2025 SABR Analytics Conference Research Presentations
SABR is excited to announce the list of research presentations to be presented at the SABR Analytics Conference, which will be held on March 14-16, 2025, in Phoenix, Arizona. Click on a link below to scroll down for abstracts, presenter bios, and schedule:
The SABR Analytics Conference brings together the leading minds in baseball analytics to discuss and debate topics relevant to the quantitative analysis of the game of baseball, including aspects of innovation and technology.
General Track presentations
Friday, March 14
2:25-2:55 p.m. MST
RP1: Evaluating MLB Hitters’ Two-Strike Swings Using Statcast Bat-Tracking Metrics
Jason Willwerscheid
It is generally accepted that MLB hitters adjust their approach on two-strike counts. Indeed, since certain outcomes have much different consequences on two-strike counts — whiffs are much worse, while fouls are merely neutral — it can be beneficial to slow one’s swing, sacrificing power in an attempt to increase contact rates.
The aim of this paper is to quantify this trade-off between power and contact. Prior to 2024, this was a difficult task. It has been possible to accurately estimate how contact rates vary across counts (controlling for, e.g., location) since at least 2015, when Statcast pitch-tracking metrics became available to the public. However, it has been difficult to quantify the other side of the trade-off — power — since only indirect measurements were available (e.g., exit velocity, which is confounded with quality of contact). Beginning in 2024, bat-tracking metrics were made available, making it possible to measure power much more directly.
In my paper, I estimate the yield of this trade-off. To limit the scope, I exclude as far as possible the effects of swing decisions (whether hitters swing) in order to focus on swing adjustments (how hitters swing). I find that most hitters’ two-strike swing adjustments add between 0 and .020 in expected wOBA to plate appearances that end on a two-strike swing, with extremes below 0 and near .040.
Interestingly, these increases do not appear to be due to simple, global trade-offs between power and contact. As an example, I include a case study of Yordan Alvarez, whose swing adjustments increased his expected wOBA by .038 in plate appearances ending on a two-strike swing. I suggest that Alvarez is especially effective because he achieves a local contact/power trade-off, raising his contact rates (at the expense of power) in areas of the strike zone where he has relatively low contact rates on non-two-strike counts, so that his two-strike contact probabilities are approximately uniform across the strike zone. These adjustments eliminate zones that pitchers might target to elicit a two-strike whiff.
This paper is, as far as I am aware, one of the first to include a detailed consideration of Statcast’s bat-tracking metrics (I focus on bat speed, but I also briefly consider swing length). As I show, such metrics make possible novel types of analysis that were not previously feasible using public data.
Jason Willwerscheid is an Assistant Professor in the Department of Mathematics & Computer Science at Providence College, where he teaches courses in statistics and data science. He holds a Ph.D. in Statistics from the University of Chicago as well as a Ph.D. in Comparative Literature from the University of California, Irvine. An unapologetic dilettante, he is currently researching Bayesian matrix factorization, single-cell multi-omics, Sony’s PlayStation 4, and the Fallout franchise. He is a fantasy baseball enthusiast and two-time winner of the 240-team Ottoneu Prestige League competition.
2:55-3:25 p.m. MST
RP2: Process +: Quality-Adjusted Hitter Metrics at the Per-Pitch Level
Kyle Bland
Adjusting performance for opponent quality has a rich and expansive history across many sports, including baseball. These adjustments are typically performed using season-level metrics, where the data is readily available (Gennaro, 2012; Loftus, 2013; Sullivan, 2015). With access to increasingly granular data, these adjustments have become proportionally more specific, now factoring in the opposing lineup, starting pitcher, and/or defense. With current pitch-level data, we can move beyond adjusting for the quality of the opposing player to evaluating the quality of individual pitches the opposing player throws. This idea has already proven useful for valuing individual hitter skills, such as decision-making (Orr, 2023).
The approach presented here analyzes the three primary skills a hitter exhibits at the plate: Decisions, Contact Ability, and Power. These skills can then be aggregated into a holistic hitter metric: Process. A generalized additive model was trained using the characteristics of individual pitches (velocity, movement, location, count, and hitter/pitcher handedness) across the 2020–2024 seasons. Multiple classification submodels were employed to predict individual intermediary steps and outcomes. These individual outcome estimates were then valued using count-based linear weights. These values are subsequently used to analyze the three key hitter skills.
Decision value examines the average outcome expectation of a swing or take for a given pitch and subtracts the pitch’s total value. Contact Ability describes the value added or lost by a hitter making contact or missing, based on the pitch’s contact expectations. Power considers the value a hitter adds to a specific pitch’s batted ball expectation, using a two-dimensional (exit velocity and launch angle) k-nearest neighbor model to evaluate the observed batted ball. These three pitch quality-adjusted skill values are summed to provide a total value for each pitch a hitter faces: the “Process” value. All four of these metrics are then aggregated and fit to the “plus” scale, with 100 representing league average and a standard deviation of 15.
In addition to narrative benefits—such as describing how a player earns their value (e.g., a player can run a high average by swinging at hittable pitches, making more contact, hitting the ball for extra bases, or a combination of these factors)—having a larger sample of observations and a focus on intrinsic skills allows Process+ to be more predictive than other total value metrics, such as wRC+ (Schwartz, 2024).
Kyle Bland is entering his fourth year at Pitcher List, where he is the Director of Data Analytics and Research. He earned his MBA from the University of LaVerne and his B.S. in Sports Medicine from Pepperdine University, where he has served as a Data Analyst. He provided the technical research for the Fantasy Sports Writers Association’s 2024 and 2025 Research Articles of the Year. His baseball focus is on providing clean visuals and analysis that help baseball fans get a deeper understanding of how players are skilled.
Saturday, March 15
3:00-3:30 p.m. MST
RP3: The Interaction of Command and Biomechanics for Pitchers
Conner Pelletier, Jack Lambert, Anthony Becerra, Sam Ehrlich
In this study, biomechanical movement data from 16 pitchers was collected using markerless motion capture technology, capturing their deliveries as they aimed to hit the intended target directly down the center of the plate with fastballs. For each pitch, miss distance from the intended target was paired with the associated biomechanical data to identify movement patterns contributing to accuracy or variance. Biomechanical movements were broken down into subcategories that described similar body movements and timings within the pitch delivery. Correlation analysis revealed key findings: lead knee extension velocity at foot plant showed a negative correlation with miss distance (r=-0.37), indicating that greater control of knee extension may enhance pitch precision. Conversely, maximal shoulder external rotation demonstrated a positive correlation (r=0.44), suggesting that greater variability in this motion may lead to less accurate outcomes. Further analysis highlighted that erratic movements towards the distal segments of the kinetic chain, such as during the final phases of arm acceleration and release, contributed significantly to variance in miss distance. A large-scale non-parametric ANOVA analysis supported this, as well as a common theme highlighting the importance of torso stability and consistency. Additionally, changes in lead knee extension angular velocity variance were strongly correlated with miss distance variability (r=0.54), reinforcing the importance of stable lower-body mechanics in mitigating inaccuracies.
