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 | 15 | 8 | 6 | 91.1% | 2.87 | 0 | 0.9400 | 79.8% |
| 2021-22 | — | USPHL-Premier | 14 | 10 | 2 | 95.3% | 1.55 | 4 | 0.9400 | 82.6% |
| 2020-21 | — | USPHL-Premier | 20 | 9 | 8 | 92.8% | 2.61 | 3 | 0.9400 | 87.2% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Fitchburg State | D3 | 9 | — | — | 90.7% | 3.03 | — |
| 2024-25 | Fitchburg State | D3 | 10 | 5 | 4 | 93.3% | 2.46 | — |
| 2023-24 | Fitchburg State | D3 | 14 | 6 | 5 | 91.1% | 2.78 | — |
| 2022-23 | Fitchburg State | D3 | 19 | 8 | 7 | 92.2% | 2.61 | — |
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 |
|---|---|---|---|---|---|---|
| Max Hildebrand | WHL | 91.8% | 79.7% | Bemidji State | 89.5% | 2.71 |
| Brandon Bussi | USHL | 91.5% | 79.8% | Western Michigan | 91.0% | 2.65 |
| Charlie Schenkel | OHL | 89.7% | 78.8% | Robert Morris | 90.9% | 2.80 |
| Patriks Berzins | USHL | 90.1% | 79.5% | Maine | 90.0% | 1.79 |
| Ben Bonisteel | OJHL | 92.6% | 79.9% | Canisius | 89.3% | 3.10 |
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 |
|---|---|---|---|---|---|---|---|
| Dane Couture | MJHL | 86.5% | 80.8% | Concordia | D3 | 87.3% | 2.94 |
| Dylan Boughen | USPHL-Premier | 90.6% | 79.8% | Rivier | D3 | 62.5% | 14.86 |
| Karl Zodda | NA3HL | 92.0% | 79.9% | Chatham | D3 | 86.8% | 4.66 |
| Emil Norrman | USPHL-Premier | 91.3% | 81.2% | Buffalo State | D3 | 93.1% | 2.30 |
| Nathan Loisel | CCHL | 88.3% | 79.6% | Southern New Hampshire | D2 | 92.5% | 2.61 |
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.