| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2018-19 | — | USHL | 2 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2019-20 | — | USHL | 44 | 14 | 31 | 45 | 1.023 | 0.6287 | 0.6287 | 3.0131 | 3.0131 |
| 2020-21 | — | USHL | 44 | 17 | 25 | 42 | 0.955 | 0.5867 | 0.5867 | 2.8121 | 2.8121 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Minnesota | D1 | BigTen | — | 40 | 21 | 21 | 42 | 1.050 |
| 2021-22 | Minnesota | D1 | BigTen | — | 33 | 15 | 18 | 33 | 1.000 |
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.