icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | NCDC | 12 | 5 | 5 | 89.3% | 3.71 | 0 | 0.9400 | 84.0% |
| 2021-22 | — | NCDC | 17 | 9 | 3 | 91.1% | 2.92 | 1 | 0.9400 | 90.7% |
| 2021-22 | — | NAHL | 3 | 0 | 3 | 81.1% | 5.25 | 0 | 0.9843 | 84.9% |
| 2019-20 | — | USHL | — | — | — | — | — | — | 0.9980 | — |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Chatham | D3 | 20 | — | — | 94.1% | 2.16 | 1 |
| 2024-25 | Chatham | D3 | 7 | 0 | 4 | 89.0% | 3.78 | 0 |
| 2023-24 | Chatham | D3 | 11 | 6 | 5 | 92.4% | 2.62 | 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 |
|---|---|---|---|---|---|---|
| Cameron Korpi | USHL | 89.2% | 83.3% | Michigan | 90.4% | 3.08 |
| Simon Latkoczy | USHL | 88.7% | 83.1% | Nebraska Omaha | 91.9% | 2.32 |
| Jarrett Fiske | CCHL | 90.9% | 85.1% | American International | — | — |
| Philip Svedebäck | USHL | 91.0% | 85.2% | Providence | 90.9% | 2.18 |
| Adam Gajan | USHL | 89.3% | 83.0% | Minnesota Duluth | 88.5% | 3.33 |
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% | 87.1% | Utica | D3 | 100.0% | — |
| Carson Ironside | AJHL | 87.8% | 84.2% | Albertus Magnus | D3 | 89.3% | 2.76 |
| Russ Decoste | USPHL-Premier | 93.1% | 87.0% | Westfield State | D3 | 93.3% | 2.38 |
| Brandon Shantz | NA3HL | 89.7% | 82.6% | Plymouth State | D3 | 92.1% | 3.00 |
| Clément Labillois | SJHL | 88.9% | 84.4% | Assumption | D2 | 93.6% | 2.08 |
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.