WNBA Free Agency kicks fully into gear on February 1st when teams and players are able to officially able to sign deals. While there hasn’t been much action (yet), it sounds like things are brewing behind the scenes.
wnba free agency might feel quiet but there's a lot happening behind the scenes and if a fraction of what's possible happens, we'll all be satisfied
— Matt Ellentuck (@mellentuck) January 25, 2021
There are quite a few stars who received core designations (Liz Cambage, Nneka Ogwumike, and Natasha Howard), plus an interesting list of reserved and restricted free agents. But among the unrestricted free agents, numerous WNBA superstars are eligible to sign with any team who will have them.
Diani Taurasi, Sue Bird, Candace Parker, Alyssa Thomas, Emma Meesseman, Kayla McBride, Eric Wheeler. The list goes on and on, making it a distinct possibility that we see multiple seismic shifts in the WNBA landscape this offseason.
We know that the WNBA is dominated by tall, athletic post players, like Elena Delle Donne, Cambage, etc. The NBA has moved into an era of “position-less” basketball, an era where LeBron James is a point guard and PJ Tucker is a center (sometimes). This has necessitated a shift from the PG-SG-SF-PF-C lineup and requires that we group players by their skill and usage, rather than how tall they are.
The WNBA hasn’t seen a shift that drastic (yet), but they still need a diversified approach to the game. Running out an All-Star lineup of Liz Cambage, Candace Parker, Elena Delle Donne, Breanna Stewart, and A’ja Wilson probably isn’t the best possible WNBA lineup (actually, on second thought, it might be since each of those players is a freaking stud). You want to complement those kinds of players with your Taurasi and Bird types who can help space the floor.
But how do we know who fits into which role? Well, that’s why I created a app that allows you to compare players in 3D, as well as a clustering method I’m going to walk through in this article. I would be remis if I didn’t mention that this project was heavily inspired by Todd Whitehead’s NBA lineup work and Alex Stern’s NBA Clustering project which introduced me to some of these concepts.
You can find the WNBA Clustering App here.
On this WNBA App, there are a variety of advanced stats, pulled from basketball-reference from the 2016-2020 season to create this app and these clusters. You can find a full breakdown of the stats available on the WNBA App. On the app, you can scroll over the bubbles and see pertinent information as to which player you are viewing.
The App contains an offensive and defensive leaderboard of these advanced stats plus one more tab, titled Similarity Scores. This is where you can see which seasons and players have similar play styles. Over the rest of this article, I’m going to break down the process of creating these scores and my clustering approach. You can find the code and data that I used for this project here.
I used two different clustering methods on this problem, which returned similar results. In the end, I opted for the Gaussian Mixture Model, which determines the optimal number of groups on its own, in this case, 6 different player archetypes. (When I first ran this model, I included Win Shares in the model, which is essentially a measure of how much a player contributes to winning. The mixture model returned three groups, a “good” group, an “average” group, and a “bad” group. That’s so helpful. Anyway, I removed those variables and got better results).
Here are the most representative seasons by cluster (note, not the BEST seasons per cluster, but the one that is most representative); some pretty clear patterns emerge.
- Cluster 1: Elena Delle Donne 2019 (and 2017 and 2016), Breanna Stewart 2018, Brittney Griner 2019
- Cluster 2: Karima Christmas-Kelly 2016 (and 2017), Tierra Ruffin-Pratt 2020, DeWanna Bonner 2016
- Cluster 3: Kiah Stokes 2017 (and 2016), Rachel Hollivay 2016, and Alaina Coates 2018
- Cluster 4: Shekinna Stricklen (2016-2020, literally all in the top 9, she IS Cluster 4), Sydney Wiese 2017, Jordan Hooper 2017
- Cluster 5: Marie Gulich 2019, Amanda Zahui B. 2020, Bella Alarie 2020
- Cluster 6: Courtney Vandersloot 2020 (and 2017-2019, literally the top 4 seasons), Sue Bird 2018, Lindsay Allen 2017
What can this visualization (and these seasons) tell us about the cluster designations?
Cluster 1 is the superstar range. The players who play stellar defense, grab rebounds, and shoot incredibly efficiently within the paint,while also managing to distribute on offense. Some of the players here shoot the three-ball, but it’s not a requirement.
Cluster 2 is home to players who are about average. These are primarily wing players who can drive to the hoop (above-average Free-throw rate), but don’t do anything that pops off the page.
Cluster 3 is basically a low-budget superstar. They don’t create for others (Assist%) that the stars do, but still provide decent defense, good rebounding, and paint play.
Cluster 4 players shoot a lot of three-pointers and are a nuisance on defense as a steals threat. That’s it.
Cluster 5 players are interesting, a combination of post play with a little bit of three-point range. Amanda Zahui B. in particular popped out to me, as the default app setting is 3PAr, BLKpct, and USGpct, putting Zahui as one of the only players who does a bit of all that.
Cluster 6, or as I like to call it, the Point God Cluster, is home to the best of the best floor generals in the WNBA. A decent amount of three-point shooting, rarely turns the ball over, and a TON of assists.
When you look at this page on the WNBA App, the lower the Uncertainty, the more that player fits in their designated cluster. Using this clustering system, there are a few particularly notable free agents (using their 2020 designation):
- Both Cheyenne Parker and Candace Parker were Cluster 1 players in 2020 and could subsequently be in line for big pay-days.
- Natalie Achonwa was a Cluster 3 player with the Fever. Could she make the jump to Cluster 1 in a new role?
- Jasmine Thomas was solidly in Cluster 6 thanks to her 2020 season; maybe a rebuilding team makes her a focal point in their offense if she’s not retained by the Sun
This is intended to be an initial look into the world of classifying WNBA players by their tendencies. The WNBA App is an interactive way for fans and analysts to explore player archetypes in 3D to provide a well rounded look at each player.