| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | New Jersey Rockets | USPHL-Premier | 1 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2023-24 | Cedar Rapids RoughRiders | USHL | 41 | 2 | 3 | 5 | 0.122 | 0.0720 | 0.0749 | 0.3661 | 0.3809 |
| 2024-25 | Danbury Jr. Hat Tricks | NAHL | 56 | 39 | 35 | 74 | 1.321 | 0.4855 | 0.5017 | 1.3966 | 1.4433 |
| 2025-26 | Moncton Wildcats | QMJHL | 64 | 43 | 31 | 74 | 1.156 | 0.5680 | 0.5376 | 3.0866 | 2.9216 |
| 2026-27 | Moncton Wildcats | QMJHL | 7 | 8 | 1 | 9 | 1.286 | 0.6317 | 0.6317 | 3.4323 | 3.4323 |
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.