| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | OHL | 56 | 6 | 8 | 14 | 0.250 | 0.1446 | 0.1555 | 0.6424 | 0.6908 |
| 2022-23 | Sudbury Wolves | OHL | 67 | 18 | 21 | 39 | 0.582 | 0.3367 | 0.3475 | 1.4957 | 1.5436 |
| 2023-24 | Sudbury Wolves | OHL | 68 | 17 | 13 | 30 | 0.441 | 0.2552 | 0.2512 | 1.1337 | 1.1158 |
| 2024-25 | Sudbury Wolves | OHL | 65 | 17 | 42 | 59 | 0.908 | 0.5251 | 0.4897 | 2.3323 | 2.1751 |
| 2025-26 | Windsor Spitfires | OHL | 67 | 16 | 26 | 42 | 0.627 | 0.3627 | 0.3200 | 1.6108 | 1.4212 |
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.