icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | NCDC | 15 | 10 | 4 | 94.1% | 2.30 | 2 | 0.9400 | 84.6% |
| 2022-23 | — | USHL | 4 | 0 | 3 | 84.3% | 4.24 | 0 | 0.9983 | 77.5% |
| 2021-22 | — | NCDC | 30 | 23 | 5 | 92.8% | 2.30 | 3 | 0.9400 | 86.1% |
| 2020-21 | — | NAHL | 7 | 5 | 2 | 89.4% | 3.26 | 0 | 0.9843 | 88.0% |
| 2019-20 | — | OHL | 1 | 0 | 0 | 85.7% | 2.06 | 0 | 1.0008 | 85.8% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Trine | D3 | 11 | — | — | 91.9% | 2.02 | 0 |
| 2024-25 | American International | D1 | 12 | 2 | 8 | 89.3% | 3.19 | 1 |
| 2023-24 | American International | D1 | — | — | — | — | — | — |
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 |
|---|---|---|---|---|---|---|
| Charles-Edward Gravel | QMJHL | 91.4% | 84.0% | Mercyhurst | 90.7% | 3.22 |
| Henry Levy | BCHL | 91.8% | 86.0% | Arizona State | 100.0% | — |
| Cole Plowman | MJHL | 93.4% | 85.4% | Alaska Fairbanks | 100.0% | — |
| Ryan Manzella | USHL | 90.4% | 83.1% | Michigan Tech | 89.5% | 3.17 |
| Carter Clafton | NAHL | 91.9% | 86.2% | Air Force | 100.0% | — |
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 |
|---|---|---|---|---|---|---|---|
| Logan Gorbitz | USPHL-Premier | 91.8% | 83.9% | Neumann | D3 | 84.4% | 5.24 |
| Dawson Rodin | NOJHL | 92.8% | 84.5% | Marian | D3 | 89.3% | 3.24 |
| Sebastian Woods | NCDC | 90.6% | 85.9% | Manhattanville | D3 | 91.4% | 3.07 |
| Jeffrey Fillion | NCDC | 89.6% | 85.2% | Wilkes | D3 | 92.4% | 1.96 |
| Matthew Dore | USPHL-Premier | 94.4% | 85.8% | Rivier | D3 | 88.8% | 4.78 |
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.