| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | AJHL | 49 | 5 | 5 | 10 | 0.204 | 0.0685 | 0.0715 | 0.1889 | 0.1973 |
| 2022-23 | Melfort Mustangs | SJHL | 39 | 13 | 24 | 37 | 0.949 | 0.2431 | 0.2453 | 0.7151 | 0.7214 |
| 2023-24 | Melfort Mustangs | SJHL | 34 | 26 | 26 | 52 | 1.529 | 0.3918 | 0.3767 | 1.1529 | 1.1083 |
| 2025-26 | Univ. of Alberta | USports-M | 22 | 2 | 6 | 8 | 0.364 | 0.1918 | 0.2004 | 1.0657 | 1.1136 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | Northern Michigan | D1 | CCHA | — | 24 | 6 | 2 | 8 | 0.333 |
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.