icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | Tri-City Storm | USHL | 47 | 26 | 13 | 89.2% | 2.98 | 2 | 0.9983 | 86.0% |
| 2022-23 | — | NAHL | 16 | 12 | 3 | 92.3% | 1.97 | 1 | 0.9843 | 94.0% |
| 2022-23 | — | USHL | 21 | 13 | 4 | 91.1% | 2.52 | 1 | 0.9983 | 91.2% |
| 2021-22 | — | USHL | 14 | 4 | 5 | 86.2% | 3.75 | 1 | 0.9983 | 89.6% |
| 2020-21 | — | NAHL | 15 | 9 | 3 | 92.6% | 2.07 | 2 | 0.9843 | 91.1% |
| 2020-21 | — | NTDP-U18 | 7 | 2 | 5 | 84.7% | 4.54 | 0 | 0.9200 | 77.9% |
| 2020-21 | — | USHL | 6 | 1 | 4 | 83.0% | 5.29 | 0 | 0.9983 | 82.9% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Union | D1 | 26 | 15 | 8 | 89.9% | 2.43 | 5 |
| 2024-25 | Michigan | D1 | 18 | 7 | 6 | 90.4% | 3.08 | 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 |
|---|---|---|---|---|---|---|
| Jarrett Fiske | CCHL | 90.9% | 86.6% | American International | — | — |
| Simon Latkoczy | USHL | 88.7% | 85.7% | Nebraska Omaha | 91.9% | 2.32 |
| Adam Gajan | USHL | 89.3% | 85.9% | Minnesota Duluth | 88.5% | 3.33 |
| Max Väyrynen | SM-Liiga-Jr | 91.2% | 86.5% | Michigan Tech | 90.3% | 2.32 |
| Mitch Gibson | USHL | 89.0% | 86.2% | Harvard | 91.6% | 2.61 |
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% | — |
| Tyler Fromolz | NA3HL | 92.0% | 86.3% | Marian | D3 | 88.3% | 3.85 |
| Jarret Bovarnick | EHL | 88.3% | 87.5% | Suffolk | D3 | 87.2% | 4.52 |
| Cameron Kuntar | NCDC | 89.3% | 89.1% | Chatham | D3 | 92.4% | 2.62 |
| Jackson Fellner | SJHL | 89.9% | 86.4% | Alvernia | D3 | 92.4% | 2.68 |
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.