icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
Detroit Red Wings
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Leksands IF | SHL | 7 | — | — | 91.7% | 2.06 | — | 1.0100 | 92.6% |
| 2023-24 | — | SHL | 28 | 19 | 9 | 92.0% | 1.93 | 5 | 1.0100 | 97.0% |
| 2022-23 | — | Allsvenskan | 36 | 16 | 18 | 92.6% | 2.31 | 3 | 1.0100 | 96.9% |
| 2021-22 | — | Allsvenskan | 18 | 13 | 4 | 91.1% | 2.25 | 2 | 1.0100 | 97.0% |
| 2021-22 | — | SHL | — | — | — | — | — | — | 1.0100 | — |
| 2020-21 | — | Allsvenskan | 6 | 1 | 4 | 88.3% | 3.54 | 1 | 1.0100 | 89.2% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2018-19 | Denver | D1 | 22 | 13 | 6 | 93.2% | 1.95 | 4 |
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 |
|---|---|---|---|---|---|---|
| Luca Di Pasquo | BCHL | 92.2% | 91.6% | Michigan | 91.0% | 2.41 |
| Nils Wallström | NAHL | 91.9% | 91.6% | American International | 92.0% | 2.27 |
| Danick Leroux | NAHL | 92.7% | 92.0% | Holy Cross | 90.6% | 2.62 |
| Johnny Hicks | BCHL | 94.3% | 93.8% | Denver | 95.5% | 1.25 |
| Petter Wickström Stumer | NAHL | 91.4% | 90.8% | Canisius | 90.4% | 3.52 |
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 |
| Brayden Ingram | EHL | 91.2% | 92.3% | Assumption | D2 | 91.4% | 2.71 |
| Justin Damon | USPHL-Premier | 94.0% | 91.2% | Gustavus Adolphus | D3 | 92.9% | 2.27 |
| Marko Belak | MJHL | 90.3% | 91.0% | Gustavus Adolphus | D3 | 87.8% | 3.08 |
| Primo Baldassarre | NCDC | 92.8% | 92.7% | Connecticut College | D3 | 87.1% | 4.43 |
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.