Spatial movement pattern recognition in soccer based on relative player movements

Abstract

Knowledge of spatial movement patterns in soccer occurring on a regular basis can give a soccer coach, analyst or reporter insights in the playing style or tactics of a group of players or team. Furthermore, it can support a coach to better prepare for a soccer match by analysing (trained) movement patterns of both his own as well as opponent players. We explore the use of the Qualitative Trajectory Calculus (QTC), a spatiotemporal qualitative calculus describing the relative movement between objects, for spatial movement pattern recognition of players movements in soccer. The proposed method allows for the recognition of spatial movement patterns that occur on different parts of the field and/or at different spatial scales. Furthermore, the Levenshtein distance metric supports the recognition of similar movements that occur at different speeds and enables the comparison of movements that have different temporal lengths. We first present the basics of the calculus, and subsequently illustrate its applicability with a real soccer case. To that end, we present a situation where a user chooses the movements of two players during 20 seconds of a real soccer match of a 2016-2017 professional soccer competition as a reference fragment. Following a pattern matching procedure, we describe all other fragments with QTC and calculate their distance with the QTC representation of the reference fragment. The top-k most similar fragments of the same match are presented and validated by means of a duo-trio test. The analyses show the potential of QTC for spatial movement pattern recognition in soccer.

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

“…The systems which evaluate the player [77] or team performance [78] have the potential to understand the game's aspects, which are not obvious to the human eye. These systems are able to evaluate the activities of players successfully [79] such as the distance covered by players, shot detection [80,81], the number of sprints, player's position, and their movements [82,83], the player's relative position concerning other players, possession [84] of the soccer ball and motion/gesture recognition of the referee [85], predicting player trajectories for shot situations [86]. The generated data can be used to evaluate individual player performance, occlusion handling [21] by the detecting position of the player [87], action recognition [88], predicting and classifying the passes [89][90][91], key event extraction [92][93][94][95][96][97][98][99][100][101], tactical performance of the team [102][103][104][105][106], and analyzing the team's tactics based on the team formation [107][108][109], along with generating highlights [110][111][112][113].…”

Spatiotemporal data mining: a survey on challenges and open problems

“…T-pattern can be mined by analysing sequences of regions of interest with time-stamps (Beernaerts et al. 2020 ) or discretisation of space to determine the regions of interest (Giannotti et al. 2007 ).…”

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

“…Trajectory pattern (T-pattern) is an example of frequent pattern defined by [78] as a set of trajectories that visit the same sequence of places consuming similar transition time. T-pattern can be mined by analysing sequences of regions of interest with time-stamps [14] or discretisation of space to determine the regions of interest [78]. Group pattern mining tends to identify movement patterns for groups of objects that move together in near space and time.…”