Baseball Simulations: Entertainment or Tech-Enabled Decision Support?
Simulated baseball seasons are capturing the attention of sports fans, but should MLB General Managers pay attention as well?
The New York Mets lost a tough one yesterday, falling to a sub-par record of 9–13. Strong offensive performances from Jeff “The Flying Squirrel” McNeil and Pete “Polar Bear” Alonso chased away Brewers starting pitcher Brent Suter in just the third inning. The potent Brewers offense, however, fired back with 6 runs as they improved to 12–10.
Unfortunately, I couldn’t watch the game. Not a single beer was sold. No grass was clipped, and no hotdogs were eaten. Baseball fans will need to keep waiting for those time-honored traditions. Rather, this game was carried out using advanced baseball simulation software, relying on artificial-intelligence empowered decision-making.
Out of The Park Baseball (OOTPB) is a complex software offering that allows users to design and carry out seasons of baseball with nearly infinite control, all for the one-time price of $39.99. Users can expand the Major League Baseball (MLB) divisions to include additional teams, recreate the 1986 Mets World Series winning season (or any other historic season from 1915 to present day), or take over their favorite team in present day and try to run it better than those good-for-nothing general managers that users always complain about. The possibilities are endless.
In an attempt to fill the current void in the hearts of baseball fans, the baseball community is using these technology-enabled baseball simulations to recreate the experience of following your favorite team’s daily schedule. For example, Baseball Reference, one of the preeminent online resources for baseball statistics, is using OOTPB to simulate each day’s previously scheduled MLB games, and publishing the outcomes right on the front page of its highly trafficked website. Baseball Reference is even publishing league standings, daily and season-long statistic leaders, and upcoming schedules. Better yet, media outlets such as ESPN and Sports Illustrated are even providing some level of coverage over the outcomes of these much-watched simulated baseball games. While they are certainly providing entertainment to eager baseball fans, one could argue that MLB front offices should also pay attention to the outcomes of these advanced simulations to help support their own strategic decision-making.
OOTPB provides the management teams of MLB organizations with an incredibly powerful tool to support data-driven decision-making. Each player’s in-game capabilities are based upon expert reviews and analytics-supported opinions of the underlying strengths and weaknesses of each player in professional baseball. Management teams can utilize OOTPB to recreate a simulated model of any possible matchup of players, and execute it an endless number of times, yielding potentially statistically relevant projections of the outcomes of various in-game scenarios.
Imagine being Met’s General Manager Brodie Van Wagenen, and wondering how your star pitcher Jacob deGrom will fare against the powerful hitter Bryce Harper this year. OOTPB provides a tool for the Mets to project an infinite amount of at bats between deGrom and Harper, with an infinite combination of variables (i.e. daytime, nighttime, rain) to help identify the probability of potential outcomes.
Further, the simulated intelligence of OOTPB is going a step further into the process of running an MLB team and beginning to execute trades! MLB fans have been eager to watch how their favorite team’s AI-based General Manager is running their team. The Toronto Blue Jays have decided to promote top prospect Nate Pearson, and include him in the opening day starting rotation. The Philadelphia Phillies have decided to trade for Jairo Muñoz. These decisions are fascinating because they address some questions raised by baseball analysts. The Blue Jays aren’t competing for a pennant this year, so why not provide opportunities for young talent to develop? The Phillies have some unproven and inconsistent infielders, so why not bolster their depth with a utility player? MLB General Managers might want to pay attention. The OOTPB AI-based decision-making software may be removing biases inherent in the process of signing, utilizing, and trading real-life baseball players in an effort to make optimized roster decisions.
Though OOTPB is, at the end of the day, a glorified spreadsheet that allows baseball fans the opportunity to play in a sandbox of infinite potential, there exists the possibility that it could be a tool worth utilizing. The opportunity is present for statisticians (or MLB front office teams) to identify the predictive capabilities of such software offerings on real-world phenomena. If the software proves to be capable of generating statistically relevant predictions, we could see a new shift in the application of technology and data in the process of running a baseball team, the biggest since the influence of Bill James’ sabermetrics or Billy Beane’s “Moneyball” practices.
