{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1992"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1992","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Analysis of pitching and active joint range of motion in baseball pitchers with markerless motion capture software","abstract":"Baseball pitching is a highly scrutinized and studied movement, with pitch velocity being an important contributor to future success of an athlete. Pitching biomechanics can reveal important insights about a pitcher’s performance, with markerless, camera-based systems providing new-found opportunities to study pitch mechanics. A potential variable that has not received much attention, but can be a potential predictor of future success, is joint range of motion. The purpose of this thesis was to investigate correlations between off-the-mound active joint ROM, pitching ROM and pitch velocity, when kinematic ROM data were obtained from a novel ProPlay AI markerless system. The AI markerless software had difficulty tracking many of the ranges of motion. However, active (i.e. off-the-mound) thoracic spine rotation and lumbar spine lateral flexion were correlated with pitch velocity (p < 0.05). When combined with demographic and anthropometric variables, strong predictive models for fastball velocity were obtained. There may be potential to include range of motion as a future predictor of velocity, but further work is needed to train and validate computer vision models to assess full ranges of motion for all major joint movements.","abstract_html":"Baseball pitching is a highly scrutinized and studied movement, with pitch velocity being an important contributor to future success of an athlete. Pitching biomechanics can reveal important insights about a pitcher’s performance, with markerless, camera-based systems providing new-found opportunities to study pitch mechanics. A potential variable that has not received much attention, but can be a potential predictor of future success, is joint range of motion. The purpose of this thesis was to investigate correlations between off-the-mound active joint ROM, pitching ROM and pitch velocity, when kinematic ROM data were obtained from a novel ProPlay AI markerless system. The AI markerless software had difficulty tracking many of the ranges of motion. However, active (i.e. off-the-mound) thoracic spine rotation and lumbar spine lateral flexion were correlated with pitch velocity (p < 0.05). When combined with demographic and anthropometric variables, strong predictive models for fastball velocity were obtained. There may be potential to include range of motion as a future predictor of velocity, but further work is needed to train and validate computer vision models to assess full ranges of motion for all major joint movements.","abstract_has_math":false,"creators":["Murphy, Adam"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Health Sciences (MHSc)","degree_level":null,"degree_discipline":"Kinesiology","degree_department":null,"school":null,"contributors":[],"advisors":["LaDelfa, Nicholas"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-01","date_published":"2025-05-01","updated_at":"2026-07-24T05:35:20Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1992","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["LaDelfa, Nicholas"]},{"key":"dc:creator","label":"Author","values":["Murphy, Adam"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-09-18T17:54:37Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-09-18T17:54:37Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Kinesiology"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Health Sciences (MHSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1992"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Baseball pitching is a highly scrutinized and studied movement, with pitch velocity being an important contributor to future success of an athlete. Pitching biomechanics can reveal important insights about a pitcher’s performance, with markerless, camera-based systems providing new-found opportunities to study pitch mechanics. A potential variable that has not received much attention, but can be a potential predictor of future success, is joint range of motion. The purpose of this thesis was to investigate correlations between off-the-mound active joint ROM, pitching ROM and pitch velocity, when kinematic ROM data were obtained from a novel ProPlay AI markerless system. The AI markerless software had difficulty tracking many of the ranges of motion. However, active (i.e. off-the-mound) thoracic spine rotation and lumbar spine lateral flexion were correlated with pitch velocity (p < 0.05). When combined with demographic and anthropometric variables, strong predictive models for fastball velocity were obtained. There may be potential to include range of motion as a future predictor of velocity, but further work is needed to train and validate computer vision models to assess full ranges of motion for all major joint movements."]},{"key":"dc:title","label":"Title","values":["Analysis of pitching and active joint range of motion in baseball pitchers with markerless motion capture software"]}]}],"canonical_facts":{"dc:contributor.advisor":["LaDelfa, Nicholas"],"dc:creator":["Murphy, Adam"],"dc:date.accessioned":["2025-09-18T17:54:37Z"],"dc:date.available":["2025-09-18T17:54:37Z"],"dc:date.issued":["2025-05-01"],"dc:description.abstract":["Baseball pitching is a highly scrutinized and studied movement, with pitch velocity being an important contributor to future success of an athlete. Pitching biomechanics can reveal important insights about a pitcher’s performance, with markerless, camera-based systems providing new-found opportunities to study pitch mechanics. A potential variable that has not received much attention, but can be a potential predictor of future success, is joint range of motion. The purpose of this thesis was to investigate correlations between off-the-mound active joint ROM, pitching ROM and pitch velocity, when kinematic ROM data were obtained from a novel ProPlay AI markerless system. The AI markerless software had difficulty tracking many of the ranges of motion. However, active (i.e. off-the-mound) thoracic spine rotation and lumbar spine lateral flexion were correlated with pitch velocity (p < 0.05). When combined with demographic and anthropometric variables, strong predictive models for fastball velocity were obtained. There may be potential to include range of motion as a future predictor of velocity, but further work is needed to train and validate computer vision models to assess full ranges of motion for all major joint movements."],"dc:identifier.uri":["https://hdl.handle.net/10155/1992"],"dc:language.iso":["en"],"dc:title":["Analysis of pitching and active joint range of motion in baseball pitchers with markerless motion capture software"],"dc:type":["Thesis"],"thesis:degree_discipline":["Kinesiology"],"thesis:degree_name":["Master of Health Sciences (MHSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:20Z"}