| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | — | USHL | 45 | 5 | 9 | 14 | 0.311 | 0.1912 | 0.1912 | 0.9166 | 0.9166 |
| 2020-21 | — | USHL | 53 | 31 | 39 | 70 | 1.321 | 0.8119 | 0.8119 | 3.8913 | 3.8913 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Arizona State | D1 | NCHC | — | 39 | 16 | 22 | 38 | 0.974 |
| 2021-22 | Arizona State | D1 | — | — | 35 | 12 | 25 | 37 | 1.057 |
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.