Counting the Baskets

Some Play by Play Numbers by Age and Height

As part of some model prep I have been working on for a possible new draft model I have been exploring some play by play data. As a part of that, I wanted to do a quick hitter on those numbers. I was able to use the data via Will Schreefer to look a little more at how players score, how that changes as they age, how it changes by height and how guys who eventually become NBA players differ from those that top out at a lower level.

The two breakdowns I was interested in for this were the split between transition and half court scoring and unassisted scoring vs points coming from an assist. Transition ratio is simply the points a player scored in transition divided by their total point scored. Creation ratio is the unassisted points from the field divided by assisted points from the field, as the play by play data does not track free throw assists.

First thing is that Transition Ratio goes down slightly by age in college, while Creation Ratio tends to be even more stable:

The slight decline in Transition Ratio is due to a larger increase in half court scoring, not a decline in transition scoring. Meanwhile it's somewhat interesting to me that on average, at least, college players are able to increase their assisted scoring numbers at a comparable rate to their self created scoring.

Then I looked at the numbers by player height. As you can see below there is a clear downward trend in percent of points scored in transition by height, and a persistent higher percentage of points scored in transition by NBA bound college players, when height is accounted for.

There is a similar downward trend in Creation ratio by player height. Again the future NBA players tend to create a higher percentage of their own points when accounting for height. However here the gap pretty much disappears for taller players. For guards,showing the ability to sore with the ball in your hands at the college level is very important to be able to get into the NBA.

 

A couple of last nuggets, I was interested to see that there is a bigger gap on Transition ratio between NBA bound bigs and non-NBA bigs bound than there was in the Creation ratio, while for guards is was the opposite. The biggest growth in raw transition scoring per forty minutes by a good distance were the guards, and especially those who eventually make the NBA.

  

A New Benchmark System

 

Last year I explored a benchmark system for draft prospects by Ed Weiland. The benchmarks track whether a prospect reaches minimum statistical marks in a number of different statistical categories. In looking at Weiland’s benchmarks, I found that the benefits of a benchmarking  system is that it allows one to highlight potential red flags and rewards versatility in a way most draft models do not. The downside is the loss of information with a simple pass fail test on the benchmarks, outstanding efficiency is treated the same as adequate efficiency. Another important factor maybe the simplicity in conveying the information, meeting or not meeting specific benchmarks may be easier to convey to less analytically inclined readers than the output of a regression or ML model.

This year I am introducing my own benchmarking system designed to work without picking a position for every player, a complication that can make the system less reliable for players with positional versatility. I am using four benchmarks:

• Scoring- Does the player score 21 points per 80 possessions?
• Efficiency- Does the player’s two point percentage and free throw percentage add up to 1.25? (Allows positional diversity, players need to demonstrate ability to score inside monstrously efficiently or some evidence of ability to shoot).
• Offensive Activity- Do assists plus offensive rebounds equal at least 6? (Strong negative correlation between the two, indicates trade off of size and skill)
• Defensive Activity- Do steals plus blocks equal 2.5 or more?

Comparison to Draft Model

While the point of the benchmarks is not to displace or mimic the methodology of a trained model, I did look at both the individual benchmarks and the total percent of benchmarks met against the data used for my draft models. In both cases the benchmark data, combined with age, had significant positive correlations to measures of success in the NBA (though less than more detailed model). Offensive Activity showed the strongest relationship followed by Defensive Activity, Efficiency and Scoring in descending order. Also notable, when combined with the data for the draft models, percent of benchmarks reached had a modest positive effect, consistent with other measures of versatility I have tested in the past.

In a boosting regression using the two models as predictors the relative importance  in predicting power was 80% to the trained model and 20% to the benchmarks. (Measure used was the max  performance in years two through four as measured by a combination of box score stats and RAPM).

Examples from this Prospect Class

Looking at the bench marks applied to this year’s top prospects should give a little better idea of how they work and what information they can reveal.

There are six major prospects that make all four of the benchmarks, DeAndre Ayton, Luka Doncic, Wendell Carter, Shake Milton, Josh Okogie and Gary Clark.

Doncic, Ayton, and Wendell Carter are well known to anyone even casually following the draft. Milton and Clark are the older prospects on this short list, which takes a little of the shine off of making the benchmarks. All of the prospects that meet all of the benchmarks also rate reasonably well in the traditional model, with all in the top 30 by the traditional model rank.

On the other side are the three significant candidates that fail to meet a single benchmark, Lonnie Walker, Sviatoslav Mykhailiuk, and Hamidou Diallo, with the highest rated prospects to fail to meet all four benchmarks being Walker and Diallo, both rated in the first round by ESPN. Walker is the only one of these prospects to rate in the top 40 via my traditional model.

Age is the big factor not explicitly addressed by the benchmarks. A 23 year old hitting all four benchmarks is still not necessarily a strong prospect, and an 18 year old only hitting two is not necessarily a non-prospect. In the attached link, I added a column that factors the prospect score by age in order to get a more approximate evaluation,