| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2010-11 | — | SDHL | 25 | 8 | 5 | 13 | 0.520 | 0.6081 | 0.7444 | — | — |
| 2011-12 | AIK | SDHL | 28 | 14 | 24 | 38 | 1.357 | 1.5871 | 1.8429 | — | — |
| 2012-13 | AIK | SDHL | 26 | 12 | 16 | 28 | 1.077 | 1.2594 | 1.4444 | — | — |
| 2013-14 | AIK | SDHL | 27 | 6 | 13 | 19 | 0.704 | 0.8230 | 0.9268 | — | — |
| 2018-19 | Connecticut Whale | PHF | 16 | 3 | 3 | 6 | 0.375 | — | — | — | — |
| 2019-20 | HV71 | SDHL | 36 | 14 | 16 | 30 | 0.833 | 0.9745 | 0.9745 | — | — |
| 2020-21 | HV71 | SDHL | 36 | 19 | 30 | 49 | 1.361 | 1.5918 | 1.5918 | — | — |
| 2021-22 | SDE HF | SDHL | 34 | 10 | 16 | 26 | 0.765 | 0.8943 | 0.7471 | — | — |
| 2022-23 | SDE HF | SDHL | 31 | 11 | 18 | 29 | 0.935 | 1.0941 | 0.8727 | — | — |
| 2023-24 | SDE HF | SDHL | 34 | 11 | 19 | 30 | 0.882 | 1.0320 | 0.7479 | — | — |
| 2024-25 | SDE HF | SDHL | 30 | 8 | 10 | 18 | 0.600 | 0.7017 | 0.4795 | — | — |
| 2025-26 | Färjestad BK | SDHL | 36 | 4 | 19 | 23 | 0.639 | 0.7472 | 0.4819 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2017-18 | Minnesota Duluth | D1 | WCHA-W | — | 35 | 2 | 9 | 11 | 0.314 |
| 2016-17 | Minnesota Duluth | D1 | WCHA-W | — | 37 | 2 | 9 | 11 | 0.297 |
| 2015-16 | Minnesota Duluth | D1 | WCHA-W | — | 37 | 3 | 5 | 8 | 0.216 |
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.