Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis
Technische Universität Darmstadt（达姆施塔特工业大学）；St. Pölten University of Applied Sciences（圣帕尔滕应用科学大学）；Fraunhofer Institute for Computer Graphics Research（弗劳恩霍夫计算机图形学研究所）
Jürgen Bernard, Christian Ritter, David Sessler, Matthias Zeppelzauer, Jörn Kohlhammer, Dieter Fellner
The definition of similarity is a key prerequisite when analyzing complex data types in data mining, information retrieval, or machine learning. However, the meaningful definition is often hampered by the complexity of data objects and particularly by different notions of subjective similarity latent in targeted user groups. Taking the example of soccer players, we present a visual-interactive system that learns users' mental models of similarity. In a visual-interactive interface, users are able to label pairs of soccer players with respect to their subjective notion of similarity. Our proposed similarity model automatically learns the respective concept of similarity using an active learning strategy. A visual-interactive retrieval technique is provided to validate the model and to execute downstream retrieval tasks for soccer player analysis. The applicability of the approach is demonstrated in different evaluation strategies, including usage scenarions and cross-validation tests.
MINES Paristech, PSL - Research University（巴黎国立高等矿业学院，巴黎文理研究大学）；SportsDynamics（运动动力学公司）；ENS Paris-Saclay（巴黎萨克雷高等师范学院）
Scientifically evaluating soccer players represents a challenging Machine Learning problem. Unfortunately, most existing answers have very opaque algorithm training procedures; relevant data are scarcely accessible and almost impossible to generate. In this paper, we will introduce a two-part solution: an open-source Player Tracking model and a new approach to evaluate these players based solely on Deep Reinforcement Learning, without human data training nor guidance. Our tracking model was trained in a supervised fashion on datasets we will also release, and our Evaluation Model relies only o…
Fitogether Inc.（Fitogether公司）；Seoul National University（首尔国立大学）；Kangwon National University（江原国立大学）
Although the values of individual soccer players have become astronomical, subjective judgments still play a big part in the player analysis. Recently, there have been new attempts to quantitatively grasp players' styles using video-based event stream data. However, they have some limitations in scalability due to high annotation costs and sparsity of event stream data. In this paper, we build a triplet network named 6MapNet that can effectively capture the movement styles of players using in-game GPS data. Without any annotation of soccer-specific actions, we use players' locations and veloci…
Explainable artificial intelligence model for identifying Market Value in Professional Soccer Players
Zhili College（致理书院）；Tsinghua University（清华大学）；Research Centre for Athletic Performance and Data Science & Division of Sports Science and Physical Education（运动表现与数据科学研究中心&体育科学与体育教育系）
In recent years, many different approaches have been proposed to quantify the performances of soccer players. Since player performances are challenging to quantify directly due to the low-scoring nature of soccer, most approaches estimate the expected impact of the players' on-the-ball actions on the scoreline. While effective, these approaches are yet to be widely embraced by soccer practitioners. The soccer analytics community has primarily focused on improving the accuracy of the models, while the explainability of the produced metrics is often much more important to practitioners. To help …
One of the most important and challenging problems in football is predicting future player performance when transferred to another club within and between different leagues. In addition to being the most valuable prediction a team makes, it is also the most complex analytics task to perform as it needs to take into consideration: a) differences in playing style between the player's current team and target team, b) differences in style and ability of other players on each team, c) differences in league quality and style, and d) the role the player is desired to play. In this paper, we present a…
Dalhousie University（达尔豪斯大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；Qom University of Technology（库姆理工大学）；Shiraz University（设拉子大学）；Polish Academy of Sciences（波兰科学院）；Memorial University of Newfoundland（纽芬兰纪念大学）
Soccer Simulation 2D League is one of the major leagues of RoboCup competitions. In a Soccer Simulation 2D (SS2D) game, two teams of 11 players and one coach compete against each other. The players are only allowed to communicate with the server that is called Soccer Simulation Server. This paper introduces the previous and current research of the CYRUS soccer simulation team, the champion of RoboCup 2021. We will present our idea about improving Unmarking Decisioning and Positioning by using Pass Prediction Deep Neural Network. Based on our experimental results, this idea proven to be effecti…
