| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Rouyn-Noranda Huskies | QMJHL | 54 | 8 | 9 | 17 | 0.315 | 0.1565 | 0.1640 | 0.8396 | 0.8800 |
| 2022-23 | Rouyn-Noranda Huskies | QMJHL | 60 | 2 | 11 | 13 | 0.217 | 0.1077 | 0.1076 | 0.5779 | 0.5774 |
| 2023-24 | Gatineau Olympiques | QMJHL | 68 | 38 | 41 | 79 | 1.162 | 0.5776 | 0.5486 | 3.0985 | 2.9431 |
| 2024-25 | Gatineau Olympiques | QMJHL | 63 | 35 | 31 | 66 | 1.048 | 0.5209 | 0.4692 | 2.7939 | 2.5164 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Bowling Green | D1 | CCHA | — | 33 | 5 | 5 | 10 | 0.303 |
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.