| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | St. Sebastian’s | NE-Prep | 24 | 10 | 11 | 21 | 0.875 | 0.1733 | 0.1733 | 0.3987 | 0.3987 |
| 2022-23 | St. Sebastian’s | NE-Prep | 24 | 5 | 19 | 24 | 1.000 | 0.1981 | 0.1981 | 0.4556 | 0.4556 |
| 2023-24 | — | NTDP-U18 | 5 | 1 | 0 | 1 | 0.200 | 0.1487 | 0.1507 | 0.7463 | 0.7565 |
| 2024-25 | Chicago Steel | USHL | 51 | 18 | 14 | 32 | 0.627 | 0.3702 | 0.3847 | 1.8846 | 1.9584 |
| 2025-26 | Moncton Wildcats | QMJHL | 54 | 31 | 37 | 68 | 1.259 | 0.6187 | 0.6164 | 3.3627 | 3.3504 |
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.