| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2020-21 | — | USHL | 6 | 0 | 2 | 2 | 0.333 | 0.2049 | 0.2049 | 0.9820 | 0.9820 |
| 2021-22 | — | USHL | 41 | 18 | 25 | 43 | 1.049 | 0.6447 | 0.6804 | 3.0900 | 3.2612 |
| 2022-23 | — | USHL | 55 | 30 | 45 | 75 | 1.364 | 0.8382 | 0.8410 | 4.0174 | 4.0310 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | Michigan | D1 | BigTen | SO | 29 | 3 | 7 | 10 | 0.345 |
| 2023-24 | Michigan | D1 | BigTen | FR | 41 | 8 | 13 | 21 | 0.512 |
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.