The Analytics Revolution in Modern Pitching

Over the past decade, Major League Baseball has undergone a dramatic transformation fueled by data. Teams now deploy entire analytics departments that track every pitch, swing, and defensive alignment. For pitchers, this means access to granular information once unimaginable—spin rate, release extension, pitch tunneling, and expected weighted on-base average (xwOBA). This data explosion has reshaped how hurlers prepare, execute, and adjust. Jacob deGrom, the two-time Cy Young Award winner for the New York Mets, stands as one of the most successful adopters of this analytical approach. His career arc—from a little-known college shortstop to a dominant force on the mound—mirrors the rise of data-driven pitching. By embracing analytics, deGrom has not only extended his prime but also continually refined his craft, setting a template for future generations.

Who Is Jacob deGrom? A Brief Background

Before diving into how deGrom uses data, it helps to understand his unusual path. Drafted in the ninth round of the 2010 MLB draft out of Stetson University, deGrom was a shortstop who converted to pitching late. He debuted in 2014 at age 26—older than most prospects—and immediately impressed with a mid-90s fastball. However, he wasn't an overnight sensation. His early success relied on raw talent and feel. Over time, deGrom realized that to compete against increasingly sophisticated hitters, he needed more than just velocity and a wipeout slider. He needed information.

That turning point came around 2018, when the Mets fully integrated Statcast data into player development. DeGrom began working with the team's analytics staff to understand advanced metrics. He didn't just accept the numbers; he studied them, asking questions about release point efficiency, pitch tunneling, and batter swing decisions. This willingness to learn separated him from peers who dismissed analytics as noise.

The Core Analytical Metrics Shaping deGrom's Approach

Spin Rate and Fastball Efficiency

Spin rate measures how many revolutions a pitch makes per minute. A high spin rate on a four-seam fastball creates a "rising" effect that fools hitters into swinging under the ball. DeGrom's four-seamer consistently ranks among the top 5% of MLB pitchers in spin rate. But he doesn't just rely on that natural gift. Analytics help him optimize when and where to throw it. Data from Baseball Savant shows that deGrom locates his fastball up in the zone more than 40% of the time—a location that maximizes whiff rates on high-spin heaters. Without analytics, he might have continued pitching to contact down in the zone, as many traditional pitchers did.

Release Point and Extension

Release extension—the distance from the pitching rubber to the point of release—affects a batter's perception of pitch speed. DeGrom has one of the longest extensions in baseball (over 6.5 feet), effectively reducing the perceived distance the ball travels. He tracks this metric religiously, using Rapsodo and Edgertronic cameras to ensure his delivery remains repeatable. Data analysis revealed that even slight deviations in release point reduced his fastball's effectiveness by increasing the batter's reaction time. By maintaining a consistent release point through mechanical drills informed by data, deGrom keeps hitters off balance.

Pitch Tunneling and Sequencing

Pitch tunneling means that different pitches look identical out of the hand for as long as possible, only diverging late. Using analytics tools, deGrom and the Mets' coaching staff plot the three-dimensional flight paths of his pitches. They discovered that his slider and fastball share a similar trajectory for the first 20-30 feet, making it extremely difficult for hitters to identify the pitch early. This insight led deGrom to pair his fastball and slider more deliberately, throwing them in sequences that exploit the same visual plane. The result? A swing-and-miss rate on his slider that exceeds 50% in many seasons.

How Analytics Changed deGrom's Pitch Arsenal

Reinventing the Slider

Early in his career, deGrom threw a slider that was more of a slurve—sweeping and slower. Analytics showed that the pitch had mediocre spin efficiency and was often left in the zone, leading to hard contact. In 2019, deGrom worked with the Mets' pitching lab to develop a tighter, harder slider with more vertical break. He increased its velocity by 2-3 mph and reduced the horizontal movement, creating a pitch that mimicked his fastball's release angle. The data confirmed that this new slider generated more whiffs and fewer barrels. Today, the slider is his primary out pitch, with batters hitting under .150 against it.

