| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Melfort Mustangs | SJHL | 54 | 8 | 30 | 38 | 0.704 | 0.1803 | 0.2025 | 0.5299 | 0.5952 |
| 2023-24 | Salmon Arm Silverbacks | BCHL | 54 | 6 | 17 | 23 | 0.426 | 0.1641 | 0.1746 | 0.6194 | 0.6590 |
| 2024-25 | Salmon Arm Silverbacks | BCHL | 51 | 13 | 14 | 27 | 0.529 | 0.2040 | 0.2068 | 0.7700 | 0.7806 |
| 2025-26 | Prince George Cougars | WHL | 59 | 7 | 5 | 12 | 0.203 | 0.0990 | 0.0927 | 0.4968 | 0.4650 |
| 2026-27 | Prince George Cougars | WHL | 5 | 1 | 0 | 1 | 0.200 | 0.0973 | 0.0973 | 0.4885 | 0.4885 |
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.