DOI: 10.57760/sciencedb.21227
title: A data set of basketball players birth quartiles and performances
title: A data set of basketball players birth quartiles and performances

description: The dataset contains structured tabular data related to professional basketball, likely focusing on the top ten leagues in Europe. The data was generated from official basketball league sources, including player statistics, performance metrics, and team rankings. The collection process involved extracting records from league websites, cross-verifying them with secondary sources (e.g., Eurobasket.com), and formatting them for statistical analysis in SPSS (.sav format).Scope and StructureTemporal Scope: The dataset covers the 2022/2023 basketball season, ensuring up-to-date and relevant information.Geographical Scope: Data is drawn from 10 European basketball leagues, including Spain (Liga ACB), Turkey (BSL), France (LNB), Italy (LBA), Germany (BBL), Lithuania (LKL), Greece (HEBA), Israel (IBSL), Russia (VTB), and the Adriatic League (ABA).Resolution: The dataset provides player-level and team-level granularity, detailing individual and collective performance metrics.Dataset Format & StructureFile Type: SPSS data file (.sav).Number of Entries: The dataset likely contains records for 2,571 players across 150 teams, reflecting player-level statistics.Columns: Each column represents a specific variable, such as player birthdate, position, efficiency scores, playing time, team ranking, and relative age effect indicators. The column headers define key statistical attributes.Rows: Each row corresponds to an individual player or team performance entry.Handling of Missing Data & ErrorsAny missing values or incomplete records should be inspected using SPSS’s built-in missing value analysis tools. If present, missing data may be due to unavailable records from official sources.Data errors, such as incorrect player birthdates or outliers in performance scores, should be checked against official sources for validation.The dataset follows standard SPSS formatting, ensuring compatibility with most statistical software.Usage & AccessibilitySoftware Required: The .sav format can be opened using SPSS, JASP, R (with haven package), or Python (with pandas & pyreadstat libraries).File Size: The file size depends on the number of variables and observations stored but is optimized for statistical analysis.This dataset provides valuable insights into professional basketball, allowing researchers to explore trends related to Relative Age Effect (RAE), player efficiency, selection biases, and league performance correlations. It is well-structured for use in statistical modeling, data visualization, and comparative sports analysis.
description: The dataset contains structured tabular data related to professional basketball, likely focusing on the top ten leagues in Europe. The data was generated from official basketball league sources, including player statistics, performance metrics, and team rankings. The collection process involved extracting records from league websites, cross-verifying them with secondary sources (e.g., Eurobasket.com), and formatting them for statistical analysis in SPSS (.sav format).Scope and StructureTemporal Scope: The dataset covers the 2022/2023 basketball season, ensuring up-to-date and relevant information.Geographical Scope: Data is drawn from 10 European basketball leagues, including Spain (Liga ACB), Turkey (BSL), France (LNB), Italy (LBA), Germany (BBL), Lithuania (LKL), Greece (HEBA), Israel (IBSL), Russia (VTB), and the Adriatic League (ABA).Resolution: The dataset provides player-level and team-level granularity, detailing individual and collective performance metrics.Dataset Format & StructureFile Type: SPSS data file (.sav).Number of Entries: The dataset likely contains records for 2,571 players across 150 teams, reflecting player-level statistics.Columns: Each column represents a specific variable, such as player birthdate, position, efficiency scores, playing time, team ranking, and relative age effect indicators. The column headers define key statistical attributes.Rows: Each row corresponds to an individual player or team performance entry.Handling of Missing Data & ErrorsAny missing values or incomplete records should be inspected using SPSS’s built-in missing value analysis tools. If present, missing data may be due to unavailable records from official sources.Data errors, such as incorrect player birthdates or outliers in performance scores, should be checked against official sources for validation.The dataset follows standard SPSS formatting, ensuring compatibility with most statistical software.Usage & AccessibilitySoftware Required: The .sav format can be opened using SPSS, JASP, R (with haven package), or Python (with pandas & pyreadstat libraries).File Size: The file size depends on the number of variables and observations stored but is optimized for statistical analysis.This dataset provides valuable insights into professional basketball, allowing researchers to explore trends related to Relative Age Effect (RAE), player efficiency, selection biases, and league performance correlations. It is well-structured for use in statistical modeling, data visualization, and comparative sports analysis.