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In this session, we will explore roster construction through mixed-integer optimization.
In this session, we will explore roster construction through mixed-integer optimization. Using individual NBA player characteristics, we will formulate an optimization problem that constructs the ideal national basketball team subject to various constraints on team composition. This example will introduce a general framework for how to formalize various strategic problems (e.g. scheduling, pricing, staffing) as optimization models. These models will be implemented using JuMP in the Julia programming language. No prior experience with Julia is necessary.
If you are interested in this session, please take a look at Holly’s GitHub to download relevant code/data.
For the past 4 years, Sports Innovation Lab has been collecting and analyzing data from across the sports, technology, and media industries. Their findings produced the industry’s first data-backed ranking of the world’s most innovative teams. Hear how these teams are have created enterprise value and learn about their path to strategic growth.
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