| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Pacific Steelers | JWHL-U19 | 17 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2017-18 | Pacific Steelers | JWHL-U19 | 27 | 0 | 5 | 5 | 0.185 | 0.0695 | 0.0695 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Aurora | D3 | NCHA | — | 11 | 0 | 1 | 1 | 0.091 |
| 2020-21 | Aurora | D3 | NCHA | — | 14 | 1 | 2 | 3 | 0.214 |
| 2019-20 | Aurora | D3 | NCHA | — | 27 | 2 | 9 | 11 | 0.407 |
| 2018-19 | Aurora | D3 | NCHA | — | 19 | 2 | 8 | 10 | 0.526 |
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.