10,418 research outputs found
Spatial movement pattern recognition in soccer based on relative player movements
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
Win-stay lose-shift strategy in formation changes in football
Managerial decision making is likely to be a dominant determinant of
performance of teams in team sports. Here we use Japanese and German football
data to investigate correlates between temporal patterns of formation changes
across matches and match results. We found that individual teams and managers
both showed win-stay lose-shift behavior, a type of reinforcement learning. In
other words, they tended to stick to the current formation after a win and
switch to a different formation after a loss. In addition, formation changes
did not statistically improve the results of succeeding matches.The results
indicate that a swift implementation of a new formation in the win-stay
lose-shift manner may not be a successful managerial rule of thumb.Comment: 7 figures, 11 table
Using network science to analyze football passing networks: dynamics, space, time and the multilayer nature of the game
From the diversity of applications of Network Science, in this Opinion Paper
we are concerned about its potential to analyze one of the most extended group
sports: Football (soccer in U.S. terminology). As we will see, Network Science
allows addressing different aspects of the team organization and performance
not captured by classical analyses based on the performance of individual
players. The reason behind relies on the complex nature of the game, which,
paraphrasing the foundational paradigm of complexity sciences "can not be
analyzed by looking at its components (i.e., players) individually but, on the
contrary, considering the system as a whole" or, in the classical words of
after-match interviews "it's not just me, it's the team".Comment: 7 pages, 1 figur
Designing multiplayer games to facilitate emergent social behaviours online
This paper discusses an exploratory case study of the design of games that facilitate spontaneous social interaction and group behaviours among distributed individuals, based largely on symbolic presence 'state' changes. We present the principles guiding the design of our game environment: presence as a symbolic phenomenon, the importance of good visualization and the potential for spontaneous self-organization among groups of people. Our game environment, comprising a family of multiplayer 'bumper-car' style games, is described, followed by a discussion of lessons learned from observing users of the environment. Finally, we reconsider and extend our design principles in light of our observations
Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories
With the explosion in the availability of spatio-temporal tracking data in
modern sports, there is an enormous opportunity to better analyse, learn and
predict important events in adversarial group environments. In this paper, we
propose a deep decision tree architecture for discriminative dictionary
learning from adversarial multi-agent trajectories. We first build up a
hierarchy for the tree structure by adding each layer and performing feature
weight based clustering in the forward pass. We then fine tune the player role
weights using back propagation. The hierarchical architecture ensures the
interpretability and the integrity of the group representation. The resulting
architecture is a decision tree, with leaf-nodes capturing a dictionary of
multi-agent group interactions. Due to the ample volume of data available, we
focus on soccer tracking data, although our approach can be used in any
adversarial multi-agent domain. We present applications of proposed method for
simulating soccer games as well as evaluating and quantifying team strategies.Comment: To appear in 4th International Workshop on Computer Vision in Sports
(CVsports) at CVPR 201
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