Martínez-Rodríguez, José AntonioNeville, JonathonCronin, John B2026-04-212026-04-212026-04-12Sensors, ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 26(8), 2378-2378. doi: 10.3390/s260823781424-82201424-8220http://hdl.handle.net/10292/20955Base running performance in baseball depends on the ability to efficiently transition between linear and curvilinear sprinting; however, current assessment approaches provide limited insight into how speed is developed, maintained, or lost across these phases. This perspective presents a methodological framework for using GPS technology to enhance the analysis and interpretation of base running performance through segment-specific velocity and time diagnostics. GPS data were collected during 54.7 m linear sprints and home-to-second-base curvilinear sprints in three high-school baseball players with differing performance profiles. Sprint paths were divided into standardized linear (L1–L4) and curvilinear (C1–C4) segments, allowing examination of speed changes between successive phases to identify acceleration, maintenance, and deceleration patterns. Comparative case analyses illustrate how athletes differ in their ability to negotiate the curve around first base, reaccelerate toward second base, and maintain speed under increasing curvilinear demands. In addition, a base running efficiency ratio (BREr) is introduced to quantify how effectively linear sprint capacity is preserved during curvilinear base running, both globally and across early and late phases of the sprint. The three players’ data illustrated that GPS-derived velocity–time profiles may provide useful insights into individual running strategies, path selection, and segment-specific performance limitations that are not captured by traditional timing methods. Rather than establishing normative benchmarks, this paper emphasizes the applied value of GPS technology as a diagnostic tool to potentially inform individualized assessment and monitoring in applied settings related to linear and curvilinear sprint performance in baseball.Copyright: © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.40 Engineering46 Information and Computing Sciences4009 Electronics, Sensors and Digital Hardware4606 Distributed Computing and Systems Softwaremonitoringtechnologybaseballcurvilinear runningsprintingJournal ArticleOpenAccess10.3390/s26082378