Baseball analytics is the use of statistical analysis and tracking technology to measure player performance, inform strategy, and make front-office decisions. The field grew out of what Bill James defined in 1980 as sabermetrics: “the search for objective knowledge about baseball.” What started as one man questioning whether batting average and pitcher wins actually told us who was good has become a multibillion-dollar infrastructure of cameras, sensors, and data scientists embedded in every Major League front office.
From Sabermetrics to Statcast
Traditional baseball statistics like batting average, RBIs, and pitcher wins dominated the sport for over a century. Sabermetric researchers challenged these measures by showing they often reflected circumstances (like teammates getting on base) more than individual skill. The goal was to isolate what a player actually controlled and assign proper value to each action on the field.
That philosophical project got a massive technological upgrade. Every MLB stadium now uses a Hawk-Eye tracking system, a network of high-speed cameras that captures the position and movement of every player, the ball, and even individual limbs in three dimensions. This system, branded as Statcast, generates data on pitch spin, bat speed, sprint speed, arm strength, and route efficiency on every single play. Before Statcast launched league-wide in 2015, analysts worked primarily with box-score data. Now they have millions of data points per game to work with.
How Hitting Is Measured Now
The shift away from batting average starts with a simple observation: not all hits are equal, and getting on base matters more than most traditional stats reflect. Modern analytics weighs each offensive event (a walk, a single, a double, a home run) by how many runs it actually produces on average. This thinking led to a stat called Weighted On-Base Average, or wOBA, which is essentially a more accurate version of OPS because it properly values each way a hitter reaches base.
The most widely used rate stat today is wRC+, or Weighted Runs Created Plus. It takes that weighted approach and adjusts for the ballpark a player hits in and the offensive environment of the league that season. The scale is intuitive: 100 is league average, and every point above or below is a percentage point better or worse. A hitter with a 125 wRC+ created 25% more runs than a league-average hitter in the same number of plate appearances. A hitter at 80 created 20% fewer. Because it accounts for context, you can use wRC+ to compare Ted Williams in 1941 to someone playing today and get a meaningful answer.
Statcast added another layer with metrics like exit velocity and launch angle. A “barrel” is a batted ball hit at the ideal combination of speed and angle for damage. To qualify, the ball must leave the bat at 98 mph or faster. At exactly 98 mph, the launch angle must fall between 26 and 30 degrees. For every additional mph of exit velocity, the acceptable angle range widens. By 116 mph, anything launched between 8 and 50 degrees counts as a barrel. Batted balls classified as barrels produce a collective batting average of at least .500 and a slugging percentage of at least 1.500. Teams use barrel rate to identify hitters who make elite contact, even when traditional stats haven’t caught up yet.
How Pitching Is Evaluated
ERA has the same problem batting average does: it’s contaminated by factors the pitcher doesn’t control. A pitcher on a team with great defenders will allow fewer hits on balls in play, and his ERA will look better than it should. Fielding Independent Pitching, or FIP, strips away defense, luck, and sequencing to estimate what a pitcher’s ERA would look like if he had league-average results on balls put in play. FIP focuses only on outcomes a pitcher directly controls: strikeouts, walks, hit batters, and home runs.
This makes FIP a more stable predictor of future performance than ERA. A pitcher whose ERA is much lower than his FIP has likely been getting lucky with his defense or the timing of hits against him. The reverse is also true: a pitcher with a high ERA but a low FIP may be better than his results suggest, making him an undervalued target in trades or free agency.
Statcast expanded pitching analysis further by tracking spin rate, spin axis, pitch movement, and extension (how close to home plate a pitcher effectively releases the ball). Teams now model how each pitch interacts with a specific hitter’s swing tendencies, allowing pitchers and catchers to build game plans at a granular level that wasn’t possible a decade ago.
Analytics and Defensive Strategy
One of the most visible impacts of analytics on the field was the defensive shift. By studying spray charts showing where a hitter tends to put the ball, teams repositioned fielders dramatically, sometimes placing three infielders on one side of second base. For pull-heavy left-handed hitters especially, the shift could turn hard-hit ground balls into outs.
The shift became so effective and so widespread that MLB intervened. Starting in 2023, new rules require all four infielders to stay within the infield boundary when the pitcher is on the rubber, and infielders cannot switch sides. A team can no longer reposition its best defender to the side where a batter is most likely to hit. Four-outfielder alignments are also banned. These changes were tested over several years in the minor leagues before being adopted. The result has been a noticeable increase in batting averages on ground balls, which was the league’s stated goal.
Injury Prevention and Workload Tracking
Analytics extends well beyond game strategy. One of its most consequential applications is protecting pitchers from injury. Pitch count has been the standard workload measure for years, but it treats every throw the same, whether it’s a 70% effort changeup in spring training or a max-effort fastball in October. Modern tracking tools aim to fix that.
Wearable sensors, like elbow-mounted devices that fit inside a compression sleeve, measure arm speed, arm slot, shoulder rotation, and estimated stress on the elbow ligament during every throw. These sensors calculate daily workload based on actual joint stress rather than simply counting pitches. The data can flag dangerous spikes in workload. In one study of high school players, those whose workload ratio spiked above 1.27 (meaning a sharp increase relative to their recent baseline) were over 15 times more likely to sustain an injury. Teams also use GPS tracking during training and games to monitor full-body demands like running volume and acceleration loads across the season.
Who Does This Work
Every MLB organization now employs a dedicated analytics staff within its baseball operations department. A typical structure includes a vice president of baseball analytics overseeing a team of senior data scientists, senior analysts, analysts, and associate analysts. Some clubs also embed analyst roles within their international scouting operations, combining traditional scouting judgment with statistical evaluation in markets like the Dominican Republic and Venezuela.
The San Francisco Giants, for example, list eight people in analytics-specific roles ranging from a VP down to associate analysts. That’s just one team, and it doesn’t count the broader data infrastructure staff, software engineers, or the analysts working in player development and scouting. Across the league, these departments have grown from a handful of outsiders with spreadsheets into core parts of organizational decision-making, influencing everything from draft picks and trade valuations to in-game pitching changes and defensive positioning.
For fans, the practical effect is that the language of baseball is changing. Broadcasts now routinely display exit velocity, expected batting average, and sprint speed. Understanding the basics of what these numbers mean, and why they replaced older stats, is increasingly part of following the sport itself.