Optimizing fantasy basketball decisions with Python
summary
Shows how PuLP and linear & integer programming encode roster rules and position eligibility, with three concrete Python examples: a start/sit optimization, a daily-salary free agency optimizer, and a multi‑category head‑to‑head model. Code constraints include salary caps, locked/excluded players, bench slots and opponent category targets; the solver yields provable optima and in an example raises the weekly projected lineup to about 68 actual fantasy points.
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chapters (16)
- 0:00Introduction
- 1:13Agenda
- 2:14Basketball stats basics
- 3:58Fantasy basketball
- 5:22League types
- 7:50Draft rules
- 9:14Integer programming
- 10:39PuLP optimization
- 11:00Daily fantasy setup
- 13:48Code walkthrough
- 17:22Adjusting lineups
- 19:50Head-to-head optimization
- 21:55Multi-category problems
- 24:01Optimizer examples
- 25:45Beyond sports applications
- 26:28Q&A
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