icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
Vancouver Canucks
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | — | DEL | 27 | 11 | 15 | 89.8% | 2.95 | 1 | 1.0100 | 72.9% |
| 2022-23 | — | SHL | 22 | 10 | 11 | 90.2% | 3.08 | 0 | 1.0100 | 71.7% |
| 2021-22 | — | SHL | 28 | 13 | 15 | 89.2% | 3.21 | 0 | 1.0100 | 72.5% |
| 2020-21 | — | SHL | 44 | 18 | 24 | 91.1% | 2.79 | 1 | 1.0100 | 92.0% |
| 2019-20 | — | Allsvenskan | 41 | 33 | 8 | 93.8% | 1.72 | 6 | 1.0100 | 94.7% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2011-12 | Merrimack | D1 | 36 | 17 | 12 | 92.5% | 2.18 | 2 |
| 2010-11 | Merrimack | D1 | 39 | 25 | 10 | 91.1% | 2.48 | 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 |
|---|---|---|---|---|---|---|
| Noah Giesbrecht | USports-M | 94.1% | 96.9% | Ferris State | 90.8% | 3.08 |
| Jakub Krbecek | USHL | 84.7% | 77.6% | RIT | 89.3% | 3.90 |
| Anton Castro | USHL | 88.6% | 81.0% | Wisconsin | 78.6% | 4.27 |
| Reid Dyck | WHL | 89.0% | 81.5% | Colgate | 89.7% | 3.17 |
| Connor Hasley | USHL | 86.7% | 79.7% | Bentley | 91.0% | 2.95 |
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 |
| Chris Branch | USPHL-Premier | 84.7% | 77.4% | Lebanon Valley | D3 | 89.3% | 4.36 |
| William Goumas | OJHL | 84.8% | 80.6% | SUNY Morrisville | D3 | 89.7% | 4.12 |
| William Goumas | OJHL | 84.8% | 80.6% | Morrisville | D3 | 89.7% | 4.12 |
| Pierce Diamond | BCHL | 88.6% | 80.6% | Albertus Magnus | D3 | 89.4% | 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.