icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | NCDC | 35 | 15 | 16 | 90.6% | 3.44 | 2 | 0.9400 | 77.1% |
| 2020-21 | — | EHL | 2 | 1 | 1 | 94.7% | 2.00 | 0 | 0.9400 | 89.0% |
| 2020-21 | — | OJHL | — | — | — | — | — | — | 0.9700 | — |
| 2019-20 | — | NOJHL | 35 | 7 | 21 | 88.4% | 4.63 | 0 | 0.9700 | 85.8% |
| 2018-19 | — | OJHL | — | — | — | — | — | — | 0.9700 | — |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | St. John Fisher | D3 | 16 | — | — | 91.1% | 2.83 | 2 |
| 2024-25 | Manhattanville | D3 | 11 | 4 | 6 | 90.8% | 3.49 | 0 |
| 2023-24 | Manhattanville | D3 | 20 | 6 | 12 | 90.1% | 3.34 | 1 |
| 2022-23 | Manhattanville | D3 | 21 | 12 | 5 | 91.4% | 3.07 | — |
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 |
|---|---|---|---|---|---|---|
| Ryan Manzella | USHL | 90.4% | 76.7% | Michigan Tech | 89.5% | 3.17 |
| Ryan Snowden | USHL | 89.8% | 76.4% | Ohio State | 90.5% | 2.52 |
| Aaron Matthews | NCDC | 90.7% | 77.9% | Providence | — | — |
| Bruno Bruveris | USHL | 89.7% | 76.1% | Miami | 86.6% | 4.15 |
| Charles-Edward Gravel | QMJHL | 91.4% | 77.9% | Mercyhurst | 90.7% | 3.22 |
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 |
|---|---|---|---|---|---|---|---|
| Paul Knapik | USPHL-Premier | 90.5% | 77.5% | Roger Williams | D3 | — | — |
| Hayden Williamson | OJHL | 85.8% | 76.4% | Arcadia | D3 | 92.0% | 3.36 |
| Bodee Weiss | BCHL | 87.9% | 76.6% | Aurora | D3 | 87.9% | 2.93 |
| Vincent Lamberti | BCHL | 88.8% | 76.8% | Amherst | D3 | 90.2% | 2.47 |
| Jeffrey Dreger | MJHL | 86.4% | 77.7% | SUNY Morrisville | D3 | 84.5% | 4.37 |
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.