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Citations since 2017
Publications
Publications (18)
Choosing the right formation is one of the coach’s most important decisions in football. Teams change formation dynamically throughout matches to achieve their immediate objective: to retain possession, progress the ball up-field and create (or prevent) goal-scoring opportunities. In this work we identify the unique formations used by teams in dist...
Choosing the right defensive corner-strategy is a crucial task for each coach in professional football (soccer). Although corners are repeatable and static situations, due to their low conversion rates, several studies in literature failed to find useable insights about the efficiency of various corner strategies. Our work aims to fill this gap. We...
Overlapping runs are a widely used group-tactical pattern in soccer. By combining variational autoencoder with a graph neural network representation of positional data, we are able to detect overlapping runs using only a very limited amount of hand-labeled data. Based on this detection, we show practical applications using data of the German nation...
Passes are by far football’s (soccer) most frequent event, yet surprisingly little meaningful research has been devoted to quantify them. With the increase in availability of so-called positional data, describing the positioning of players and ball at every moment of the game, our work aims to determine the difficulty of every pass by calculating i...
We study the automatic annotation of situations in soccer games. At first sight, this translates nicely into a standard supervised learning problem. However, in a fully supervised setting, predictive accuracies are supposed to correlate positively with the amount of labeled situations: more labeled training data simply promise better performance. U...
Detecting counterpressing is an important task for any professional match-analyst in football (soccer), but is being done exclusively manually by observing video footage. The purpose of this paper is not only to automatically identify this strategy, but also to derive metrics that support coaches with the analysis of transition situations. Addition...
We propose to analyse the origin of goals in professional football (soccer) in a purely data-driven approach. Based on positional and event data of 3,457 goals from two seasons German Bundesliga and 2nd Bundesliga (2018/20,219 and 2019/2020), we devise a rich set of 37 features that can be extracted automatically and propose a hierarchical clusteri...
Due to the low scoring nature of football (soccer), shots are often used as a proxy to evaluate team and player performances. However, not all shots are created equally and their quality differs significantly depending on the situation. The aim of this study is to objectively quantify the quality of any given shot by introducing a so-called expecte...
Key Performance Indicators (KPIs) are used to evaluate the offensive success of a soccer team (e.g. penalty box entries) or player (e.g. pass completion rate). However, knowledge transfer from research to applied practice is understudied. The current study queried practitioners (n = 145, mean ± SD age: 36 ± 9 years) from 42 countries across differe...
A possible objective in analyzing trajectories of multiple simultaneously moving objects, such as football players during a game, is to extract and understand the general patterns of coordinated movement in different classes of situations as they develop. For achieving this objective, we propose an approach that includes a combination of query tech...
It is common practice amongst coaches and analysts to search for key performance indicators related to attacking play in football. Match analysis in professional football has predominately utilised notational analysis, a statistical summary of events based on video footage, to study the sport and prepare teams for competition. Recent increases in t...
Context: Across different domains, Artificial Neural Networks (ANNs) are used more and more in safety-critical applications in which erroneous outputs of such ANN can have catastrophic consequences. However, the development of such neural networks is still immature and good engineering practices are missing. With that, ANNs are in the same position...
Im Auftrag der Arbeitsgruppe Curriculum 4.0 des Hochschulforums Digitalisierung führten das Fraunhofer-Institut für Experimentelles Software-Engineering IESE und die Gesellschaft für Informatik eine Studie durch, um umsetzbares Wissen für Hochschulen und Fächer für die Curriculum-Entwicklung im Hinblick auf Data Literacy zusammenzustellen. Der Foku...
Der vorliegende Bericht fasst den Stand der Forschung für den Aufbau von Strukturen und Kollaborationsformen zur erfolgreichen Vermittlung von Data-Literacy-Kompetenzen zusammen. Er ist im Rahmen einer Studie entstanden, welche das Ziel verfolgt, umsetzbares Wissen für Hochschulen und Fächer für die Curriculum-Entwicklung im Hinblick auf Data Liter...
Projects
Projects (4)
Artificial Intelligence (AI) methods such as Machine Learning (ML) and especially Deep Learning (DL) are widely used in non-critical applications such as language assistants. In safety-critical environments, there are also many use cases with huge economic potential, but this potential cannot be fully exploited yet at present. One prominent example is the use of ML-based object recognition for automated driving. There are many approaches aimed at making ML-based components more dependable, and due to intensive research, more and more new approaches are emerging. Manufacturers are supposed to keep up with the state of the art and the state of the practice, but how can they do this if research keeps publishing new results almost daily, and if these results may even represent different viewpoints? Safety standards are intended to describe the assured state of the practice, but traditional standards such as the basic safety standard IEC 61508 do not take AI developments into account. They assume that safety functions are realized without AI and that AI components are assured through these traditional safety functions. Although there are many standards that deal with AI, they are not sufficient with regard to safety or refer to traditional safety standards, such as the technical report ISO/IEC TR 24028 “Overview of trustworthiness in artificial intelligence”. The technical report ISO/IEC AWI TR 5469 “Functional safety of AI-based systems” currently under development may be able to answer the question of what is generally accepted when it comes to the use of AI in safety-critical contexts. However, it will not be able to provide custom-tailored safety concepts. To do this, safety and AI experts must work together and mutually understand each other’s mindset and terminology.
The use of ML, in particular, signifies a change of the development paradigm: In the development of a classical system, the system is typically specified, refined, implemented, and tested by engineers, who consider safety requirements. If, on the other hand, ML is used for specific, usually complex, tasks, a set of example data serves as a detailed specification; on this basis, a learning algorithm generates a model that is used in the final system (after being checked on more example data) to implement a specific function. However, it is known that the way in which these models are developed is (often) not deterministic. In addition, the quality of such models depends, among other things, on the data used, the process, the learning algorithms used (product), and the expertise of the developers (humans). The fact that many established verification and validation methods are only applicable partially due to the lack of a real specification and the non-interpretability of ML-based solutions further complicates the situation.
