{"title":"精英篮球比赛结果与性别差异的表现分析","authors":"Marcos Bezerra","doi":"10.21134/eurjhm.2022.49.7","DOIUrl":null,"url":null,"abstract":"Traditionally, basketball research has used game-related statistics in order to discriminate winning and losing teams. Unfortunately, most investigations refer exclusively to the analysis of men's competitions. In this sense, Tokyo 2020 Olympic Games created a unique opportunity to make gender performance comparison feasible, since it provided same period and environmental conditions for both genders. The aim of this study was to identify which game-related statistics best discriminated winning and losing teams in men and women’s team playing Tokyo 2020 Basketball Olympic Tournament. All statistical data for the 26 games of each gender were obtained from the FIBA website. The MANOVA and discriminant analysis models were run to check differences according to gender and game outcome. The main results revealed 2-point field-goals percentage, defensive rebounds, assists, points in the paint, and effective field goal percentage as key variables to succeed in men and women’s games. However, there were gender discrepancies on 2-point field-goals made, points from turnovers, turnover per ball possession rate (relevant only when discriminating men’s winning teams), and fastbreak points, offensive rebounds percentage, and free-throw rate (only for women’s winning teams). Winning and losing discriminant statistics were quite similar for both, men and women’s teams when only considering traditional box-score stats, but not when analyzing advanced stats.","PeriodicalId":36150,"journal":{"name":"European Journal of Human Movement","volume":"18 1","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Performance Analysis in Elite Basketball Differentiating Game Outcome And Gender\",\"authors\":\"Marcos Bezerra\",\"doi\":\"10.21134/eurjhm.2022.49.7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditionally, basketball research has used game-related statistics in order to discriminate winning and losing teams. Unfortunately, most investigations refer exclusively to the analysis of men's competitions. In this sense, Tokyo 2020 Olympic Games created a unique opportunity to make gender performance comparison feasible, since it provided same period and environmental conditions for both genders. The aim of this study was to identify which game-related statistics best discriminated winning and losing teams in men and women’s team playing Tokyo 2020 Basketball Olympic Tournament. All statistical data for the 26 games of each gender were obtained from the FIBA website. The MANOVA and discriminant analysis models were run to check differences according to gender and game outcome. The main results revealed 2-point field-goals percentage, defensive rebounds, assists, points in the paint, and effective field goal percentage as key variables to succeed in men and women’s games. However, there were gender discrepancies on 2-point field-goals made, points from turnovers, turnover per ball possession rate (relevant only when discriminating men’s winning teams), and fastbreak points, offensive rebounds percentage, and free-throw rate (only for women’s winning teams). Winning and losing discriminant statistics were quite similar for both, men and women’s teams when only considering traditional box-score stats, but not when analyzing advanced stats.\",\"PeriodicalId\":36150,\"journal\":{\"name\":\"European Journal of Human Movement\",\"volume\":\"18 1\",\"pages\":\"\"},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Human Movement\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21134/eurjhm.2022.49.7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"SPORT SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Human Movement","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21134/eurjhm.2022.49.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"SPORT SCIENCES","Score":null,"Total":0}
Performance Analysis in Elite Basketball Differentiating Game Outcome And Gender
Traditionally, basketball research has used game-related statistics in order to discriminate winning and losing teams. Unfortunately, most investigations refer exclusively to the analysis of men's competitions. In this sense, Tokyo 2020 Olympic Games created a unique opportunity to make gender performance comparison feasible, since it provided same period and environmental conditions for both genders. The aim of this study was to identify which game-related statistics best discriminated winning and losing teams in men and women’s team playing Tokyo 2020 Basketball Olympic Tournament. All statistical data for the 26 games of each gender were obtained from the FIBA website. The MANOVA and discriminant analysis models were run to check differences according to gender and game outcome. The main results revealed 2-point field-goals percentage, defensive rebounds, assists, points in the paint, and effective field goal percentage as key variables to succeed in men and women’s games. However, there were gender discrepancies on 2-point field-goals made, points from turnovers, turnover per ball possession rate (relevant only when discriminating men’s winning teams), and fastbreak points, offensive rebounds percentage, and free-throw rate (only for women’s winning teams). Winning and losing discriminant statistics were quite similar for both, men and women’s teams when only considering traditional box-score stats, but not when analyzing advanced stats.