{"id":{"repo_id":"brock","oai_identifier":"oai:brocku.scholaris.ca:10464/18370"},"canonical_url":"https://search.dev.ndltd.org/etd/brock/oai:brocku.scholaris.ca:10464/18370","repository":{"repo_id":"brock","name":"Brock University","base_url":"https://brocku.scholaris.ca/server/oai/request"},"display":{"title":"Relationships Between Key Performance Indicators Across Four Swimming Strokes and by Distances in Competitive Swimmers","abstract":"Key performance indicators (KPIs) are skill-based metrics, used by coaches and athletes to adjust technique and develop race strategies. The purpose of the study was to investigate the effect of key performance indicators (KPIs), such as stroke rate (SR;#/s), stroke count (SC;#), stroke length (SL;m) and kick frequency (KF;#/s) on total swim time (s), across four swimming strokes (butterfly, backstroke, breaststroke, freestyle) of the same swim distance, and within a swimming stroke between swim distances (50m and 100 m). Varsity-caliber competitive swimmers (n=12 males; 19yrs1.4) were recruited. Anthropometric measures including height (m), seated height (m), weight (kg), wingspan (m), hand length (m) and leg length (m) were recorded. Shoulder and ankle range of motion (ROM) measurements and a Y-balance test (YBT) were conducted to profile upper and lower limb mobility. Athletes completed four swim sessions; each session consisted of a standardized warm up, 50m kick, 50m pull, 50m swim and 100m swim distances per swimming stroke. Swimming KPIs and total swim time (s) were collected by a portable Triton 2 device (TritonWear, ON, Canada). A GoPro Hero 8 device (GoPro, California, USA) collected underwater video to facilitate calculating KF (#/s). Descriptives were calculated for all variables across all four swimming strokes and two swim distances. Pearson product-moment correlations revealed significant relationships between select anthropometrics and ROM and performance times (p<0.005), suggesting that both anthropometric and ROM measures have the potential to influence swim performance times. A series of repeated-measure ANOVAs with Greenhouse-Geisser corrections revealed significant differences in select KPIs [SR (#/s), SC (#), SL (m) and KF (#/s)] across the four swimming strokes and between 50m and 100m swim distances within a swimming stroke (p<0.05), suggesting that both swimming strokes and swim distances may utilize different KPI related strategies. A multiple regression analyses was conducted to identify the contribution of pull time (s) versus kick time (s) to total swim time (s) within 50m swim distance and each swimming stroke. Percent contributions were calculated and revealed differences by each stroke. Data-driven metrics obtained during swim performances facilitated a better understanding of the relationships between KPIs, anthropometrics, ROM, and YBT measures. Furthermore, metrics provided support to adjust select KPIs relative to an athlete’s anthropometrics. These sport specific skill-based metrics can empower coaches and athletes to use KPIs to optimize athlete potential and target performance goals.","abstract_html":"Key performance indicators (KPIs) are skill-based metrics, used by coaches and athletes to adjust technique and develop race strategies. The purpose of the study was to investigate the effect of key performance indicators (KPIs), such as stroke rate (SR;#/s), stroke count (SC;#), stroke length (SL;m) and kick frequency (KF;#/s) on total swim time (s), across four swimming strokes (butterfly, backstroke, breaststroke, freestyle) of the same swim distance, and within a swimming stroke between swim distances (50m and 100 m). Varsity-caliber competitive swimmers (n=12 males; 19yrs1.4) were recruited. Anthropometric measures including height (m), seated height (m), weight (kg), wingspan (m), hand length (m) and leg length (m) were recorded. Shoulder and ankle range of motion (ROM) measurements and a Y-balance test (YBT) were conducted to profile upper and lower limb mobility. Athletes completed four swim sessions; each session consisted of a standardized warm up, 50m kick, 50m pull, 50m swim and 100m swim distances per swimming stroke. Swimming KPIs and total swim time (s) were collected by a portable Triton 2 device (TritonWear, ON, Canada). A GoPro Hero 8 device (GoPro, California, USA) collected underwater video to facilitate calculating KF (#/s). Descriptives were calculated for all variables across all four swimming strokes and two swim distances. Pearson product-moment correlations revealed significant relationships between select anthropometrics and ROM and performance times (p<0.005), suggesting that both