icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | MJHL | 9 | 3 | 4 | 86.5% | 4.02 | 0 | 0.9700 | 80.3% |
| 2022-23 | — | NAHL | 12 | 4 | 7 | 88.1% | 3.41 | 0 | 0.9843 | 83.9% |
| 2021-22 | — | NCDC | 21 | 9 | 9 | 91.7% | 2.56 | 2 | 0.9400 | 86.3% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Concordia | D3 | 13 | — | — | 90.8% | 2.69 | 0 |
| 2024-25 | Concordia | D3 | 25 | — | — | 90.2% | 2.66 | 0 |
| 2023-24 | Concordia | D3 | 8 | — | — | 87.3% | 2.94 | 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 |
|---|---|---|---|---|---|---|
| Alec Calvaruso | USHL | 89.0% | 83.1% | RPI | 86.9% | 3.91 |
| Quentin Sigurdson | USHL | 89.6% | 83.5% | Northeastern | 92.0% | 2.13 |
| Henry Hunt | USPHL-Premier | 88.1% | 79.6% | Mercyhurst | — | — |
| Beckham Dempsey | NCDC | 90.4% | 82.7% | Colorado College | — | — |
| Charlie Schenkel | OHL | 89.7% | 84.0% | Robert Morris | 90.9% | 2.80 |
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 |
|---|---|---|---|---|---|---|---|
| Will Pinney | NA3HL | 90.3% | 81.2% | Gustavus Adolphus | D3 | 85.8% | 4.05 |
| Karl Zodda | NA3HL | 92.0% | 82.6% | Chatham | D3 | 86.8% | 4.66 |
| John Simon | NA3HL | 92.1% | 83.2% | Post | D2 | 91.6% | 3.15 |
| Dylan Boughen | USPHL-Premier | 90.6% | 83.5% | Rivier | D3 | 62.5% | 14.86 |
| Emil Norrman | USPHL-Premier | 91.3% | 84.6% | Buffalo State | D3 | 93.1% | 2.30 |
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.