Ayden McCarthy,Joel Thomas Fuller,Jodie Anne Wills,Steve Cassidy,Mita Lovalekar,Bradley C Nindl,Tim L A Doyle
{"title":"力量,跳跃高度,着陆和机动性指标预测火力和移动评估的高低:一种机器学习方法。","authors":"Ayden McCarthy,Joel Thomas Fuller,Jodie Anne Wills,Steve Cassidy,Mita Lovalekar,Bradley C Nindl,Tim L A Doyle","doi":"10.1249/mss.0000000000003822","DOIUrl":null,"url":null,"abstract":"PURPOSE\r\nCombat manoeuvrability is critical for soldier survivability. Military organisations ensure effective combat manoeuvrability through routine assessments. Advanced statistical analyses may improve combat movement efficiency practices. This study grouped physical qualities (e.g., strength, power, mobility) via an Exploratory Factor Analysis (EFA) and extracted factors to compare high and low performers and develop predictive models.\r\n\r\nMETHODS\r\n34 participants completed two sessions assessing physical qualities and combat movement performance. Participants were classified as either \"high\" or \"low\" performers (i.e., completed 50 laps of the assessment or completed less than 50 laps, respectively). An EFA was conducted to reduce physical quality dataset dimensions into specific factors. T-test and effect size compared factors between high and low performers. Logistic regression, multilayer perceptron, and random forest models were trained and tested to classify performers based on factor values. Feature importance scores determined factors most influential in classifying participants.\r\n\r\nRESULTS\r\nEFA resulted in four factors (81.46% variance explained). Factor 1 represented isometric strength, jumping, and drop landing ability. Factors 2-4 represent isometric strength and rate of force development in the lower and upper body, and overhead squat ability, respectively. All factors significantly differed between groups, with high performers demonstrating higher mean values than low performers (p < 0.05). Factor 1 demonstrated a very large effect size (d = 2.15), while factors 2-4 were moderate-large (d = 0.72-0.81). The logistic regression model had 100% accuracy in the testing phase, while other models achieved 86%. Factor 1 was the most influential factor across models (approximately six times more than other factors).\r\n\r\nCONCLUSIONS\r\nUtilised models show military applicability in classifying high or low performers for combat manoeuvrability. Physical interventions optimising Factor 1 may enhance combat manoeuvrability.","PeriodicalId":18500,"journal":{"name":"Medicine & Science in Sports & Exercise","volume":"14 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Strength, Jump Height, Landing, and Mobility Metrics Predict High and Low Performers of a Fire and Move Assessment: A Machine Learning Approach.\",\"authors\":\"Ayden McCarthy,Joel Thomas Fuller,Jodie Anne Wills,Steve Cassidy,Mita Lovalekar,Bradley C Nindl,Tim L A Doyle\",\"doi\":\"10.1249/mss.0000000000003822\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PURPOSE\\r\\nCombat manoeuvrability is critical for soldier survivability. Military organisations ensure effective combat manoeuvrability through routine assessments. Advanced statistical analyses may improve combat movement efficiency practices. This study grouped physical qualities (e.g., strength, power, mobility) via an Exploratory Factor Analysis (EFA) and extracted factors to compare high and low performers and develop predictive models.\\r\\n\\r\\nMETHODS\\r\\n34 participants completed two sessions assessing physical qualities and combat movement performance. Participants were classified as either \\\"high\\\" or \\\"low\\\" performers (i.e., completed 50 laps of the assessment or completed less than 50 laps, respectively). An EFA was conducted to reduce physical quality dataset dimensions into specific factors. T-test and effect size compared factors between high and low performers. Logistic regression, multilayer perceptron, and random forest models were trained and tested to classify performers based on factor values. Feature importance scores determined factors most influential in classifying participants.\\r\\n\\r\\nRESULTS\\r\\nEFA resulted in four factors (81.46% variance explained). Factor 1 represented isometric strength, jumping, and drop landing ability. Factors 2-4 represent isometric strength and rate of force development in the lower and upper body, and overhead squat ability, respectively. All factors significantly differed between groups, with high performers demonstrating higher mean values than low performers (p < 0.05). Factor 1 demonstrated a very large effect size (d = 2.15), while factors 2-4 were moderate-large (d = 0.72-0.81). The logistic regression model had 100% accuracy in the testing phase, while other models achieved 86%. Factor 1 was the most influential factor across models (approximately six times more than other factors).\\r\\n\\r\\nCONCLUSIONS\\r\\nUtilised models show military applicability in classifying high or low performers for combat manoeuvrability. Physical interventions optimising Factor 1 may enhance combat manoeuvrability.\",\"PeriodicalId\":18500,\"journal\":{\"name\":\"Medicine & Science in Sports & Exercise\",\"volume\":\"14 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medicine & Science in Sports & Exercise\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1249/mss.0000000000003822\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medicine & Science in Sports & Exercise","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1249/mss.0000000000003822","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Strength, Jump Height, Landing, and Mobility Metrics Predict High and Low Performers of a Fire and Move Assessment: A Machine Learning Approach.
PURPOSE
Combat manoeuvrability is critical for soldier survivability. Military organisations ensure effective combat manoeuvrability through routine assessments. Advanced statistical analyses may improve combat movement efficiency practices. This study grouped physical qualities (e.g., strength, power, mobility) via an Exploratory Factor Analysis (EFA) and extracted factors to compare high and low performers and develop predictive models.
METHODS
34 participants completed two sessions assessing physical qualities and combat movement performance. Participants were classified as either "high" or "low" performers (i.e., completed 50 laps of the assessment or completed less than 50 laps, respectively). An EFA was conducted to reduce physical quality dataset dimensions into specific factors. T-test and effect size compared factors between high and low performers. Logistic regression, multilayer perceptron, and random forest models were trained and tested to classify performers based on factor values. Feature importance scores determined factors most influential in classifying participants.
RESULTS
EFA resulted in four factors (81.46% variance explained). Factor 1 represented isometric strength, jumping, and drop landing ability. Factors 2-4 represent isometric strength and rate of force development in the lower and upper body, and overhead squat ability, respectively. All factors significantly differed between groups, with high performers demonstrating higher mean values than low performers (p < 0.05). Factor 1 demonstrated a very large effect size (d = 2.15), while factors 2-4 were moderate-large (d = 0.72-0.81). The logistic regression model had 100% accuracy in the testing phase, while other models achieved 86%. Factor 1 was the most influential factor across models (approximately six times more than other factors).
CONCLUSIONS
Utilised models show military applicability in classifying high or low performers for combat manoeuvrability. Physical interventions optimising Factor 1 may enhance combat manoeuvrability.