implementing the Expected Goal (xG) model to predict scores in soccer matches

Football is a sport that has the most fans in the world. What makes sebak patterns so popular are their uncertain and unpredictable results. There are many factors that affect the outcome of a football match, including strategy, skill, and even luck. Therefore, guessing the results of a soccer match is an interesting problem. All shots are grouped into sections on the playing field and theoretical goal scores are applied to each area. The factors analyzed are: distance of shot from goal and angle of shot in relation to goal. When calculating xG, it is recommended that the distance and angle of the shot are important. The combination of the two xG factors is better calculated than each variable only. In addition, this xG check has been able to relatively accurately identify the mid-table teams that score and concede goals.

[1]  Iain Matthews,et al.  "Quality vs Quantity": Improved Shot Prediction in Soccer using Strategic Features from Spatiotemporal Data , 2015 .

[2]  Alex A. T. Rathke An examination of expected goals and shot efficiency in soccer , 2017 .

[3]  Harukazu Igarashi,et al.  Learning of soccer player agents using a policy gradient method: Coordination between kicker and receiver during free kicks , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[4]  Filip Zelezný,et al.  Deep Learning from Spatial Relations for Soccer Pass Prediction , 2018, MLSA@PKDD/ECML.

[5]  Carlos Lago-Peñas,et al.  Differences in performance indicators between winning and losing teams in the UEFA Champions League , 2011 .

[6]  Qiao-yan Qi Research on the Optimal Design of Soccer Robot based on the Mechanical Analysis , 2016 .

[7]  Wen Gao,et al.  A new method to calculate the camera focusing area and player position on playfield in soccer video , 2005, Visual Communications and Image Processing.

[8]  Martin Lauer,et al.  Calculating the Perfect Match: An Efficient and Accurate Approach for Robot Self-localization , 2005, RoboCup.

[9]  Luke Bornn,et al.  A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions , 2020, Machine Learning.

[10]  F. Cardoso,et al.  Young Soccer Players With Higher Tactical Knowledge Display Lower Cognitive Effort , 2019, Perceptual and motor skills.

[11]  Ayman Hashim Idlan Developing a Cooperative Behavior for Multi Agents System Application to Robot Soccer , 2007 .

[12]  Songyang Lao,et al.  Automatic line mark recognition and its application in camera calibration in soccer video , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[13]  Nuno Lau,et al.  Machine Learning algorithms applied to the classification of robotic soccer formations and opponent teams , 2010, 2010 IEEE Conference on Cybernetics and Intelligent Systems.

[14]  Improving decision making for shots , .

[15]  홍성진,et al.  An Analysis of Comparison on Performances in Soccer Attacking-Third , 2013 .

[16]  Aboul Ella Hassanien,et al.  Event Detection Based Approach for Soccer Video Summarization Using Machine learning , 2012 .

[17]  Jesse Davis,et al.  Valuing On-the-Ball Actions in Soccer: A Critical Comparison of xT and VAEP , 2020 .

[18]  A. N. Fitriana,et al.  Color-based segmentation and feature detection for ball and goal post on mobile soccer robot game field , 2016, 2016 International Conference on Information Technology Systems and Innovation (ICITSI).

[19]  Konstantinos Pelechrinis,et al.  A Skellam regression model for quantifying positional value in soccer , 2021, Journal of Quantitative Analysis in Sports.