| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Cobourg Cougars | OJHL | 8 | 2 | 0 | 2 | 0.250 | 0.0698 | 0.0701 | 0.1725 | 0.1734 |
| 2023-24 | Cobourg Cougars | OJHL | 55 | 16 | 24 | 40 | 0.727 | 0.2032 | 0.1833 | 0.5019 | 0.4527 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Canton | D3 | SUNYAC | SO | 24 | 12 | 7 | 19 | 0.792 |
| 2024-25 | Canton | D3 | SUNYAC | FR | 15 | 10 | 4 | 14 | 0.933 |
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.