| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Blackfalds Bulldogs | AJHL | 57 | 14 | 20 | 34 | 0.597 | 0.1979 | 0.2063 | 0.5528 | 0.5762 |
| 2022-23 | Blackfalds Bulldogs | AJHL | 27 | 6 | 6 | 12 | 0.444 | 0.1475 | 0.1464 | 0.4119 | 0.4090 |
| 2023-24 | Humboldt Broncos | SJHL | 55 | 47 | 41 | 88 | 1.600 | 0.4874 | 0.4676 | 1.1858 | 1.1376 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | St. Lawrence | D1 | ECAC | — | 5 | 0 | 0 | 0 | 0.000 |
| 2024-25 | St. Lawrence | D1 | ECAC | — | 32 | 3 | 1 | 4 | 0.125 |
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.