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cs.LG2026
Machine learning modeling of hit-into-play probability and quantification of pitch sequence effects
Ryota Takamido, Hiroki Nakamoto
Although pitch sequencing is a central topic in baseball analytics, previous studies have primarily focused on improving predictive performance for the final pitch or its outcome,…
cs.HC2026
Cross-individual generalizability of machine learning models for ball speed prediction in baseball pitching
Ryota Takamido, Chiharu Suzuki, Hiroki Nakamoto
Although machine learning (ML)-based performance outcome prediction is an important topic in contemporary sports science, one important issue is the limited understanding of the cr…
cs.HC2026
Data-driven modelling of low-dimensional dynamical structures underlying complex full-body human movement
Ryota Takamido, Chiharu Suzuki, Hiroki Nakamoto
One of the central challenges in the study of human motor control and learning is the degrees-of-freedom problem. Although the dynamical systems approach (DSA) has provided valuabl…