is no dummy, so you can bet that exception will turn into something useful.

The other team that intrigued me was the Memphis Grizzlies.  They made two separate trades: one in which they gave up a player (Kyle Lowry) for a draft pick, and another in which they gave up a draft pick and received a player (Chris Mihm) along with cash considerations.  The net effect is Memphis turning a conditional 2013 second-round pick (not exactly valuable) into a first rounder in next year’s draft, and being paid a few bucks in the process.  Sure they gave up Lowry for Mihm, but Mike Conley is the point guard of the future anyways.

In case we forgot, the trade deadline reminded us that this thing we hold so dear is certainly a business.  And I’m ok with that.  I prefer teams to be scheming penny-pinchers instead of trial-and-error free spenders.  The next goal should be to avoid giving out those bad contracts in the first place (easier said than done).

February 19, 2009 Posted by | Commentary | Leave a comment

How Did Those Guys Do It

The Boston Celtics were discussed ad nauseum last season, so I feel bad bringing them up, but they still baffle me.  I think many people don’t realize how dominant their defense was last season.  Their 98.6 defensive efficiency was three points better than the next closest team, a sizable margin.

Even more confusing is the fact that many of the players that made up their squad did not have stellar defensive reputations.  Paul Pierce, who ranked 6th in Defensive Composite Score last season, had not ranked higher than 94th in any of the previous four seasons.  Ray Allen, who ranked 34th last season, never previously topped 183rd.  Eddie House ranked 38th in 07-08 after finishing as one of the worst defenders in the league in 2004, 2005, and 2007, and not too great in 2006.  In limited minutes, Tony Allen ranked 4th last season after finishing 89th in the previous year.  The list goes on and on.

The Celtics did have Kevin Garnett, the Defensive Player of the Year.  Unlike the previous players, Defensive Composite Score has always loved Garnett, so his year was no fluke.  Because Garnett is a big man who does a great job guarding the paint but also has the quickness to extend his D, he certainly makes everyone around him better.  Still, I’m sold on his defensive abilities having this large of an impact.

After some extensive soul searching, I’ve learned that the reason I can’t figure out last year’s Celtics is because the differences can’t be seen in any specific numbers.  As much as I hate to admit it, the stats don’t tell the story here.

Garnett’s abilities had an impact on the team, but his mindset had might have been the key.  There’s simply no way to explain someone such as Eddie House becoming a good defender other than figuring he changed his attitude.  Like the rest of the Celtics, House appeared to give maximum effort on every defensive possession, something he hasn’t been known for.  Defense has a lot to do with natural ability, but it also requires effort.  The Celtics displayed unreal amounts of effort for all 82 games and then kept it going in the playoffs.

The players don’t get all the credit here, though.  The Celtics coaching staff clearly got messages through to the players.  Tom Thibodeau has been known as a defensive expert, and he did more than enough to cement that reputation last season.  Mike D’Antoni and Vinny Del Negro have many positive qualities, but if I were running the Knicks or Bulls I would not have passed on hiring Thibodeau in the offseason.

If you look at the numbers for Doc Rivers and Thibodeau, some things start to stand out.  The average defensive rating rank of the previous three Celtics teams that Rivers coached was 16.67.  Prior to that, he coached four full seasons with the Magic, and those teams ranked 13.5.  Although those numbers aren’t terrible, it’s clear he did not lead the turnaround on his own.

Thibodeau, on the other hand, has an excellent track record as an assistant.  In his two seasons with the Spurs, the teams ranked on average 9.5 in defensive efficiency.  The next two years he helped coach a terrible 76ers team, and their defensive numbers were quite poor.  However, after that, his numbers were stellar.  The Knicks’ average rank during his tenure there was 8.57.  After seven seasons, Thibodeau followed Van Gundy to the Rockets, who had a rank of 4.5 during those four seasons.  Thibodeau’s best years came with Van Gundy, so it’s impossible to pinpoint how much credit belongs to each.

It’s unclear how much credit Thibodeau deserves for the Celtics’ resurgence, but struggling teams should at least be giving this guy a shot at being their head coach.  If you could drastically improve your defense (which, we must not forget, is half of the game) by hiring just one man, how could you pass that up?

NBA teams ought to be doing their homework right now and trying to figure out how the Celtics became such a defensive powerhouse.  The next team to replicate that strategy will also be a force to be reckoned with.

October 27, 2008 Posted by | Commentary | Leave a comment

How Much Diesel is Left in the Tank?

