Based on 40 comparable players
Most women's college players come directly from high school hockey. Junior or HS season stats below (when available on Elite Prospects).
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-W PPG | Age-Adjusted |
|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | Mounds View High (women) | USHS-MN-W | 25 | 3 | 5 | 8 | 0.320 | 0.0483 | 0.0483 |
| 2020-21 | Mounds View High (women) | USHS-MN-W | 20 | 9 | 8 | 17 | 0.850 | 0.1283 | 0.1283 |
| 2021-22 | Mounds View High (women) | USHS-MN-W | 24 | 13 | 8 | 21 | 0.875 | 0.1321 | 0.1321 |
| Season | School | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Gustavus Adolphus | MIAC | SR | 26 | 0 | 1 | 1 | 0.038 |
| 2024-25 | Gustavus Adolphus | MIAC | JR | 28 | 3 | 9 | 12 | 0.429 |
| 2023-24 | Gustavus Adolphus | MIAC | SO | 19 | 0 | 2 | 2 | 0.105 |
| 2022-23 | Gustavus Adolphus | MIAC | FR | 29 | 1 | 0 | 1 | 0.034 |
| Player | League · Season · PPG | FR College PPG | School |
|---|---|---|---|
| Amanda Bäckebo | SDHL · 2012-13 · 0.11 PPG | 0.056 | Syracuse |
| Lydia Passolt | USHS-MN-W · 2012-13 · 0.89 PPG | 0.095 | Bemidji State |
| Vilma Tanskanen | SMLIIGA-W · 2012-13 · 0.35 PPG | 0.182 | North Dakota |
| Holly Dietzler | USHS-MN-W · 2012-13 · 0.84 PPG | 0.108 | Lindenwood |
| Jessica Bonfe | USHS-MN-W · 2012-13 · 0.84 PPG | 0.206 | Merrimack |
| Player | Junior Season · PPG | FR College PPG | School | Conference |
|---|---|---|---|---|
| Jana Lesch | 2023-24 · 1.44 PPG | 0.222 | Wisconsin-Eau Claire | WIAC |
| Sam Mugge | 2015-16 · 0.80 PPG | — | Wisconsin-Superior | WIAC |
| Alyson Vogelsang | 2023-24 · 0.81 PPG | — | St. Catherine | MIAC |
| Abby Reule | 2022-23 · 0.69 PPG | 0.450 | Concordia | MIAC |
| Brooke Klemz | 2023-24 · 1.15 PPG | 0.280 | Hamline | MIAC |
Women's college hockey projections draw primarily on high school and junior stats when available. Because most women go directly from HS to college, projections with limited pre-college data use conference-based priors.
NCAAe-W factors are derived from historical pre-college→NCAA-W transitions. For many players, projection confidence is lower than men's due to sparse pre-college tracking.