Sports teams are nowadays collecting huge amounts of data from training sessions and matches. The teams are becoming increasingly interested in exploiting these data to gain a competitive advantage over their competitors. One of the most prevalent types of new data is event stream data from matches. These data enable more advanced descriptive analysis as well as the potential to investigate an opponent's tactics in greater depth. Due to the complexity of both the data and game strategy, most tactical analyses are currently performed by humans reviewing video and scouting matches in person. As a result, this is a time-consuming and tedious process. This paper explores the problem of automatic tactics detection from event-stream data collected from professional soccer matches. We highlight several important challenges that these data and this problem setting pose. We describe a data-driven approach for identifying patterns of movement that account for both spatial and temporal information which represent potential offensive tactics. We evaluate our approach on the 2015/2016 season of the English Premier League and are able to identify interesting strategies per team related to goal kicks, corners and set pieces.

Transfer fees for soccer players are at an all-time high. To make the most of their budget, soccer clubs need to understand the type of players they have and the type of players that are on the market. Current insights in the playing style of players are mostly based on the opinions of human soccer experts such as trainers and scouts. Unfortunately, their opinions are inherently subjective and thus prone to faults. In this paper, we characterize the playing style of a player in a more rigorous, objective and data-driven manner. We characterize the playing style of a player using a so-called 'player vector' that can be interpreted both by human experts and machine learning systems. We demonstrate the validity of our approach by retrieving player identities from anonymized event stream data and present a number of use cases related to scouting and monitoring player development in top European competitions.

Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process. Unfortunately, most traditional metrics fall short in addressing this task as they either focus on rare actions like shots and goals alone or fail to account for the context in which the actions occurred. This paper introduces (1) a new language for describing individual player actions on the pitch and (2) a framework for valuing any type of player action based on its impact on the game outcome while accounting for the context in which the action happened. By aggregating soccer players' action values, their total offensive and defensive contributions to their team can be quantified. We show how our approach considers relevant contextual information that traditional player evaluation metrics ignore and present a number of use cases related to scouting and playing style characterization in the 2016/2017 and 2017/2018 seasons in Europe's top competitions.

Patients with sports-related injuries need to learn to perform rehabilitative exercises with correct movement patterns. Unfortunately, the feedback a physiotherapist can provide is limited by the number of physical therapy appointments. We study the feasibility of a system that automatically provides feedback on correct movement patterns to patients using a Microsoft Kinect camera and Machine Learning techniques. We discuss several challenges related to the Kinect's proprietary software, the Kinect data's heterogeneity, and the Kinect data's temporal component. We introduce AMIE, a machine learning pipeline that detects the exercise being performed, the exercise's correctness, and if applicable, the mistake that was made. To evaluate AMIE, ten participants were instructed to perform three types of typical rehabilitation exercises (squats, forward lunges and side lunges) demonstrating both correct movement patterns and frequent types of mistakes, while being recorded with a Kinect. AMIE detects the type of exercise almost perfectly with 99% accuracy and the type of mistake with 73% accuracy.

Despite the fact that objectively assessing the impact of the individual actions performed by soccer players during games is a crucial task, most traditional metrics have substantial shortcomings. First, many metrics only consider rare actions like shots and goals which account for less than 2% of all on-the-ball actions. Second, they fail to account for the context in which the actions occurred. This work summarizes several important contributions. First, we describe a language for representing individual player actions on the pitch. This language unifies several existing formats which greatly simplifies automated analysis and this language is becoming widely used in the soccer analytics community. Second, we describe our framework for valuing any type of player action based on its impact on the game outcome while accounting for the context in which the action happened. This framework enables giving a broad overview of a player's performance, including quantifying a player's total offensive and defensive contributions to their team. Third, we provide illustrative use cases that highlight the working and benefits of our framework.