I am working on a player-based projection system, similar to how Bart Torvik does it.
For now, the main projections are team-based, with the main inputs being:
1. Reversion to conference mean.
2. Coaching change factor = a fired coach results in a positive benefit, a good coach hired away produces a negative one.
3. Generic increase in performance for remaining players on roster.
It's okay, and generally mirrors some of the early subjective predictions. But we can do better.
| Player | Class | Height | Avg Minutes | Avg Points | Avg Rebounds | Avg Assists | 2025 Team |
|---|---|---|---|---|---|---|---|
| Brittany Harshaw | Fr | 6-01 | 5.0 | 1.9 | 0.6 | 0.2 | Kansas |
| Danai Papadopoulou | Jr | 6-04 | 7.5 | 1.0 | 2.3 | 0.2 | Kansas |
| Laia Conesa | Fr | 5-11 | 5.2 | 1.0 | 0.5 | 0.2 | Kansas |
| McKenzie Smith | Fr | 6-00 | 5.8 | 1.3 | 0.5 | 0.3 | Kansas |
| S'Mya Nichols | Fr | 6-00 | 29.5 | 15.4 | 2.8 | 2.7 | Kansas |
| Sania Copeland | So | 5-07 | 29.8 | 7.3 | 2.6 | 2.1 | Kansas |
| Skyler Gill | Jr | 5-10 | 10.0 | 2.0 | 1.2 | 0.3 | Kansas |