Blue Jays.
The year-round intelligence desk for Toronto.
01 / Record
The numbers, last window.
Official BETs
13
Executable
13
archive-backed
Resolved
13
Won
6
Lost
7
Win rate
46.2%
Brier (per-resolved): 0.2202Voided: 0
02 / Calibration
Model probability vs realised rate.
- 0–20%n=0—model—realised
- 20–30%n=126.1%model0.0%realised
- 30–40%n=0—model—realised
- 40–50%n=0—model—realised
- 50–60%n=1056.8%model40.0%realised
- 60–70%n=262.4%model100.0%realised
- 70–80%n=0—model—realised
- 80–90%n=0—model—realised
- 90–100%n=0—model—realised
When the model says X%, the event happened Y% of the time. Closer the two columns sit, the better calibrated the model is at that confidence band.
03 / Players
By sample size.
| Player | Official | Resolved | Won | Win rate | Brier |
|---|---|---|---|---|---|
| Kazuma Okamoto | 3 | 3 | 2 | 67% | 0.2276 |
| Josh Smith | 2 | 2 | 0 | 0% | 0.2771 |
| Myles Straw | 2 | 2 | 1 | 50% | 0.2639 |
| Andrés Giménez | 1 | 1 | 1 | 100% | 0.1443 |
| Brandon Valenzuela | 1 | 1 | 0 | 0% | 0.2748 |
| Charles McAdoo | 1 | 1 | 1 | 100% | 0.1390 |
| Ernie Clement | 1 | 1 | 0 | 0% | 0.0681 |
| Jesús Sánchez | 1 | 1 | 0 | 0% | 0.3091 |
| Nathan Lukes | 1 | 1 | 1 | 100% | 0.1617 |
04 / Mispriced
Largest edges that hit.
| Player | Market | Model | Sharp | Edge | Result |
|---|---|---|---|---|---|
| Myles Straw | 1+ Hits | 59.0% | 52.0% | +7.0pp | WON |
| Nathan Lukes | 1+ Hits | 59.8% | 53.0% | +6.8pp | WON |
| Charles McAdoo | 1+ Hits | 62.7% | 56.4% | +6.4pp | WON |
| Kazuma Okamoto | 1+ Hits | 58.1% | 51.9% | +6.2pp | WON |
| Kazuma Okamoto | 1+ Hits | 59.3% | 55.0% | +4.2pp | WON |
| Andrés Giménez | 1+ Hits | 62.0% | 58.3% | +3.7pp | WON |