ABSTRACT
Soccer matches involve two teams interacting as they contest the match. When in possession of the ball teams try to create space (Bangsbo and Peitersen, 2004) while the defending team tries to deny space (Bangsbo and Peitersen, 2002). Therefore, many performance variables reflect both one team's attacking play and the other team's defensive play. Player locations on playing surfaces can now be tracked automatically or semi-automatically using a range of technologies with varying degrees of reliability (Carling et al., 2008). These systems can provide information on player movement such as distances covered in different speed ranges as well as the locations of any sprints and accelerations. Some systems also include outputs where player locations can be animated allowing qualitative assessment of sub-units within teams such as the defenders (Dijk, 2011). The player trajectory data that is available to clubs may also be used to provide quantitative information on tactical aspects of movement (Lemmink and Frencken, 2011; Lames and Siegle, 2011). Spatial aspects of performance such as depth and width (Daniel, 2003), concentration of players and delay (Worthington, 1980) and balance of defence (Olsen, 1981) have been discussed in soccer coaching literature which predates player tracking technology. Some simple research has been done on team centroids (Lames et al., 2011; Duarte et al., 2011; Lemmink and Frencken, 2011) and more advanced algorithms have been produced to determine sectors of coverage (Grehaigne et al., 1997), balance of the defence (O'Donoghue, 2011) and other spatial variables relating to tactics (Robles et al., 2011; Duarte et al., 2012). There is, however, still a great need to define variables to represent the concepts of space creation and restriction described by Bangsbo and Peitersen (2002, 2004), Daniel (2003), Olsen (1981) and Worthington (1980) as well as to develop algorithms to measure these variables using player tracking data.