Conner Pelletier is a Product Engineer at Driveline Baseball, specializing in extracting and delivering data-driven insights from diverse sources and pipelines. Previously, he worked in Data Science and Sports Science at Driveline, leveraging analytics to enhance player development. He holds a bachelor’s degree in Statistics from the University of California, Davis.
Jack Lambert is the Director of Baseball Operations at Driveline Baseball, where he specializes in quantitative modeling and developing deliverables for professional athletes. His work at Driveline focuses on translating innovative analytical methods into real-world applications for player development and performance optimization. He studied Computer Science with a concentration in Data Science at the University of Notre Dame, where he also served as a quantitative analyst for the baseball team. In 2024, he presented research on quantifying arsenal effects at Saberseminar.
3:30-4:00 p.m. MST
RP4: Linking Neural Activity to Hitting Expertise: A Live-Pitch Analysis to Mental Processes in Baseball Players
Jason R. Themanson
Research has shown meaningful relationships between hitters’ neural activity in computerized pitch perception tasks and their performance in those tasks. This research has recently expanded to show strong correlations between neural activity and actual in-game hitting performance. However, these studies have been limited to college players in laboratory settings. The current project extends these findings to live pitch scenarios using the Trajekt Arc™ pitching machine across players with varying levels of both collegiate and professional experience, to provide a more ecologically valid understanding of the mental processes underlying hitting performance including inhibitory control, response inhibition, and motor control.
In collaboration with a professional baseball organization, former professional and collegiate players of varying experience and expertise levels, along with non-player baseball office staff members, participated in three studies. In each study, participants faced live pitches thrown by the Trajekt Arc™ pitching machine, simulating real game pitches and conditions. In the first two studies, participants identified pitches as balls or strikes, making live verbal swing or no-swing decisions. In the third study, participants recreated real game situations, swinging at strikes and not swinging at balls. Participants’ neural activity was recorded throughout each task.
The first study demonstrated that neural responses to live pitches could clearly differentiate players from non-players, with players exhibiting distinct neural activation patterns; validating the use of mobile EEG technology to measure neural activity in live pitch settings and confirmed previous laboratory-based results. The second study went further, revealing that patterns of neural activity could differentiate not only between players and non-players, but also among players with varying levels of expertise and experience. Higher-skill players showed distinct activation patterns that highlighted differences in inhibitory and response control processing across a range of player performance levels.
In the third study, which included live swings and more precise neural measurement, the findings replicated earlier results by differentiating players from non-players. Additionally, the study revealed timing differences in neural activation, with players showing earlier neural responses reflecting inhibitory and motor control than non-players. This timing advantage suggests that high-performing hitters process pitches both more quickly and more effectively than non-players.
The research provides a more objective view of the control processes that contribute to successful hitting. Just as high-speed cameras and force plates measure physical aspects of performance, neural activity offers a way to assess and analyze the mental processes involved in hitting performance as well as broader areas of performance in baseball. The psychological activity indexed by these neural measures is adaptable and trainable. These insights have practical implications related to hitting performance as well as providing players and organizational personnel with a competitive data advantage and data-driven insights into inhibitory and motor control processes. These control processes not only contribute to successful hitting, they also impact motor learning, injury prevention, and other areas of performance within baseball.
Jason R. Themanson is a Professor in the Department of Psychology and the Neuroscience Program at Illinois Wesleyan University. He received his B.S. in Psychology from the University of Illinois, his M.A. in Social Psychology from the University of Connecticut, and his Ph.D. in Kinesiology (with an emphasis in biobehavioral kinesiology and exercise psychology) from the University of Illinois. Dr. Themanson’s work utilizes both neural and behavioral measures to examine neurocognitive control processes related to learning, decision-making, and enhanced task performance.
Sunday, March 16
9:00-9:30 a.m. MST
RP5: Baseball Cinematography: Using Open Source CV Algorithms to Track and Quantify Pitcher Mechanics
Nathan Backman
Lit Review: The application of computer vision to the world of baseball is fairly new in the public sphere. “Utilizing Single-Angle Broadcast Feeds and Computer Vision to Extract 3D MLB Biomechanical Data” (Drummey) showcases this application to MLB broadcast video for a variety of purposes. Pose estimation algorithms, such as “You Only Look Once: Unified, Real-Time Object Detection” (Redmon et. al) and “RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose” (Jiang et. al) allow for the estimation of biomechanical data and are integrated in Drummey’s work. “A Visual Scouting Primer: Part One” (Taruskin) provides a foundation for biomechanical analysis from a scouting perspective. Additionally, “Alignment of Curves by Dynamic Time Warping” (Wang and Gasser) offers a useful multivariate technique for comparing biomechanical motion sequences of differing lengths.
Methodology: In this study, I leveraged the biomechanics pipeline outlined in Drummey’s work to extract pitching mechanics from video. My approach differed slightly, as I cropped the video’s frame dimensions prior to the pose estimation process in order to reduce computational resources. I then calculated the difference (in Euclidean space) of each estimated keypoint from the estimated keypoint in the previous frame and compared these sequential data points between pitchers utilizing multivariate Dynamic Time Warping with a minimum Euclidean distance function. The main results focus on quantifying and comparing arm action of different pitchers as well as measuring mechanical consistency throughout the course of a start using the estimated keypoints on the throwing arm and back leg.
Results: The arm action of Lucas Giolito was compared to the arm action of three different pitchers: Shane Bieber, Tyler Rogers, and Hans Crouse. Giolito was utilized as the reference due to Taruskin’s use of him as an example for having “short arm action” in his delivery. The model suggests that Bieber is the most similar to Giolito relative to the other two pitchers’ arm actions, which can be confirmed with the eye test. In looking at mechanical consistency throughout the course of a pitcher’s start, a game between the Marlins and Blue Jays was used in order to compare the starts made by Eury Perez and Yusei Kikuchi in that game. Once again, Perez was utilized due to Taruskin’s use of him as an example for a pitcher with consistent mechanics. The final results showed Kikuchi with overall higher levels of volatility in his mechanics than Perez, although these results are a bit misleading.
