| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2020-21 | — | MJHL | 4 | 2 | 2 | 89.7% | 3.89 | 0 | 0.9700 | 87.0% |
| 2019-20 | — | MJHL | 12 | 8 | 3 | 90.3% | 2.29 | 1 | 0.9700 | 87.6% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Anna Maria | D3 | 7 | — | — | 88.0% | 3.99 | — |
| 2024-25 | Anna Maria | D3 | 7 | 5 | 0 | 93.2% | 2.07 | — |
| 2023-24 | Anna Maria | D3 | 7 | 2 | 2 | 92.9% | 2.77 | — |
| 2022-23 | Anna Maria | D3 | 13 | 5 | 5 | 89.1% | 3.42 | — |
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.