| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Lloydminster Bobcats | AJHL | 2 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2022-23 | Lloydminster Bobcats | AJHL | 36 | 2 | 3 | 5 | 0.139 | 0.0466 | 0.0511 | 0.1285 | 0.1409 |
| 2023-24 | Surrey Eagles | BCHL | 36 | 3 | 7 | 10 | 0.278 | 0.1070 | 0.1125 | 0.4043 | 0.4250 |
| 2024-25 | Surrey Eagles | BCHL | 4 | 2 | 3 | 5 | 1.250 | 0.4816 | 0.4821 | 1.8191 | 1.8209 |
| 2025-26 | Chicago Steel | USHL | 18 | 0 | 4 | 4 | 0.222 | 0.1311 | 0.1214 | 0.6674 | 0.6179 |
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.