| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | AJHL | 59 | 13 | 18 | 31 | 0.525 | 0.1743 | 0.1787 | 0.4869 | 0.4992 |
| 2022-23 | — | USHL | 62 | 9 | 36 | 45 | 0.726 | 0.4461 | 0.4257 | 2.1384 | 2.0406 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Denver | D1 | NCHC | JR | 43 | 10 | 19 | 29 | 0.674 |
| 2024-25 | Denver | D1 | NCHC | JR | 41 | 9 | 21 | 30 | 0.732 |
| 2023-24 | Denver | D1 | NCHC | SO | 44 | 5 | 22 | 27 | 0.614 |
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.