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 | 4 | 4 | 0 | 93.4% | 1.75 | 1 | 0.9400 | 78.9% |
| 2023-24 | — | MJHL | 1 | 0 | 1 | 86.7% | 4.11 | 0 | 0.9700 | 74.8% |
| 2022-23 | — | MJHL | 25 | 10 | 10 | 88.3% | 3.83 | 0 | 0.9700 | 82.5% |
| 2021-22 | — | USPHL-Elite | 2 | 0 | 2 | 90.6% | 2.50 | 0 | 0.9400 | 82.9% |
| 2021-22 | — | USPHL-Premier | 9 | 5 | 1 | 90.4% | 3.31 | 0 | 0.9400 | 88.0% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Rivier | D3 | 13 | — | — | 88.0% | 4.96 | 0 |
| 2024-25 | Rivier | D3 | 2 | 0 | 1 | 89.7% | 3.54 | — |
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 |
|---|---|---|---|---|---|---|
| Aaron Matthews | NCDC | 90.7% | 77.9% | Providence | — | — |
| Charles-Edward Gravel | QMJHL | 91.4% | 77.9% | Mercyhurst | 90.7% | 3.22 |
| Henry Levy | BCHL | 91.8% | 80.8% | Arizona State | 100.0% | — |
| Jacob Zacharewicz | NAHL | 89.2% | 79.5% | Brown | 86.8% | 4.19 |
| Ryan Manzella | USHL | 90.4% | 76.7% | Michigan Tech | 89.5% | 3.17 |
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 |
|---|---|---|---|---|---|---|---|
| Colby Entz | SJHL | 89.8% | 79.2% | St. Norbert | D3 | 93.0% | 1.88 |
| Logan Gorbitz | USPHL-Premier | 91.8% | 79.4% | Neumann | D3 | 84.4% | 5.24 |
| Cameron Hrdlicka | SJHL | 89.6% | 79.6% | Concordia | D3 | 89.0% | 3.97 |
| Anthony Bonaldi | USPHL-Premier | 90.6% | 78.2% | Nichols | D3 | 81.8% | 7.78 |
| Dawson Rodin | NOJHL | 92.8% | 79.6% | Marian | D3 | 89.3% | 3.24 |
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.