| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Malmö Redhawks U20 | SHL-J20 | 27 | 0 | 1 | 1 | 0.037 | 0.0204 | 0.0216 | 0.0494 | 0.0522 |
| 2023-24 | Malmö Redhawks U20 | SuperElit | 48 | 3 | 13 | 16 | 0.333 | 0.1306 | 0.1316 | 0.4094 | 0.4126 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Ferris State | D1 | CCHA | — | 37 | 1 | 16 | 17 | 0.460 |
| 2024-25 | Ferris State | D1 | CCHA | — | 20 | 1 | 3 | 4 | 0.200 |
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.