icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | AJHL | 35 | 13 | 16 | 90.4% | 3.58 | 2 | 0.9700 | 81.9% |
| 2021-22 | — | AJHL | 43 | 16 | 21 | 89.8% | 3.79 | 2 | 0.9700 | 84.6% |
| 2020-21 | — | AJHL | 1 | 1 | 0 | 92.5% | 3.00 | 0 | 0.9700 | 89.7% |
| 2019-20 | — | AJHL | 2 | 1 | 0 | 94.9% | 1.50 | 0 | 0.9700 | 92.0% |
| 2018-19 | — | AJHL | 3 | 1 | 1 | 95.3% | 1.35 | 0 | 0.9700 | 97.0% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Marian | D3 | 11 | — | — | 88.8% | 3.41 | 0 |
| 2024-25 | Marian | D3 | 8 | — | — | 93.3% | 1.85 | 0 |
| 2023-24 | Marian | D3 | 8 | — | — | 91.0% | 3.04 | 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 |
|---|---|---|---|---|---|---|
| Luke Lush | AJHL | 91.9% | 83.0% | Sacred Heart | 91.5% | 2.11 |
| Jacob Zacharewicz | NAHL | 89.2% | 83.4% | Brown | 86.8% | 4.19 |
| Aaron Matthews | NCDC | 90.7% | 81.4% | Providence | — | — |
| Bruno Bruveris | USHL | 89.7% | 82.4% | Miami | 86.6% | 4.15 |
| Reid Dyck | WHL | 89.0% | 81.5% | Colgate | 89.7% | 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 |
|---|---|---|---|---|---|---|---|
| Jeffrey Dreger | MJHL | 86.4% | 82.1% | SUNY Morrisville | D3 | 84.5% | 4.37 |
| Jeffrey Dreger | MJHL | 86.4% | 82.1% | Morrisville | D3 | 84.5% | 4.37 |
| Vincent Lamberti | BCHL | 88.8% | 81.2% | Amherst | D3 | 90.2% | 2.47 |
| Anthony Bonaldi | USPHL-Premier | 90.6% | 82.7% | Nichols | D3 | 81.8% | 7.78 |
| Hayden Williamson | OJHL | 85.8% | 82.1% | Arcadia | D3 | 92.0% | 3.36 |
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.