| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Drumheller Dragons | AJHL | 44 | 11 | 7 | 18 | 0.409 | 0.1372 | 0.1456 | 0.3792 | 0.4025 |
| 2022-23 | — | AJHL | 46 | 12 | 17 | 29 | 0.630 | 0.2114 | 0.2139 | 0.5843 | 0.5913 |
| 2023-24 | — | AJHL | 49 | 22 | 13 | 35 | 0.714 | 0.2396 | 0.2312 | 0.6620 | 0.6388 |
| 2024-25 | Camrose Kodiaks | AJHL | 37 | 20 | 19 | 39 | 1.054 | 0.3535 | 0.3223 | 0.9769 | 0.8907 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Albertus Magnus | D3 | UCHC | — | 17 | 8 | 4 | 12 | 0.706 |
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.