| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Winkler Flyers | MJHL | 48 | 2 | 13 | 15 | 0.312 | 0.0602 | 0.0620 | 0.1969 | 0.2029 |
| 2023-24 | Winkler Flyers | MJHL | 51 | 0 | 32 | 32 | 0.627 | 0.1208 | 0.1182 | 0.3955 | 0.3868 |
| 2024-25 | Winkler Flyers | MJHL | 39 | 2 | 29 | 31 | 0.795 | 0.1530 | 0.1412 | 0.5009 | 0.4624 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | St. Olaf | D3 | MIAC | — | 20 | 1 | 6 | 7 | 0.350 |
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.