| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | KalPa U20 | SM-Liiga-Jr | 13 | 1 | 2 | 3 | 0.231 | 0.1251 | 0.1435 | 0.3490 | 0.4002 |
| 2023-24 | KalPa U20 | SM-Liiga-Jr | 34 | 2 | 14 | 16 | 0.471 | 0.2551 | 0.2789 | 0.7117 | 0.7781 |
| 2024-25 | KalPa U20 | SM-Liiga-Jr | 42 | 19 | 26 | 45 | 1.071 | 0.5807 | 0.6062 | 1.6203 | 1.6915 |
| 2025-26 | KalPa U20 | SM-Liiga-Jr | 33 | 8 | 41 | 49 | 1.485 | 0.8048 | 0.8001 | 2.2455 | 2.2324 |
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.