Oklahoma State Cowgirls Are Big 12’s 2026-27 Preseason Favorite
Oklahoma State opens 2026-27 as the Big 12’s preseason favorite for the first time, with a revamped roster built to turn rising expectations into a breakthrough season. After a 24-win campaign and its first NCAA Tournament victory since 2021, the Cowgirls add standout transfers Audi Crooks and Liv McGill around veteran leader Stailee Heard.
By the Numbers- OSU went 24-10 last season and nearly half of its regular-season games this year are against teams that reached the previous NCAA Tournament.
- The roster features 11 newcomers: nine transfers and two freshmen.
- Crooks averaged 25.8 points and shot 64.9% from the field last season; McGill averaged 6.3 assists per game, ninth nationally.
- The Cowgirls have 12 nationally televised games, including a program-first trip to face South Carolina on ABC.
Preseason expectations are high, but the roster has undergone a major overhaul, with Heard the only returning player who made a start last season. A demanding nonconference schedule and a strong Big 12 will test how quickly the newcomers build chemistry.
State of Play- Heard brings continuity after three seasons of double-digit scoring and enters the year with 95 career starts.
- Crooks provides an elite scoring presence inside, while McGill adds proven playmaking at point guard.
- OSU’s recruiting haul also includes experienced transfers and a Division II scoring leader, giving coach Jacie Hoyt a deep, versatile group.
The Cowgirls will face an early measure of their potential against top competition, including South Carolina, before navigating a Big 12 slate designed to leave them ready for March.
Bottom LineOSU has the talent and visibility to pursue a historic season; its biggest challenge is turning a roster of high-impact newcomers into a cohesive team under the weight of favorite status.
Read more at Oklahoma State University Athletics
The summary of the linked article was generated with the assistance of artificial intelligence technology from OpenAI

