| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Drayton Valley Thunder | AJHL | 56 | 4 | 9 | 13 | 0.232 | 0.0778 | 0.0798 | 0.2151 | 0.2206 |
| 2022-23 | Drayton Valley Thunder | AJHL | 58 | 1 | 26 | 27 | 0.466 | 0.1561 | 0.1524 | 0.4314 | 0.4211 |
| 2023-24 | New Mexico Ice Wolves | NAHL | 56 | 3 | 21 | 24 | 0.429 | 0.1522 | 0.1446 | 0.4500 | 0.4277 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Nichols | D3 | CNE | SO | 27 | 3 | 16 | 19 | 0.704 |
| 2024-25 | Nichols | D3 | CNE | — | 24 | 1 | 10 | 11 | 0.458 |
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.