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 |
---|---|---|---|---|---|---|---|
Alexis Parker | So | 5-09 | 8.4 | 1.4 | 1.2 | 0.4 | UTSA |
Aysia Proctor | Fr | 5-08 | 25.2 | 9.7 | 5.1 | 1.4 | UTSA |
Cheyenne Rowe | Fr | 6-02 | 8.5 | 2.3 | 2.4 | 0.2 | UTSA |
Elyssa Coleman | Jr | 6-03 | 25.4 | 10.4 | 7.2 | 0.7 | UTSA |
Emma Lucio | Fr | 5-09 | 7.2 | 1.2 | 0.8 | 0.5 | UTSA |
Idara Udo | Fr | 6-01 | 20.6 | 7.4 | 5.9 | 0.8 | UTSA |
Madison Cockrell | So | 5-04 | 7.5 | 1.6 | 1.4 | 0.4 | UTSA |
Maya Linton | So | 5-11 | 14.7 | 2.9 | 3.7 | 0.3 | UTSA |
Sidney Love | So | 5-08 | 30.4 | 9.6 | 3.7 | 3.2 | UTSA |
Siena Guttadauro | So | 5-06 | 9.9 | 2.9 | 1.3 | 0.7 | UTSA |