| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Lincoln Stars | USHL | 14 | 1 | 3 | 4 | 0.286 | 0.1756 | 0.1834 | 0.8417 | 0.8790 |
| 2023-24 | Lincoln Stars | USHL | 62 | 19 | 14 | 33 | 0.532 | 0.3272 | 0.3255 | 1.5683 | 1.5599 |
| 2024-25 | Lincoln Stars | USHL | 56 | 27 | 34 | 61 | 1.089 | 0.6696 | 0.6319 | 3.2093 | 3.0284 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Northeastern | D1 | HockeyEast | FR | 36 | 3 | 4 | 7 | 0.194 |
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.