icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | NAHL | 44 | 17 | 21 | 91.4% | 3.24 | 0 | 0.9843 | 85.2% |
| 2019-20 | — | OJHL | 37 | 19 | 13 | 91.2% | 2.82 | 4 | 0.9700 | 88.5% |
| 2018-19 | — | OJHL | 30 | 7 | 17 | 89.5% | 3.85 | 1 | 0.9700 | 96.9% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Hobart | D3 | 21 | — | — | 95.1% | 0.99 | 7 |
| 2024-25 | Hobart | D3 | 18 | 17 | 1 | 95.2% | 1.14 | 5 |
| 2023-24 | Hobart | D3 | 20 | 18 | 1 | 96.2% | 0.94 | 7 |
| 2022-23 | Hobart | D3 | 20 | 18 | 2 | 95.5% | 1.04 | — |
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 |
|---|---|---|---|---|---|---|
| Gergely Orosz | NAHL | 91.9% | 85.4% | Alaska Anchorage | 91.5% | 2.75 |
| Carson Dorfman | NAHL | 90.1% | 84.7% | RPI | 100.0% | — |
| Rorke Applebee | BCHL | 90.2% | 83.3% | Lake Superior State | 90.7% | 3.00 |
| Beni Halasz | NAHL | 92.1% | 85.2% | Northern Michigan | 91.9% | 2.32 |
| Brandon Perrone | NAHL | 91.2% | 85.4% | Alaska Anchorage | 87.2% | 3.66 |
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 |
|---|---|---|---|---|---|---|---|
| Conor Sullivan | NAHL | 88.2% | 85.2% | Middlebury | D3 | 92.1% | 2.46 |
| Tyler Roy | EHL | 91.3% | 86.0% | Neumann | D3 | 87.2% | 3.68 |
| Matthew O'Donnell | NAHL | 88.6% | 84.8% | Aurora | D3 | 91.0% | 2.97 |
| Connor Graham | AJHL | 91.8% | 85.2% | Alvernia | D3 | 93.2% | 2.19 |
| Jacob Jaslow | NCDC | 90.7% | 86.1% | Roger Williams | D3 | 91.0% | 3.49 |
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.