icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | USHL | 25 | 11 | 11 | 90.8% | 2.60 | 3 | 0.9983 | 84.5% |
| 2020-21 | — | USHL | 18 | 3 | 8 | 87.6% | 4.02 | 0 | 0.9983 | 87.5% |
| 2019-20 | — | USHL | 28 | 12 | 8 | 89.8% | 3.03 | 0 | 0.9983 | 89.6% |
| 2018-19 | — | NAHL | 39 | 21 | 13 | 89.7% | 2.85 | 4 | 0.9843 | 94.7% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2024-25 | Notre Dame | D1 | 1 | 0 | 0 | 88.9% | 5.75 | 0 |
| 2023-24 | Notre Dame | D1 | 1 | 0 | 0 | 89.3% | 4.50 | 0 |
| 2022-23 | Notre Dame | D1 | 2 | 0 | 0 | 88.9% | 2.90 | 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 |
|---|---|---|---|---|---|---|
| Ben Bonisteel | OJHL | 92.6% | 84.6% | Canisius | 89.3% | 3.10 |
| Brendan Holahan | NAHL | 89.7% | 85.0% | Union | 85.7% | 4.48 |
| Brandon Bussi | USHL | 91.5% | 85.3% | Western Michigan | 91.0% | 2.65 |
| Yan Shostak | USHL | 91.1% | 84.5% | St. Cloud State | 89.7% | 3.08 |
| Max Hildebrand | WHL | 91.8% | 85.6% | Bemidji State | 89.5% | 2.71 |
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 |
|---|---|---|---|---|---|---|---|
| Dylan Kruss | NCDC | 88.5% | 84.7% | Lake Forest | D3 | 91.8% | 2.84 |
| Gabe Rosek | NOJHL | 91.3% | 83.7% | Concordia | D3 | 90.2% | 4.56 |
| Nathan Loisel | CCHL | 88.3% | 83.8% | Southern New Hampshire | D2 | 92.5% | 2.61 |
| Diego D'Alessandro | CCHL | 89.2% | 84.9% | King's | D3 | 86.5% | 4.68 |
| Liam Gross | USPHL-Premier | 90.2% | 83.4% | Buffalo State | D3 | 85.7% | 5.75 |
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.