Expected Contribution: The present state of computer vision and AI have allowed for the expansion of data sources in the baseball analytics community. In capturing a pitcher’s mechanics through broadcast feeds, aspects that were once seemingly unquantifiable and were previously only identifiable by scouts and industry experts are now publicly accessible, to a limited capacity. This presentation will aim to show how the application of computer vision to baseball video can be used to compare and assess pitching mechanics, but also how this new data can allow for the emergence of new areas of baseball research in the public sphere.
Nathan Backman is an undergraduate senior majoring in sport analytics at Syracuse University with a minor in writing. He serves as the Head Data Specialist for the men’s basketball team at SU and is the former president of the on-campus SU Baseball Statistics and Sabermetrics Club. A self-proclaimed “mad data scientist,” he has participated in numerous different sport-based data competitions and hackathons, including SABR’s Diamond Dollars Case Competition. He aims to pursue a position in sport analytics post-graduation.
9:30-10:00 a.m. MST
RP6: Predicting On-Field Baseball Performance with Reaction Time Testing
Ross Hoehn
There is a broad and growing acceptance that baseball is a highly cognitive sport — often referred to as the mental game. There is also growing interest (and need) to start exploring the contribution of cognitive and psychomotor abilities as a tool for scouting, coaching, and training. It has been shown that simple psychomotor tests such as the Simple Reaction Time and the Go/No-Go (Mental Agility) tests can provide critical insight into complex physical activities, including the sport of baseball.
Wearable devices made by Timex and powered by Pison’s neural sensors are making it possible for baseball teams to integrate reaction time testing into their training and game preparation programs. We suggest how these tests can be used to:
- Predict individual’s performance trajectories
- Understand player’s readiness to assume or continue tasks
- Determine if brief cognitive tasks can contribute (correlate) to players’ outcomes.
Additionally, we shall introduce the tools available through our technology and dashboards, our growing team and elite player testing, and outline preliminary findings comparing cognitive performance and on-field metrics. We will highlight findings from the White Paper, and several other explorations we are undertaking in the domain of baseball, including at Lansing Community College, Penn State, West Virginia Univeristy, Auburn University, Oklahoma State, and Oral Roberts.
Ross Hoehn is Principal Investigator and Research Scientist at Pison Technology, Inc.
11:15-11:45 a.m. MST
RP7: MLB Ownership, Atlanta Braves, Inc., and Financial Analysis
Rob Mains
For years, there’s been an information vacuum regarding the finances of MLB teams. Several outlets, including, most notably, Forbes, have provided estimates of the profitability of MLB franchises. The appreciation of team valuations (most recently, the sale of the Orioles, purchased by Peter Angelos in October 1993 for $173 million, to David Rubenstein for $1.725 billion in March 2024, an 8.1% compound annual return) have suggested that team ownership is lucrative. But many owners and the Commissioner’s Office have repeatedly complained that baseball is a poor investment.
On July 19, 2023, Liberty Media, a conglomerate that owns, among other properties, Formula 1 and Live Nation Entertainment (and, at the time SiriusXM), split off its baseball investment into a new publicly-traded company, Atlanta Braves Holdings, Inc. The Braves consequently are required to file detailed financial reports every quarter. This is a significant development, not only because it provides transparency into a baseball team’s results, but also because it removes any incentive its owners have to downplay their finances. The stock market punishes unprofitable businesses.
In this presentation, I will use the tools I used over a 25-year career as an equities analyst to illustrate the Braves’ results and their applicability to the baseball industry as a whole. I will describe the team’s overall profitability, the seasonality of its earnings, the value of its Battery Atlanta real estate holdings, its non-baseball sources of income and appreciation, the impact of revenue sharing, and the nonstandard structure of the company’s stock. While the Braves are one of the sport’s most successful franchises, many of the characteristics of its earnings are shared by the other 29 teams that don’t have to publicly report on their financial results.
Rob Mains is a writer for Baseball Prospectus and manages the site’s Spanish content. He is a former equities analyst and the winner of the 2024 SABR Analytics Conference John Dewan Defensive Analytics Research Award. He is a SABR Analytics Certification course reviewer.
11:45 a.m.-12:15 p.m. MST
RP8: A Data-Driven Approach to Analyzing Pitch Decay: A New Way of Breaking Down Pitchers
David Bailit, William Haray, Owen Lefkowicz, Jared Markowitz, Andrew Possi
Often people will mention the idea of the “third time through the order” when discussing baseball, however, they don’t explain why pitchers get worse as the game goes on. The central reason for this phenomenon is a concept called pitch decay. However, not as much research has been done on the concept of pitch decay as the “third time through the order” phenomenon. Analyzing which pitches “decay” in value the more a specific hitter sees them allows us to build a greater understanding of how a batter can capitalize against a fatigued arm. The “decay” refers to the decay in value on each individual pitch and the pitches’ relevant data, such as velocity, induced vertical break, horizontal break, spin rate, and spin efficiency.
Jim Albert’s essay “Times Through The Order Effects” and Jon Roegele’s “The Effect of Seeing Pitches” both look at how the TTTO effect impacts wOBA. Albert compared the wOBA change once a pitcher pitches farther into the game using data from starting pitchers during the 2019 MLB season and Roegele quantified which pitch types saw the most decay in data from 2011 to 2013. Do velocity, spin rate, vertical and horizontal movement, and spin efficiency affect the rate of decay?
Managers can make better in-game pitching decisions by using pitch decay to determine which pitches might not be as successful the third time around the order. They can also use the effects of pitch decay to optimize the arsenal of a pitcher before they even step onto the mound. This is due to the fact that pitch decay affects different types and shapes of pitches in different ways. Our preliminary data showed that curveballs and sliders show more effectiveness in relation to fastballs as a pitcher throws more of one pitch. Early data also reveals that all six major pitch types decay at least to some extent. As we dive deeper into our data we expect to see a negative correlation between spin rate and pitch decay specifically on fastballs and cutters. Additionally, clubs can determine if a pitcher is a true starter or a reliever by evaluating how their arsenal is affected by pitch decay. If teams genuinely think a pitcher has the skills and efficiency to pitch longer outings, they can then determine early in the player’s development whether he has starting potential. They can then use this value to determine which pitching prospects to keep and which ones to trade if other teams aren’t yet aware of the pitcher’s true value.
