| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | Pacific Steelers | JWHL-U19 | 27 | 2 | 1 | 3 | 0.111 | 0.0417 | 0.0417 | — | — |
| 2015-16 | Pacific Steelers | JWHL-U19 | 29 | 0 | 3 | 3 | 0.103 | 0.0388 | 0.0388 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | Johnson & Wales | D3 | — | — | 24 | 5 | 11 | 16 | 0.667 |
| 2018-19 | Johnson & Wales | D3 | — | — | 27 | 5 | 8 | 13 | 0.481 |
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.