The Emergence of a Changeup

DeGrom's changeup was once considered a show-me pitch. But as left-handed hitters began to sit on his fastball, analytics indicated that a changeup with similar arm speed and release point could neutralize platoon advantages. Using TrackMan data, deGrom and his coaches tweaked his grip and arm angle to produce more fade (arm-side run). The changeup now has a whiff rate above 40%, making it a legitimate third weapon. This evolution would have been unlikely without the granular feedback that analytics provides.

Limited Use of the Curveball

Interestingly, deGrom has reduced his curveball usage over time. Analytics revealed that his curveball, while a decent pitch, didn't tunnel well with his fastball or slider. Its lower spin rate and different release characteristics made it easier for hitters to identify. By de-emphasizing the curve, deGrom simplified his approach, relying on two primary pitches (fastball and slider) and a changeup. This data-driven simplification improved his command and reduced mechanical inconsistency.

Game Planning with Data

Analytics don't just influence pitch design; they shape how deGrom attacks each hitter. Before every series, he reviews scouting reports that include heat maps of opponent swing tendencies. For example, if a batter struggles with high fastballs and whiffs on sliders down and away, deGrom's game plan will target those zones relentlessly. He also studies batters' zone profiles—where they chase pitches outside the strike zone—and adjusts his tunnel strategy accordingly.

During games, deGrom often consults a tablet in the dugout between innings to review his own pitch data. He looks for trends: Is his fastball losing velocity? Is his slider's vertical break flattening? These in-game adjustments, powered by real-time analytics, allow him to make micro-corrections that prevent a bad inning from spiraling.

Mechanical Adjustments Driven by Data

Perhaps the most critical impact of analytics on deGrom has been in injury prevention and mechanical efficiency. He has a history of elbow and back issues. Using motion-capture technology and force plate data, deGrom's training staff identified inefficiencies in his delivery that placed excess stress on his ulnar collateral ligament. By altering his stride length and hip-shoulder separation, based on biomechanical analysis, deGrom reduced torque on his arm without sacrificing velocity. This analytical approach to health has been a key reason why, even in his late 30s, deGrom can still throw 100 mph.

Additionally, release point consistency—tracked through high-speed cameras—helped him eliminate a subtle arm slot drift that caused his pitches to flatten out. DeGrom now uses a "tunnel vision" drill where he throws from a mound while a sensor measures his release point in real time. Any deviation beyond a certain threshold triggers a correction. This level of precision is only possible through data.

The Technology Behind the Data

To fully appreciate deGrom's analytics journey, it helps to understand the tools he uses. Rapsodo units measure spin rate, spin axis, and movement. Edgertronic high-speed cameras capture release point and arm angle at thousands of frames per second. TrackMan radar systems track ball flight from release to the plate. And Statcast provides game-level metrics like expected batting average (xBA) and expected slugging (xSLG). DeGrom works closely with the Mets' and Rangers' analytics staffs to interpret this data, often asking for specific reports on how his pitches performed in different counts or against different hitter types.

This technology ecosystem has become standard across MLB, but deGrom uses it more actively than most. He regularly requests side-by-side comparisons of his mechanics from different outings, looking for even minor changes in his delivery. The result is a pitcher who can self-correct between starts, a skill that has extended his dominance.

Performance Results: The Numbers Speak

The results of deGrom's analytics-driven evolution are staggering. From 2018 to 2021, he posted a combined 1.94 ERA, with strikeout rates exceeding 13 per nine innings. His 2021 season, where he had a 1.08 ERA and 0.55 WHIP, was one of the greatest pitching seasons ever. Advanced metrics like xERA (expected ERA) and SIERA (Skill-Interactive Earned Run Average) consistently ranked him first among all starters. Even during injury-shortened 2023 and 2024, when he returned with the Texas Rangers, his underlying data—such as swing-and-miss rate and chase rate—remained elite.

Analytics also explain why deGrom's dominance has aged well. Unlike pitchers who rely solely on velocity, deGrom's success stems from deception, tunneling, and location. Those skills are less susceptible to age-related decline. Data from FanGraphs shows that his fastball whiff rate has actually improved in his 30s, a rare feat that correlates directly with his use of analytical insights to refine his release and sequencing.

