| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Moncton Wildcats | QMJHL | 8 | 0 | 1 | 1 | 0.125 | 0.0614 | 0.0675 | 0.3338 | 0.3667 |
| 2022-23 | Moncton Wildcats | QMJHL | 58 | 11 | 17 | 28 | 0.483 | 0.2372 | 0.2490 | 1.2892 | 1.3533 |
| 2023-24 | Moncton Wildcats | QMJHL | 68 | 19 | 38 | 57 | 0.838 | 0.4118 | 0.4119 | 2.2382 | 2.2390 |
| 2024-25 | Moncton Wildcats | QMJHL | 64 | 17 | 27 | 44 | 0.688 | 0.3378 | 0.3213 | 1.8358 | 1.7462 |
| 2025-26 | Moncton Wildcats | QMJHL | 64 | 20 | 22 | 42 | 0.656 | 0.3224 | 0.2914 | 1.7523 | 1.5840 |
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.