icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
Montreal Canadiens
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | USHL | 28 | 18 | 10 | 91.7% | 2.46 | 0 | 0.9980 | 89.0% |
| 2021-22 | — | USHL | 35 | 16 | 16 | 89.9% | 3.00 | 1 | 0.9980 | 93.8% |
| 2020-21 | — | USHL | 16 | 7 | 6 | 88.7% | 3.67 | 0 | 0.9980 | 88.5% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Dartmouth | D1 | 24 | 15 | 5 | 92.2% | 1.93 | 2 |
| 2024-25 | Dartmouth | D1 | 18 | 13 | 4 | 90.3% | 2.10 | 1 |
| 2023-24 | Clarkson | D1 | 6 | 2 | 3 | 83.5% | 3.53 | 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 |
|---|---|---|---|---|---|---|
| Arsenii Sergeev | USHL | 91.8% | 89.5% | UConn | 91.2% | 2.61 |
| John Parsons | USHL | 90.3% | 88.2% | Providence | 92.2% | 2.05 |
| Alexis Cournoyer | QMJHL | 94.2% | 91.6% | Cornell | 91.5% | 2.05 |
| Carson Cherepak | MJHL | 92.1% | 89.2% | RPI | 89.6% | 3.11 |
| Blake Pietila | USHL | 91.2% | 89.7% | Michigan Tech | 86.8% | 3.24 |
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 |
|---|---|---|---|---|---|---|---|
| Nevin Tardif | USPHL-Premier | 90.4% | 88.0% | Worcester State | D3 | 89.8% | 3.54 |
| Brody Haynes | NCDC | 92.5% | 92.3% | Elmira | D3 | 94.8% | 1.22 |
| Justin Damon | USPHL-Premier | 94.0% | 91.5% | Gustavus Adolphus | D3 | 92.9% | 2.27 |
| Michael Paterson-Jones | USPHL-Premier | 92.0% | 88.7% | Wilkes | D3 | 89.5% | 3.08 |
| Zach Dosan | NA3HL | 92.1% | 87.8% | Gustavus Adolphus | D3 | 72.5% | 6.60 |
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.