| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Anoka High (women) | USHS-MN-W | 25 | 13 | 11 | 24 | 0.960 | 0.1542 | 0.1542 | — | — |
| 2017-18 | Anoka High (women) | USHS-MN-W | 25 | 8 | 12 | 20 | 0.800 | 0.1285 | 0.1285 | — | — |
| 2018-19 | Anoka High (women) | USHS-MN-W | 24 | 21 | 12 | 33 | 1.375 | 0.2208 | 0.2208 | — | — |
| 2019-20 | Anoka/Spring Lake Park | USHS-MN-W | 25 | 25 | 21 | 46 | 1.840 | 0.2955 | 0.2955 | — | — |
| 2025-26 | Färjestad BK | SDHL | 14 | 1 | 1 | 2 | 0.143 | 0.1650 | 0.1621 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | Minnesota | D1 | WCHA-W | GR | 37 | 11 | 18 | 29 | 0.784 |
| 2024-25 | Minnesota State | D1 | CHA-W | GR | 37 | 11 | 18 | 29 | 0.784 |
| 2023-24 | Minnesota | D1 | WCHA-W | SR | 26 | 3 | 8 | 11 | 0.423 |
| 2023-24 | Minnesota State | D1 | CHA-W | SR | 25 | 3 | 8 | 11 | 0.440 |
| 2022-23 | Minnesota | D1 | WCHA-W | JR | 36 | 10 | 12 | 22 | 0.611 |
| 2022-23 | Minnesota State | D1 | CHA-W | JR | 36 | 10 | 12 | 22 | 0.611 |
| 2021-22 | Minnesota | D1 | WCHA-W | SO | 35 | 5 | 7 | 12 | 0.343 |
| 2020-21 | Minnesota | D1 | WCHA-W | FR | 20 | 2 | 2 | 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.