NBA Draft Class of 2016. Image from nba.com

Post NBA Finals, NBA GMs and sports-fans alike look forward to the only basketball event of the summer — the NBA Draft. The draft is notorious for making the careers of GMs, dashing the hopes of fans, and creating future stars of the league.

Everyone scratches their head at how Portland drafted Gred Oden over Kevin Durant, or how Isaiah Thomas, darling of the 2017 NBA Playoffs, could have slipped to the last pick of the draft. Hindsight is of course 20–20; at the time, no one in their right mind would have drafted a 5 foot 9 point guard any place but the second round, and maybe the Trailblazers really needed a center.

Stories like those of Oden and Thomas’s, the prototypical “busts” and “steals” of drafts, are ones that GMs must be able to predict. Being able to differentiate potential NBA success from a college career is crucial to building a solid NBA franchise. For small market teams struggling to compete against the NBA elite, this ability is even more important.

NBA Draft History

Before diving into the analytics, I’d like to take a look at how draft picks have been made.

Figure 1

For domestic, players who have been drafted between the years of 2003 and 2012*, we don’t see anything surprising. The majority of the first and second year players are taken within the first round. These players typically show high potential for growth in ability and are described as having a high ceiling. Third year players are split, while the college veterans are taken mostly in the second round. These players have typically shown their ceiling in college; they usually fall to and are taken during the latter end of each round by well-established teams looking for role players rather than stars.

Figure 2

Here, I’ve split the draft into 6 sections (i.e. Picks 1–10 are section 1 and so forth) and looked at the height of players selected in each of these sections.

The most interesting cell is the first, where we see a bi-modal distribution of height. It seems that during the first 10 picks, teams either select a point-guard or center type. The first ten picks are usually teams in the lottery; these teams have struggled for quite a while and are in desperate need for important positional players. These teams are looking for centers and point-guards to bring value and scoring to their teams.

Examining Draft Results: A Look at Win Share

It is often difficult for teachers to individually grade members of a group project. They often must answer the question “How much did each student contribute to the grade?”. Lucky for us, we are not teachers, and the NBA has already come up with the win share, a way to determine individual contribution to team success. A win share is defined more explicitly here.

Let’s take a look at how college experience and draft position are correlated to NBA win shares.

Figure 3

We see that one-and-done players have more variance in their future NBA win shares, while 4 year players have less variance. This data suggests strongly that older college players indeed have a lower ceiling than younger players, but are not as risky to draft. GMs are on the right track when they pick 1st or 2nd year players to give their franchise a boost (although correlation does not imply causation)

Now let’s take a look at how draft positions are related to future NBA win shares.

Figure 4

It looks as if there is a negative correlation between draft position and win shares. After about the 30th pick, there seems to be no change in future NBA win shares. However, this graph leaves much to be explained. What, in fact, are the biggest predictors of future NBA win shares? In order to understand this, I’ve plotted every college statistic against future NBA win shares. Let’s take a look at the results.