icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
Chicago Blackhawks
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | Green Bay Gamblers | USHL | 43 | 23 | 12 | 89.3% | 3.35 | 0 | 0.9983 | 85.9% |
| 2022-23 | — | NAHL | 34 | 19 | 12 | 91.7% | 2.57 | 1 | 0.9843 | 93.2% |
| 2022-23 | — | USHL | 6 | 5 | 1 | 90.6% | 2.48 | 1 | 0.9983 | 90.5% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Minnesota Duluth | D1 | 33 | 19 | 13 | 90.8% | 2.25 | 2 |
| 2024-25 | Minnesota Duluth | D1 | 21 | 7 | 12 | 88.5% | 3.33 | 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 Grannan | USHL | 89.1% | 85.8% | Penn State | 88.5% | 3.40 |
| Samuel Urban | USHL | 89.9% | 86.4% | Arizona State | 90.4% | 3.53 |
| Cameron Korpi | USHL | 89.2% | 86.0% | Michigan | 90.4% | 3.08 |
| Jaxson Stauber | USHL | 88.3% | 85.0% | Minnesota | 93.3% | 1.00 |
| Simon Latkoczy | USHL | 88.7% | 85.7% | Nebraska Omaha | 91.9% | 2.32 |
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 |
|---|---|---|---|---|---|---|---|
| Andrew Doran | NA3HL | 93.4% | 87.0% | Utica | D3 | 100.0% | — |
| Jackson Fellner | SJHL | 89.9% | 86.4% | Alvernia | D3 | 92.4% | 2.68 |
| Carson Ironside | AJHL | 87.8% | 86.2% | Albertus Magnus | D3 | 89.3% | 2.76 |
| Tyler Fromolz | NA3HL | 92.0% | 86.3% | Marian | D3 | 88.3% | 3.85 |
| Russ Decoste | USPHL-Premier | 93.1% | 88.5% | Westfield State | D3 | 93.3% | 2.38 |
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.