[Underdog]

How to Identify an NBA Draft Bust in Just 10 Games
Philly’s Markelle Fultz hasn’t earned the label yet, but analyzing the surprising lessons from two key metrics in the lottery era shows that he’s not far away

https://www.theringer.com/nba/2017/1...markelle-fultz

First, this analysis examines top-10 picks based on the thought that anyone picked outside the top 10 doesn’t generate the same “bust” smears. Second, it generally examines their play during their first four seasons in the NBA (the standard length of a first-rounder’s contract) because a failed prospect who bounces back as a veteran still engenders the “bust” label. And third, it uses two advanced statistics designed to encompass a player’s overall performance. Basketball doesn’t break down so neatly into single numbers as, say, baseball does with WAR, but these metrics work on a macro level. To encapsulate career value, the analysis will rely on win shares, which estimate how many wins a player added or subtracted to his team’s total. Win shares cohere with the eye test, too: By this metric, the half-dozen best top-10 picks in the lottery era (from 1985’s draft onward) are LeBron James, Tim Duncan, Dirk Nowitzki, Kevin Garnett, Shaquille O’Neal, and David Robinson; the half-dozen worst (not counting players still on their rookie contracts) are Nikoloz Tskitishvili, Adam Morrison, Bobby Hurley, Jonny Flynn, Chris Washburn, and Rafael Araujo. That’ll do.

But win shares don’t vary much on a game-to-game basis, so game score will fit for shorter spans of games. This metric, which was created by John Hollinger, condenses a box score line into one number and, according to Basketball-Reference’s glossary, is supposed to “give a rough measure of a player’s productivity for a single game.” An obvious caveat is that because game score considers only numbers that appear in a box score, it doesn’t account for a player’s entire impact on the court, but it works as the best proxy for short-term player value.