This post originally appeared on Hickory-High.com, on May 2nd.
There have been some significant disappointments in the 2013 draft class. Not only from number one pick Anthony Bennett, but number three pick Otto Porter, number five selection Alex Len, and seventh pick Ben McLemore have all disappointed to varying degrees. So, that raises the question as to how much should we downgrade a player's prospects based on a disappointing rookie year? And does production in college still have any value in predicting the player's production at the end of the rookie year?
To answer those questions, I took the data I used to train my draft models, using the classes from 2002 through 2011, and added the rookie year production to the pre-NBA data and tested against the same independent variable their maximum production as measured by Alt. Win Score (AWS) in the third or fourth years.
Unsurprisingly, adding the rookie production data increases the predictive power of the models, however, the draft model and the elements that make up the model still had independent and significant predictive contributions to the end of rookie contract AWS.
In addition to the elements of the draft model, both the rookie production per forty minutes and the player's total minutes played were consistently indicators of better production later. Interestingly, the pick the player was taken with didn't have a significant contribution if the player's total minutes were included, nor did the logarithmic transformation of the pick taken. Teams tend to let high draft picks play, either because they have more confidence in them or because they have more prestige riding on their success. However, if a lottery pick can't stay on the floor that can be a bad sign independent of their box score production, and the reverse for a second rounder that gets minutes. Other analysts, like Daniel Myers examining RAPM have found that minutes played reveal an independent source of player value.
In terms of contribution to the prediction of future production in the Update Model, about 60% of the predictive ability comes from rookie production, 30% from the P-AWS draft model and 10% from total minutes played as a rookie. In the training data the average of the End of Contract AWS was 3.97, the P-AWS model average was 3.95, average rookie AWS was 2.73, and the average rookie minutes played was 943.
The 2013 Draft Class
Below the 2013 draft class is shown with the original P-AWS model, their rookie production, and the Updated Prediction model. The biggest mover is Mason Plumlee, who the original model didn't like much but lead his draft class in AWS. A couple of older bigs in the draft Gorgui Dieng who was second in AWS and Jeff Withey also moved up. In terms of the disappointments Bennett and Len look to be in the most trouble, though the model doesn't have a good way to control for the nagging injuries that limited both their playing time and productivity. The value pick of the draft may have been Nate Wolters, picked in the second round. The original model liked him and he put up very solid numbers in his rookie year.
Update: To clarify, without reliable numbers Giannis Antetokoumpo was projected with his pre-draft numbers assumed as average.
And neither model nor I have any idea what possessed Golden State to use the 30th overall pick to draft Nemanja Nedovic.
The second model I developed for the draft is a Logit model called % Player that estimates the odds of a draftee becoming a 'quality starter', estimated as a having a stabilized AWS of 5. The nice thing about the % Player model is that the results are given as probabilities. Because the independent variable is a Yes/No variable it is not as good at finding star players, but it does well identifying solid players.
An Updated Starter Probability model was created using both rookie production and the original P-AWS model. The new model is similar in results, but is more straight forward to interpret and the Logit model mimics Bayes theorem so that's an added bonus.
For Bennett, for example, the odds of him becoming a quality starter were 62% based on his age and college stats, but after his poor rookie campaign, the odds have dropped to a 27% chance. Otto Porter was very well liked by the draft model coming out of college, but had a similarly poor rookie year and drops from an estimated 86% chance to a 55% chance. On the flip side, Plumlee has an 86% chance of putting up starter type numbers, with a huge jump from the 27% chance the original model gave him.
I would be remiss if I didn't mention that Kelly Olynyk saw his odds of putting up quality starter type numbers from 59% to 81%.
As for Sergei Karasev and Peyton Siva and Nedovic, so I am saying there is still a chance.
Comments