| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Yorkton Terriers | SJHL | 46 | 14 | 15 | 29 | 0.630 | 0.1615 | 0.1789 | 0.4752 | 0.5263 |
| 2023-24 | Moose Jaw Warriors | WHL | 63 | 15 | 21 | 36 | 0.571 | 0.2780 | 0.2836 | 1.4020 | 1.4303 |
| 2024-25 | Moose Jaw Warriors | WHL | 68 | 17 | 30 | 47 | 0.691 | 0.3363 | 0.3251 | 1.6960 | 1.6395 |
| 2025-26 | Moose Jaw Warriors | WHL | 65 | 17 | 49 | 66 | 1.015 | 0.4940 | 0.4547 | 2.4915 | 2.2932 |
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.