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 | 33 | 14 | 15 | 86.7% | 3.55 | 1 | 0.9980 | 73.6% |
| 2020-21 | — | NAHL | 31 | 6 | 19 | 89.2% | 3.17 | 2 | 0.9843 | 87.8% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Arizona State | D1 | 24 | 10 | 11 | 90.7% | 3.12 | 0 |
| 2024-25 | Bentley | D1 | 36 | 21 | 13 | 92.5% | 1.96 | 11 |
| 2023-24 | Bentley | D1 | 21 | 10 | 9 | 90.5% | 2.22 | 3 |
| 2022-23 | Bentley | D1 | 20 | 6 | 10 | 91.0% | 2.95 | 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 |
|---|---|---|---|---|---|---|
| Andrew Oke | OHL | 87.6% | 74.6% | Lake Superior State | 87.9% | 3.95 |
| Anton Castro | USHL | 88.6% | 74.4% | Wisconsin | 78.6% | 4.27 |
| Bruno Bruveris | USHL | 89.7% | 76.1% | Miami | 86.6% | 4.15 |
| Reid Dyck | WHL | 89.0% | 74.5% | Colgate | 89.7% | 3.17 |
| Ryan Snowden | USHL | 89.8% | 76.4% | Ohio State | 90.5% | 2.52 |
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 |
|---|---|---|---|---|---|---|---|
| Chris Branch | USPHL-Premier | 84.7% | 73.2% | Lebanon Valley | D3 | 89.3% | 4.36 |
| Hayden Williamson | OJHL | 85.8% | 76.4% | Arcadia | D3 | 92.0% | 3.36 |
| William Goumas | OJHL | 84.8% | 74.6% | SUNY Morrisville | D3 | 89.7% | 4.12 |
| Pierce Diamond | BCHL | 88.6% | 75.8% | Albertus Magnus | D3 | 89.4% | 3.82 |
| Vincent Lamberti | BCHL | 88.8% | 76.8% | Amherst | D3 | 90.2% | 2.47 |
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.