icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | NAHL | 19 | 9 | 8 | 90.5% | 3.06 | 1 | 0.9843 | 85.8% |
| 2022-23 | — | USHL | 6 | 2 | 4 | 85.9% | 4.22 | 0 | 0.9983 | 79.8% |
| 2021-22 | — | NAHL | 21 | 8 | 8 | 91.6% | 2.93 | 0 | 0.9843 | 90.1% |
| 2020-21 | — | NAHL | 11 | 2 | 9 | 88.7% | 3.14 | 0 | 0.9843 | 87.3% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Ferris State | D1 | 5 | 1 | 4 | 85.0% | 4.96 | 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 |
|---|---|---|---|---|---|---|
| Brendan Holahan | NAHL | 89.7% | 85.0% | Union | 85.7% | 4.48 |
| Max Hildebrand | WHL | 91.8% | 85.6% | Bemidji State | 89.5% | 2.71 |
| Brandon Bussi | USHL | 91.5% | 85.3% | Western Michigan | 91.0% | 2.65 |
| Noah Pak | OJHL | 94.5% | 86.1% | Yale | 87.2% | 3.86 |
| Ben Bonisteel | OJHL | 92.6% | 84.6% | 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 |
|---|---|---|---|---|---|---|---|
| Tucker Hanson | EHL | 89.0% | 85.7% | Saint Anselm | D2 | 100.0% | — |
| Cam Prodin | USPHL-Premier | 93.6% | 86.0% | Roger Williams | D3 | 75.8% | 9.18 |
| Freddy Soderberg | USPHL-Premier | 93.1% | 86.2% | Fitchburg State | D3 | 93.0% | 2.48 |
| Michael Cammasso | USPHL-Premier | 92.4% | 85.1% | Johnson & Wales | D3 | 82.2% | 8.99 |
| Sam Edmands | USPHL-Premier | 93.4% | 86.5% | Post | D2 | 93.9% | 2.10 |
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.