| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Newmarket Hurricanes | OJHL | 2 | 1 | 0 | 1 | 0.500 | 0.1226 | 0.1362 | 0.3443 | 0.3825 |
| 2023-24 | Newmarket Hurricanes | OJHL | 54 | 21 | 19 | 40 | 0.741 | 0.1815 | 0.1922 | 0.5100 | 0.5401 |
| 2024-25 | Newmarket Hurricanes | OJHL | 30 | 8 | 9 | 17 | 0.567 | 0.1389 | 0.1400 | 0.3902 | 0.3932 |
| 2025-26 | Guelph Storm | OHL | 38 | 2 | 7 | 9 | 0.237 | 0.1370 | 0.1292 | 0.6085 | 0.5737 |
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.