icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
Vancouver Canucks
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | SM-Liiga-Jr | 27 | 13 | 10 | 89.7% | 2.79 | 2 | 0.9600 | 88.3% |
| 2020-21 | — | SM-Liiga-Jr | 13 | — | — | 89.3% | 2.92 | — | 0.9600 | 85.7% |
| 2019-20 | — | SM-Liiga-Jr | 1 | — | — | 90.5% | 2.00 | — | 0.9600 | 86.9% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2024-25 | Harvard | D1 | 20 | 8 | 9 | 90.2% | 2.81 | 1 |
| 2023-24 | Harvard | D1 | 17 | 5 | 6 | 91.0% | 2.95 | 2 |
| 2022-23 | Harvard | D1 | 2 | 1 | 1 | 87.5% | 3.56 | 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 |
|---|---|---|---|---|---|---|
| Owen Bartoszkiewicz | USHL | 88.9% | 88.1% | Minnesota | 88.3% | 3.72 |
| Mikhail Yegorov | USHL | 91.2% | 90.0% | Boston University | 90.4% | 2.73 |
| John Parsons | USHL | 90.3% | 88.2% | Providence | 92.2% | 2.05 |
| Jackson Irving | USHL | 88.8% | 87.2% | UMass | 100.0% | — |
| 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 |
|---|---|---|---|---|---|---|---|
| Brody Haynes | NCDC | 92.5% | 92.3% | Elmira | D3 | 94.8% | 1.22 |
| Nevin Tardif | USPHL-Premier | 90.4% | 88.0% | Worcester State | D3 | 89.8% | 3.54 |
| Cooper Rautenstrauch | NCDC | 90.6% | 93.8% | Colby | D3 | 93.4% | 2.08 |
| Cooper Rautenstrauch | NAHL | 88.9% | 94.0% | Colby | D3 | 93.4% | 2.08 |
| Michael Paterson-Jones | USPHL-Premier | 92.0% | 88.7% | Wilkes | D3 | 89.5% | 3.08 |
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.