Big & Tall

Source: Anthony Bennett Facebook

Source: Anthony Bennett Facebook

Since 2003, 73% of the number one picks in the NBA Draft have been big men.  Some of these picks have been spectacular—think LeBron James, while others have been real busts—think Greg Oden.  With the recent firing of the Cavaliers’ General Manager, Chris Grant, I am sure you are wondering what the deal with Anthony Bennett is.  I am talking about the guy who was taken with the first pick in last summer’s draft and showed up to training camp out of shape…


Before I dive into the forwards of the league, I want to quickly provide some perspective on NBA Centers—a dying breed (take a look at this year’s All-Star roster if you disagree).  Starting out with a list of the top 15 picks from each draft since 2003, I was left with only 31 centers after filtering for those who played at least 20 games in the pre-All-Star period of their first active NBA season.  Using the same methodology from my previous post on rookie guards, I ended up with 4 clusters of centers.  Here is a table of summary statistics to give you a snapshot of the style and strengths of each cluster:

Summary Statistics: Rookie Centers

Summary Statistics: Rookie Centers

Unlike the relatively obvious breakdown of guards, many of these clusters of centers are difficult to decouple from one another.  Cluster 4 jumps off the page because the centers in this group are noticeably better across almost all dimensions considered.  Cluster 3 just looks flat out bad.  Though there have been some pretty good centers who have had terrible rookie seasons, it is not a place you want your team’s picks to be.  To give you some context, Kris Humphries is in Cluster 3—not even Kim Kardashian wanted to deal with that.  The remaining clusters (Clusters 1 and 2) are unremarkable, but there is the potential of a solid player who is unlikely to be an All-Star.

Since the statistics considered are just basic pre-All-Star box score stats from a player’s rookie season, I bet you are wondering how much these results match up with current levels of play.  Take a look at the players in the cluster that appears to be the “statistical winner” (Cluster 4):

Centers: Cluster 4

Rookie Centers: Cluster 4

Not bad, huh?  The only very obvious flops here are Greg Oden (the Blazers could have taken Kevin Durant instead) and Emeka Okafor.  Please note that none of the current rookies made this cluster, but the promising sophomore Andre Drummond did.  Out of this season’s rookie class, Cody Zeller and Kelly Olynyk are both part of Cluster 2 and Steven Adams is in Cluster 1.  So, how about the rest of the rookies from this year’s class–the much anticipated forwards?


Using the same eligibility requirements as before, I methodologically divided the 61 forwards into 4 clusters based on the k-means clustering algorithm.  Take a look at the between groups differences in the summary table below.

Summary Statistics: Forwards

Summary Statistics: Rookie Forwards

Unlike the previous centers analysis, there were fairly clear distinctions between groups.  The most promising overall performances came from those in the “High Volume” and “Efficient & Strong Defensively” clusters.  The players in the “Potentially Solid” cluster are unlikely to be All-Stars, but they are look slightly better than the “Unremarkable” cluster, on paper at least.

Both Anthony Bennett and Otto Porter from the current rookie class fall into the “Unremarkable” cluster.  With only half of the season in the books, things can potentially change.  Bennett, in particular, has had a really great February.  Some call his career performance against the Sacramento Kings a breakout game.  Coincidentally, the Cavaliers are also 3-0 since Grant’s departure.  Only time will tell whether Bennett can turn things around.

For what it is worth, pre All-Star rookie performance seems to be a fairly good predictor for career success.  Take a look at a sampling of the forwards who fall into each of the aforementioned clusters:

Clusters of Rookie Forwards

Clusters of Rookie Forwards

I will leave it as an exercise to you to pick out the outliers and dark horses in each cluster…


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s