icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | SHL | — | — | — | — | — | — | 1.0100 | — |
| 2022-23 | — | SHL-J20 | 42 | 26 | 16 | 92.1% | 2.23 | 3 | 0.9600 | 83.5% |
| 2021-22 | — | SHL | — | — | — | — | — | — | 1.0100 | — |
| 2021-22 | — | SuperElit | 25 | 16 | 8 | 91.0% | 2.87 | 0 | 0.9600 | 88.4% |
| 2021-22 | — | SHL-J20 | 25 | 16 | 8 | 91.0% | 2.87 | 0 | 0.9600 | 88.4% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Maine | D1 | 20 | 10 | 7 | 89.9% | 2.59 | 4 |
| 2024-25 | Maine | D1 | 37 | 23 | 8 | 92.8% | 1.82 | 4 |
| 2023-24 | Maine | D1 | 18 | 10 | 6 | 91.6% | 2.01 | 2 |
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 |
|---|---|---|---|---|---|---|
| Max Weilandt | USHL | 88.7% | 83.8% | Northern Michigan | 87.0% | 3.43 |
| Brayden Gillespie | OHL | 88.1% | 83.2% | Union | 91.5% | 2.19 |
| Calvin Vachon | USHL | 89.6% | 84.4% | Alaska Fairbanks | 89.7% | 3.11 |
| Kevin Reidler | USHL | 90.2% | 85.9% | Nebraska Omaha | 92.0% | 2.74 |
| Mitch Gibson | USHL | 89.0% | 83.9% | Harvard | 91.6% | 2.61 |
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 |
|---|---|---|---|---|---|---|---|
| Zach Dosan | NA3HL | 92.1% | 87.8% | Gustavus Adolphus | D3 | 72.5% | 6.60 |
| Michael Paterson-Jones | USPHL-Premier | 92.0% | 88.7% | Wilkes | D3 | 89.5% | 3.08 |
| Tyler Fromolz | NA3HL | 92.0% | 87.1% | Marian | D3 | 88.3% | 3.85 |
| Ryan Kenny | USPHL-Premier | 93.5% | 89.3% | Stevenson | D3 | 89.6% | 2.95 |
| Jarret Bovarnick | EHL | 88.3% | 87.8% | Suffolk | D3 | 87.2% | 4.52 |
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.