| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | — | USHL | 11 | 0 | 3 | 3 | 0.273 | 0.1676 | 0.1676 | 0.8034 | 0.8034 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Minnesota | D1 | BigTen | — | 35 | 5 | 18 | 23 | 0.657 |
| 2022-23 | Minnesota Duluth | D1 | NCHC | — | 35 | 5 | 18 | 23 | 0.657 |
| 2021-22 | Minnesota | D1 | BigTen | — | 34 | 2 | 17 | 19 | 0.559 |
| 2021-22 | Minnesota Duluth | D1 | NCHC | — | 34 | 2 | 17 | 19 | 0.559 |
| 2020-21 | Minnesota Duluth | D1 | NCHC | FR | 28 | 0 | 10 | 10 | 0.357 |
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.