How much predictability is there in fantasy football? Many football fanatics like myself wonder year after year if there is any path to consistent success in fantasy leagues. Let's use JMP to find out!
With enhancements to JMP's Python integration, getting your fantasy football league data into JMP is easier than ever. After briefly showing how to use JMP's native HTTP Request functionality to access documented APIs from fantasy football providers like ESPN and Sleeper, this talk focuses on showcasing how to use existing API wrappers from the Python community to pull in years of data from fantasy leagues. From league standings, playoff results, NFL player statistics, and fantasy outputs to fantasy draft steals and busts, there is so much data to explore!
Once the data is imported and organized into tables, we can then get to work using JMP's statistical discovery tools to both explore and analyze our league-specific data, finding possible trends and patterns to give us an edge.
Does success mainly come from nailing the draft or from making trades and acquisitions throughout the year? Are breakout fantasy performances repeatable from players, year over year? How important is having a top three fantasy producer on your team? Are running backs still the biggest impact positions in fantasy, or have things shifted in favor of wide receivers and quaterbacks? These are the questions we explore to discover if JMP can help you win your next league.
