I’ve used hierarchical clustering for three separate basketball projects: once, to group Hall of Famers by Win Shares, then, to group historic rosters by VORP distributions, and, most recently, to group offensive roles using play-type data.


1. Grouping Hall of Famers

Here’s how the clustering worked out for the collection of Hall of Fame players.


The clusters reflect combinations of career longevity and average Win Shares per season.


From there, I found HOF comps for the 2016 inductees.



2. Grouping Historic Rosters

Here I grouped NBA rosters by the way talent was distributed between players. Talent Distribution 02 - Figure 2 - Cluster Plot

Below is one example of the VORP distribution from one team in each cluster of rosters.

Talent Distribution 03 - Figure 3 - An example of each cluster

Many roster constructions have yielded championship success, but

For a given level of overall team quality, an unbalanced roster has been more likely to produce a championship than a balanced one.

Talent Distribution 04 - Table 1 - Championship probabilities by cluster


3. Grouping offensive roles

I used play-type frequencies and hierarchical clustering to define three positions,

01 Ranking scorers by role - Three major player types on offense

six groups,

02 Ranking scorers by role - Six big groups of offensive roles

and 18 roles.

03 Ranking scorers by role - A hierarchy of offensive roles



Leave a Reply

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

WordPress.com Logo

You are commenting using your WordPress.com 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