Match Analysis and Game Preparation
The results showed that players movements were more regular with respect to the centroid of their respective groups compared to the other groups. Player numbers varied between 4 versus 3, 4 versus 5, and 4 versus 7. The results showed that in experts an increase in the number of opponents increased the regularity in team behavior with respect to the opponents. Although the application of ApEn is becoming more prominent, it still remains to be shown what this measure really represents as the regularity behavior of team centroids in itself represent a highly abstract description of team behavior.
Nevertheless, team centroid measures increasingly are being used to capture team behavior and many interesting applications have been reported in the literature in recent years. Another more recent group of approach to study team tactics focuses on the control of space. On such approach uses for example the team surface area as calculated from the convex hull which encloses all players from one team Frencken et al.
Results from this line of research indicates that greater surface areas are covered by the attacking compared to the defensive teams Frencken et al. Similar, more experienced players also cover a greater area compared to less experienced players Duarte et al. Fradua et al. The results showed that individual playing areas become smaller when the ball moved into the central pitch area.
Another approach uses Voronoi-diagrams to investigate space control Nakanishi et al. Here the controlled space is determined using the location and distances between individual players to determine the controlled space. Results using Voronoi-diagrams show similar results compared to the team surface area approach Fonseca et al. Together these results indicate that space control is a central aspect of soccer tactics and further highlight the interactive nature underlying soccer games Duarte et al. Another emerging analysis approach to study team tactics studies investigates team passing behavior using network approaches Watts and Strogatz The basic rationale of this approach is to model the players of a team as nodes and the passes occurring between them as weighted vertices where the number of passes between two players determine the weights Duarte et al.
This representation of team passing behavior allows to easily identify key players within in a team as they display more connection to other vertices accompanied by greater vertex weights Gama et al. Recent network analyses which included next to the player information also pass position information were able to predict game outcomes and the final ranking of the top teams using a K-Nearest Neighbor classifier Cintia et al.
Similar, Wang et al. The obtained model was able to automatically identify different tactical patterns across teams. By combining the obtained tactical information with attacking success the authors were further able to show which specific tactical patterns were more efficient across teams. By investigating the contributions by the individual players to each tactical pattern the authors were further able to determine individual contributions by the players to each tactical pattern Wang et al.
Together these results suggest that players interactions mediated through passing behavior in combination with spatial information provides an interesting new approaches to analyze tactical behavior in elite soccer thereby providing much more information compared to traditional notational analysis approaches. Increasingly tactical decision making in elite soccer is also investigated using machine learning ML algorithms based on game position data Bialkowski et al. Machine learning algorithms allow to identify specific data patterns in large datasets by building an a priori unknown model from the data Haykin ; Jordan and Mitchell ; Waljee and Higgins Although this approach has been discussed in sports research for some time Bartlett ; Borrie et al.
For example, application of an expectation maximization algorithm with position data from an entire English Premier League season allowed the automatic identification of team formations Bialkowski et al. The results further showed that teams used more defensive formations during away games Bialkowski et al.
The authors used a two-step algorithm where the formations were identified only after each player was assigned a specific role. Knauf et al. Pairwise similarities between trajectories during attacking phases were compared using a specific metric and subsequently a clustering algorithms grouped the trajectories into clusters.
Again, one of the underlying features of the algorithm used by the authors is that the comparison between trajectories is invariant to permutations between players Knauf et al. Using spatial tracking data, Kihwan et al. By calculating a flow-field from the running directions of the players the authors were able to determine convergence points of flow-field which predicted future positions of the ball with good agreement Kihwan et al. Hirano and Tsumoto used a multiscale comparison technique with combined event data type and event location data to automatically identify reoccurring attacking sequences leading to a goal.
The multiscale comparison technique allowed to compare event sequences of varying length with each other. For example, in the spatial-kernel method this problem has been resolved by time-normalizing the data Knauf et al. Similar, Fernando et al. Recently, Montoliu et al.
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The authors divided the pitch into ten areas and calculated the optical flow representing the moving direction of players during short video sequences extracted from two complete soccer game recording. Thus, the application relied on the pre-segmentation of the raw video data by experts Montoliu et al. A second group of ML approaches featuring prominent in the soccer literature uses neural network modeling compare Dutt-Mazumder et al.
