How a point spread becomes a win probability
A point spread is the market's best single guess at the final margin. When a WNBA team is listed at -6, the market is saying "our central expectation is that they win by about 6," with home court, travel, rest, and tonight's injury report already priced in. But games do not land on the expectation; they scatter around it. Some nights the 6 point favorite wins by 18, some nights the underdog steals it at the buzzer. If you sketch that scatter it looks roughly like a bell curve centered on the spread, and the win probability is simply the share of the curve that sits on the winning side of zero.
That one idea turns a spread into a probability with a single extra ingredient: how wide the bell curve is, the standard deviation of outcomes around the spread, which statisticians call sigma. And sigma is where this page has to be more honest than most, because for the WNBA, nobody actually knows it very precisely. More on that below, because it is the most important section on this page.
The formula
Φ is the standard normal CDF, the share of a bell curve below a given point. Spread ÷ σ asks how many standard deviations of cushion the favorite has; the underdog's chance is simply the complement. On the moneyline route, implied is each price converted to a probability (for -240, that is 240 ÷ 340 = 70.59%), and dividing by the two-side total strips out the bookmaker's fee, because posted prices deliberately sum to more than 100. For every odds format, payouts, and the book's hold, our odds calculator owns that job; this page borrows just enough of it to produce a fair number.
Worked example
A 6 point favorite. z = 6 ÷ 10.5 = 0.57, and Φ(0.57) = 71.6%. Sensitivity, because the sigma is an assumption: at sigma 10 the same line gives 72.6%, and at sigma 12 (the NBA research figure) it gives 69.1%. So a 6 point WNBA favorite lands between 69.1% and 72.6%, and our best single answer is 71.6%. The assumption moves the answer by about 3.4 points, which is exactly why we show it instead of hiding it.
The moneyline route, -240 vs +195. The -240 implies 240 ÷ 340 = 70.59% and the +195 implies 100 ÷ 295 = 33.90%. Together that is 104.49%, so 4.49 points of it is the book's fee. Scale back to a 100 total and the fair chance is 67.56% for the favorite, 32.44% for the underdog. Note the spread model said 71.6% for a 6 point favorite: a 4.1 point disagreement with the market. When that happens, trust the market.
The sigma nobody has published
Here is the part almost no other calculator will tell you. For the NFL, this problem was settled in 1991: Hal Stern measured the standard deviation of final margins around the point spread and got 13.86 points, a figure sharp bettors still quote from memory. For the NBA, published work (Wayne Winston, among others) puts the equivalent figure near 12. For the WNBA, we searched, and there is no rigorously published equivalent. Betting models that quote WNBA win probabilities to two decimal places are quoting their private assumption, not a measured fact.
So we state ours and show its seams. Start from the NBA's 12. A WNBA game is 40 minutes instead of 48, and variance in the final margin grows roughly with playing time, so the shorter clock scales sigma by the square root of 40/48, which lands at about 10.95. WNBA teams also score fewer points per possession, which trims the per-possession scatter a touch further. We land on 10.5 as our house assumption, and because it is an assumption, every result on this page is printed three times: at sigma 10, at our 10.5, and at the NBA's 12. If someone eventually publishes the real figure, we will happily switch to it and retire this paragraph. Until then, an honest range beats a confident guess.
The shorter game, and what it does and does not do
Two things are true about the 40 minute clock, and they pull in opposite directions, so it pays to be careful. First: 6 points of expected margin over 40 minutes is a bigger per-minute edge than 6 points over 48, which pushes a same-number WNBA favorite toward safer. Second: fewer minutes means fewer possessions, a smaller sample for the better team's edge to grind down luck, which pushes the other way per unit of true skill gap. The spread already encodes the skill gap, though, so for a given posted number the first effect wins: the shorter game shrinks the scatter around the spread, and a 6 point WNBA favorite at our sigma (71.6%) rates a little safer than a 6 point NBA favorite at sigma 12 (69.1%). A real but modest difference of a couple of percentage points, not a different sport. Anyone claiming a dramatic effect in either direction is overclaiming; the honest statement is "slightly safer, with uncertainty we have shown you."
Thin markets: where the fee hides
WNBA betting has grown enormously through the 2020s, but the market is still far thinner than the NBA's: less money wagered, fewer sharp models grinding the lines, lower limits, slower reactions to news. Thin markets have two practical consequences for you. The lines can be a touch less efficient, which sounds like an opportunity and mostly is not (the books protect themselves with the second consequence): the fee is often wider. Books vary, and the only way to know what yours charges is arithmetic, which is precisely what the moneyline mode above does: add the two implied probabilities, and everything over 100 is the fee. A thick NBA market might run 4 to 5 points of overround; if your WNBA pair sums fatter than that, you now know, and knowing is the whole point of a devig calculator.
One honest paragraph before you go. Because the fee is baked into every posted price, the expected value of a bet is negative by construction unless you genuinely know something the market does not, and the market has watched more WNBA basketball than both of us combined. This page exists to teach you what a line means, not to beat one. If the games have stopped feeling like entertainment and started feeling like a problem, the best next step is a conversation with someone you trust, not a calculator.