(I use a lot of different advanced statistics in this piece.  If you’re confused about any of them, check out the explanation of Composite Score found here:

One of the most criticized moves during the past NBA season was Steve Kerr’s decision to trade Shawn Marion and Marcus Banks for Shaquille O’Neal.  The Suns felt they had many reasons to trade Marion – his contract demands, potential locker room problems with Amare Stoudemire, his age, etc.  However, couldn’t they have gotten more for him than the once great but now aging Shaq?  Not only was Shaq’s game slipping, but he’s due a lot of money until 2010.

Regardless of the criticisms last season, the trade happened, and there aren’t any do-overs.  The Suns must figure out what they have going forward in Shaq, and they need to figure out how best to utilize him.

First of all, what do the player rating systems say about Shaq?  Composite Score had Shaq ranked as the 41st best player in the league last season, down from 33rd in 2007 and superb numbers from 2004-06.  Shaq’s PER was 17.17, declining for the 3rd straight year.  Hollinger expects Shaq’s numbers to continue to decline and fall below league average.  O’Neal’s plus-minus was +1.2 overall last season, a one point decline from the previous year.  His plus-minus is another statistic that has been declining every year.  His offensive rating was a career low last season (this is bad) and his defensive rating was a career high (also bad).  The biggest culprit for these declines appear to be his turnovers, which have become a huge (no pun intended) problem.

If his decline stays at its current pace, Composite Score thinks he’ll still be an above-average player.  PER thinks he’ll be around average, as does plus-minus.  His offensive and defensive ratings suggest he’ll be below average.  All of the numbers agree that Shaq will be a worse player than Shawn Marion next season.

However, the Suns may have already known that before they made the trade.  The reason they acquired O’Neal was for a strong post presence on both ends of the floor.  What do the numbers say about Shaq’s potential to meet those expectations?

His offensive rebound rate ranked 21st among centers and his defensive rebound rate ranked 10th last season.  He blocked 2.33 shots per 48 minutes, which is not particularly impressive.  He committed 6.3 fouls per 48 minutes, another negative.  He did shoot a high percentage near the basket.  He is excellent at drawing fouls, although bad at converting foul shots.  Overall, he’s a decent post presence because of his solid rebounding and sheer size, but you can do better.  For $20 million, you can do a lot better.

It may seem like I’m beating a dead horse, but the Shaq trade really does not look like a good one for the Suns, and the stats confirm this.  If he was making a third of his current salary, it would be a different story.

I will say this though: Shaq is one of the greats and should not be totally ignored.  He understands his legacy and will try not to end his career on a bad note.  If nothing else, he’s an experienced veteran who can teach guys like Stoudemire a thing or two.

October 13, 2008 Posted by | Commentary | Leave a comment

My Thoughts on Team Chemistry

Although it would be silly for me to try to speak for everyone, I would guess that the modern NBA “stathead” thinks the whole notion of team chemistry is ridiculous.  The fact that team chemistry can’t be statistically measured isn’t our beef with it (we acknowledge there are a lot of things that can’t be measured).  The problem is that team chemistry is usually the catch-all reason for why teams are good when it doesn’t seem like they should be.  Stats guys tend to take a more scientific look at things.  They often see things that explain why these teams mysteriously play so well.  Maybe a team has an efficient offense that is much better than it looks because it plays at a slow pace and doesn’t score a ton of points every game.  Maybe it has some players that are considered “scrubs” by most people but are surprisingly adept at defense.  Or maybe the team is just lucky.

Whatever the case may be, I’ve historically never been a fan of talk of team chemistry, intangibles, and all the warm and fuzzy things that help NBA fans sleep at night.  As I wrote in one of my previous articles,

“The problem is when things like locker room chemistry are given as reasons for team success.  It’s easy to pick examples of teams that have won with a group of players that got along and shared the same mentality, but it is just as easy to pick examples of teams that were quite the opposite.  Michael Jordan once punched out Steve Kerr in practice, and MJ often verbally tormented his players.  Shaquille O’Neal and Kobe Bryant led the Lakers to three championships despite not being the best of friends.  Dwyane Wade and Gary Payton exchanged verbal jabs with each other during a playoff series last year against the Bulls, and then held up the championship trophy a few weeks later.”

With all that being said, I’ve warmed up to the idea very slowly.  The Celtics last year at least made you think a little bit about all those intangible things.  I will never feel that teams need to be large groups of best friends to win, but I will admit that a strong team philosophy that includes hustle and teamwork (especially on the defensive end) is crucial to winning it all.  As someone who tries to get people to look at defense too when analyzing players, it would be foolish of me not to concede that.  The key is just not taking things too far.  Cute off-the-court stories make for good newspaper articles, but they generally don’t make people better at playing the game of basketball.

October 3, 2008 Posted by | Commentary | Leave a comment