Giants.
The year-round intelligence desk for San Francisco.
01 / Record
The numbers, last window.
Official BETs
20
Executable
20
archive-backed
Resolved
20
Won
13
Lost
7
Win rate
65.0%
Brier (per-resolved): 0.2144Voided: 0
02 / Calibration
Model probability vs realised rate.
- 0–20%n=0—model—realised
- 20–30%n=125.4%model0.0%realised
- 30–40%n=0—model—realised
- 40–50%n=0—model—realised
- 50–60%n=459.6%model75.0%realised
- 60–70%n=1562.6%model66.7%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 |
|---|---|---|---|---|---|
| Jung Hoo Lee | 6 | 6 | 3 | 50% | 0.2625 |
| Rafael Devers | 3 | 3 | 2 | 67% | 0.2206 |
| Willy Adames | 3 | 3 | 2 | 67% | 0.2358 |
| Bryce Eldridge | 2 | 2 | 2 | 100% | 0.1634 |
| Drew Gilbert | 2 | 2 | 1 | 50% | 0.2686 |
| Casey Schmitt | 1 | 1 | 0 | 0% | 0.0647 |
| Heliot Ramos | 1 | 1 | 1 | 100% | 0.1308 |
| Jonah Cox | 1 | 1 | 1 | 100% | 0.1362 |
| Osleivis Basabe | 1 | 1 | 1 | 100% | 0.1483 |
04 / Mispriced
Largest edges that hit.
| Player | Market | Model | Sharp | Edge | Result |
|---|---|---|---|---|---|
| Willy Adames | 1+ Hits | 61.1% | 54.1% | +7.0pp | WON |
| Osleivis Basabe | 1+ Hits | 61.5% | 55.3% | +6.2pp | WON |
| Jung Hoo Lee | 1+ Hits | 63.2% | 69.4% | -6.2pp | — |
| Bryce Eldridge | 1+ Hits | 59.8% | 53.7% | +6.1pp | WON |
| Jung Hoo Lee | 1+ Hits | 65.8% | 59.7% | +6.1pp | WON |
| Heliot Ramos | 1+ Hits | 63.8% | 58.0% | +5.8pp | WON |
| Willy Adames | 1+ Hits | 59.9% | 54.4% | +5.4pp | WON |
| Jung Hoo Lee | 1+ Hits | 59.2% | 63.5% | -4.2pp | — |
| Rafael Devers | 1+ Hits | 61.3% | 64.8% | -3.4pp | — |
| Jung Hoo Lee | 1+ Hits | 63.9% | 66.3% | -2.4pp | — |