| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2020-21 | — | USHL | 51 | 9 | 13 | 22 | 0.431 | 0.2652 | 0.2652 | 1.2710 | 1.2710 |
| 2021-22 | — | USHL | 51 | 14 | 28 | 42 | 0.824 | 0.5062 | 0.5435 | 2.4262 | 2.6048 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | Michigan | D1 | BigTen | — | 40 | 25 | 28 | 53 | 1.325 |
| 2022-23 | Michigan | D1 | BigTen | — | 41 | 12 | 26 | 38 | 0.927 |
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.