icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
San Jose Sharks
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | Erie Otters | OHL | 35 | 21 | 11 | 88.9% | 3.31 | 0 | 1.0008 | 81.4% |
| 2022-23 | — | OHL | 44 | 24 | 12 | 88.9% | 3.07 | 1 | 1.0008 | 84.5% |
| 2021-22 | — | OHL | 44 | 19 | 18 | 89.1% | 3.71 | 2 | 1.0008 | 87.6% |
| 2020-21 | — | OHL | — | — | — | — | — | — | 1.0008 | — |
| 2019-20 | — | OHL | 28 | 10 | 13 | 89.0% | 4.34 | 0 | 1.0008 | 89.1% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2026-27 | Clarkson | D1 | — | — | — | — | — | — |
Historical goalies with similar age-adjusted SVe profiles who went on to play NCAA D1.
| Name | Junior League | Junior SV% | Adj SVe | College | NCAA SV% | NCAA GAA |
|---|---|---|---|---|---|---|
| Reid Dyck | WHL | 89.0% | 81.5% | Colgate | 89.7% | 3.17 |
| Anton Castro | USHL | 88.6% | 81.1% | Wisconsin | 78.6% | 4.27 |
| Lukas Renaud | AJHL | 92.4% | 83.4% | Long Island Univ. | 89.7% | 2.57 |
| Aaron Matthews | NCDC | 90.7% | 81.4% | Providence | — | — |
| Daniel Hauser | WHL | 91.3% | 83.7% | Wisconsin | 90.0% | 2.49 |
Historical goalies with similar age-adjusted SVe profiles who went on to play NCAA D2/D3.
| Name | Junior League | Junior SV% | Adj SVe | College | Div | SV% | GAA |
|---|---|---|---|---|---|---|---|
| Hunter Thomas | USPHL-Premier | 90.2% | 82.0% | Salem State | D3 | 87.3% | 5.20 |
| Jeremy Skaife | USPHL-Premier | 89.7% | 81.5% | Johnson & Wales | D3 | 83.0% | 5.54 |
| William Goumas | OJHL | 84.8% | 80.6% | SUNY Morrisville | D3 | 89.7% | 4.12 |
| William Goumas | OJHL | 84.8% | 80.6% | Morrisville | D3 | 89.7% | 4.12 |
| Pierce Diamond | BCHL | 88.6% | 80.6% | Albertus Magnus | D3 | 89.4% | 3.82 |
How to read this: NCAAe and D3e factors convert a player's junior PPG into expected NCAA scoring at the D1 or D3 level. Harder conferences → lower projected PPG for the same player. A strong junior player (e.g. USHL 0.90 PPG) will project much higher in NESCAC than Big Ten because the D3 scoring environment is lower-difficulty.
Strength factor: conferences above 1.0 are harder than average; below 1.0 are easier. The formula is: Base NCAAe PPG ÷ Conference Strength = Projected PPG.