| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Olds Grizzlys | AJHL | 6 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2022-23 | Olds Grizzlys | AJHL | 53 | 6 | 8 | 14 | 0.264 | 0.0886 | 0.0954 | 0.2449 | 0.2636 |
| 2023-24 | Winkler Flyers | MJHL | 53 | 5 | 24 | 29 | 0.547 | 0.1053 | 0.1087 | 0.3448 | 0.3560 |
| 2024-25 | Winkler Flyers | MJHL | 56 | 15 | 37 | 52 | 0.929 | 0.1788 | 0.1748 | 0.5852 | 0.5720 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Long Island Univ. | D1 | AHA | FR | 23 | 0 | 2 | 2 | 0.087 |
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.