| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Olds Grizzlys | AJHL | 44 | 6 | 10 | 16 | 0.364 | 0.1220 | 0.1371 | 0.3365 | 0.3782 |
| 2022-23 | Olds Grizzlys | AJHL | 51 | 11 | 15 | 26 | 0.510 | 0.1710 | 0.1837 | 0.4718 | 0.5069 |
| 2023-24 | Fairbanks Ice Dogs | NAHL | 31 | 8 | 4 | 12 | 0.387 | 0.1422 | 0.1491 | 0.4086 | 0.4285 |
| 2024-25 | Winkler Flyers | MJHL | 36 | 19 | 26 | 45 | 1.250 | 0.2406 | 0.2347 | 0.7883 | 0.7689 |
| 2025-26 | Winkler Flyers | MJHL | 48 | 29 | 45 | 74 | 1.542 | 0.2968 | 0.2753 | 0.9722 | 0.9019 |
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.