icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | Saginaw Spirit | OHL | 50 | 26 | 21 | 87.6% | 3.80 | 1 | 1.0008 | 74.6% |
| 2023-24 | Saginaw Spirit | OHL | 41 | 29 | 10 | 89.0% | 2.89 | 2 | 1.0008 | 82.2% |
| 2022-23 | — | OHL | 24 | 10 | 10 | 89.2% | 3.45 | 0 | 1.0008 | 88.4% |
| 2021-22 | — | OHL | 36 | 13 | 16 | 84.8% | 4.63 | 0 | 1.0008 | 89.2% |
| 2020-21 | — | OHL | — | — | — | — | — | — | 1.0008 | — |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Lake Superior State | D1 | 8 | 1 | 4 | 87.9% | 3.95 | 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 |
|---|---|---|---|---|---|---|
| Lucas Massie | USHL | 87.9% | 75.1% | Union | 80.5% | 5.73 |
| Bruno Bruveris | USHL | 89.7% | 76.1% | Miami | 86.6% | 4.15 |
| Connor Hasley | USHL | 86.7% | 73.6% | Bentley | 91.0% | 2.95 |
| Ryan Manzella | USHL | 90.4% | 76.7% | Michigan Tech | 89.5% | 3.17 |
| Mason Kucenski | USHL | 84.9% | 72.5% | St. Lawrence | 82.6% | 8.00 |
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 |
|---|---|---|---|---|---|---|---|
| Bodee Weiss | BCHL | 87.9% | 76.6% | Aurora | D3 | 87.9% | 2.93 |
| 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 |
| Nolan Hildebrand | AJHL | 87.4% | 77.5% | Babson | D3 | 87.8% | 3.82 |
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.