A pitcher has just completed six shutout innings, but the manager takes him out because the computer tells him that it’s too risky to leave him in.
This is a situation that several MLB pitchers have faced over the last few years due to the integration of analytics. Baseball used to be a game won by instincts, trust, and the experience of a manager. Now, it is driven by statistics. Baseball has always been a numbers game, but in the last two decades, complex analytics have been used by all thirty MLB teams for everything, ranging from in-game decisions to player development to free-agent signings. Analytics have completely changed the operations of a baseball team, but baseball conservatives argue that this newfound attachment to the computer has stripped away the intimacy that fans have long enjoyed in baseball.
In order to comprehend the current state of analytics in baseball, it is important to understand the catalyst for the rise of analytics. Before the hiring of ex-MLB player Billy Beane as the general manager of the Oakland Athletics in 1997, baseball scouting was primarily ruled by what is called “the eye test.” Scouts would look for the superficial traits of a solid ballplayer: a smooth swing, speed, and slick fielding. Oftentimes, the eye test would not be a great way to find the best baseball talent. Additionally, teams would only look at a player’s home run total or his batting average to determine his worth. By contrast, Beane and his team of statisticians, who were often not even baseball fans, would look deeper at the players they scouted. They concentrated on stats, such as on-base percentage and the number of walks a player had, which allowed them to find players who were succeeding at the same rate as the big-name free agents yet did not receive the same attention. Following the A’s rapid success, the strategy employed by Beane became known as “Moneyball”. This concept has continued to grow since Beane’s tenure, and it is still evolving today.
Analytics have changed the way that MLB hitters and teams approach their in-game offensive strategy. New and constantly changing data enable hitters to look back on their previous swings and analyze the underlying metrics, such as the exit velocity and launch angle. These metrics and their accessibility allow players to make technical changes to their swings to achieve their desired results. On the coaching side, the creation of hundreds of new complex metrics, such as weighted runs created (wRC) and weighted on-base average (wOBA), allows teams to construct a more balanced lineup in a methodical manner. Before the rise of analytics, the best contact hitter would hit first, and the best hitters on the team would follow, most commonly batting third or cleanup. Today, pure power hitters, such as Kyle Schwarber, are hitting leadoff, and a team’s best hitters, like New York Yankees star Aaron Judge and New York Mets star Juan Soto, are now hitting second in the order. This development is largely due to the analytics and the metrics that they produce.
On the other side, analytics and new metrics have also revolutionized the way teams create strategies for their pitchers and in the field. As with hitting, teams use several metrics to monitor their pitchers, such as expected earned-run average (xERA) and spin rate. The latter is universally used by players, coaches, broadcasters, and even the casual fan. Spin rate measures how many times the ball spins per pitch, which varies depending on the type of pitch thrown. A fastball typically has fewer revolutions per minute (RPM) than a curveball or slider. Teams can use a pitcher’s spin rate, or usually a lack thereof, to pinpoint specific pitches that are weaker. Similar to the application of the hitting metrics, coaches and players are able to make technical adjustments that will help the pitchers become better overall. In the field, teams can apply patterns in their opponents’ offensive data to create gameplans that involve unique pitch sequencing, like throwing more breaking balls to hitters who have proven to be weaker versus those pitches, and defensive shifts, which means fielders moving based on where data shows the opponent most often hits the ball. Defensive shifts used to be very extreme; some fielders would be playing on the outfield grass or on the opposite side of the diamond from their “normal” position. Hitters would commonly have clean base-hits taken away due to these extreme shifts, so the MLB implemented new rules that limited, but did not eliminate, shifts.
Despite what seems to be very helpful applications of analytics, several fans, players, and coaches, usually of the older demographic, have begun to speak out against the use of analytics. Some managers feel that the use and, in their minds, the overuse of analytics has diminished the role of the manager, as well as diminished their influence and the trust placed in them.
Detroit Tigers manager AJ Hinch stated in an interview that he doesn’t “think you can manage games simply on your stomach, and [can’t] manage games simply on a script. The guys that can blend it are the best.”
Former players have also voiced their displeasure with the new computer-driven game. Hall of Fame pitcher and now TV analyst John Smoltz cited the new focus on analytics, specifically spin rate and velocity, as the reason so many pitchers are getting seriously hurt (“John Smoltz unleashes…”). Some current players have even begun to voice their opinions on the matter. Seasoned veteran Max Scherzer, who has seen the rise of analytics first-hand throughout his career, has been at the forefront of players against the computer. His solution to the criticism of starting pitchers not pitching as far into games anymore is to eliminate the team’s right to a designated hitter if the starting pitcher does not meet the predetermined “expectations.” The all-encompassing argument being made by all these analytics haters is simple: the unpredictable nature of the sport is what makes baseball enjoyable, but analytics inherently takes away all the unpredictability of the game.
On the contrary, being able to manipulate the data to find those diamond-in-the-rough players, like Beane did 25 years ago, has allowed small-market teams to succeed in an era of big spending. Teams like the Brewers, Guardians, and Rays can compete with big-market teams, including the Dodgers and Yankees. Additionally, new access to biometric data and high-tech pitching “labs” have allowed coaches, medical specialists, and players to pinpoint technical flaws that result in injury. The argument being made by the pro-analytics side is that these crazy formulas do not ruin baseball; they level the playing field, allowing teams without the vast financial resources of owners like Steve Cohen and Hal Steinbrenner to compete.
Indirectly, the MLB has tried to bring back the “older” style of baseball. As mentioned before, defensive shifts were banned, making it easier to hit singles. Bases were made bigger, making it easier to steal, which is a lost art for many teams. The biggest change implemented has been the new pace of play rules, including the addition of a pitch clock, limited pickoff attempts, and the inability to remove a pitcher before facing three batters. The latter is most directly related to the use of analytics, as most teams used to lean on a relief pitcher for only one batter because the computer predicted a favorable matchup. Eliminating this adds strategy back into the game for managers. The MLB has made it clear that its goal is to reinstate the unpredictability of the game without disregarding innovation.
Now more than ever, analytics are at the forefront of every baseball fan’s mind. With artificial intelligence growing more powerful by the day, teams are continuing to incorporate it into their systems for scouting, player forecasts, biometric analysis, and player development. Most recently, the MLB has said that it will be using automated umpires for balls and strikes in the 2026 season. While they are not removing the home plate umpire entirely, the MLB is still showing signs of further integration with AI.
With all that being said, baseball is still just a game. No matter what data is used, what metric is applied, and what numbers are crunched, any team can win on any given day. The best part of baseball has always been its unpredictable nature–the idea that every pitch, every play, every moment can completely change the game. While analytics have made the game smarter, no robot will ever be able to take away the raw emotion that has defined baseball for generations.