| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Barrie Colts | OHL | 18 | 2 | 2 | 4 | 0.222 | 0.1285 | 0.1416 | 0.5709 | 0.6292 |
| 2022-23 | Pickering Panthers | OJHL | 38 | 11 | 14 | 25 | 0.658 | 0.1613 | 0.1738 | 0.6424 | 0.6802 |
| 2023-24 | — | OJHL | 40 | 23 | 31 | 54 | 1.350 | 0.3309 | 0.3393 | 0.9295 | 0.9531 |
| 2024-25 | Kingston Frontenacs | OHL | 64 | 13 | 11 | 24 | 0.375 | 0.2169 | 0.2081 | 0.9636 | 0.9245 |
| 2025-26 | Sarnia Sting | OHL | 64 | 24 | 17 | 41 | 0.641 | 0.3706 | 0.3369 | 1.6460 | 1.4964 |
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.