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.

clustering-hall-of-fame-careers

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

hall-of-fame-clusters-in-scatter-plot

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

hof-careers-similar-to-shaq-oneal-allen-iverson-yao-ming

 

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

 

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