Estimating flight trajectories of breaking balls from four-seam fastballs by Arata Kimura, Hirotaka Nakashima, Hirotaka Nakashima, Shuntaro Kuroyanagi, Yuka Ando, Penhao Liao, Shinji Sakurai

Subjects: “…baseball pitching…”
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Instructional Design for Accelerated Macrocognitive Expertise in the Baseball Workplace by Peter J. Fadde

“…It also uses a case study to trace the development and implementation of a macrocognitive training program in the very challenging workplace of the baseball batters’ box. This training, which was embedded for a full season in a college baseball team, targeted the perceptual-cognitive skill of pitch recognition that allows expert batters to circumvent limitations of human reaction time in order to hit a 90 mile-per-hour slider. …”
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Performance-environment mutual flow model using big data on baseball pitchers by Yasuhiro Hashimoto, Yasuhiro Hashimoto, Hiroki Nakata

“…IntroductionThe study investigated the baseball pitching performance in terms of release speed, spin rate, and 3D coordinate data of the release point depending on the ball and strike counts.MethodsWe used open data provided on the official website of Major League Baseball (MLB), which included data related to 580 pitchers who pitched in the MLB between 2015 and 2019.ResultsThe results show that a higher ball count corresponds to a slower release speed and decreased spin rate, and a higher strike count corresponds to a faster release speed and increased spin rate. …”
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You Can’t Think and Hit at the Same Time: Neural Correlates of Baseball Pitch Classification by Jason eSherwin, Jordan eMuraskin, Paul eSajda

“…Hitting a baseball is often described as the most difficult thing to do in sports. …”
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The relationship between pitching parameters and release points of different pitch types in major league baseball players by Yasuhiro Hashimoto, Tomoyuki Nagami, Shinji Yoshitake, Hiroki Nakata

“…ObjectivesThe purpose of this study was to deepen our understanding of pitches and to obtain basic knowledge about pitches by comparing 4-seam and other pitches in Major League Baseball (MLB).MethodsWe analyzed big data for 1,820 professional baseball pitchers of MLB on release speed, spin rate, release point 3D coordinates (X, Y, and Z axes), amount of change for 4-seam, and seven changing ball types (sinker, slider, changeup, cutter, curve, split finger, and knuckle curve), using PITCHf/x and TrackMan. …”
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Exploring wearable sensors as an alternative to marker-based motion capture in the pitching delivery by Kyle J. Boddy, Joseph A. Marsh, Alex Caravan, Kyle E. Lindley, John O. Scheffey, Michael E. O’Connell

“…Methods A total of 10 healthy subjects threw five to seven fastballs followed by five to seven breaking pitches (slider or curveball) in the motion capture lab. Subjects wore retroreflective markers and the MotusBASEBALL sensor simultaneously. …”
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Categorical Nature of Major Factor Selection via Information Theoretic Measurements by Ting-Li Chen, Elizabeth P. Chou, Hsieh Fushing

“…We then apply this protocol on two data sets pertaining to two somewhat related but distinct pitching dynamics of two pitch types: slider and fastball. In particular, we refer to a specific Major League Baseball (MLB) pitcher and we consider data of multiple seasons.…”
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Unraveling Hidden Major Factors by Breaking Heterogeneity into Homogeneous Parts within Many-System Problems by Elizabeth P. Chou, Ting-Li Chen, Hsieh Fushing

“…We study one artificially designed MSP and then two real collectives of Major League Baseball (MLB) pitching dynamics with 62 slider pitchers and 199 fastball pitchers, respectively. …”
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