icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | CCHL | 41 | 22 | 12 | 92.5% | 2.39 | 5 | 0.9700 | 84.0% |
| 2021-22 | — | CCHL | 25 | 17 | 7 | 92.0% | 2.43 | 1 | 0.9700 | 86.5% |
| 2021-22 | — | NAHL | 5 | 3 | 2 | 88.3% | 4.03 | 0 | 0.9843 | 85.7% |
| 2020-21 | — | OJHL | — | — | — | — | — | — | 0.9700 | — |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Norwich | D3 | 23 | — | — | 93.3% | 2.01 | 2 |
| 2024-25 | Norwich | D3 | 27 | 14 | 10 | 92.0% | 2.36 | 1 |
| 2023-24 | Norwich | D3 | 15 | 7 | 6 | 94.3% | 1.62 | 1 |
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 |
|---|---|---|---|---|---|---|
| Thomas Heaney | NAHL | 90.0% | 84.2% | UConn | 86.7% | 4.39 |
| Daniel Hauser | WHL | 91.3% | 83.7% | Wisconsin | 90.0% | 2.49 |
| Jacob Zacharewicz | NAHL | 89.2% | 83.4% | Brown | 86.8% | 4.19 |
| Cullen DeYoung | NAHL | 91.0% | 85.0% | Sacred Heart | 94.3% | 1.73 |
| Luke Lush | AJHL | 91.9% | 83.0% | Sacred Heart | 91.5% | 2.11 |
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 |
|---|---|---|---|---|---|---|---|
| Josh Nadler | AJHL | 89.5% | 84.2% | Hamilton | D3 | 68.8% | 12.03 |
| Pierce Diamond | SJHL | 91.5% | 84.2% | Albertus Magnus | D3 | 89.4% | 3.82 |
| Nikolas Trakakis | MJHL | 87.9% | 83.1% | Chatham | D3 | 93.4% | 2.45 |
| Dawson Rodin | NOJHL | 92.8% | 84.5% | Marian | D3 | 89.3% | 3.24 |
| Ben Dardis | AJHL | 90.3% | 85.0% | Buffalo State | D3 | 89.9% | 2.82 |
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.