For example, Grunz et al.mindworksedu.com/images/2019-11-09/255.html
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In summary, numerous machine learning studies of have used soccer data to study tactical decision making with little guidance for non-experts. Common to these approaches is that mostly a certain facet of team tactics, predominantly team formations, was investigated. Accordingly, information how to combine the information across tactical domains Fig. For example it is not clear how group formations interact with the individual technical and tactical skills of players.
As it is clear that different tactical positions within a team have different physiological demands there has been no research addressing how this information can be used in combination with tactical formations used by the attacking and defensive teams Carling et al.
Match Analysis and Game Preparation
Furthermore, with respect to the tactics hierarchy introduced in the introduction compare also Fig. Accordingly, how team formations influence group tactics of subgroups and individual tactics has not been investigated so far. An interesting side-note of the presented studies is the fact that most ML soccer analyses are performed by computer scientist research group with little apparent involvement by sports scientists. This short overview shows that although many interesting analyses are available what is lacking is a conceptual connection between them. Accordingly, it appears that the main obstacle to study team tactics stems from the lack of a theoretical model Garganta ; Glazier ; Mackenzie and Cushion One model which has been repeatedly proposed in the literature is based on a Dynamic system theoretical framework Duarte et al.
However, although this approach merits great potential, at present already the basic definition of a relevant phase space is lacking.
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In the dynamic systems theoretical approaches, the phase space constitutes a key concept which describes a theoretical abstractions describing mathematically a space where the system resides in and which enable to capture the dynamics of the system in a meaningful manner Nevill et al. Current suggestions regarding appropriate phase space variables in team game vary widely Duarte et al.
In this regard, a common approach for example is to use the relative phase as a measure to capture coordination phenomena between players Duarte et al. Relative phase approaches stem from the domain of physical dynamical systems were oscillators typically constitute the building blocks of the systems Pikovsky et al. Accordingly, the question of whether an oscillator assumption is justified to model team games is an open question at present.
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Modeling efforts of soccer games as a dynamic system which go beyond a purely phenomenological description are therefore not available at present. The lack of a higher-order description about soccer team dynamics also prevents the current analytical approaches from making a real impact with practitioners Carling et al. One of the challenges for tactical match analysis in elite soccer will be to work towards an explanatory theoretical model which is able to integrate information from various domains including tactics, physiology, and motor skills Garganta ; Sarmento et al. In particular, so-called deep learning networks are becoming increasingly powerful in modeling domains previously considered computational intractable Hinton and Salakhutdinov ; LeCun et al.
However, these approaches rely on large training datasets to determine network parameters Jones ; Xue-wen and Xiaotong , which at present have not been used in tactical analyses in soccer. In this regard, recent machine learning models using neural networks have been extended such to allow to incorporate a priori information into the models Bishop This might be of great relevance to develop novel approach to model team tactical behaviors as for example insights gained from the studies summarized above might be used to constrain network modeling efforts and at the same time allowing the connection between physiological, tactical and skill related information.
Accordingly, modern algorithm from AI might prove highly useful for tactical analysis in elite soccer and fulfill previous proposals Dutt-Mazumder et al. A potential solution with respect to model building and the combination various data sources might present itself through the recent rise of big data technologies which has been already suggested as shaping the future of performance analysis in elite soccer Cassimally ; Kasabian ; Lohr ; Medeiros ; Norton As the phenomenon of big data is relatively recent first a definition of the relevant concepts will be provided.
Surprisingly, no universally agreed definition of big data is available and big data is rather described by its characteristics Baro et al. Volume describes the magnitude of the data, Variety refers to the heterogeneity of data, and Velocity characterizes the data production rate Noor et al. With respect to tactical analytics in soccer these concept can be mapped in the following way: 1 Volume refers to the size of datasets in soccer. For example, a current dataset for positional data typically encoded using Extensible Markup Language XML ranges between 86 and megabytes mb.
Thus, storing position, event and video data from a single complete Bundesliga season results in gigabytes of tracking data. Accordingly the data volume increases with the addition of other sources including for example physiological or event data. Common solutions using Excel sheets do not scale well with these data. Big data technologies in contrast provide specific solutions for storing such data sets and make them accessible through specific user interfaces and application programming interfaces API.