Our study will be utilizing Statcast data, specifically the pybaseball library, in order to analyze pitch-level data for MLB games. The dataset includes detailed metrics such as pitch type, release velocity, spin rate, and play outcomes. We have developed a unique metric, pitch count (by type) against batter, which tracks the number of times a batter has seen a specific pitch type from the same pitcher within a single game. Our analysis will focus on how pitch effectiveness changes as this count increases.
David Bailit is a senior studying Political Economy at Tulane University. He has worked in the past for the Boston Red Sox and Hyannis Harbor Hawks and currently works as an analytics assistant for the Tulane Baseball Team. He is passionate about the intersection of data and player performance. David is pursuing a career in baseball operations and player development following graduation.
William Haray is a freshman student at Tulane University, studying economics. He has experience writing about baseball with the online blog, Half Street High Heat. Haray aims to pursue a career in baseball in the future.
Owen Lefkowicz is currently a sophomore at Tulane University, pursuing a major in digital media production. He serves as a camera operator for both Tulane Athletics and ESPN+, which has enabled him to specialize in visual data regarding swing and pitch mechanics. A native of San Francisco, Owen is an avid supporter of the Giants.
Jared Markowitz is a senior studying Mathematics and Computer Science at Tulane University. He has professional experience in data analysis and AI from his internships at Harris Blitzer Sports & Entertainment (HBSE) and Quarter4, where he developed predictive models and data-driven insights. He is passionate about applying analytics and predictive modeling not only within sports, but also to a variety of challenging problems across many different industries. After graduation, he will pursue a career using his data analytical background to help organizations make smarter decisions through data analysis and AI.
Andrew Possi is a graduate student at Tulane University pursuing a master’s degree in business analytics. He attended UMass Dartmouth for four years as a collegiate baseball player. He presently serves as both an analytics assistant for Tulane’s baseball team and an associate scout for the Tampa Bay Rays.
Student Track presentations
Saturday, March 15
10:00-10:15 a.m. MST
SP1: Route Acumen: Grading Routes on a Curve
Ian Chang
In the past, Route Efficiency was considered a critical statistic for evaluating outfielder performance. However, selection bias and lack of contextual information has since diminished its popularity.
In this presentation, we introduce Route Acumen, a metric that comprehensively considers an outfielder’s path. Using SMT’s 6th Tool player-tracking data, our model evaluates how well a player conforms to his apparently-intended (rather than a strictly ideal) route, taking into account the deliberation of his movements, how he positions himself for a catch, and the impact of misreads and poor decision making.
A still-evolving metric, Route Acumen grounds its data-driven variables and calculations in assumptions about player decision-making and the “quality” of on-field performance. The ultimate goal is a well-defined quantitative means of route evaluation that is both intuitive and consistent with the “eye test,” providing value both on the field and in the broadcast booth.
Ian Chang is currently an undergraduate senior at the University of Utah, majoring in Mathematics and minoring in Computer Science. He started as a data science intern at SMT in January of 2022. He also works as a student manager and Video & Analytics Intern for the Mens and Womens basketball program at Utah. He is pursuing graduate studies or a position in sports analytics after graduation.
10:15-10:30 a.m. MST
SP2: Using Eliteness Early: Influence of the Opener on Run Prevention
Lyev Pitram
Suppose you have an excellent closing pitcher. There are theoretical reasons to believe his talents would be better utilized as an opening pitcher: the first inning always matters whereas the ninth doesn’t always matter (i.e., the game may have already effectively been won or lost), the first inning features the opposing team’s best batters, and it may allow managers to play matchups more effectively (e.g., via a beneficial platoon effect). Hence, in this work, we use a data-driven simulation approach to evaluate the efficacy of re-assigning the closer as an opener. The opener was widely re-introduced in 2018, when the Tampa Bay Rays received significant publicity for starting veteran closer Sergio Romo in back to back games, facing only a handful of batters each time. Though many articles have discussed the opener and its effectiveness, to our knowledge we have yet to find any focusing specifically on using a team’s closer as the opener, particularly in a data-driven way. Our simulation methodology relies on a multinomial logistic regression model for the outcome of a plate appearance, which is a nonlinear and interacting function of pitcher quality, batter quality, and handedness. Then, we compare mass simulations of games in pairs, one in which we move the actual closer to the spot of the opener, and another keeping the order of pitchers the same. In a variety of instances, switching an elite closer to the opener position yields significantly fewer runs allowed on average. While the “year of the opener” may have passed, the opener is still a commonly discussed idea, with MLB pitchers continuing to face strict pitch and inning limits, and bullpen strategies becoming more creative. The results of this work can help teams understand when and who to use as an opener, advancing the planning of pitching staffs as we see today.
Lyev Pitram is a high school senior in Philadelphia with a passion for math and statistics, especially their applications in sports. He participated in the University of Pennsylvania’s Moneyball Academy summer program, and has followed by conducting baseball analytics research under the mentorship of UPenn PhD student Ryan Brill. His passion for sports analytics comes from being an avid MLB fan and playing high school baseball, and he hopes to continue pursuing the field in college.
10:35-10:50 a.m. MST
SP3: Evaluating Outfielder Route Efficiency and Route Paths by Level
Brett Cerenzio
Outfielders have one of the most important jobs in today’s game. In a game where power and extra bases win baseball games, great outfielders must excel at limiting the damage done on hard-hit balls. There are a couple of attributes that an outfielder may possess to prevent big plays, among them is their route to the baseball. A few steps in the wrong direction could mean the difference between an out, hit, or extra bases. The aim of this project is to identify the optimal route path on any given flyball.
I couldn’t find much research about route optimization in baseball, so the majority of my research involved modeling techniques. Specifically, I looked into a new splitting technique used in growing a Random Forest called Extremely Randomized Trees. A link to this method can be found here. I also looked into different methods of dealing with class imbalances in the y variable, and ultimately chose to use the inverse of class frequencies approach shown here in step 3.
