| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Guelph Storm | OHL | 36 | 4 | 2 | 6 | 0.167 | 0.0964 | 0.1058 | 0.4283 | 0.4701 |
| 2022-23 | — | OHL | 40 | 10 | 2 | 12 | 0.300 | 0.1736 | 0.1830 | 0.7709 | 0.8128 |
| 2023-24 | Sudbury Wolves | OHL | 61 | 10 | 12 | 22 | 0.361 | 0.2087 | 0.2101 | 0.9268 | 0.9328 |
| 2024-25 | Sudbury Wolves | OHL | 65 | 24 | 19 | 43 | 0.661 | 0.3827 | 0.3654 | 1.6997 | 1.6230 |
| 2025-26 | Sudbury Wolves | OHL | 59 | 18 | 15 | 33 | 0.559 | 0.3236 | 0.2927 | 1.4371 | 1.3000 |
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.