| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2018-19 | — | AJHL | 60 | 10 | 51 | 61 | 1.017 | 0.3396 | 0.3739 | 0.9438 | 1.0392 |
| 2019-20 | — | AJHL | 54 | 12 | 63 | 75 | 1.389 | 0.4639 | 0.4639 | 1.2893 | 1.2893 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Denver | D1 | NCHC | JR | 39 | 13 | 21 | 34 | 0.872 |
| 2021-22 | Denver | D1 | NCHC | SO | 41 | 15 | 23 | 38 | 0.927 |
| 2020-21 | Denver | D1 | NCHC | FR | 21 | 3 | 8 | 11 | 0.524 |
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.