| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Toronto Aeros U22 AA | OWHL-U22 | 41 | 5 | 7 | 12 | 0.293 | 0.1023 | 0.1023 | — | — |
| 2023-24 | Whitby Wolves | OWHL-U22 | 53 | 3 | 16 | 19 | 0.358 | 0.1253 | 0.1253 | — | — |
| 2024-25 | Whitby Wolves | OWHL-U22 | 41 | 6 | 14 | 20 | 0.488 | 0.1705 | 0.1705 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Maine | D1 | HEA-W | FR | 34 | 1 | 12 | 13 | 0.382 |
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.