Nagoya University（名古屋大学）；RIKEN（理化学研究所）；Japan Science and Technology Agency（日本科学技术振兴机构）
Evaluating the individual movements for teammates in soccer players is crucial for assessing teamwork, scouting, and fan engagement. It has been said that players in a 90-min game do not have the ball for about 87 minutes on average. However, it has remained difficult to evaluate an attacking player without receiving the ball, and to reveal how movement contributes to the creation of scoring opportunities for teammates. In this paper, we evaluate players who create off-ball scoring opportunities by comparing actual movements with the reference movements generated via trajectory prediction. Fir…
We present a framework that gives a deep insight into the link between physical and technical-tactical aspects of soccer and it allows associating physical performance with value generation thanks to a top-down approach. First, we estimate physical indicators from tracking data. Then, we contextualize each player's run to understand better the purpose and circumstances in which it is done, adding a new dimension to the creation of team and player profiles. Finally, we assess the value-added by off-ball high-intensity runs by linking with a possession-value model. This novel approach allows ans…
Faculdade de Engenharia, Universidade do Porto（波尔图大学工程学院）；LIAAD - INESC TEC（系统与计算机工程、技术与科学研究所 - 人工智能与决策支持实验室）；FC Porto – Futebol SAD（波尔图足球俱乐部股份有限公司）
In soccer (or association football), players quickly go from heroes to zeroes, or vice-versa. Performance is not a static measure but a somewhat volatile one. Analyzing performance as a time series rather than a stationary point in time is crucial to making better decisions. This paper introduces and explores I-VAEP and O-VAEP models to evaluate actions and rate players' intention and execution. Then, we analyze these ratings over time and propose use cases to fundament our option of treating player ratings as a continuous problem. As a result, we present who were the best players and how thei…
University of Pisa（比萨大学）；ISTI-CNR（意大利国家研究委员会信息科学与技术研究所）
Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of data that describe all the spatio-temporal events that occur in each match. These events (e.g., passes, shots, fouls) are collected by human operators manually, constituting a considerable cost for data providers in terms of time and economic resources. In this paper, we describe PassNet, a method to recognize the most frequent events in soccer, i.e., passes, from video streams. Our model combines a set of artificial neural networks that perform feature extraction from video streams, obje…
In this paper we present an early Apprenticeship Learning approach to mimic the behaviour of different players in a short adaption of the interactive fiction Anchorhead. Our motivation is the need to understand and simulate player behaviour to create systems to aid the design and personalisation of Interactive Narratives (INs). INs are partially observable for the players and their goals are dynamic as a result. We used Receding Horizon IRL (RHIRL) to learn players' goals in the form of reward functions, and derive policies to imitate their behaviour. Our preliminary results suggest that RHIRL…
Engineering Features to Improve Pass Prediction in Soccer Simulation 2D Games
Institute for Big Data Analytics, Dalhousie University（达尔豪斯大学大数据分析研究所）；Shiraz University（设拉子大学）；Qom University of Technology（库姆理工大学）；Institute for Computer Science, Polish Academy of Sciences（波兰科学院计算机科学研究所）；Memorial University of Newfoundland（纽芬兰纪念大学）
Electronic Arts（美国艺电公司）；Institute of Computational Modelling（计算建模研究所）
There is a high demand for high-quality Non-Player Characters (NPCs) in video games. Hand-crafting their behavior is a labor intensive and error prone engineering process with limited controls exposed to the game designers. We propose to create such NPC behaviors interactively by training an agent in the target environment using imitation learning with a human in the loop. While traditional behavior cloning may fall short of achieving the desired performance, we show that interactivity can substantially improve it with a modest amount of human efforts. The model we train is a multi-resolution …
University of Southampton（南安普顿大学）；Sentient Sports（Sentient体育）
Understanding agent behaviour in Multi-Agent Systems (MAS) is an important problem in domains such as autonomous driving, disaster response, and sports analytics. Existing MAS problems typically use uniform timesteps with observations for all agents. In this work, we analyse the problem of agent location imputation, specifically posed in environments with non-uniform timesteps and limited agent observability (~95% missing values). Our approach uses Long Short-Term Memory and Graph Neural Network components to learn temporal and inter-agent patterns to predict the location of all agents at ever…
