Football (or Soccer) is a game of tactics and knowing how to move players at the right place and time. This makes the game a spatial strategy game that GIS has practical applications in addressing. Game analysis has become competitive among professional teams at national and international competitions.
The use of GPS technologies has been applied in training, where optimized locations for player placement, among other uses, are determined by tracking. Such data can be used in cluster or proximity analyses that look at how often and how close players are to each other in the course of given plays. New big data techniques have now also allowed teams to monitor their own players over the course of an entire season to determine the value given players have on game outcomes.
Other ways to analyze effectiveness of players is to monitor morphological changes in defensive players’ positioning relative to a person with the ball. Those who cause the most number of players to change their position when they have the ball are considered to be the most influential or relatively effective in that they cause more reaction. Players with relatively high reputation, for instance, tend to cause more defensive reaction or morphological changes to defensive positioning. Companies have emerged, such as STATS, that provide analytics that are mostly statistical; however, these data also incorporate spatial analysis of players and opponents during matches. At their core, such companies monitor players using video or GPS data, where the data are then analyzed for performance measures such as location during matches relative to where the ball is or where opponents are located. Players are evaluated during the season to see if their play forms a pattern and to determine the worth of players to football clubs. Such examples show that as more money is invested on play, then spatial analysis and GIS will continue to have an important role in assessing teams’ investments on players and training.
 For more on how cluster and proximity analysis are used to understand plays, see: Kotzbeg, K G., Kainz, W., 2014: Football Game Analysis: A New Application Area for Cartographers and GI-Scientists? In: Proceedings, Vol.1 and Vol.2 of the 5th International Conference on Cartograpghy and GIS [Link], June 15-21, 2014, Riviera, Bulgaria, pp. 299-306.
 For more on GPS-driven big data techniques for soccer matches, see: Bialkowski, A., Lucey, P., Carr, P., Yue, Y., Sridharan, S., & Matthews, I. (2014). Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data (pp. 725–730). IEEE.
 For more on assessing position changes to defensive players when an offensive player has a ball, see: Kim, H.-C., Kwon, O., & Li, K.-J. (2011). Spatial and spatiotemporal analysis of soccer (p. 385). ACM Press.
 For more on STATS, see: https://www.stats.com/football/