{"title":"Athletic Monitoring in Basketball: A Qualitative Exploratory Approach","authors":"B. Serrano, M. Comer, Geoff Puls","doi":"10.37722/aoasm.2022302","DOIUrl":null,"url":null,"abstract":"The competitiveness of sport continues to increase with the corollary increase in sport technology. Sport technology is an umbrella that encompasses biometrics, wearable technology, and data analytics. Central to sport technology is a close relative, sport analytics. If sport technology is the general term for measuring what the athlete is experiencing from a performance standpoint, then sport analytics is the process of cleaning, processing, and manipulating these variables into meaningful output. The number of variables that can be collected are extensive but can be generally categorized into internal and external metrics. Internal metrics seek to measure how the athlete responds to training load (HR, HRV, Hormones, Sleep patterns). External metrics seek to measure how much work the athlete is performing (Live player tracking, Countermovement Jump, Isometric Mid-Thigh Pull). Internal and external metrics are continually evolving in the manner they are collected which depends on the time, resources, and staff allocation of an organization. This is where the collaboration between sport scientists and sport performance staff can come together to overlay variables and find relationships. There is no correct way to analyze data because each organization will have their own goals. For example, data could be used for injury mitigation, readiness assessment, or simply to establish baseline values. Some common statistical techniques that can be used include correlation, regression, and principal component analysis (PCA). However, performing these calculations may be complex and some organizations may lack a practitioner which can limit their ability to use data. In seeing this gap, the authors propose a qualitative approach to workload monitoring made for basketball but can be applied to most sports. The purpose of this paper is to empower sports performance staffs to learn and implement the basics of qualitative athletic monitoring approach.","PeriodicalId":7354,"journal":{"name":"Advances in Orthopedics and Sports Medicine","volume":"29 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Orthopedics and Sports Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37722/aoasm.2022302","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The competitiveness of sport continues to increase with the corollary increase in sport technology. Sport technology is an umbrella that encompasses biometrics, wearable technology, and data analytics. Central to sport technology is a close relative, sport analytics. If sport technology is the general term for measuring what the athlete is experiencing from a performance standpoint, then sport analytics is the process of cleaning, processing, and manipulating these variables into meaningful output. The number of variables that can be collected are extensive but can be generally categorized into internal and external metrics. Internal metrics seek to measure how the athlete responds to training load (HR, HRV, Hormones, Sleep patterns). External metrics seek to measure how much work the athlete is performing (Live player tracking, Countermovement Jump, Isometric Mid-Thigh Pull). Internal and external metrics are continually evolving in the manner they are collected which depends on the time, resources, and staff allocation of an organization. This is where the collaboration between sport scientists and sport performance staff can come together to overlay variables and find relationships. There is no correct way to analyze data because each organization will have their own goals. For example, data could be used for injury mitigation, readiness assessment, or simply to establish baseline values. Some common statistical techniques that can be used include correlation, regression, and principal component analysis (PCA). However, performing these calculations may be complex and some organizations may lack a practitioner which can limit their ability to use data. In seeing this gap, the authors propose a qualitative approach to workload monitoring made for basketball but can be applied to most sports. The purpose of this paper is to empower sports performance staffs to learn and implement the basics of qualitative athletic monitoring approach.