icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | — | USPHL-Premier | 19 | 11 | 7 | 93.4% | 2.48 | 2 | 0.9400 | 80.6% |
| 2022-23 | — | USPHL-Premier | — | — | — | — | — | — | 0.9400 | — |
| 2022-23 | — | OJHL | 13 | 2 | 9 | 88.9% | 4.38 | 0 | 0.9700 | 83.0% |
| 2021-22 | — | USPHL-Elite | 18 | 16 | 1 | 93.3% | 2.15 | 2 | 0.9400 | 87.0% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Post | D2 | 2 | — | — | 86.4% | 5.61 | 0 |
| 2024-25 | Post | D2 | 1 | 0 | 1 | 93.9% | 2.10 | 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 |
|---|---|---|---|---|---|---|
| Ben Bonisteel | OJHL | 92.6% | 79.9% | Canisius | 89.3% | 3.10 |
| Brandon Bussi | USHL | 91.5% | 79.8% | Western Michigan | 91.0% | 2.65 |
| Max Hildebrand | WHL | 91.8% | 79.7% | Bemidji State | 89.5% | 2.71 |
| Jack Williams | USHL | 90.8% | 79.1% | Notre Dame | 88.9% | 2.90 |
| Noah Pak | OJHL | 94.5% | 81.2% | Yale | 87.2% | 3.86 |
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 |
|---|---|---|---|---|---|---|---|
| Liam Gross | USPHL-Premier | 90.2% | 80.0% | Buffalo State | D3 | 85.7% | 5.75 |
| Diego D'Alessandro | CCHL | 89.2% | 80.7% | King's | D3 | 86.5% | 4.68 |
| Nathan Loisel | CCHL | 88.3% | 79.6% | Southern New Hampshire | D2 | 92.5% | 2.61 |
| Dylan Kruss | NCDC | 88.5% | 81.6% | Lake Forest | D3 | 91.8% | 2.84 |
| Gabe Rosek | NOJHL | 91.3% | 79.3% | Concordia | D3 | 90.2% | 4.56 |
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.