In soccer, the most frequent event that occurs is a pass. For a trained eye, there are a myriad of adjectives which could describe this event (e.g., "majestic pass", "conservative" to "poor-ball"). However, as these events are needed to be coded live and in real-time (most often by human annotators), the current method of grading passes is restricted to the binary labels 0 (unsuccessful) or 1 (successful). Obviously, this is sub-optimal because the quality of a pass needs to be measured on a continuous spectrum (i.e., 0 to 100%) and not a binary value. Additionally, a pass can be measured across multiple dimensions, namely: i) risk -- the likelihood of executing a pass in a given situation, and ii) reward -- the likelihood of a pass creating a chance. In this paper, we show how we estimate both the risk and reward of a pass across two seasons of tracking data captured from a recent professional soccer league with state-of-the-art performance, then showcase various use cases of our deployed passing system.

The last two years have been somewhat of a rollercoaster for English Premier League (EPL) team Leicester City. In the 2015/16 season, against all odds and logic, they won the league to much fan-fare. Fast-forward nine months later, and they are battling relegation. What could describe this fluctuating form? As soccer is a very complex and strategic game, common statistics (e.g., passes, shots, possession) do not really tell the full story on how a team succeeds and fails. However, using machine learning tools and a plethora of data, it is now possible to obtain some insights into how a team performs. To showcase the utility of these new tools (i.e., expected goal value, expected save value, strategy-plots and passing quality measures), we first analyze the EPL 2015/16 season which a specific emphasis on the champions Leicester City, and then compare it to the current one. Finally, we show how these features can be used to predict future performance.

Blind signal separation (BSS) is a powerful tool to open-up complex signals into component sources that are often interpretable. However, BSS methods are generally unsupervised, therefore the assignment of class membership from the elements of the mixing matrix may be sub-optimal. This paper proposes a three-stage approach using Fisher information metric to define a natural metric for the data, from which a Euclidean approximation can then be used to drive BSS. Results with synthetic data models of real-world high-dimensional data show that the classification accuracy of the method is good for challenging problems, while retaining interpretability.