How good are these forecasts?

Every forecast scored on seasons the model never saw while its settings were chosen.

Held-back seasons

ForecastForecastsLog lossFavourite won
The model122980.45278%
Base rate122980.65864%
Recent form122980.53373%

Against the betting markets

Each market's price before the start, scored on the same finished games as the model. A negative figure means the market was more accurate.

MarketSincePricedModel log lossMarket log lossModel vs market
Bookmakers (Pinnacle, DraftKings, bet365)21 Jan 20262730.5080.506−0.3%
Polymarket3 Nov 202558490.4550.57220.5%
Kalshi20 Nov 202543400.4550.437−4.3%

Every Division I game since 2002 moves the ratings, and so do games against teams from lower divisions; a team new to the file starts 150 points below average. Only Division I teams are in the tables and the title odds.

The title odds use an approximate tournament: each conference's best team by record gets in, the rest of the 64 are the best left by simulated wins and rating, seeded on an S-curve on neutral floors. The committee, conference tournaments and the First Four are not modelled.

Roster turnover between seasons is a pull toward average in the rating; the adjustment for who is playing adds what each returning player and transfer did in their box scores, but freshmen start at a bench player's value and recruiting rankings are not used.

Between April and November there are no games; the pages show how the last season finished.

Questions and answers

How accurate are PodiumOracle's NCAA women's basketball predictions?

On 12,298 games from held-back seasons (2024–25 to 2026–27), which the model never saw while it was tuned, the side it favoured won 78% of the time. Its log loss was 0.452, against 0.658 for the base rate and 0.533 for recent form. Against the betting markets on the same finished games, its log loss was 0.508 against the bookmakers' 0.506 over 273 games, 0.455 against Polymarket's 0.572 over 5,849 games and 0.455 against Kalshi's 0.437 over 4,340 games (lower is better).