Utilizing ball and player tracking data, I calculated numerous metrics including player distance to ball bounce/catch, fielder speed, and hang time. Using these metrics, I created a catch probability model which I then implemented in a route optimization model. The route optimization model utilizes fielder speed to first determine how far a fielder can move in a given time interval, and then determine which point would maximize the fielder’s catch probability. I also created a new statistic I call Route Score, which compares the catch probability at the end of the play to the catch probability at contact to see if the fielder took a good route to the baseball: Route Score = Catch Prob at end of play – Catch Prob at contact. Then, I created a model that predicted the route taken to a ball at different levels of the farm system to see if there were any major or minor differences between routes at different levels that impacted the fielder’s catch probability using similar metrics that I mentioned above.
I found that although most fielders who had good OAA had good Route Scores and vice versa, there were some players who score highly on Route Score but poorly on OAA, suggesting that there may be some flaws in OAA. And although there weren’t huge differences in the average route taken at each level of the minors, the variance between routes were much higher at the lower levels, which makes sense since these players haven’t seen much development yet.
Overall, I can see this being used in a player development or player evaluation setting. The route optimization model allows fielders to easily analyze whether or not they took a good route to the baseball and gives them immediate feedback on how they can improve it next time. The fielder path model has the ability to compare an outfielder’s route to his peers and make a determination on if their fielding is good enough for the next level of professional baseball.
Brett Cerenzio is a senior at Syracuse University studying Sport Analytics who is also pursuing a master’s degree in Applied Data Science. He has placed highly in numerous sports data competitions such as the 2024 SMT Data Challenge and the 2025 Reds Hackathon, and is currently an analyst for the Syracuse softball team. In the summer, he will join Prep Baseball for the summer as a Baseball Analytics intern.
10:50-11:05 a.m. MST
SP4: Relationship Between Impulse and Velocity in Baseball Pitching
Cameron Jensen
Introduction: Impulse represents the total force exerted on an object over a given time, providing insights into powerful athletic movements. Unlike peak force or instantaneous power, which only capture brief moments of force application, impulse offers a more comprehensive understanding of force and momentum. This makes it particularly relevant for movements that demand both speed and strength, like sprinting, jumping, and directional changes. The limited research on impulse in rotational sports has primarily focused on its role in energy flow, while its direct contribution to performance metrics remains minimally explored. This study aimed to determine the relationship between impulse and pitch velocity in baseball pitching. It was hypothesized that higher impulse values would generally correlate positively with increased pitch velocities. Understanding this relationship could identify impulse as a key indicator for pitching performance. This insight has the potential to guide targeted training interventions aimed at optimizing performance and reducing injury risk.
Methods: As part of a larger protocol, 45 collegiate baseball pitchers (Age: 20.1 ± 1.7 years; Height: 1.84 ± .06 m; Weight: 88.5 ± 8.3 kg; Right-handed: 31, Left-handed: 14) completed 3 fastball pitches on an instrumented pitching mound (960 Hz). The force-time and moment-time curves were obtained from pitch initiation to ball release for the drive leg, and from stride foot contact to ball release for the stride leg. Linear and angular impulses were calculated by integrating the force-time curves and moment-time curves, respectively. Pitch velocity was measured with a radar gun. Spearman correlation coefficients and significance levels were obtained to examine the strength and direction of the relationships between the average impulse values for each pitcher in the drive and stride legs (across the X, Y, Z directions, magnitude, and ratio of drive leg to stride leg impulse) with pitch velocity.
Results: Two impulse values were significantly correlated with pitch velocity: the propulsive impulse generated by the drive leg toward the target (home plate) and the ratio of the drive leg impulse to the stride leg propulsive impulse toward the target (Drive: J = 0.17 ± 0.49 Ns/kg, rho = 0.33, p = 0.03; Ratio: -0.29 ± 0.88, rho = -0.32, p = 0.03). The drive leg impulse showed a weak positive relationship, while the impulse ratio demonstrated a weak negative relationship.
Significance: Generating greater drive leg propulsive linear impulse positively influences pitch velocity, demonstrating the drive leg’s crucial role in initiating forward momentum. Increasing the drive/stride propulsive impulse ratio without reducing stride leg braking impulse (negative propulsive impulse) is preferred, as lowering braking impulse contradicts previous research recommending greater braking impulse for improved performance and energy transfer. Thus, this further reinforces the importance of maximizing drive leg propulsive impulse, positioning it as a potential key performance indicator for pitching velocity. The lack of significance from other impulse variables suggests that linear and angular impulse are not primary determinants of pitch velocity in elite collegiate pitchers, indicating that other biomechanical factors may play a greater role.
Cameron Jensen is a second-year master’s student at the University of Nebraska at Omaha, completing a degree in biomechanics with an emphasis in sports biomechanics. As an integral member of the UNO Sports Medicine and Biomechanics Lab, he utilizes motion capture, clinical assessments, and force plates to analyze athletes of all sports, specifically, baseball, softball, and golf. He double-majored in Physics and Kinesiology at the University of Utah, where he gained hands-on experience as an Applied Health and Performance Science Intern, collecting and analyzing data to improve athlete performance and reduce injury risk. After earning his master’s degree, he plans to complete a doctorate degree at UNO before pursuing a career as a biomechanist with an MLB team.
11:05-11:20 a.m. MST
SP5: Pitcher Dynamics xPLained: Creating a Model that Determines When to Pull a Starting Pitcher
Atul Venkatesh, Ishan Kinikar, Levon Sarian
The decision to pull a starting pitcher is one of the most difficult in professional baseball. Managers often have to tread the fine line between not pulling a pitcher too early and not keeping them in too late. Often times, these choices are pre-determined based on a certain pitch limit, leading to ill-advised decisions that can be the difference between a win and a loss. Controversial decisions, such as pulling Blake Snell early in the 2020 World Series or keeping Matt Harvey in too late in the 2015 World Series have put this controversial issue at the forefront of baseball. In this project, we use a combination of situational factors and machine learning to determine the proper time to pull a pitcher. We create two models using an XGBoost: one that predicts the expected pitches left in the game and one that predicts the expected pitches left in the inning. We decided that if the pitcher’s expected pitches left in the game was fewer than the pitcher’s expected pitches left in the inning (the model projects that the pitcher wouldn’t be able to make it through the inning), that would be the appropriate time to pull the starting pitcher. We trained our model on pitch-by-pitch data from all levels of a certain team’s farm system for a given season.
