Game analysis in the era of big data: A synthesis approach - Duarte, R. (21st Annual Congress of the European College of Sport Science (ECSS), Vienna, 6. -9. July 2016, 2016)
In the last 15 years, we entered in a period of accelerated access to large amount of data (Barris & Button, 2008). Today, tracked positional data and multiple-event notation systems are undoubtedly available at professional club setting. Performance analysis, like most scientific disciplines, lives in the era of `big data` (Howe et al., 2008). As such, one important consequence is that performance analysts are struggling with large amount of data. This implies a re-definition of the more appropriate methods and tools to use and, consequently, the way analysts are educated to deal with them (Donovan, 2008). Thus, despite the need to have sophisticated technologies compiling all the statistical information from match performance, there is also a need to develop `synthetic` methods and tools to enhance the usability of such `big data` (e.g., Lames & McGarry, 2007). According to the 2011 McKinsey Global Institute report (Manyika et al., 2011), this need is particularly important when practitioners are interested to extract meaningful information from large amount of data sets and not only on how to store it in long-term. Here, we review and propose some modelling and visualization tools allowing to objectively quantifying the individual, group and team performance in game sports. The modelling tools that will be under our scope are social networks analysis, cluster phase analysis and dominant region diagrams. Also, we will introduce the multiplexity concept once team players forgeand-broke different kind of networked relationships (e.g., passing relations, switching positioning relations, positioning stability relations, etc., Hanneman & Riddle, 2005).