Giants.
The year-round intelligence desk for San Francisco.
01 / Record
The numbers, last window.
Official BETs
16
Executable
16
archive-backed
Resolved
16
Won
10
Lost
6
Win rate
62.5%
Brier (per-resolved): 0.2384Voided: 0
02 / Calibration
Model probability vs realised rate.
- 0–20%n=0—model—realised
- 20–30%n=0—model—realised
- 30–40%n=0—model—realised
- 40–50%n=0—model—realised
- 50–60%n=359.5%model66.7%realised
- 60–70%n=1362.3%model61.5%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 | 5 | 5 | 2 | 40% | 0.2915 |
| Rafael Devers | 3 | 3 | 2 | 67% | 0.2206 |
| Bryce Eldridge | 2 | 2 | 2 | 100% | 0.1634 |
| Drew Gilbert | 2 | 2 | 1 | 50% | 0.2686 |
| Willy Adames | 2 | 2 | 1 | 50% | 0.2733 |
| 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 | 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 | — |
| Drew Gilbert | 1+ Hits | 62.7% | 61.8% | +1.0pp | WON |