By Horia Tipi
For 23 seasons, starting in 1982, Holly and Henry Stephenson, a husband and wife duo, were the masterminds behind the Major League Baseball (MLB) schedule. The Stephensons were profiled in an ESPN 30 for 30 Shorts film, which describes how the couple, with a computer, a pencil, and a whole lot of permutations and variables, took on the daunting and thankless job of MLB scheduling from an upstairs bedroom in their Staten Island home.
In 2005, Holly and Henry were replaced by analysts specializing in computational methods in optimization. These modern day schedule makers with “random” computational optimization scheduling software were blamed for issues in subsequent MLB schedules. For example, the film points out that the 2013 Yankees season ended at an away game in Houston, which also happened to be the last game played by Yankees great Mariano Rivera. Of course, Rivera announced his retirement after the 2013 season schedule was set, he could have retired anytime during the season, and the Yankees didn’t even make the postseason! Hardly a snafu by modern day sports schedule makers – but it speaks more to the inability of the league and the teams to know in advance or clearly articulate their requirements.
Sports scheduling is complex business involving variables such as venue availability, fairness in balancing travel times and distance, league rules, sponsorships and advertising, TV schedules, and fan attendance. Scheduling games for a single season can involve literally trillions of scheduling permutations with thousands of variables and tens of thousands of constraints. Each year, team management, sportswriters and broadcasters, athletes, and fans ridicule the scheduling logic and blame the schedule makers.
Schedule making is as much science as it is art. Modern schedule makers – like our client Bortz Media, which produces season schedules for professional, collegiate and recreational baseball, football, basketball, hockey and tennis leagues – develop mathematical models that can find the best scheduling permutations.
Coming up with the best schedules that are fair and most closely reflect the goals of the league (e.g., marketing, competitive balance) and the sensitivities of individual teams (e.g., travel, attendance, etc.) also takes a fair amount of domain expertise. A sports season needs to tell a story, and television ratings and fan attendance depend on the drama created by great match ups and rivalries. For example, the NFL often opens the season with a Super Bowl rematch, the NBA plays on Christmas day, and many leagues end their seasons with battles between divisional rivals.
This domain expertise is a critical asset that helps the modern day schedule maker develop the intuition to create the best mathematical models. The scheduler needs to elicit any and all requirements from all stakeholders, translate these requirements into mathematical equations (objectives and constraints), and use an optimization engine to rapidly identify schedules that meet most or all of the constraints. Mathematical optimization software delivers the automation and the execution speed that allows the schedulers to do their work of understanding and extracting value. But it’s the schedulers and their expertise that bring the real alchemy in this scheduling equation.
