| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2012-13 | Linköping HC | SDHL | 16 | 0 | 1 | 1 | 0.062 | 0.0722 | 0.0722 | — | — |
| 2013-14 | Linköping HC | SDHL | 28 | 0 | 2 | 2 | 0.071 | 0.0825 | 0.0825 | — | — |
| 2014-15 | Linköping HC | SDHL | 27 | 4 | 7 | 11 | 0.407 | 0.4705 | 0.4705 | — | — |
| 2015-16 | Brynäs IF | SDHL | 34 | 2 | 4 | 6 | 0.176 | 0.2039 | 0.2039 | — | — |
| 2016-17 | Brynäs IF | SDHL | 32 | 5 | 8 | 13 | 0.406 | 0.4692 | 0.4692 | — | — |
| 2017-18 | Djurgårdens IF | SDHL | 28 | 3 | 7 | 10 | 0.357 | 0.4125 | 0.4125 | — | — |
| 2021-22 | Linköping HC | SDHL | 33 | 6 | 19 | 25 | 0.758 | 0.8750 | 0.8750 | — | — |
| 2022-23 | Linköping HC | SDHL | 23 | 4 | 10 | 14 | 0.609 | 0.7030 | 0.6773 | — | — |
| 2023-24 | Linköping HC | SDHL | 8 | 1 | 1 | 2 | 0.250 | 0.2888 | 0.2572 | — | — |
| 2024-25 | HV71 | SDHL | 13 | 0 | 4 | 4 | 0.308 | 0.3554 | 0.3018 | — | — |
| 2025-26 | SDE HF | SDHL | 14 | 5 | 2 | 7 | 0.500 | 0.5775 | 0.4682 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2020-21 | Penn State | D1 | WCHA-W | — | 21 | 4 | 7 | 11 | 0.524 |
| 2019-20 | Penn State | D1 | WCHA-W | — | 12 | 0 | 2 | 2 | 0.167 |
| 2018-19 | Penn State | D1 | WCHA-W | — | 35 | 5 | 7 | 12 | 0.343 |
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.