| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | MoDo Hockey | SDHL | 16 | 2 | 0 | 2 | 0.125 | 0.1462 | 0.1462 | — | — |
| 2015-16 | MoDo Hockey | SDHL | 36 | 2 | 1 | 3 | 0.083 | 0.0974 | 0.0974 | — | — |
| 2016-17 | MoDo Hockey | SDHL | 32 | 0 | 2 | 2 | 0.062 | 0.0731 | 0.0731 | — | — |
| 2017-18 | MoDo Hockey | SDHL | 34 | 0 | 6 | 6 | 0.176 | 0.2064 | 0.2064 | — | — |
| 2018-19 | MoDo Hockey | SDHL | 36 | 2 | 8 | 10 | 0.278 | 0.3249 | 0.3249 | — | — |
| 2020-21 | MoDo Hockey | SDHL | 22 | 1 | 5 | 6 | 0.273 | 0.3189 | 0.3189 | — | — |
| 2021-22 | MoDo Hockey | SDHL | 33 | 2 | 6 | 8 | 0.242 | 0.2835 | 0.2835 | — | — |
| 2024-25 | Frölunda HC | SDHL | 35 | 3 | 8 | 11 | 0.314 | 0.3676 | 0.3219 | — | — |
| 2025-26 | Frölunda HC | SDHL | 30 | 2 | 6 | 8 | 0.267 | 0.3119 | 0.2612 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | Minnesota Duluth | D1 | WCHA-W | JR | 38 | 0 | 4 | 4 | 0.105 |
| 2022-23 | LIU | D1 | CHA-W | SO | 33 | 4 | 13 | 17 | 0.515 |
| 2022-23 | Long Island Univ. | D1 | CHA-W | — | 33 | 4 | 13 | 17 | 0.515 |
| 2020-21 | Long Island Univ. | D1 | CHA-W | — | 12 | 1 | 3 | 4 | 0.333 |
| 2019-20 | Long Island Univ. | D1 | CHA-W | — | 28 | 6 | 12 | 18 | 0.643 |
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.