Challenges and Criticisms of Analytics-Heavy Pitching

No revolution is without its drawbacks. Some critics argue that over-reliance on data can make pitchers robotic, reducing the art of pitching to a formula. DeGrom himself has acknowledged that processing so much information can lead to overthinking. He works with the Mets' and Rangers' mental skills coaches to separate data analysis from in-game execution. "I look at the data before the game and after the game, but during the game I trust my feel," he told The Athletic.

Another challenge is the risk of confirmation bias. If a pitcher believes a certain pitch is effective based on data, he may continue throwing it even when the situation demands a different approach. DeGrom mitigates this by constantly cross-referencing his own data with opposing hitters' tendencies. He understands that analytics are a guide, not a rule book. Additionally, an overemphasis on metrics can lead to pitchers neglecting the mental game—learning to read a batter's body language or adjust to umpire tendencies. DeGrom balances this by maintaining a strong rapport with his catchers, using their feedback alongside the numbers.

What Young Pitchers Can Learn from deGrom's Approach

DeGrom's journey offers valuable lessons for aspiring pitchers. First, embrace data but don't let it paralyze you. Second, use analytics to identify your unique strengths rather than trying to copy someone else. For instance, deGrom didn't try to become a sinkerballer; he amplified his already elite fastball. Third, combine quantitative data with qualitative feedback from coaches and personal feel. The best pitchers, like deGrom, operate at the intersection of science and instinct.

Many minor league systems now require pitchers to engage with Rapsodo units and video analysis tools. Teams like the Tampa Bay Rays have pioneered a data-driven pitching philosophy that emphasizes tunneling and spin efficiency. DeGrom's success validates that approach, showing that even a pitcher with a "plus" fastball can become unhittable when he understands the "why" behind his pitches. For young pitchers, the takeaway is clear: the era of ignoring data is over. Those who embrace it—without losing the art of pitching—will have a competitive edge.

Comparative Analysis: deGrom vs. Peers

How does deGrom's analytics usage compare to other elite pitchers? Gerrit Cole, for example, also relies heavily on spin rate and tunneling, but he incorporates more pitch types (four-seamer, cutter, slider, curveball, changeup). deGrom uses a simpler arsenal but executes it with greater precision. Max Scherzer, another data-savvy pitcher, relies more on pitch sequencing and location rather than extreme spin metrics. deGrom's unique advantage is his combination of elite fastball characteristics—high spin, high velocity, outstanding extension—with a slider that tunnels perfectly. This synergy, optimized through analytics, makes him virtually unhittable when healthy.

Another peer, Shohei Ohtani, has a comparable fastball but uses a different mix (sweeper, splitter) and relies less on data for fine-tuning. DeGrom's approach is more methodical, treating every start as a scientific experiment. This difference highlights that analytics are a tool, not a one-size-fits-all solution. Each pitcher must adapt data to their own strengths.

The Future: deGrom and Analytics Beyond 2025

As deGrom enters the twilight of his career, analytics will remain central to his strategy. New metrics like Stuff+ and Location+ (which measure pitch quality independent of results) help him identify small declines before they become problematic. Wearable sensors that track arm angle and workload are becoming standard, potentially extending careers. DeGrom has already expressed interest in biomechanical analysis for designing his off-season throwing program.

Even if his velocity drops, data suggests he can remain effective by leaning more heavily on his slider and changeup, while using a lower-velocity fastball with movement. The blueprint exists because analytics allowed him to understand his own arsenal so deeply. Looking ahead, the integration of AI and machine learning may further refine pitch selection and injury prediction, and deGrom is likely to be at the forefront of adopting these technologies.

Conclusion

Jacob deGrom’s integration of analytics is far more than a trend; it’s a case study in how a talented player can leverage information to reach an elite level and stay there. From pitch design and sequencing to mechanical efficiency and health management, data touches every aspect of his preparation and execution. He hasn't abandoned the human element—the feel for spinning a ball or the competitive fire—but he has supercharged it with objective insight. In doing so, deGrom has not only changed his own approach but also influenced the next wave of pitchers who will view a Rapsodo unit as essential as a glove. As baseball continues to evolve, the story of Jacob deGrom and his data-driven journey will remain a benchmark for excellence on the mound.

For further reading, explore Baseball Savant for Statcast data, FanGraphs for advanced metrics, and The Athletic for in-depth player analysis.