icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | OJHL | 22 | 2 | 20 | 85.8% | 6.34 | 0 | 0.9700 | 72.5% |
| 2021-22 | — | NOJHL | 9 | 6 | 2 | 88.6% | 3.69 | 0 | 0.9700 | 80.4% |
| 2021-22 | — | OJHL | 1 | 0 | 0 | 50.0% | 18.24 | 0 | 0.9700 | 45.7% |
| 2020-21 | — | NOJHL | 2 | 0 | 1 | 93.7% | 2.79 | 0 | 0.9700 | 90.9% |
| 2019-20 | — | OJHL | — | — | — | — | — | — | 0.9700 | — |
| 2018-19 | — | OJHL | — | — | — | — | — | — | 0.9700 | — |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Arcadia | D3 | 10 | — | — | 93.4% | 2.40 | 1 |
| 2024-25 | Arcadia | D3 | 13 | 4 | 4 | 93.3% | 2.44 | 1 |
| 2023-24 | Arcadia | D3 | 5 | 1 | 2 | 92.0% | 3.36 | 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 |
|---|---|---|---|---|---|---|
| Connor Hasley | USHL | 86.7% | 73.6% | Bentley | 91.0% | 2.95 |
| Mason Kucenski | USHL | 84.9% | 72.5% | St. Lawrence | 82.6% | 8.00 |
| Jakub Krbecek | USHL | 84.7% | 71.3% | RIT | 89.3% | 3.90 |
| Andrew Oke | OHL | 87.6% | 74.6% | Lake Superior State | 87.9% | 3.95 |
| Bruno Bruveris | USHL | 89.7% | 76.1% | Miami | 86.6% | 4.15 |
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 |
| William Goumas | OJHL | 84.8% | 74.6% | SUNY Morrisville | D3 | 89.7% | 4.12 |
| Paul Knapik | USPHL-Premier | 90.5% | 77.5% | Roger Williams | D3 | — | — |
| 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.