Our analysis revealed a few striking results. First, managers tend to overwork pitchers in lower levels of the minor leagues. Pitchers on average tend to throw over three more pitches than expected per outing. This does not seem like much, but it can accumulate over the course of a season. However, managers seem to correct this in higher levels of the farm system as pitchers only throw 0.14 more pitches than expected per outing. Second, in the scenario where pitchers throw more pitches than our model expects them to, more often than not, it ends up being a mistake. In several cases, after our model recommended to pull the pitcher, we observed the pitcher giving up home runs, base hits, and throwing far more pitches than they should have. We obviously have no way of observing the counterfactual, but one would imagine that deploying a reliever in these instances could at least save the starter’s arm. In fact, our model made the “correct” decision 88 percent of the time.
Our ultimate product is a manager assistance tool that combines these factors with qualitative variables to provide immediate, in-game feedback on when to pull the pitcher. We have created a web app (snelltool.com) that allows users to directly input game specific statistics and receive a recommendation based on our model. This project was submitted as an entry in the 2024 SportsMedia Technology Data Challenge and was selected as the overall winner of the undergraduate division. All of the data for this project was provided by SMT.
Atul Venkatesh is a sophomore at Dartmouth College majoring in Quantitative Social Science and Applied Mathematics. He is currently Head of Research at Dartmouth Sports Analytics and a Data Science Intern with The 33rd Team. Atul’s ultimate goal is to work for a professional team in a data science capacity and he has conducted several research projects across multiple sports.
Ishan Kinikar is a sophomore at the University of Massachusetts Amherst, majoring in Computer Science with Multidisciplinary Honors. He was part of the winning undergraduate team at the SMT Data Challenge for Baseball Analytics with the project “Pitcher Dynamics Xplained: Creating a Model That Determines the Right Time to Pull a Starting Pitcher.” He currently works as a Data Science and Software Engineering Intern at the UMass Amherst Center for Data Science and is a senior quantitative researcher at the Minuteman Alternative Investment Fund, where he develops machine learning algorithms for financial trading. In 2024, he interned as a software engineer at EverTrue, refining his skills in data-driven financial software development.
Levon Sarian is a sophomore at Dartmouth College studying Quantitative Social Science and Computer Science. During his time at Dartmouth, he has worked for a data analytics company where he helped in the development of an autonomous generative AI data analysis, processing, and gathering system. For the past two summers, he has interned at a telecommunications company outside of Boston. Recently, he participated in the SMT Data Challenge.
Poster Presentations
Saturday, March 15
1:05-1:35 p.m. MST
Presented by Cleat Street Capital
P1: How Do Stride Foot Ground Interactions Affect Stride Foot Ground Impulses in Baseball Pitchers?
Brandon Doehne
Introduction: The role of the stride leg in baseball pitching is to reduce the pitcher’s linear momentum towards home-plate to provide a stable base of support for the upper body to rotate. Compared to peak ground reaction forces (GRF), impulse provides a more comprehensive display of the force-time relationship. While stride leg impulse has been correlated with ball velocity and energy flow, there has not been research investigating the contributors of impulse. The purpose of this study is to investigate the effects of stride foot ground interactions and other stride variables on the stride leg impulse. We hypothesize that a footstrike which lands in an adducted and inverted heel strike orientation with a greater distance between the pitcher’s center of mass (COM) and heel location will have greater anteroposterior and vertical impulses due to its ability to direct energy into the body which could create more stride force.
Methods: As part of a larger protocol, thirty-two collegiate baseball pitchers (aged 18 to 24) pitched three fastballs which were captured by motion capture (320 Hz) and force plates (1280 Hz). The stride leg impulse was calculated as the integral of GRF produced from stride foot contact (SFC) to ball release. The footstrike angle was calculated by finding the difference between the degree of heel strike or toe strike relative to the slope of the pitching mound, where a positive angle was a heel strike. The foot abduction and foot inversion angles were calculated by finding the difference between the long axes of the stride foot relative to the pitching mound, positive angle is adduction, and the difference between the degree of inversion or eversion relative to the pitching mound surface, a positive value is inversion, respectively. The distance between COM and the heel location, and the mediolateral and anteroposterior GRF angle at SFC were determined. A positive mediolateral GRF angle represents a medial angle while a positive anteroposterior GRF angle represents a posterior angle.
Results: While no significant correlations were found, there were several trends found (p<0.10) between the distance from COM to heel location at SFC and anteroposterior impulse, foot abduction angle at SFC and mediolateral impulse, mediolateral GRF angle and anteroposterior impulse, and stride length and anteroposterior impulse.
Significance: Based on the findings, we speculate that as the orientation of the stride foot becomes more extreme the force produced by the stride leg will decrease due to the negative progression of the muscle’s optimal muscle length. We believe that there may not be an optimal stride foot position that results in greater stride leg impulse, and that the orientation of a pitcher’s foot at SFC is individual and based on their mechanical strategy and mobility. We advise pitchers to avoid landing in extreme stride foot orientations. Regarding the distance between the pitcher’s COM and heel location as well as stride length, we cannot advise a pitcher to modify their mechanics in reference to these based off lack of significance.
Brandon Doehne is a graduate student at the University of Nebraska at Omaha, pursuing a master’s degree in biomechanics with an expected graduation in Spring 2025. He holds a bachelor’s degree in mechanical engineering from the University of New Mexico and plans to pursue a Ph.D. in biomechanics. Additionally, Brandon has personal collegiate baseball experience as both a pitcher and hitter at the junior college and Division II levels, and a pitcher at the Division I level.
P2: Enhancing the Art of WAR: Pitching Plus Model with Arsenal Points
Henry Gliedman, Benjamin Reinhard, Liam Kennedy
Pitching+ models have taken over baseball analytics as an advanced way to predict and evaluate pitching performance. Last year, we developed a set of Division III Stuff+, Location+, and Pitching+ models. Our Stuff+ quantifies the value of pitches in a vacuum, a simple way to evaluate pitch value. However, pitchers can also succeed using a well-rounded arsenal that works together to deceive hitters. Stuff+ might evaluate the physical tools of a pitcher, but arsenal design is their blueprint for success. This project proposes a new way to quantify and evaluate arsenal coherence at the collegiate level. Research on arsenal coherence has become a topic of growing interest. Marek Ramilo and Jack Lambert of Driveline released their Mix+ and Match+ statistics. Match+ is described as “how long pitches in a player’s arsenal remain on the same trajectory.” They define Mix+ as “the ability to make things move in different ways.” Their work helped advance Pitching+ to evaluate a pitcher more comprehensively. Another similar approach was Dylan Drummey of Prospects Live, titled Arsenal+. Arsenal+ is described as a “Pitching+ metric on steroids” an evaluation dependent on changes from one pitch to another. Arsenal+ considers prior pitches thrown, another interesting technique to quantify arsenal.
