| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Olds Grizzlys | AJHL | 59 | 18 | 18 | 36 | 0.610 | 0.2047 | 0.2091 | 0.5655 | 0.5776 |
| 2022-23 | — | AJHL | 60 | 24 | 27 | 51 | 0.850 | 0.2851 | 0.2771 | 0.7878 | 0.7657 |
| 2023-24 | Whitecourt Wolverines | AJHL | 57 | 26 | 42 | 68 | 1.193 | 0.4001 | 0.3700 | 1.1057 | 1.0225 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Colby | D3 | NESCAC | SO | 24 | 18 | 10 | 28 | 1.167 |
| 2024-25 | Northern Michigan | D1 | CCHA | — | 19 | 0 | 1 | 1 | 0.053 |
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.