icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | USHL | 43 | 23 | 13 | 89.7% | 2.60 | 4 | 0.9983 | 82.4% |
| 2021-22 | — | USHL | 31 | 15 | 12 | 87.3% | 3.02 | 3 | 0.9983 | 83.6% |
| 2020-21 | — | KHL | 2 | 0 | 1 | 84.8% | 4.07 | 0 | 1.0100 | 85.7% |
| 2020-21 | — | MHL-RU | 17 | 3 | 11 | 90.8% | 3.25 | 0 | 0.9600 | 87.2% |
| 2019-20 | — | MHL-RU | 29 | 9 | 12 | 90.6% | 2.90 | 2 | 0.9600 | 87.0% |
| 2018-19 | — | MHL-RU | 22 | 3 | 12 | 90.4% | 3.26 | 2 | 0.9600 | 93.4% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | RPI | D1 | 13 | 3 | 7 | 88.4% | 3.44 | 0 |
| 2024-25 | Miami | D1 | 19 | 1 | 1 | 86.6% | 4.14 | 0 |
| 2023-24 | Miami | D1 | 15 | 0 | 1 | 86.6% | 4.15 | 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 |
|---|---|---|---|---|---|---|
| Ryan Snowden | USHL | 89.8% | 82.7% | Ohio State | 90.5% | 2.52 |
| Ryan Manzella | USHL | 90.4% | 83.1% | Michigan Tech | 89.5% | 3.17 |
| Aaron Matthews | NCDC | 90.7% | 81.4% | Providence | — | — |
| Luke Lush | AJHL | 91.9% | 83.0% | Sacred Heart | 91.5% | 2.11 |
| Gavin Moffatt | USHL | 89.3% | 82.4% | Western Michigan | — | — |
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% | 82.3% | Roger Williams | D3 | — | — |
| Hayden Williamson | OJHL | 85.8% | 82.1% | Arcadia | D3 | 92.0% | 3.36 |
| Colby Entz | SJHL | 89.8% | 82.9% | St. Norbert | D3 | 93.0% | 1.88 |
| Anthony Bonaldi | USPHL-Premier | 90.6% | 82.7% | Nichols | D3 | 81.8% | 7.78 |
| Jeffrey Dreger | MJHL | 86.4% | 82.1% | 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.