Nichols and Dimes

Innovative Basketball Research

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 http://en.wikipedia.org/wiki/Coefficient_of_determination 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