The purpose of our research into Arsenal Points is to understand arsenal coherence at the college level. How does pitch interaction and proper sequencing affect collegiate pitching? We want to account for not only the quality of a pitch (with Stuff+) but also how pitches interact in an arsenal. We found trends in players whose performance deviates from their predicted Stuff+, modeling how arsenal effects play a role in quantifying their overperformance or underperformance. Using both the physical interactions of pitches, and sequencing effects we quantify arsenal quality for college pitchers. Results found that large amounts of the variation in our Stuff+ model could be accounted for by Arsenal Points
Our model is unique both in methodology and results due to differences between professional and collegiate data. In MLB, there are millions of data points from a talent pool that is much more consistent than that of the college level. Our goal is to create a publicly accessible and easy to use model for arsenal coherence that does not rely on technology that is cost prohibitive for the majority college baseball programs.
Arsenal Points make Pitching+ a more complete statistic for holistic evaluation, continuing to solve the problem of predicting future success with the smallest possible sample. However, Arsenal Points also leads to more detail for development with college pitching. This research provides a quantitative understanding of what arsenals are most effective at the collegiate level, with detail on how a player could adjust their arsenal to make it more efficient. Using Arsenal Points as a foundation, pitchers can receive actionable insights for development beyond Stuff+.
Henry Gliedman is currently a senior mathematics major with concentrations in engineering, data science, and statistics at St. Olaf College in Minnesota. He serves as the Director of Analytics & Assistant Pitching Coach for the school’s Division III baseball team. Gliedman also co-founded a baseball technology company (Art of W.A.R. Baseball Technologies), with a mission of lowering the financial threshold for data driven baseball development through software. Henry is pursuing a position working in baseball after graduation.
Liam Kennedy is a recent graduate from the Florida State University Sports Management program, where he also received his undergraduate degree in Business Management. During his time at FSU, he helped found the school’s baseball analytics program. He currently serves as a baseball analyst for Florida International University. Liam is passionate about player development, and is looking for opportunities in this field.
P3: Understanding the Correlation Between Static Range of Motion and Joint Torque Power Capabilities
Dimitri Haan
During a baseball pitch, the pitcher is required to generate and transfer significant amounts of energy and power through all joints of the kinetic chain. The energy in the kinetic chain of a baseball pitch originates with joint torque power (JTP) in the drive leg, which is transferred to the pelvis and through the rest of the chain. The transfer of JTP through the kinetic chain has been found to have a strong positive correlation to fastball velocity in youth pitchers, indicating the importance JTP with relation to performance. Current literature presents competing theories about the effects of stretching and flexibility on performance in sports, with some researchers citing benefits to performance, and others cautioning flexibility past the functional range of motion for the sport. Fields et al. suggests that in sports where joint stability is required, such as baseball, increased flexibility may not be beneficial for performance. Currently, the relationship between flexibility and the performance measurement of JTP remains unexplored. The purpose of this research was to measure the correlation between pitchers’ passive range of motion in clinically relevant movements and the maximum JTP experienced at the associated joint during pitching. It was hypothesized that a significant relationship exists between these two variables at the ankles, hips, lumbosacral joint and shoulder.
Fifty-eight (age=20.89±2.12 years, height=185±5.84 cm, weight=89.84±11.1 kg) college-aged pitchers were included in this study. Range of motion in ankle dorsiflexion, hip internal rotation, hip flexion, trunk rotation, shoulder internal and external rotation, shoulder flexion, and shoulder horizontal adduction was measured by certified athletic trainers.
Pitchers threw in front of motion capture cameras (Qualisys, Gothenburg, SWE) capturing at 320Hz, using an instrumented pitching mound with embedded force plates (Bertec, Columbus, OH, USA) collecting at 1280Hz. The three fastest fastballs for each pitcher were taken for analysis in Visual3D (HasMotion, Kingston, ON, CA). Total joint power as a scalar value was collected for the ankles, hips, lumbosacral joint and shoulder, and exported to custom MATLAB code (MathWorks, Natick, MA, USA). In MATLAB, the maximum value of JTP for each joint was collected and correlated to clinical measurements of those joints. Significance was set at α=0.05.
No significant correlations were reported between the flexibility of the ankles, hips, lumbopelvic and shoulder joints and the power with the associated joint. The lumbosacral joint experienced the highest magnitude of power, absorbing an average of -33.2 N/kg*°/s, but showed no significant correlation to trunk flexibility (p=0.56), or hip internal rotation of the drive leg (p=0.43) or stride leg (p=0.50).
Research is conflicted on the effects of stretching and flexibility on athletic performance. The preliminary results show that flexibility in the joints involved in the baseball pitch show insignificant correlation with JTP generation and absorption. The preliminary results do not lend support to the conclusion made by Fields et al., which states that sports requiring joint stability may see decreased performance with increased flexibility, however, within athlete analysis would be needed as a next step.
Dimitri Haan is a second-year master’s student at the University of Nebraska at Omaha (UNO) in the Sports Biomechanics program. He received his undergraduate degree in biomechanics from UNO in May 2023, and will continue to pursue his doctorate at UNO following graduation in May 2025. Dimitri has worked in the UNO Sports Medicine and Biomechanics Lab and the UNO Pitching Lab for three years and leads biomechanics evaluations of baseball pitchers, as well as assisting with softball, golf and other athlete evaluations. His primary interests in baseball research are the effects of different center of pressure loading strategies in the drive leg and their effects on pitching biomechanics, and the acute effects of ground reaction force biofeedback and external cues on baseball pitchers’ biomechanics and performance.
P4: Bayesian Analysis of Inning Shares via the Stick-Breaking Process
Sebastian Kirkpatrick
Background: Pitcher usage has long been a central question in baseball research, with workload management shaping how teams build rotations and bullpens. Understanding how teams distribute innings across a full season helps explain not just how pitchers are used, but also how strategies around health, effectiveness, and matchups have evolved. Historically, researchers like Daniel Levitt have tracked the decline in innings pitched by starters across the 20th century, noting that while innings pitched had certainly decreased from the earlier parts of the 1900s, pitch counts from the 1990s suggested actual workloads didn’t decrease as sharply as expected due to evolving offensive approaches (SABR Baseball Research Journal). More recently, Ben Clemens showed that while innings per start have ticked up slightly since 2020, they remain well below the early 2000s, highlighting how teams have taken a more cautious approach (FanGraphs). However, most of this past work focuses on either starters or relievers in isolation. My work shifts the focus to a more comprehensive question: how do teams divide the full season’s innings across the entire pitching staff, and how has that changed over time?
