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WNBA Free Agent Playing Styles: The Beginning of Figuring Out Who Fits Where

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Unrestricted free agent Penny Taylor was a key piece of the Phoenix Mercury's championship run and arguably one of the top free agents on the market in 2010. <em>Photo by Max Simbron.</em>
Unrestricted free agent Penny Taylor was a key piece of the Phoenix Mercury's championship run and arguably one of the top free agents on the market in 2010. Photo by Max Simbron.

Free agency is fun, especially if you feel your team is one or two players away from the promised land of a championship.

One of the things to take into consideration with free agency is how exactly a prospective free agent addition will "fit" with their new team.

That is not merely a matter of production, but moreso how well a player’s skillset meshes with their teammates.

It has been proven time and time again in professional sports that simply loading up on all-star talent is not a formula for success, nor can a team win merely by being one-dimensional – building a successful team requires bringing together the right combination of players, not necessarily the best players.

One way to look at player combinations is in terms of a player’s style or function on a team. For example, knowing whether a player tends to be more of a scorer or a player who can influence the game without scoring or more interior than perimeter.

David Sparks’ playing style spectrum can be quite useful for looking at player combinations.

NBA playing style spectrum " The Arbitrarian
Very rudimentary factor and cluster analysis I performed a long time ago indicated that there are distinctions in the data between players who tend to try to score a lot, those who play a "smaller" game, and those who play like "big men." In terms of the NBA’s tracked counting statistics, this translates to a differentiation between those who specialize in points and field goal attempts, rebounds and blocks, and steals and assists. I have chosen to call each of these three tendencies Scorer, Perimeter, and Interior, and collectively they form the SPI Style Trichotomy.

I’ve referred to this spectrum in the past and have used it more of a reference point for myself than anything else. Since Sparks was not around to update it – or the pretty graphic – for the 2009 season, the ideas sort of just sat in the archives. However, I did track the formulas underlying the graphic last season and I actually find the numbers themselves as much or more useful for evaluating a player’s "goodness of fit" for prospective teams.

What I’ve noticed is that if we look at an individual’s playing style plus their individual contribution to team wins, we can not only determine what players add to a team, but more precisely determine what a team might lose if they let a player go.

So before launching into the analysis of each team’s chances in free agency, I thought I would first describe the player styles of the league’s unrestricted free agents, those probably most likely to change teams.

Statistically determining qualitative playing styles

The calculation of individual playing style is actually quite simple:

NBA playing style spectrum " The Arbitrarian
To identify each player’s style is conceptually simple, but computationally somewhat more complex. Essentially, one sums each player’s fga + tr + bk + as + st, and determines what percentage of the total each SPI factor constitutes:

* Scorer percentage = fga / (fga + tr + bk + as + st)
* Perimeter percentage = (as + st) / (fga + tr + bk + as + st)
* Interior percentage = (tr + bk) / (fga + tr + bk + as + st)

These numbers are interesting on their own, but for the calculation of an index of style, they require further manipulation. In the league as a whole, the Scorer percentage is around 50%, the Perimeter percentage around 20%, and Interior 30%. Thus, if using these percentages, the vast majority of players would appear to be very scoring-centered. My concern here, in constructing a useful index, is to identify player propensities relative to other players, and for that, I calculate the percentile of each player’s percentages.

Once you determine each player’s SPI percentile triplet, it’s easy to find her relative style. This can be done with Sparks’ graphic quite easily and I’ve done that previously, outlining 7 player styles:

Statistics Primer - Swish Appeal - Commentary on the WNBA and women's NCAA basketball
Just to make the language a bit easier to apply, here is a key for interpreting how the colors match the labels, which is hopefully helpful in understanding the color spectrum: Colors ranging from...

...turquoise to green: perimeter utility player (PU)

...green to yellow: distributor (D)

...yellow to red: perimeter scorers (PS)

...red to purple: interior scorer (IS)

...purple to dark blue: post presence (PP)

...dark blue to turquoise: interior utility player (IU)

...blends surrounding the middle: mixed (M)

However, looking at the numbers actually allows for more specificity than the graphic just because it’s easy to clearly compare not only if one player is more of a scorer than another, but exactly how much more relative to their peers.

So here’s what I did with the SPI percentiles for each player:

  • Consistent with the graph, if a player is in the top third of the league as a scoring, perimeter, or interior player that is a primary attribute. To add further specificity, if a player is say (70, 90, 30) I would label them a perimeter scorer whereas a (90, 70, 30) would be a scoring perimeter – both perimeter players, but their games clearly have a different character.
  • The middle third means you're average in all three categories and thus not defined by any, but as it turns out, most players who are average in two end up being average in all three, making them a versatile "mixed" player in Sparks’ terms.
  • Sparks has essentially described players in the bottom third of scoring as a "scorer’s opposite" – players that do things for their team but are not scorers. So I’ve labeled those players "utility" players. So for example, a (20, 40, 20) would be a utility player, but a (20, 40, 70) would be an interior utility player.
  • For the record, there is no player who is in the highest third of the league in all three categories, so essentially, what Sparks’ style typology looks at is perimeter vs interior and scorer vs. non-scorer. Nevertheless, it results in 7 pretty strong categories of player.

