| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Rockland Nationals | CCHL | 51 | 2 | 10 | 12 | 0.235 | 0.0751 | 0.0813 | 0.1821 | 0.1972 |
| 2022-23 | Moncton Wildcats | QMJHL | 59 | 4 | 14 | 18 | 0.305 | 0.1517 | 0.1541 | 0.8137 | 0.8265 |
| 2023-24 | Sherbrooke Phoenix | QMJHL | 65 | 2 | 11 | 13 | 0.200 | 0.0994 | 0.0961 | 0.5334 | 0.5155 |
| 2024-25 | Sherbrooke Phoenix | QMJHL | 62 | 2 | 23 | 25 | 0.403 | 0.2005 | 0.1839 | 1.0753 | 0.9863 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Alaska Fairbanks | D1 | WCHA | FR | 18 | 0 | 6 | 6 | 0.333 |
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.