icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2018-19 | — | CCHL | 44 | 13 | 23 | 90.9% | 3.29 | 1 | 0.9700 | 85.1% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2023-24 | Niagara | D1 | 29 | — | — | 91.2% | 2.72 | 0 |
| 2022-23 | American International | D1 | 25 | 6 | 5 | 92.3% | 2.38 | 0 |
| 2021-22 | American International | D1 | 1 | 0 | 0 | 100.0% | — | 0 |
| 2020-21 | American International | D1 | 1 | 0 | 0 | 71.4% | 23.45 | 0 |
| 2019-20 | American International | D1 | 2 | 0 | 0 | — | — | 0 |
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 |
|---|---|---|---|---|---|---|
| Philip Svedebäck | USHL | 91.0% | 85.2% | Providence | 90.9% | 2.18 |
| Cameron Whitehead | USHL | 90.4% | 84.7% | Northeastern | 91.7% | 2.62 |
| Max Väyrynen | SM-Liiga-Jr | 91.2% | 85.5% | Michigan Tech | 90.3% | 2.32 |
| Matej Marinov | USHL | 91.7% | 85.9% | Quinnipiac | 91.3% | 1.85 |
| Cameron Korpi | USHL | 89.2% | 83.3% | Michigan | 90.4% | 3.08 |
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 |
|---|---|---|---|---|---|---|---|
| Andrew Doran | NA3HL | 93.4% | 87.1% | Utica | D3 | 100.0% | — |
| Tyler Fromolz | NA3HL | 92.0% | 87.1% | Marian | D3 | 88.3% | 3.85 |
| Russ Decoste | USPHL-Premier | 93.1% | 87.0% | Westfield State | D3 | 93.3% | 2.38 |
| Jarret Bovarnick | EHL | 88.3% | 87.8% | Suffolk | D3 | 87.2% | 4.52 |
| Jackson Fellner | SJHL | 89.9% | 85.7% | Alvernia | D3 | 92.4% | 2.68 |
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.