Playing styles of 2010 unrestricted free agents

However, one interesting thing about playing styles is that they say little about quality or what a player actually added to a team. So in addition to playing styles, I’ve provided each player’s valpct from last year as a way to describe a player’s valuable contributions to their team:

Valpct " The Arbitrarian
Valpct is derived from val, or "valuable contributions," began as a straightforward, unweighted sum of player and team statistics: (pts+tr+as+st+bk-to).

However, it has since been modified to weigh assists and blocks somewhat differently: pts+as/(tfgm-fgm)/(tfga-fga)+tr+st+bk/(tdr/(tdr+oor))-to)

Similarly for teams. valpct for each player is that player’s val divided by his team’s total tval, the idea is to somewhat crudely capture the player’s contribution to total team statistical output.

So now we can approximate both a player’s style and how valuable that style was to their team. For the sake of determining what a team might lose if the player leaves, I’ve also provided Sparks' boxscores metric from 2009, which is mathematically redundant, but conceptually different from valpct. Minutes per game are also useful just to know how much a player was on the floor, but more useful once looking at team rotations.

Player S% P% I% Type MPG ValPct Boxscores
brown,kiesha 58.64% 64.81% 37.65% PU 10.97 0.04 0.68
canty, dominique 41.97% 91.35% 8.02% D 22.21 0.09 1.40
dixon, tamecka 74.07% 61.11% 30.86% S 12.84 0.04 0.89
harrower, kristi 22.83% 90.12% 29.62% D 16.42 0.05 0.93
jackson, lauren 71.60% 12.96% 66.04% SI 31.92 0.20 3.93
lawson, kara 85.80% 70.37% 6.17% SP 23.48 .07 .873
maiga-ba, hamchetou 88.27% 45.06% 32.09% S 19.29 0.10 1.17
mcwilliams, taj 20.37% 59.25% 70.98% IU 29.53 0.17 3.05
miller, coco 69.13% 61.72% 32.71% SP 11.56 0.04 0.71
miller, kelly 35.80% 82.09% 33.95% PU 19.32 0.05 0.77
montanana, anna 11.11% 86.41% 50.61% PU 10.19 0.02 0.28
riley, ruth 9.87% 24.69% 96.29% IU 20.29 0.08 1.26
robinson, ashley 6.17% 4.32% 98.76% IU 6.91 0.00 0.03
smith, katie 96.91% 62.96% 4.32% S 32.30 0.11 1.95
taylor, penny 69.75% 77.16% 14.19% PS 19.71 0.04 1.01
teasley, nikki 7.40% 96.91% 19.75% D 20.71 0.04 0.79
willingham, le'coe 66.66% 29.01% 58.02% S 20.53 0.11 2.47

A few notes:

  1. While there are players available for all of your free agent needs, the most value in this free agent cohort seems to exist at the interior sports, with McWilliams, Riley, and Willingham. Jackson is said to have made a verbal commitment to the Seattle Storm.
  2. Obviously, defense is not necessarily taken into account with these numbers, as with any box score based statistics. However, do consider that stls and blks factor into what makes a player a perimeter or interior player. So in effect, those are as much defensive as offensive numbers.
  3. valpct is based upon the team's performance over the whole season so injured players (e.g. Lawson, Jackson, and Smith) are actually underrated in terms of their value to the team by that particular number.
  4. There are no mixed free agents, which is consistent an interesting observation in both the NBA and WNBA -- there just aren't many mixed players. For a little perspective, two that are mixed in the WNBA: Diana Taurasi and Tamika Catchings. It's an indication of versatility more than not having strengths.

It’s also worth noting that no style is inherently good or bad – it all depends on the combinations (this was discussed at some length on the APBRmetrics board in 2008). Nevertheless, it does allow us to make some grounded claims about where players might be redundant or ineffective with another.

The next step is obviously to look at team situations and figure out who fits best where, which I’ll do sporadically with a few teams over the course of the next week or so.


Transition Points:

  • An interesting thought experiment would be to imagine who would win a round-robin style tournament of teams made up completely of one-dimensional teams of each style or even a tournament between one-dimension teams composed of the purest of each of the three underlying elements (scorer, perimeter, and interior).
  • I will describe restricted free agents with their individual teams as they're normally less likely to move.
  • Sparks' framework is obviously based on NBA regressions. But, this comment is interesting: "In the league as a whole, the Scorer percentage is around 50%, the Perimeter percentage around 20%, and Interior 30%." The WNBA is almost exactly the same, so it's a reasonable approximation of both men's and women's basketball.
  • Hopefully this goes without saying, but I'll say it anyway: this is by no means the final word on how a player will fit with a team. Players often change their games slightly when switching teams due to differences in strategy, playing a different role, or even age. Then there's always interpersonal factors that we cannot know about. This framework is merely an approximation based upon past performance, but better than "arbitrarily" projecting what a player might look like.