With the massive MLB Savant pitch-by-pitch dataset and Python programming, I was able to quickly get each pitcher's schedule data based on the list of hitters they actually faced, rather than just looking at stats from the teams they faced.
It is important for me to detail how I went about this, because it is probably not what you'd expect. Editor's Note: Our incredible team of writers received 13 award nominations by the Fantasy Sports Writers Association including Baseball Writer of the Year, Football Writers of the Year, Golf Writer of the Year and many more! Be sure to follow their analysis, rankings and advice all year long, and win big with RotoBaller! Read More! It's also true that the schedules aren't balanced out, teams play almost half of their games against teams in their division, which does end up giving some teams much easier goes of it throughout the long season. It feels ridiculous that the Pittsburgh Pirates and the Los Angeles Dodgers are in the same league and have to play each other. The lack of a salary cap in Major League Baseball has really led to huge differences in the firepower on the league's teams. Most people realize this inherently without it needing to be proven, so we don't often bother analyzing the schedules. Well, I bothered to do it. Strength of schedule metrics tend to even out over that big of a data sample.
If you get to face the league's worst offense twice while other teams don't face them at all, that makes a big difference. With just 16 (17 now, I guess) games on the schedule every year, the list of teams on your schedule can make a big difference in your final year metrics. Strength of schedule analysis is a common thing you'll see in fantasy football analysis.
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