| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Rosemount High (women) | USHS-MN-W | 20 | 2 | 4 | 6 | 0.300 | 0.0482 | 0.0482 | — | — |
| 2017-18 | Rosemount High (women) | USHS-MN-W | 25 | 12 | 13 | 25 | 1.000 | 0.1606 | 0.1606 | — | — |
| 2018-19 | Rosemount High (women) | USHS-MN-W | 24 | 17 | 13 | 30 | 1.250 | 0.2007 | 0.2007 | — | — |
| 2019-20 | Rosemount High (women) | USHS-MN-W | 24 | 29 | 13 | 42 | 1.750 | 0.2810 | 0.2810 | — | — |
| 2020-21 | Rosemount High (women) | USHS-MN-W | 17 | 21 | 18 | 39 | 2.294 | 0.3684 | 0.3684 | — | — |
| 2021-22 | Rosemount High (women) | USHS-MN-W | 28 | 39 | 32 | 71 | 2.536 | 0.4072 | 0.4072 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Minnesota | D1 | WCHA-W | SR | 37 | 5 | 15 | 20 | 0.540 |
| 2025-26 | Minnesota State | D1 | CHA-W | SR | 37 | 5 | 15 | 20 | 0.540 |
| 2024-25 | Minnesota | D1 | WCHA-W | JR | 35 | 13 | 9 | 22 | 0.629 |
| 2024-25 | Minnesota State | D1 | CHA-W | JR | 35 | 13 | 9 | 22 | 0.629 |
| 2023-24 | Minnesota | D1 | WCHA-W | SO | 38 | 10 | 16 | 26 | 0.684 |
| 2023-24 | Minnesota State | D1 | CHA-W | SO | 38 | 10 | 16 | 26 | 0.684 |
| 2022-23 | Minnesota | D1 | WCHA-W | FR | 36 | 2 | 7 | 9 | 0.250 |
| 2022-23 | Minnesota State | D1 | CHA-W | FR | 36 | 2 | 7 | 9 | 0.250 |
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.