| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | Chicago Steel | USHL | 11 | 0 | 3 | 3 | 0.273 | 0.1676 | 0.1705 | 0.8034 | 0.8172 |
| 2015-16 | Chicago Steel | USHL | 60 | 20 | 28 | 48 | 0.800 | 0.4918 | 0.4774 | 2.3570 | 2.2880 |
| 2016-17 | Chicago Steel | USHL | 59 | 17 | 29 | 46 | 0.780 | 0.4793 | 0.4408 | 2.2972 | 2.1127 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2020-21 | Minnesota | D1 | BigTen | — | 28 | 4 | 12 | 16 | 0.571 |
| 2020-21 | Minnesota State | D1 | WCHA | SR | 28 | 4 | 12 | 16 | 0.571 |
| 2019-20 | Minnesota | D1 | BigTen | — | 24 | 8 | 8 | 16 | 0.667 |
| 2019-20 | Minnesota State | D1 | WCHA | JR | 24 | 8 | 8 | 16 | 0.667 |
| 2018-19 | Minnesota | D1 | BigTen | — | 37 | 7 | 12 | 19 | 0.513 |
| 2018-19 | Minnesota State | D1 | WCHA | SO | 37 | 7 | 12 | 19 | 0.513 |
| 2017-18 | Minnesota | D1 | BigTen | — | 40 | 15 | 24 | 39 | 0.975 |
| 2017-18 | Minnesota State | D1 | WCHA | FR | 40 | 15 | 24 | 39 | 0.975 |
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.