UTK Special 12/17/24

More Metrics in an Imperial Land

In Major League Baseball (MLB), the search for better metrics — more insightful, actionable data — is perpetual. As technology evolves and competition intensifies, front office executives and analysts continually seek new ways to measure performance and potential. Insights from anonymous MLB insiders highlight emerging priorities and trends in scouting, development, and evaluation. Maybe some of these exist or are under development, which is part of the problem. Teams don’t share. I asked around about what execs, scouts, and development staffers are looking for.

(Graphic created by DALL-E by entering the text of this article!)

“I have bat speed and path, but I need something that tells me about the contact a guy is making, or lack thereof,” one executive shared. Tools like exit velocity and launch angle have revolutionized how we evaluate hitters, but they don’t always provide a complete picture. A 105 mph line drive to center is far more valuable than a 105 mph grounder into the shift. The next wave of metrics needs to dive into these nuances. A “Contact Profile” metric could combine exit velocity, launch angle, spin rate, and spray direction to categorize and value contact events. By connecting this data with swing metrics, scouts could identify mechanical inefficiencies or strategic adjustments to unlock potential. For hitters struggling despite doing “everything right,” a “Missed Opportunity” score could quantify the swings that fail to produce quality contact under favorable conditions.

Defense remains one of baseball’s toughest areas to quantify. “We actually had this built last year, but I want a bullseye chart that shows how far each player can range defensively. I think positioning is based on the hitter too much and not minimizing the outliers,” an executive revealed. Metrics like Outs Above Average (OAA) and Defensive Runs Saved (DRS) rely heavily on positioning, but raw ability often gets obscured. A “Range Bullseye” — mapping the area each player can cover from a neutral starting point — could evaluate range independently. This approach would show concentric zones representing different probabilities of making a play, providing clarity on true athletic ability and defensive potential. Teams could use this information to balance hitter tendencies against defender capabilities, optimizing positioning strategies to reduce the impact of extreme outcomes. (The exec said that while the chart they had built worked, it wasn’t well absorbed by their coaching staff. It also wasn’t publicly available.)

Speed, while undeniably valuable, has its own set of measurement issues. “I hate the speed metrics. Meters per second and MPH are great for NASCAR, not for a runner,” one insider argued. Sprint speed and baserunning efficiency offer insights but lack nuance. A “Situational Speed” metric could address this by evaluating how effectively players use their speed in different scenarios, measuring acceleration out of the box, top speed on the basepaths, and efficiency in turns. Combining this with reaction time and decision-making data would give a fuller picture of baserunning value. Additionally, “Defensive Agility” metrics could prioritize first-step quickness and reaction time over raw speed, helping teams identify hidden value in defenders.

Pitching analytics have come a long way, but a critical piece remains missing: energy management. “You talk about a battery gauge for a pitcher’s arm. Give me something like that, where we know how much each pitch takes out of him, an energy measure,” another executive said. A “Pitch Energy Expenditure” (yeah, the name isn’t going to work unless you like the acronym) metric could estimate the physical toll of each pitch, factoring in velocity, spin rate, arm angle, and mechanics. This real-time data could help teams monitor fatigue and injury risk, informing decisions on pitch counts, bullpen usage, and recovery protocols. For young pitchers struggling with durability, the metric could optimize mechanics for longevity, while relievers might benefit from tracking energy levels across outings to reduce overuse.

This is drastically harder than you’d think. A simple analysis of energy expenditure would show that aside from base level metabolism, the caloric output is very small, perhaps as small as a fraction of a kcal. Over the course of even a high pitch count, the ability to have enough energy. Even factoring in a 5x increase in energy over the course of 100 pitches - a huge increase for an activity that does not go to failure - doesn’t challenge the body. The difficulty here doesn’t mean it’s not worth doing, but that of all these ideas, this one may be the most complex and the farthest off.

These ideas reflect a broader shift in baseball analytics. The emphasis is moving toward integrated metrics that capture the complexity of the game. Scouting, development, and evaluation are no longer siloed. Scouting validates observations with advanced metrics to uncover hidden potential. Development uses those insights to tailor training and refine skills. Evaluation integrates them into decisions about playing time, roster construction, and long-term planning. Yet, as metrics grow more sophisticated, the human element must not be ignored. Baseball remains a human game and no data point can fully capture the intangibles that make players successful. The challenge lies in balancing cutting-edge analytics with traditional scouting wisdom.

Looking ahead, many of these proposed metrics seem poised to become reality. Advancements in tracking technology and machine learning make it easier to bridge the gap between concept and implementation. The teams that adapt quickly will gain a competitive edge, while those resistant to innovation risk falling behind. For fans, these developments offer a deeper understanding of the game, shedding light on its hidden intricacies. Metrics like the “Range Bullseye” and “Pitch Energy Expenditure” won’t just benefit teams, they’ll enrich the experience for everyone, if they’ll just let them get past a bias against analytics and, frankly, against expertise.

Baseball’s data-driven evolution shows no signs of slowing. The next generation of metrics won’t just measure performance — they’ll redefine how we think about the game.

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Several great comments and emails about the Devin Williams article from earlier this week. Reader Ian B made a great point about part of the value being the salary differential between Williams and Nestor Cortes. Cortes at about $8m and Williams estimated arb value of around $12m (per Matt Swartz at MLB Trade Rumors) doesn’t seem like much, and with $2m tossed in, Milwaukee cut about $6m off their payroll. That might be as important to them as anything with the Brewers caught in the Diamond tv rights mess. Yes, Mark Attanasio could just pony up the $6m easily - he did just up his stake in Norwich City for much more than that - but baseball continues to reward the cheap and punish the payors.