anthropometric and ROM measures have the potential to influence swim performance times. A series of repeated-measure ANOVAs with Greenhouse-Geisser corrections revealed significant differences in select KPIs [SR (#/s), SC (#), SL (m) and KF (#/s)] across the four swimming strokes and between 50m and 100m swim distances within a swimming stroke (p<0.05), suggesting that both swimming strokes and swim distances may utilize different KPI related strategies. A multiple regression analyses was conducted to identify the contribution of pull time (s) versus kick time (s) to total swim time (s) within 50m swim distance and each swimming stroke. Percent contributions were calculated and revealed differences by each stroke. Data-driven metrics obtained during swim performances facilitated a better understanding of the relationships between KPIs, anthropometrics, ROM, and YBT measures. Furthermore, metrics provided support to adjust select KPIs relative to an athlete’s anthropometrics. These sport specific skill-based metrics can empower coaches and athletes to use KPIs to optimize athlete potential and target performance goals.","abstract_has_math":false,"creators":["Bozyigit, Gokhan"],"institution":"Brock University","degree_name":"M.Sc. Applied Health Sciences","degree_level":"Masters","degree_discipline":"Faculty of Applied Health Sciences","degree_department":"Applied Health Sciences Program","school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-04-29T17:23:18Z","date_published":"2024-04-29T17:23:18Z","updated_at":"2026-07-24T01:22:54Z","subjects":["swimming","strokes","KPI","stature","kick","pull","sprint distance"],"languages":["eng"],"rights":["Attribution-NonCommercial-ShareAlike 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by-nc-sa/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10464/18370","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.department","label":"Department","values":["Applied Health Sciences Program"]},{"key":"dc:creator","label":"Author","values":["Bozyigit, Gokhan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-04-29T17:23:18Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-04-29T17:23:18Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-04-29T17:23:18Z"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Faculty of Applied Health Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.Sc. Applied Health Sciences"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Brock University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["swimming","strokes","KPI","stature","kick","pull","sprint distance"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Attribution-NonCommercial-ShareAlike 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by-nc-sa/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10464/18370"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Key performance indicators (KPIs) are skill-based metrics, used by coaches and athletes to adjust technique and develop race strategies. The purpose of the study was to investigate the effect of key performance indicators (KPIs), such as stroke rate (SR;#/s), stroke count (SC;#), stroke length (SL;m) and kick frequency (KF;#/s) on total swim time (s), across four swimming strokes (butterfly, backstroke, breaststroke, freestyle) of the same swim distance, and within a swimming stroke between swim distances (50m and 100 m). Varsity-caliber competitive swimmers (n=12 males; 19yrs1.4) were recruited. Anthropometric measures including height (m), seated height (m), weight (kg), wingspan (m), hand length (m) and leg length (m) were recorded. Shoulder and ankle range of motion (ROM) measurements and a Y-balance test (YBT) were conducted to profile upper and lower limb mobility. Athletes completed four swim sessions; each session consisted of a standardized warm up, 50m kick, 50m pull, 50m swim and 100m swim distances per swimming stroke. Swimming KPIs and total swim time (s) were collected by a portable Triton 2 device (TritonWear, ON, Canada). A GoPro Hero 8 device (GoPro, California, USA) collected underwater video to facilitate calculating KF (#/s). Descriptives were calculated for all variables across all four swimming strokes and two swim distances. Pearson product-moment correlations revealed significant relationships between select anthropometrics and ROM and performance times (p<0.005), suggesting that both anthropometric and ROM measures have the potential to influence swim performance times. A series of repeated-measure ANOVAs with Greenhouse-Geisser corrections revealed significant differences in select KPIs [SR (#/s), SC (#), SL (m) and KF (#/s)] across the four