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 method which addresses these issues and enables us to make accurate predictions of future performance. Our Transfer Portal model utilizes a personalized neural network accounting for both stylistic and ability level input representations for players, teams, and leagues to simulate future player performance at any chosen club. Furthermore, we use a Bayesian updating framework to dynamically modify player and team representations over time which enables us to generate predictions for rising stars with small amounts of data.
The widespread popularity of soccer across the globe has turned it into a multi-billion dollar industry. As a result, most professional clubs actively engage in talent identification and development programmes. Contemporary research has generally supported the use of executive functions - a class of neuropsychological processes responsible for cognitive behaviours - in predicting a soccer player's future success. However, studies on the developmental evolution of executive functions have yielded differing results in their structural form (such as inverted U-shapes, or otherwise). This article …
In basketball and hockey, state-of-the-art player value statistics are often variants of Adjusted Plus-Minus (APM). But APM hasn't had the same impact in soccer, since soccer games are low scoring with a low number of substitutions. In soccer, perhaps the most comprehensive player value statistics come from video games, and in particular FIFA. FIFA ratings combine the subjective evaluations of over 9000 scouts, coaches, and season-ticket holders into ratings for over 18,000 players. This paper combines FIFA ratings and APM into a single metric, which we call Augmented APM. The key idea is reca…
We consider the task of determining the number of chances a soccer team creates, along with the composite nature of each chance-the players involved and the locations on the pitch of the assist and the chance. We propose an interpretable Bayesian inference approach and implement a Poisson model to capture chance occurrences, from which we infer team abilities. We then use a Gaussian mixture model to capture the areas on the pitch a player makes an assist/takes a chance. This approach allows the visualization of differences between players in the way they approach attacking play (making assists…
Goals are results of pin-point shots and it is a pivotal decision in soccer when, how and where to shoot. The main contribution of this study is two-fold. At first, after showing that there exists high spatial correlation in the data of shots across games, we introduce a spatial process in the error structure to model the probability of conversion from a shot depending on positional and situational covariates. The model is developed using a full Bayesian framework. Secondly, based on the proposed model, we define two new measures that can appropriately quantify the impact of an individual in s…
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 …
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…
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）
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 propos…
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…
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…
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）
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…
Polytechnic University of Catalonia（加泰罗尼亚理工大学）；FC Barcelona（巴塞罗那足球俱乐部）；Simon Fraser University（西蒙弗雷泽大学）
We present a fully convolutional neural network architecture that is capable of estimating full probability surfaces of potential passes in soccer, derived from high-frequency spatiotemporal data. The network receives layers of low-level inputs and learns a feature hierarchy that produces predictions at different sampling levels, capturing both coarse and fine spatial details. By merging these predictions, we can produce visually-rich probability surfaces for any game situation that allows coaches to develop a fine-grained analysis of players' positioning and decision-making, an as-yet little-…
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…
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）
Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning
Institute for Big Data Analytics, Dalhousie University（达尔豪斯大学大数据分析研究所）；Qom University of Technology（库姆科技大学）；Shiraz University（设拉子大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；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）
本文介绍了 RoboCup 2021 2D 足球仿真冠军 CYRUS 的三大核心功能：多动作盘带、传球预测和盯人决策，分别提升了盘带成功率、团队协作和防守策略。
Forecasting the future development in quality and value of professional football players
1 Delft Institute of Applied Mathematics, Delft University of Technology, 2628 CD Delft, The Netherlands；\ 2 Data Science \& Computer Vision, SciSports, 3703 HX Zeist, The Netherlands
KAIST（韩国科学技术院）；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
The transfer fees of sports players have become astronomical. This is because bringing players of great future value to the club is essential for their survival. We present a case study on the key factors affecting the world's top soccer players' transfer fees based on the FIFA data analysis. To predict each player's market value, we propose an improved LightGBM model by optimizing its hyperparameter using a Tree-structured Parzen Estimator (TPE) algorithm. We identify prominent features by the SHapley Additive exPlanations (SHAP) algorithm. The proposed method has been compared against the ba…
