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

Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation

A variety of machine learning models have been proposed to assess the performance of players in professional sports. However, they have only a limited ability to model how player performance depends on the game context. This paper proposes a new approach to capturing game context: we apply Deep Reinforcement Learning (DRL) to learn an action-value Q function from 3M play-by-play events in the National Hockey League (NHL). The neural network representation integrates both continuous context signals and game history, using a possession-based LSTM. The learned Q-function is used to value players' actions under different game contexts. To assess a player's overall performance, we introduce a novel Game Impact Metric (GIM) that aggregates the values of the player's actions. Empirical Evaluation shows GIM is consistent throughout a play season, and correlates highly with standard success measures and future salary.Comment: This paper has been accepted by IJCAI 201

Data-driven action-value functions for evaluating players in professional team sports

As more and larger event stream datasets for professional sports become available, there is growing interest in modeling the complex play dynamics to evaluate player performance. Among these models, a common player evaluation method is assigning values to player actions. Traditional action-values metrics, however, consider very limited game context and player information. Furthermore, they provide directly related to goals (e.g., shots), not all actions. Recent work has shown that reinforcement learning provided powerful methods for addressing quantifying the value of player actions in sports. This dissertation develops deep reinforcement learning (DRL) methods for estimating action values in sports. We make several contributions to DRL for sports. First, we develop neural network architectures that learn an action-value Q-function from sports events logs to estimate each team\u27s expected success given the current match context. Specifically, our architecture models the game history with a recurrent network and predicts the probability that a team scores the next goal. From the learned Q-values, we derive a Goal Impact Metric (GIM) for evaluating a player\u27s performance over a game season. We show that the resulting player rankings are consistent with standard player metrics and temporally consistent within and across seasons. Second, we address the interpretability of the learned Q-values. While neural networks provided accurate estimates, the black-box structure prohibits understanding the influence of different game features on the action values. To interpret the Q-function and understand the influence of game features on action values, we design an interpretable mimic learning framework for the DRL. The framework is based on a Linear Model U-Tree (LMUT) as a transparent mimic model, which facilitates extracting the function rules and computing the feature importance for action values. Third, we incorporate information about specific players into the action values, by introducing a deep player representation framework. In this framework, each player is assigned a latent feature vector called an embedding, with the property that statistically similar players are mapped to nearby embeddings. To compute embeddings that summarize the statistical information about players, we implement a Variational Recurrent Ladder Agent Encoder (VaRLAE) to learn a contextualized representation for when and how players are likely to act. We learn and evaluate deep Q-functions from event data for both ice hockey and soccer. These are challenging continuous-flow games where game context and medium-term consequences are crucial for properly assessing the impact of a player\u27s actions

Temporal consistency in learning action values for volleyball

Learning actions values is a key idea in sports analytics with applications such as player ranking, tactical insight and outcome prediction. We compare two fundamentally different approaches for learning action values on a novel play-by-play volleyball dataset. In the first approach, we employ regression models that implicitly assume statistical independence of data samples. In the second approach, we use a deep reinforcement learning model, explicitly enforcing the sequential nature of the data in the learning process. We find that temporally independent regression can in certain settings outperform the reinforcement learning approach in terms of predictive accuracy, but the latter performs much better when temporal consistency is required. We also consider a mimic regression tree as a way to add interpretability to the deep reinforcement learning approach. Finally, we examine the computed action values and perform a number of example analyses to verify their validity

Presenting Multiagent Challenges in Team Sports Analytics

This paper draws correlations between several challenges and opportunities within the area of team sports analytics and key research areas within multiagent systems (MAS). We specifically consider invasion games, defined as sports where players invade the opposing team's territory and can interact anywhere on a playing surface such as ice hockey, soccer, and basketball. We argue that MAS is well-equipped to study invasion games and will benefit both MAS and sports analytics fields. Our discussion highlights areas for MAS implementation and further development along two axes: short-term in-game strategy (coaching) and long-term team planning (management).Comment: 5 pages, 1 figure, In Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023

Leaving Goals on the Pitch: Evaluating Decision Making in Soccer

Analysis of the popular expected goals (xG) metric in soccer has determined that a (slightly) smaller number of high-quality attempts will likely yield more goals than a slew of low-quality ones. This observation has driven a change in shooting behavior. Teams are passing up on shots from outside the penalty box, in the hopes of generating a better shot closer to goal later on. This paper evaluates whether this decrease in long-distance shots is warranted. Therefore, we propose a novel generic framework to reason about decision-making in soccer by combining techniques from machine learning and artificial intelligence (AI). First, we model how a team has behaved offensively over the course of two seasons by learning a Markov Decision Process (MDP) from event stream data. Second, we use reasoning techniques arising from the AI literature on verification to each team's MDP. This allows us to reason about the efficacy of certain potential decisions by posing counterfactual questions to the MDP. Our key conclusion is that teams would score more goals if they shot more often from outside the penalty box in a small number of team-specific locations. The proposed framework can easily be extended and applied to analyze other aspects of the game

Predicting the Current Season\u27s Win Percentages in the National Hockey League Using Data from the Previous Season: Can Game-Level Data Help?

Researchers have tried to predict winning percentages for the National Hockey League (NHL) teams based on their performance in the previous seasons. However, these predictions have not been very accurate. This study hypothesizes that incorporating pair-wise game-level data with season-level data can be useful in improving the prediction of a team’s win percentage. Season-level data and pair-wise game-level data from the 2005-2006 season to the 2015-2016 season has been used to predict winning percentages for the pairs in each of the following seasons. Significant results were not found for any of the pair-wise game-level data variables except for two pair-wise variables. This helps establish the idea that including more granular information does not necessarily increase the predictive power of models. One of the pair-wise variables found to be significant (at the 10% level of significance) was when high goal differential was observed in the interaction term between high goal differential for a team in its home games against the other pair-wise team and the goal differential for a team in its home games against the other pair-wise team. This provides marginal support for the claim that extreme game-level outcomes from the previous season can help in predicting a team’s win percentage in the following season. Another pair-level variable found to be significant (at the 5% level of significance) was when high goal differential was observed and at least 4 games played was not observed in the interaction term between at least 4 games played against the other pair-wise team and high goal differential for a team in its home games against the other pair-wise team. This suggests that only in the games a team plays outside its own division, the extreme game-level data helps in predicting a team’s win percentage in the following season

Small but Mighty: Examing the Utility of Microstatistics in Modeling Ice Hockey

As research into hockey analytics continues, an increasing number of metrics are being introduced into the knowledge base of the field, creating a need to determine whether various stats are useful or simply add noise to the discussion. This paper examines microstatistics – manually tracked metrics which go beyond the NHL’s publicly released stats – both through the lens of meta-analytics (which attempt to objectively assess how useful a metric is) and modeling game probabilities. Results show that while there is certainly room for improvement in understanding and use of microstats in modeling, the metrics overall represent an area of promise for hockey analytics