swimming strokes and between 50m and 100m swim distances within a swimming stroke (p<0.05), suggesting that both swimming strokes and swim distances may utilize different KPI related strategies. A multiple regression analyses was conducted to identify the contribution of pull time (s) versus kick time (s) to total swim time (s) within 50m swim distance and each swimming stroke. Percent contributions were calculated and revealed differences by each stroke. Data-driven metrics obtained during swim performances facilitated a better understanding of the relationships between KPIs, anthropometrics, ROM, and YBT measures. Furthermore, metrics provided support to adjust select KPIs relative to an athlete’s anthropometrics. These sport specific skill-based metrics can empower coaches and athletes to use KPIs to optimize athlete potential and target performance goals."]},{"key":"dc:title","label":"Title","values":["Relationships Between Key Performance Indicators Across Four Swimming Strokes and by Distances in Competitive Swimmers"]}]}],"canonical_facts":{"dc:contributor.department":["Applied Health Sciences Program"],"dc:creator":["Bozyigit, Gokhan"],"dc:date.accessioned":["2024-04-29T17:23:18Z"],"dc:date.available":["2024-04-29T17:23:18Z"],"dc:date.issued":["2024-04-29T17:23:18Z"],"dc:description.abstract":["Key performance indicators (KPIs) are skill-based metrics, used by coaches and athletes to adjust technique and develop race strategies. The purpose of the study was to investigate the effect of key performance indicators (KPIs), such as stroke rate (SR;#/s), stroke count (SC;#), stroke length (SL;m) and kick frequency (KF;#/s) on total swim time (s), across four swimming strokes (butterfly, backstroke, breaststroke, freestyle) of the same swim distance, and within a swimming stroke between swim distances (50m and 100 m). Varsity-caliber competitive swimmers (n=12 males; 19yrs1.4) were recruited. Anthropometric measures including height (m), seated height (m), weight (kg), wingspan (m), hand length (m) and leg length (m) were recorded. Shoulder and ankle range of motion (ROM) measurements and a Y-balance test (YBT) were conducted to profile upper and lower limb mobility. Athletes completed four swim sessions; each session consisted of a standardized warm up, 50m kick, 50m pull, 50m swim and 100m swim distances per swimming stroke. Swimming KPIs and total swim time (s) were collected by a portable Triton 2 device (TritonWear, ON, Canada). A GoPro Hero 8 device (GoPro, California, USA) collected underwater video to facilitate calculating KF (#/s). Descriptives were calculated for all variables across all four swimming strokes and two swim distances. Pearson product-moment correlations revealed significant relationships between select anthropometrics and ROM and performance times (p<0.005), suggesting that both anthropometric and ROM measures have the potential to influence swim performance times. A series of repeated-measure ANOVAs with Greenhouse-Geisser corrections revealed significant differences in select KPIs [SR (#/s), SC (#), SL (m) and KF (#/s)] across the four swimming strokes and between 50m and 100m swim distances within a swimming stroke (p<0.05), suggesting that both swimming strokes and swim distances may utilize different KPI related strategies. A multiple regression analyses was conducted to identify the contribution of pull time (s) versus kick time (s) to total swim time (s) within 50m swim distance and each swimming stroke. Percent contributions were calculated and revealed differences by each stroke. Data-driven metrics obtained during swim performances facilitated a better understanding of the relationships between KPIs, anthropometrics, ROM, and YBT measures. Furthermore, metrics provided support to adjust select KPIs relative to an athlete’s anthropometrics. These sport specific skill-based metrics can empower coaches and athletes to use KPIs to optimize athlete potential and target performance goals."],"dc:identifier.uri":["http://hdl.handle.net/10464/18370"],"dc:language.iso":["eng"],"dc:rights":["Attribution-NonCommercial-ShareAlike 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by-nc-sa/4.0/"],"dc:subject":["swimming","strokes","KPI","stature","kick","pull","sprint distance"],"dc:title":["Relationships Between Key Performance Indicators Across Four Swimming Strokes and by Distances in Competitive Swimmers"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Faculty of Applied Health Sciences"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.Sc. Applied Health Sciences"],"thesis:institution_name":["Brock University"]},"updated_at":"2026-07-24T01:22:54Z"}