| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | Greater Sudbury Cubs | NOJHL | 58 | 10 | 27 | 37 | 0.638 | 0.1086 | 0.1220 | 0.2703 | 0.3036 |
| 2024-25 | Collingwood Blues | OJHL | 50 | 8 | 33 | 41 | 0.820 | 0.2010 | 0.2150 | 0.5644 | 0.6038 |
| 2025-26 | Sudbury Wolves | OHL | 62 | 1 | 14 | 15 | 0.242 | 0.1399 | 0.1406 | 0.6334 | 0.6365 |
| 2026-27 | Sudbury Wolves | OHL | 6 | 0 | 0 | 0 | 0.000 | — | — | — | — |
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.