| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | North York Rangers | OJHL | 53 | 12 | 12 | 24 | 0.453 | 0.1110 | 0.1200 | 0.3118 | 0.3370 |
| 2022-23 | — | OJHL | 49 | 19 | 25 | 44 | 0.898 | 0.2201 | 0.2266 | 0.6183 | 0.6367 |
| 2023-24 | Blackfalds Bulldogs | AJHL | 43 | 15 | 16 | 31 | 0.721 | 0.2418 | 0.2388 | 0.6671 | 0.6589 |
| 2024-25 | — | BCHL | 51 | 8 | 17 | 25 | 0.490 | 0.1889 | 0.1775 | 0.7134 | 0.6703 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Mercyhurst | D1 | AHA | FR | 28 | 3 | 2 | 5 | 0.179 |
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.