icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | — | OJHL | 26 | 4 | 19 | 84.8% | 4.91 | 1 | 0.9700 | 70.8% |
| 2022-23 | — | OJHL | 25 | 12 | 11 | 89.4% | 2.93 | 0 | 0.9700 | 81.0% |
| 2021-22 | — | NCDC | 8 | 3 | 3 | 90.6% | 2.94 | 0 | 0.9400 | 88.2% |
| 2021-22 | — | OJHL | 3 | 2 | 0 | 87.5% | 2.72 | 0 | 0.9700 | 85.3% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | SUNY Morrisville | D3 | 6 | — | — | 88.7% | 5.17 | — |
| 2024-25 | SUNY Morrisville | D3 | 14 | 1 | 10 | 89.7% | 4.12 | — |
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 |
|---|---|---|---|---|---|---|
| Jakub Krbecek | USHL | 84.7% | 71.3% | RIT | 89.3% | 3.90 |
| Reid Dyck | WHL | 89.0% | 74.5% | Colgate | 89.7% | 3.17 |
| Anton Castro | USHL | 88.6% | 74.4% | Wisconsin | 78.6% | 4.27 |
| 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 |
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 |
| Pierce Diamond | BCHL | 88.6% | 75.8% | Albertus Magnus | D3 | 89.4% | 3.82 |
| Hunter Thomas | USPHL-Premier | 90.2% | 77.3% | Salem State | D3 | 87.3% | 5.20 |
| Jeremy Skaife | USPHL-Premier | 89.7% | 76.7% | Johnson & Wales | D3 | 83.0% | 5.54 |
| Brian Tallieu | USPHL-Premier | 91.3% | 77.8% | Lawrence | D3 | 88.6% | 4.50 |
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.