| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Dauphin Kings | MJHL | 49 | 24 | 22 | 46 | 0.939 | 0.1807 | 0.2071 | 0.5920 | 0.6786 |
| 2022-23 | — | WHL | 57 | 5 | 8 | 13 | 0.228 | 0.1110 | 0.1185 | 0.5597 | 0.5973 |
| 2023-24 | Prince Albert Raiders | WHL | 66 | 17 | 12 | 29 | 0.439 | 0.2138 | 0.2176 | 1.0782 | 1.0976 |
| 2024-25 | Prince Albert Raiders | WHL | 68 | 31 | 36 | 67 | 0.985 | 0.4793 | 0.4623 | 2.4176 | 2.3319 |
| 2025-26 | Prince Albert Raiders | WHL | 55 | 24 | 27 | 51 | 0.927 | 0.4511 | 0.4142 | 2.2753 | 2.0892 |
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.