{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/139345"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/139345","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Automated Force-Velocity Profiling of NFL Athletes via High-Frequency Tracking Data","abstract":"The ability to measure key physical parameters of athletes is becoming increasingly critical for today’s sports organizations. Force-velocity profiling is a well-understood and studied technique for measuring the relationship between speed and output force in sport-specific contexts. Accurate force-velocity profiling systems can enable a wide variety of applications for sports organizations to improve player performance, cater better training programs, and potentially reduce injury rates in the long term. A current limitation of many of these systems is that they can require context-specific testing that impacts workflows for players, coaches, and trainers. Given the recent rise of wearable sensor technologies that track player movement in dynamic contexts, there is a clear opportunity to leverage new data streams to enhance this process. We present a novel system for automated force-velocity profiling using publicly available high-frequency tracking data of NFL players. We demonstrate that our derived force-velocity envelopes match observed position and player performance, and provide a proof of concept framework that would allow teams to leverage automated force-velocity profiling in their internal operations.","abstract_html":"The ability to measure key physical parameters of athletes is becoming increasingly critical for today’s sports organizations. Force-velocity profiling is a well-understood and studied technique for measuring the relationship between speed and output force in sport-specific contexts. Accurate force-velocity profiling systems can enable a wide variety of applications for sports organizations to improve player performance, cater better training programs, and potentially reduce injury rates in the long term. A current limitation of many of these systems is that they can require context-specific testing that impacts workflows for players, coaches, and trainers. Given the recent rise of wearable sensor technologies that track player movement in dynamic contexts, there is a clear opportunity to leverage new data streams to enhance this process. We present a novel system for automated force-velocity profiling using publicly available high-frequency tracking data of NFL players. We demonstrate that our derived force-velocity envelopes match observed position and player performance, and provide a proof of concept framework that would allow teams to leverage automated force-velocity profiling in their internal operations.","abstract_has_math":false,"creators":["Lyons, Kevin Andrew"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Force-velocity profiling is a well-understood and studied technique for measuring the relationship between speed and output force in sport-specific contexts. Accurate force-velocity profiling systems can enable a wide variety of applications for sports organizations to improve player performance, cater better training programs, and potentially reduce injury rates in the long term. A current limitation of many of these systems is that they can require context-specific testing that impacts workflows for players, coaches, and trainers. Given the recent rise of wearable sensor technologies that track player movement in dynamic contexts, there is a clear opportunity to leverage new data streams to enhance this process. We present a novel system for automated force-velocity profiling using publicly available high-frequency tracking data of NFL players. We demonstrate that our derived force-velocity envelopes match observed position and player performance, and provide a proof of concept framework that would allow teams to leverage automated force-velocity profiling in their internal operations."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Automated Force-Velocity Profiling of NFL Athletes via High-Frequency Tracking Data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Hosoi, Anette"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Lyons, Kevin Andrew"],"dc:date.accessioned":["2022-01-14T15:05:25Z"],"dc:date.available":["2022-01-14T15:05:25Z"],"dc:date.issued":["2021-06"],"dc:description.abstract":["The ability to measure key physical parameters of athletes is becoming increasingly critical for today’s sports organizations. Force-velocity profiling is a well-understood and studied technique for measuring the relationship between speed and output force in sport-specific contexts. Accurate force-velocity profiling systems can enable a wide variety of applications for sports organizations to improve player performance, cater better training programs, and potentially reduce injury rates in the long term. A current limitation of many of these systems is that they can require context-specific testing that impacts workflows for players, coaches, and trainers. Given the recent rise of wearable sensor technologies that track player movement in dynamic contexts, there is a clear opportunity to leverage new data streams to enhance this process. We present a novel system for automated force-velocity profiling using publicly available high-frequency tracking data of NFL players. We demonstrate that our derived force-velocity envelopes match observed position and player performance, and provide a proof of concept framework that would allow teams to leverage automated force-velocity profiling in their internal operations."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/139345"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Automated Force-Velocity Profiling of NFL Athletes via High-Frequency Tracking Data"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:39Z"}