| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Collingwood Blues | OJHL | 34 | 3 | 8 | 11 | 0.324 | 0.0793 | 0.0819 | 0.2227 | 0.2301 |
| 2022-23 | Collingwood Blues | OJHL | 52 | 21 | 17 | 38 | 0.731 | 0.1791 | 0.1759 | 0.5032 | 0.4942 |
| 2023-24 | Collingwood Blues | OJHL | 56 | 39 | 33 | 72 | 1.286 | 0.3151 | 0.2931 | 0.8852 | 0.8235 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Niagara | D1 | AHA | SO | 37 | 9 | 12 | 21 | 0.568 |
| 2024-25 | Niagara | D1 | AHA | — | 34 | 7 | 5 | 12 | 0.353 |
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.