| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | BCHL | 7 | 0 | 1 | 1 | 0.143 | 0.0532 | 0.0570 | 0.2082 | 0.2232 |
| 2022-23 | Langley Rivermen | BCHL | 37 | 0 | 4 | 4 | 0.108 | 0.0403 | 0.0412 | 0.1575 | 0.1612 |
| 2023-24 | — | AJHL | 47 | 2 | 8 | 10 | 0.213 | 0.0706 | 0.0689 | 0.1972 | 0.1923 |
| 2024-25 | Ogden Mustangs | NCDC | 53 | 7 | 23 | 30 | 0.566 | 0.3156 | 0.2989 | 0.4577 | 0.4335 |
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.