icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | — | OJHL | 17 | 11 | 3 | 91.4% | 2.92 | 1 | 0.9700 | 84.0% |
| 2022-23 | — | NOJHL | 41 | 11 | 22 | 89.8% | 3.73 | 3 | 0.9700 | 86.0% |
| 2021-22 | — | NOJHL | 20 | 9 | 9 | 91.4% | 3.25 | 0 | 0.9700 | 90.3% |
| 2021-22 | — | OJHL | 3 | 0 | 2 | 82.7% | 5.18 | 0 | 0.9700 | 82.1% |
| 2020-21 | — | OJHL | — | — | — | — | — | — | 0.9700 | — |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Curry | D3 | 14 | — | — | — | — | — |
| 2024-25 | Curry | D3 | 2 | 1 | 0 | 87.0% | 2.11 | — |
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 |
|---|---|---|---|---|---|---|
| Charlie Schenkel | OHL | 89.7% | 84.0% | Robert Morris | 90.9% | 2.80 |
| Patriks Berzins | USHL | 90.1% | 84.4% | Maine | 90.0% | 1.79 |
| Ethan Dahlmeir | USHL | 90.0% | 84.4% | Miami | 87.0% | 4.04 |
| Quentin Sigurdson | USHL | 89.6% | 83.5% | Northeastern | 92.0% | 2.13 |
| Connor Murphy | CCHL | 91.7% | 84.9% | Northeastern | 68.8% | 7.04 |
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 |
|---|---|---|---|---|---|---|---|
| Dane Couture | MJHL | 86.5% | 83.8% | Concordia | D3 | 87.3% | 2.94 |
| Emil Norrman | USPHL-Premier | 91.3% | 84.6% | Buffalo State | D3 | 93.1% | 2.30 |
| Alexander Timmons | USPHL-Premier | 90.5% | 84.4% | Framingham State | D3 | 84.9% | 5.20 |
| Tyler Sayger | USPHL-Premier | 90.8% | 84.5% | King's | D3 | 90.5% | 4.40 |
| Dylan Boughen | USPHL-Premier | 90.6% | 83.5% | Rivier | D3 | 62.5% | 14.86 |
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.