icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | — | BCHL | 5 | 2 | 3 | 87.6% | 3.65 | 0 | 0.9990 | 81.9% |
| 2023-24 | — | NAHL | 24 | 18 | 4 | 94.0% | 1.83 | 2 | 0.9843 | 88.0% |
| 2022-23 | — | NAHL | 30 | 18 | 11 | 92.3% | 2.60 | 2 | 0.9843 | 89.6% |
| 2021-22 | — | AJHL | 28 | 21 | 3 | 89.8% | 2.55 | 4 | 0.9700 | 87.7% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Minnesota State | D1 | 1 | 1 | 0 | 83.3% | 3.00 | 0 |
| 2024-25 | Bentley | D1 | 4 | 2 | 1 | 90.2% | 1.84 | 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 |
|---|---|---|---|---|---|---|
| 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 |
| Jacob Zacharewicz | NAHL | 89.2% | 83.4% | Brown | 86.8% | 4.19 |
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 |
|---|---|---|---|---|---|---|---|
| Jeremy Skaife | USPHL-Premier | 89.7% | 81.5% | Johnson & Wales | D3 | 83.0% | 5.54 |
| Hunter Thomas | USPHL-Premier | 90.2% | 82.0% | Salem State | D3 | 87.3% | 5.20 |
| Graham Burke | NAHL | 86.2% | 82.8% | Hobart | D3 | 95.1% | 1.00 |
| Jeffrey Dreger | MJHL | 86.4% | 82.1% | SUNY Morrisville | D3 | 84.5% | 4.37 |
| Jeffrey Dreger | MJHL | 86.4% | 82.1% | 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.