| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Blackfalds Bulldogs | AJHL | 49 | 17 | 12 | 29 | 0.592 | 0.1985 | 0.2028 | 0.5459 | 0.5577 |
| 2022-23 | New Mexico Ice Wolves | NAHL | 55 | 19 | 14 | 33 | 0.600 | 0.2132 | 0.2120 | 0.6329 | 0.6292 |
| 2023-24 | New Mexico Ice Wolves | NAHL | 47 | 27 | 25 | 52 | 1.106 | 0.3931 | 0.3720 | 1.1670 | 1.1045 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | Alaska Anchorage | D1 | WCHA | — | 33 | 11 | 14 | 25 | 0.758 |
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.