You really don't even need data to make these types of inferences. It follows from truisms like:
1. players selected early are better than players selected later
2. younger players have a smaller body of work and less is known about them
3. older players have a larger body of work and more is known about them
4. as more is known, variance between expected and actual production will decrease
5. younger players have more room to improve
6. truly exceptional players tend to leave school early
7. older players who remained in college 4 yrs are less likely to be truly exceptional
8. young players who are not selected top 3 are more of a gamble
9. young players who are selected top 3 tend to be exceptional
And so on. The conclusions in the article can either be derived logically or they're probably wrong.
Good thoughts. It is nice to see it backed up with data. This allows us to step back and really look at guys without becoming biased positively or negatively about them. Who would you pick in this draft if you couldn't trade?
You're missing the point. I love data and I value it. But you have to apply a layer of common sense.
- A model that disagrees with known facts is wrong.
- Logical inference is stronger than statistical inference. e.g. 20 year olds tend to be taller than 10 year olds. A model that tells me this is correct, but useless. I can infer it logically from the fact that people grow as they age.
- A model that produces surprising results deserves extra skepticism.
- models lose fidelity as arbitrary parameters are added
This one in particular is a model without a purpose. If it's hard to clearly explain what your model is trying to predict, it's a non-starter. e.g. It seems that it's trying
"to predict the relative likelihood that a player of a given age will tend to be an extreme outlier in terms of NBA production, as measured by win shares, given that a player was drafted either in the top 3 or drafted 4-60th, where outliers are players who either jumped 20 positions, fell 30 positions, or fell 250 points, where points are defined by minutes played, win shares, All-Star appearances, ROY Awards, and MVP awards".
This is not how real modeling works.