| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2018-19 | — | SM-Liiga-Jr | 45 | 9 | 26 | 35 | 0.778 | 0.4143 | 0.4327 | 1.0995 | 1.1485 |
| 2019-20 | — | SM-Liiga-Jr | 48 | 15 | 34 | 49 | 1.021 | 0.5438 | 0.5438 | 1.4430 | 1.4430 |
| 2022-23 | — | Liiga | 60 | 5 | 15 | 20 | 0.333 | — | — | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Denver | D1 | NCHC | SO | 35 | 1 | 8 | 9 | 0.257 |
| 2020-21 | Denver | D1 | NCHC | FR | 24 | 2 | 9 | 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.