icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2020-21 | — | USHL | 34 | 9 | 20 | 88.6% | 3.72 | 2 | 0.9980 | 88.4% |
| 2019-20 | — | NAHL | 35 | 17 | 14 | 92.0% | 2.61 | 3 | 0.9843 | 90.6% |
| 2018-19 | — | NAHL | 2 | 1 | 0 | 100.0% | — | 0 | 0.9843 | 97.0% |
| 2018-19 | — | USHL | — | — | — | — | — | — | 0.9980 | — |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | RIT | D1 | 2 | 0 | 1 | 95.5% | 0.83 | 0 |
| 2024-25 | RIT | D1 | 9 | 1 | 7 | 85.5% | 4.34 | 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 |
|---|---|---|---|---|---|---|
| Arthur Smith | NAHL | 92.8% | 96.9% | Princeton | 89.9% | 2.96 |
| Lawton Zacher | NAHL | 92.0% | 94.0% | Brown | 90.9% | 2.91 |
| Jere Huhtamaa | SM-Liiga-Jr | 90.3% | 89.8% | Merrimack | 86.4% | 3.47 |
| Mikhail Yegorov | USHL | 91.2% | 90.0% | Boston University | 90.4% | 2.73 |
| Zachary Sandy | NAHL | 93.2% | 94.6% | Minnesota Duluth | 100.0% | — |
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 |
| Cooper Rautenstrauch | NAHL | 88.9% | 94.0% | Colby | D3 | 93.4% | 2.08 |
| Vaughn Makar | NAHL | 90.9% | 95.6% | St. Norbert | D3 | 93.2% | 1.73 |
| Cooper Rautenstrauch | NCDC | 90.6% | 93.8% | Colby | D3 | 93.4% | 2.08 |
| Brayden Ingram | EHL | 91.2% | 94.4% | Assumption | D2 | 91.4% | 2.71 |
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.