This chapter discusses a Big Data field study that analyzed more than 300,000,000 position data points from the German Bundesliga. These data were used to explore and validate multiple advanced Key Performance Indicators (KPIs) based on Voronoi Diagrams or Neural Networks to analyze team and player performances, and to answer sport scientific questions such as the factors which vary between winning and losing or good and bad teams. With such a large sample, this example offers a great opportunity to examine the application of positional data analysis in practice. In addition to the study results, this chapter provides an in-depth discussion of the possible applications of positional data and derived KPIs in match analysis. Importantly, critical concepts, such as the operational definitions, validity and reliability of performance metrics, are highlighted. With a special emphasis on hands-on applications, this chapter finally discusses the interpretation of advanced KPIs and gives tips on how to apply these in a practical setting.
|Title of host publication||Match Analysis : How to Use Data in Professional Sport|
|Number of pages||8|
|Place of Publication||New York|
|Publication status||Published - 2021|