icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | — | NAHL | 10 | 3 | 6 | 87.4% | 3.89 | 0 | 0.9843 | 78.7% |
| 2023-24 | Youngstown Phantoms | USHL | 21 | 12 | 6 | 89.3% | 3.01 | 2 | 0.9980 | 75.8% |
| 2021-22 | — | USHL | 12 | 3 | 6 | 88.9% | 3.32 | 1 | 0.9980 | 88.1% |
| 2020-21 | — | NAHL | 41 | 23 | 14 | 91.7% | 2.44 | 6 | 0.9843 | 90.3% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Michigan Tech | D1 | 38 | 23 | 12 | 91.3% | 2.52 | 2 |
| 2022-23 | Minnesota | D1 | 6 | 3 | 0 | 88.3% | 3.72 | 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 |
|---|---|---|---|---|---|---|
| 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% | — |
| Ryan Manzella | USHL | 90.4% | 76.7% | Michigan Tech | 89.5% | 3.17 |
| Jacob Zacharewicz | NAHL | 89.2% | 79.5% | Brown | 86.8% | 4.19 |
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 |
| Anthony Bonaldi | USPHL-Premier | 90.6% | 78.2% | Nichols | D3 | 81.8% | 7.78 |
| Cameron Hrdlicka | SJHL | 89.6% | 79.6% | Concordia | D3 | 89.0% | 3.97 |
| Paul Knapik | USPHL-Premier | 90.5% | 77.5% | Roger Williams | D3 | — | — |
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.