> correct_score · Friday, 19 June 2026
2 fixtures on today's slate, ranked by Bawler's model probability for the Correct Score market. Every prediction is logged before kickoff and settled publicly — and cryptographically anchored to Bitcoin so we mathematically can't edit the picks after the result is in.
> today.correct_score()
Ranked by model probability. Higher = more confident.
| League | Fixture | Pick | Prob |
|---|---|---|---|
| World Cup | ScotlandvMorocco 23:00 | 1-1 λ home = 1.24 · λ away = 1.51 | 12% |
| World Cup | United StatesvAustralia 20:00 | 1-1 λ home = 1.51 · λ away = 1.22 | 12% |
> upcoming.correct_score()
| Date | League | Fixture | Pick | Prob |
|---|---|---|---|---|
| 20 Jun | World Cup | TürkiyevParaguay 04:00 | 1-1 λ home = 1.06 · λ away = 1.35 | 13% |
| 21 Jun | World Cup | EcuadorvCuraçao 01:00 | 1-1 λ home = 1.47 · λ away = 1.22 | 12% |
| 20 Jun | World Cup | BrazilvHaiti 01:30 | 1-1 λ home = 1.49 · λ away = 1.23 | 12% |
> how_the_model_calls_it()
Correct Score pays out when you predict the exact full-time scoreline. It's the hardest mainstream market (typical favourite scoreline ~12-15% probability), which means even small model edges translate into big returns. Bawler computes the joint probability of every realistic scoreline from 0-0 to 5-5 and surfaces the most likely.
Every probability above comes from the same Poisson model that powers our public track record — bivariate Poisson on rolling expected goals, FIFA-ranked international adjustment, recent form, and a small home-advantage prior. Probabilities are the model's honest estimate, not sales copy. No favourite-longshot bias compression.
Want the “why” on a specific pick? Click into the match page for the full breakdown. See the methodology page for the full mathematical write-up, or /verify for proof every prediction above was published before kickoff.
> other_markets()