icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | Kingston Frontenacs | OHL | 47 | 24 | 16 | 89.7% | 3.13 | 2 | 1.0008 | 84.0% |
| 2023-24 | Soo Greyhounds | OHL | 47 | 28 | 12 | 88.3% | 3.10 | 2 | 1.0008 | 86.1% |
| 2022-23 | — | OHL | 31 | 9 | 14 | 89.4% | 3.49 | 2 | 1.0008 | 90.3% |
| 2021-22 | — | CCHL | 8 | 3 | 3 | 91.6% | 3.41 | 1 | 0.9700 | 94.4% |
| 2021-22 | — | OHL | 7 | 2 | 3 | 84.3% | 3.51 | 0 | 1.0008 | 87.9% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Robert Morris | D1 | 30 | 11 | 15 | 90.9% | 2.80 | 1 |
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 |
|---|---|---|---|---|---|---|
| Patriks Berzins | USHL | 90.1% | 84.4% | Maine | 90.0% | 1.79 |
| Ethan Dahlmeir | USHL | 90.0% | 84.4% | Miami | 87.0% | 4.04 |
| Connor Murphy | CCHL | 91.7% | 84.9% | Northeastern | 68.8% | 7.04 |
| Mathis Rousseau | QMJHL | 90.5% | 84.7% | Maine | 89.6% | 2.59 |
| Quentin Sigurdson | USHL | 89.6% | 83.5% | Northeastern | 92.0% | 2.13 |
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 |
|---|---|---|---|---|---|---|---|
| Dane Couture | MJHL | 86.5% | 83.8% | Concordia | D3 | 87.3% | 2.94 |
| Emil Norrman | USPHL-Premier | 91.3% | 84.6% | Buffalo State | D3 | 93.1% | 2.30 |
| Alexander Timmons | USPHL-Premier | 90.5% | 84.4% | Framingham State | D3 | 84.9% | 5.20 |
| Tyler Sayger | USPHL-Premier | 90.8% | 84.5% | King's | D3 | 90.5% | 4.40 |
| Tyler Roy | USPHL-Premier | 91.1% | 84.8% | Neumann | D3 | 87.2% | 3.68 |
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.