313 research outputs found
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
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
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
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
Flexible marked spatio-temporal point processes with applications to event sequences from association football
We develop a new family of marked point processes by focusing the
characteristic properties of marked Hawkes processes exclusively to the space
of marks, providing the freedom to specify a different model for the occurrence
times. This is possible through a decomposition of the joint distribution of
marks and times that allows to separately specify the conditional distribution
of marks given the filtration of the process and the current time. We develop a
Bayesian framework for the inference and prediction from this family of marked
point processes that can naturally accommodate process and point-specific
covariate information to drive cross-excitations, offering wide flexibility and
applicability in the modelling of real-world processes. The framework is used
here for the modelling of in-game event sequences from association football,
resulting not only in inferences about previously unquantified characteristics
of the game dynamics and extraction of event-specific team abilities, but also
in predictions for the occurrence of events of interest, such as goals, corners
or fouls, in a specified interval of time
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