Methods: To model this, we used a Bayesian framework. For each team-season, we ranked every pitcher by innings pitched and calculated their Inning Share (IS), which is the proportion of the team’s total innings that said pitcher covered. We built a Bayesian multinomial regression to model these ordered Inning Shares across the seasons in the Lahman database. This gave us posterior distributions for each slot in the rotation or bullpen – P1 for first in innings, P2 for second, and so on – for every team and season. From there, we pulled the posterior means for each role, giving us the expected Inning Share for that spot in the given season. To see which players and teams excelled, we found the biggest overperformers (pitchers or rotations who took on a much larger share than expected) and the biggest stragglers. Finally, to track how the makeup of pitching staffs evolved over time, we calculated the entropy of each season’s inning distribution to quantify how evenly (or unevenly) innings were spread out across staffs.
Results: Our results show clear historical patterns in how teams distribute innings. The IS of the top 5 pitchers has had a steady decrease, and the entropy for the whole staff has consistently increased, confirming the idea that innings are being spread out across more players throughout the season. We also observed trends in IS around historical changes and pitching strategy development, like the widespread use of a 5-man rotation in the 1970’s, the change from using a rest day to skip a lesser pitcher to instead give the entire rotation a rest day, or the more recent utilization of a 6-man rotation. Even though there is a correlation between skill and workload, ISOA (Inning Shares Above Average) uncovered some unheralded names and rotations who have not gotten adequate praise given their workloads respective to the rest of baseball history. As teams feature more and more pitchers during the 162-game season, we hope our model gives insights on how the game got here, and how it may continue to change.
Sebastian Kirkpatrick is an applied statistics graduate student at Loyola University Chicago, working as a statistics consultant for Loyola’s Center for Data Science and Consulting and for the Biostatistics Collaborative Core. Last year, he interned with the Seattle Mariners and DC United. He hopes to continue working in sports after his graduation in May. Outside of sports statistics, he enjoys competitive Pokémon VGC and casual Mario Kart.
P5: The Relationship Between Drive Leg Generation and Ball Velocity in Different Age Groups
Takato Ogasawara
Introduction: Recent studies emphasize the role of the lower body in generating energy during pitching, particularly through the drive leg with coordinated movements at the lower extremity joints to maximize ball velocity (BV). Relying solely on ground reaction forces data may not capture the intricate mechanics involved in energy generation during a pitch. Previous research has highlighted the characteristics of joint torque power (JTP), which reflects power generation, however, the strategy of power generation remains underexplored. Therefore, this study aims to investigate the relationship between JTP magnitudes at the drive leg joints and BV across the two age groups and hypothesize that there is a positive correlation.
Methods: Ninety-three competitive baseball pitchers aged 12 to 24 were extracted from the UNO Pitching Lab database (51 aged 12-18, 42 aged 19-24). Pitchers performed 15 to 20 maximal-effort pitches from a mound, with the fastest pitch used for analysis. High-speed motion capture (320 Hz), and ground reaction forces (1280 Hz) were recorded using 41 reflective markers and force plates. JTP at the drive leg ankle, knee and hip were calculated using the following formula. JTP = Tp x (Wd – Wp). Tp is the joint toque vector acting on a segment at the proximal joint p, and w is the segmental angular velocity vector of a segment at proximal segment s and distal segment d. The peak magnitude of JTP at each joint was extracted from maximal knee height to stride foot contact. Pearson correlation coefficients were calculated to assess the relationship between peak JTP magnitude and BV joint torque, and independent samples t-tests were conducted to compare JTP magnitudes under-18 and over-19 groups, with significance set at p < 0.05.
Results: The analysis showed positive correlations between BV and JTP magnitudes at the knee and hip shown in Table 1. In contrast, the t-test results showed a significant difference in knee JTP between the two age groups (p<0.01, d=0.61, Figure 1). However, there were no significant differences observed in ankle or hip between the two groups.
Discussion: The result highlighted that higher power generation at the knee relates to higher BV. The lower knee JTP in younger pitchers suggests they may still be developing efficient mechanics, which could impact energy transfer during pitching. Note that high standard deviations in JTP Ankle Knee Hip R-Value 0.190 0.428** 0.223* indicate variability in strategies regardless of the player level, and future analysis is needed on the timing of the peak, joint coordination pattern, and the ratio of JTP strength across joints to identify the different strategies during the drive phase.
Conclusion: The result suggests that JTP metrics may be useful in evaluating the potential performance of youth pitchers in power generation by looking at knee JTP. Potential research can identify the drive phase strategy such as knee/hip dominant and it allows coaches to design targeted and individualized training programs. Incorporating JTP metrics into player development strategy and scouting decision-making can help teams optimize pitching mechanics and improve overall efficiency.
Takato Ogasawara is currently a second-year master’s student in biomechanics at the University of Nebraska at Omaha. He is working on baseball and softball pitching biomechanics research advised by Dr. Brian Knarr. He worked as an athletic trainer and strength coach at Chukyo University Swimming Club and received his bachelor’s degree in Sports Science from Chukyo University in 2021. He is expected to earn a master’s degree in Biomechanics this May.
P6: The Million Dollar Reversal: Using Baseball Data to Scout Cricket Talent
Ramon Sanchez
This essay examines the feasibility of transitioning baseball players who are underperforming in their sport to cricket. Drawing inspiration from Million Dollar Arm, which successfully adapted cricket bowlers to baseball, the study investigates how metrics from baseball, such as RBIs and OPS, could align with cricket’s scoring methods. The analysis utilizes advanced data techniques to correlate baseball hits with cricket runs and explores how elements like foul balls and pop-ups could influence cricket scoring. The findings indicate that converting baseball performance metrics could reveal potential cricket talent, offering new opportunities for players and contributing to cricket’s global expansion.
Ramon Sanchez is a second-year graduate student at San Diego State University studying Physics. As a full-time Teaching Assistant, he instructs undergraduate students in the principles of physics and guides them through fundamental lab experiments. His current research investigates human skeletal muscle mechanics using MRI imaging. He is passionate about bridging physics and baseball analytics to revolutionize player evaluation and health monitoring.
For more information on the 2025 SABR Analytics Conference, visit SABR.org/analytics.