The NBA is a personality cult, created and cultivated by men who invest in men

When the question arises “Will the WNBA ever succeed like the NBA?”, my answer is: “No, professional women’s basketball will never be more than a curiosity in the U.S. Mostly because the NBA is a cult of personality, the kind of which the WNBA couldn’t develop.”

Dr. David Berri, who teaches gender economics at Southern Utah University, presented a talk about the WNBA at the Basketball Analytics Summit last Thursday. Berri gave this example:

The corporate sponsorship money for the NBA and WNBA are pies labeled A and B. The NBA players receive J percent of A, while the WNBA players receive K percent of B.

If K were increased to halfway toward J — still not as much as the NBA players get, but improved — Courtney Vandersloot would’ve earned a more equitable and acceptable $1.2 million in the example year.

Why can’t the WNBA players get a larger share (K) of the
pie, or a larger pie to share (B)?

“Because men invest in other men,” said Berri.

Boom! that’s the phrase I’ve always needed. The NBA is a personality cult, in which men invest in other men. Men who own shoe and beverage companies invest sponsorship money in growing the legends surrounding male athletes, who play boyhood games for the enjoyment of mostly other men.

The size of the pie or the weight of the slice for women’s sports could be increased to fair amounts with an insignificant hit to men’s bottom line. But men have been in a war against women longer then they’ve been making Nikes and Gatorade, and men don’t want women’s basketball (or women’s anything) to prosper.


The data-driven scientific approach to basketball is in practice about 40 fewer years than sabrmetrics to baseball. Basketball analytics will catch up to baseball in fewer than 40 years, though.

Technical baseball analysts spent years devising methods for reducing a player’s whole value to one number, and then years settling in agreement that it’s a dumb thing to do. Basketball analysts were never as likely to fall into that trap, given they understood at the start that a numerical expression pinned to one player is derived so heavily from what nine other players are doing.

I was relieved to see at the Basketball Analytics Summit that analysts are still focused on tools for using relevant big data. Big data results in big solutions for big problems that don’t always exist.

Pythagorean win expectations

Everything is Bill James’ fault. Bill James is the nerd who began the trend toward baseball analytics (James coined the term “sabrmetrics”) as a household term.

Baseball analytics works, because baseball performance is usually an individual thing: one guy throws it, another guy hits it 500 feet or swings and misses. You can chart that with some basis in reality.

After the application of baseball analytics began saving and making major league baseball teams billions of dollars, basketball people naturally wanted to participate. But I don’t think basketball performance can soundly be quantified as an individual thing; even when Chamberlain scored 100, it was a cooperative effort of 10 players. Chamberlain’s teammates had to give him the ball, and their opponents had to give Chamberlain’s team the ball; the other team was committing turnovers on purpose at the end of that game.

Team-based analytics have a better shot at genuine usefulness. Take the Pythagorean Winning Percentage, which James concocted as a method for determining an expected winning percentage based on runs scored:

pwp = (runs_scored^2) / (runs_scored^2 + runs_allowed^2)

The 2021 San Francisco Giants won 107 regular season games (107-55, .660). The Giants scored 804 runs, and allowed 594, for a Pythagorean projection of .647, or 105 wins.

For reasons that escape me, Daryl Morey (NBA Philadelphia) and John Hollinger (NBA Memphis) use exponents of 13.91 and 16.5, respectively. Look at what this does to the 2022 Utah Utes women.

pwp = (points_scored^some_exponent) / (points_scored^some_exponent + points_allowed^some_exponent)

The Utes scored 2505 points, and allowed 2251, and went 21-12 (.636).

Using Morey’s exponent 13.91, Utah’s Pythagorean expectation was .816, or 27-6. Using Hollinger’s exponent 16.5, the Utes’ expectation was .854, or 28-5. I tried a range of exponents until landing on 5, which returned .631.

TeamScoredAllowedactual winsPythagorean expected
Stanford245618653026
Oregon228319612022
Utah250522512121
Arizona194916642020
Colorado203517892220
UCLA193117771717
Oregon St.193218661716
Arizona St.164515571215
Washington St.180218001915
Southern Cal180118111214
California164315941113
Washington1292140679