Actions Speak Louder Than Goals: Valuing Player Actions in Soccer

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.Comment: Significant update of the paper. The same core idea, but with a clearer methodology, applied on a different data set, and more extensive experiments. 9 pages + 2 pages appendix. To be published at SIGKDD 201

Soccer Analytics Meets Artificial Intelligence: Learning Value and Style from Soccer Event Stream Data

Soccer analytics has seen an explosion of interest in the last decade. The success of data analysis in other sports has driven soccer clubs and other stakeholders in soccer to wonder if they could also deepen their understanding of the game by analyzing data and translate this deepened understanding into tangible results such as signing good players and winning matches. Consequently, more data than ever is being collected in soccer. One prominent data source is event stream data, which is collected by human annotators who watch video feeds of soccer matches through special annotation software and rigorously describe all on-the-ball actions performed on the pitch such as passes, dribbles, interceptions, tackles, and shots. While event stream data is an incredibly rich data source, gleaning useful soccer insights from it has proven to be difficult in practice. One part of the problem is soccer being a fluid sport that involves many complex interactions between players. Furthermore, soccer's low-scoring nature and susceptibility to chance make it hard to correlate player skill with match results. Another part of the problem is event stream data being hard to analyze in its raw form. Analysts typically have to deal with a number of issues such as parsing complicated data structures, adapting to vendor-specific terminologies, dealing with data sparsity, scaling to millions of data points, and incorporating domain knowledge. These issues have motivated researchers to apply techniques from the field of artificial intelligence (AI) to event stream data, as these techniques are often intended to be used semi-autonomously on large and complicated data sets. Consequently, researchers have successfully used AI techniques such as classification, reinforcement learning, pattern mining, and network analysis to address soccer analytics tasks such as estimating shot quality, rating players, and detecting tactics. However, existing literature on learning from event stream data with AI techniques shows a number of shortcomings. First, no efforts have been made to address the data engineering challenges of event stream data, severely obstructing the reproducibility of papers within the field. Second, no approaches exist for valuing on-the-ball actions that consider the full context in which actions are performed or recognize the value of defensive actions such as tackles and clearances. Third, existing works have not sufficiently explored how to best model the locations and directions of actions when capturing the playing style of teams and players. Most approaches that attempt to capture playing style either rudimentarily divide the pitch into zones or ignore the spatial component of event stream data all together. This dissertation makes three main contributions to the field of soccer analytics that attempt to address these shortcomings. First, to better represent event stream data, we construct a new language that simplifies and unifies the data of different event stream data vendors, alleviating many data engineering challenges and encouraging the reproducibility of soccer analytics research. Second, we propose a framework for assigning values to on-the-ball actions that, compared to simpler metrics and possession-based approaches, considers a more complete view of the context in which actions occur. Our framework uses a simple and elegant formula that formalizes the intuition that all actions in a match are performed with the intention of increasing the chance of scoring a goal and/or decreasing the chance of conceding a goal. The latter point is what allows our framework to recognize the value of defensive actions. Third, we introduce a number of approaches that express the playing style of teams and players based on where on the pitch they perform certain types of actions. Our approaches improve over earlier work by modelling the spatial component of event stream data in a data-driven manner using decomposition techniques such as non-negative matrix factorization and mixture models.status: publishe

Interpretable Prediction of Goals in Soccer

Objectively quantifying a soccer player’s contributions within a match is a challenging and crucial task in soccer analytics. Many of the currently available metrics focus on measuring the quality of shots and assists only, although these represent less than 1% of all on-the-ball actions. Most recently, several approaches were proposed to bridge this gap. By valuing how actions increase or decrease the likelihood of yielding a goal, these models are effective tools for quantifying the performances of players for all sorts of actions. However, we lack an understanding of their differences, both conceptually and in practice. Therefore, this paper critically compares two such models: expected threat (xT) and valuing actions by estimating probabilities (VAEP). Both approaches exhibit variety in their design choices, that leads to different top player rankings and major differences in how they value specific actions.status: Published onlin

Player Vectors: Characterizing Soccer Players’ Playing Style from Match Event Streams

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Characterizing Soccer Players' Playing Style from Match Event Streams

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 capture the playing style of a player in 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 recovering commonly known player types (e.g., left-winger, right-center defender) through unsupervised clustering and by substantiating a number of claims in popular media about soccer players (e.g., "Paolo Dybala is the new Lionel Messi") with our results.status: publishe

Solving Euclidean Steiner tree problems with multi swarm optimization

A new iterative heuristic algorithm is presented for the Steiner Tree Problem (STP) in the Euclidean plane. It is based on Multi Swarm Optimization. The underlying principle of the basic algorithm and its inner workings are explained, as well as optimizations based on dynamic programming and a graph theory theorem. The algorithm's performance is compared to perfect solutions for the classic Steiner Tree Problem and to a deterministic heuristic for the k-bottleneck STP, a variant of STP. The experimental evaluation shows that the algorithm often produces near optimal solutions with very limited resources.nrpages: 9status: publishe

SoccerMix: Representing Soccer Actions with Mixture Models

status: accepte

Predicting the potential of professional soccer players

Projecting how a player’s skill level will evolve in the future is a crucial problem faced by sports teams. Traditionally, player projections have been evaluated by human scouts, who are subjective and may suffer from biases. More recently, there has been interest in automated projection systems such as the PECOTA system for baseball and the CARMELO system for basketball. In this paper, we present a projection system for soccer players called APROPOS which is inspired by the CARMELO and PECOTA systems. APROPOS predicts the potential of a soccer player by searching a historical database to identify similar players of the same age. It then bases its prediction for the target player’s progression on how the similar previous players actually evolved. We evaluate APROPOS on players from the five biggest European soccer leagues and show that it clearly outperforms a more naive baseline.status: publishe

VAEP: An Objective Approach to Valuing On-the-Ball Actions in Soccer (Extended Abstract)

status: Published onlin