| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Chicago Steel | USHL | 11 | 0 | 2 | 2 | 0.182 | 0.1118 | 0.1222 | 0.5356 | 0.5856 |
| 2017-18 | Chicago Steel | USHL | 54 | 23 | 29 | 52 | 0.963 | 0.5920 | 0.6179 | 2.8372 | 2.9614 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Minnesota | D1 | BigTen | SR | 39 | 13 | 20 | 33 | 0.846 |
| 2020-21 | Minnesota | D1 | BigTen | JR | 31 | 12 | 16 | 28 | 0.903 |
| 2019-20 | Minnesota | D1 | BigTen | SO | 37 | 8 | 16 | 24 | 0.649 |
| 2018-19 | Minnesota | D1 | BigTen | FR | 35 | 5 | 15 | 20 | 0.571 |
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.