Antonio Losada’s research while affiliated with Universidad de Salamanca and other places

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Publications (3)


A project review under the focus of "complexities" on the example of exploreAT!
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July 2019

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Antonio Losada
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TABLE 1 | Description from the speed intervals used in the application.
Proposed workflow. The user starts the analysis tasks at either of the two entry points to the tool.
(A) Centroid of a group of players with radial distances. (B) The same group of players captured at two different instants of the game. Despite being equally dispersed, the players on the right covered a larger effective playing space. (C) Intra-group coordination in a series of movements. Player number 6 performs in the opposite direction of his group.
Top: Virtual player and group analysis. Bottom-left: A Comparison in the speed and distance covered by two different players. Bottom-right: Line chart showing the evolution of each group's stretch index in the selected period.
Build-up phases of a scored goal. The area covered by attackers and defenders is depicted in the system.

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Data-Driven Visual Performance Analysis in Soccer: An Exploratory Prototype

December 2018

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1,578 Reads

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18 Citations

In soccer, understanding of collective tactical behavior has become an integral part in sports analysis at elite levels. Evolution of technology allows collection of increasingly larger and more specific data sets related to sport activities in cost-effective and accessible manner. All this information is minutely scrutinized by thousands of analysts around the globe in search of answers that can in the long-term help increase the performance of individuals or teams in their respective competitions. As the volume of data increases in size, so does the complexity of the problem and the need for suitable tools that leverage the cognitive load involved in the investigation. It is proven that visualization and computer-vision techniques, correctly applied to the context of a problem, help data analysts focus on the relevant information at each stage of the process, and generally lead to a better understanding of the facts that lie behind the data. In the current study, we presented a software prototype capable of assisting researchers and performance analysts in their duty of studying group collective behavior in soccer games and trainings. We used geospatial data acquired from a professional match to demonstrate its capabilities in two different case studies. Furthermore, we successfully proved the efficiency of the different visualization techniques implemented in the prototype and demonstrated how visual analysis can effectively improve some of the basic tasks employed by sports experts on their daily work, complementing more traditional approaches.


Citations (1)


... Unsurprisingly, a substantial portion of research investigating tactical behaviour in team sport has identified Australian-rules Football (AF) teams with more kicks, time in possession, entrances into the attacking zone per shot taken at goal, and goal conversions than their opponent, being more likely to win matches [1,2]. Further, studies of the characteristics of collective team behaviours show that teams aim to occupy a larger area of space during offensive phases compared with defensive phases of play [3][4][5]. The combination of tallies of key performance indicators and characteristics of collective team behaviour, defined in industry terms as athlete spatio-temporal variables, provides a guide to ideal space occupancy and overall ball use. ...

Reference:

Clustering Offensive Strategies in Australian-Rules Football Using Social Network Analysis
Data-Driven Visual Performance Analysis in Soccer: An Exploratory Prototype