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.”
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.