icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | AJHL | 42 | 26 | 12 | 90.9% | 2.72 | 4 | 0.9700 | 82.2% |
| 2021-22 | — | AJHL | 40 | 26 | 5 | 91.3% | 2.64 | 4 | 0.9700 | 85.8% |
| 2020-21 | — | AJHL | 6 | 2 | 4 | 91.0% | 3.10 | 0 | 0.9700 | 88.3% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Anna Maria | D3 | 24 | — | — | 94.4% | 1.87 | — |
| 2024-25 | Anna Maria | D3 | 22 | 14 | 8 | 91.5% | 2.67 | — |
| 2023-24 | Anna Maria | D3 | 17 | 10 | 6 | 93.2% | 2.59 | — |
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 |
|---|---|---|---|---|---|---|
| Reid Dyck | WHL | 89.0% | 81.5% | Colgate | 89.7% | 3.17 |
| Anton Castro | USHL | 88.6% | 81.0% | Wisconsin | 78.6% | 4.27 |
| Daniel Hauser | WHL | 91.3% | 83.7% | Wisconsin | 90.0% | 2.49 |
| Luke Lush | AJHL | 91.9% | 83.0% | Sacred Heart | 91.5% | 2.11 |
| Lukas Renaud | AJHL | 92.4% | 83.4% | Long Island Univ. | 89.7% | 2.57 |
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 |
|---|---|---|---|---|---|---|---|
| Hunter Thomas | USPHL-Premier | 90.2% | 82.0% | Salem State | D3 | 87.3% | 5.20 |
| Brian Tallieu | USPHL-Premier | 91.3% | 82.8% | Lawrence | D3 | 88.6% | 4.50 |
| Graham Burke | NAHL | 86.2% | 82.8% | Hobart | D3 | 95.1% | 1.00 |
| Jeremy Skaife | USPHL-Premier | 89.7% | 81.5% | Johnson & Wales | D3 | 83.0% | 5.54 |
| Blake Bjella | USPHL-Premier | 91.5% | 83.1% | Worcester State | D3 | 87.0% | 3.54 |
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.