icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | NAHL | 33 | 18 | 6 | 92.8% | 1.59 | 7 | 0.9843 | 96.9% |
| 2021-22 | — | NAHL | 34 | 23 | 3 | 90.9% | 1.94 | 3 | 0.9843 | 97.0% |
| 2021-22 | — | USHL | 6 | 0 | 5 | 78.1% | 6.33 | 0 | 0.9980 | 83.6% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Princeton | D1 | 22 | 11 | 9 | 91.3% | 2.36 | 0 |
| 2024-25 | Princeton | D1 | 13 | 5 | 7 | 89.2% | 3.00 | 1 |
| 2023-24 | Princeton | D1 | 20 | 7 | 7 | 89.9% | 2.96 | 1 |
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 |
|---|---|---|---|---|---|---|
| Hampton Slukynsky | USHL | 92.3% | 93.3% | Western Michigan | 92.2% | 1.90 |
| Carsen Musser | USHL | 90.5% | 90.7% | Colorado College | 87.9% | 3.94 |
| Mathis Langevin | QMJHL | 91.2% | 92.0% | Miami | 93.8% | 2.00 |
| Jere Huhtamaa | SM-Liiga-Jr | 90.3% | 89.8% | Merrimack | 86.4% | 3.47 |
| Matthew Thiessen | USHL | 88.9% | 89.7% | Maine | 50.0% | 25.59 |
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 |
|---|---|---|---|---|---|---|---|
| Brody Haynes | NCDC | 92.5% | 92.3% | Elmira | D3 | 94.8% | 1.22 |
| Cooper Rautenstrauch | NAHL | 88.9% | 94.0% | Colby | D3 | 93.4% | 2.08 |
| Vaughn Makar | NAHL | 90.9% | 95.6% | St. Norbert | D3 | 93.2% | 1.73 |
| Cooper Rautenstrauch | NCDC | 90.6% | 93.8% | Colby | D3 | 93.4% | 2.08 |
| Brayden Ingram | EHL | 91.2% | 94.4% | Assumption | D2 | 91.4% | 2.71 |
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.