Proceedings ArticleDOI
A Data Science Approach to Football Team Player Selection
P. Rajesh,Bharadwaj,Mansoor Alam,Mansour Tahernezhadi +3 more
- pp 175-183
TLDR
A data science approach to minimize the time taken in selecting a player for a team by considering the cost and player's skills as constraints is presented and shows that it leads to improved business profits through a systematic enhancement to football data sets.Abstract:
FIFA (Federation Internationale de Football Association) is world football (soccer) league that is separate from Olympics. FIFA been largely instrumental for making soccer as the most popular game in the world. It has led to development of many private soccer clubs all over the world. Creating new clubs with young players, loaning players from other clubs, picking choice positions, determining wages and remuneration to players based on performance and international rankings is complicated decision process in terms of global business perspective. This paper presents a data science approach to minimize the time taken in selecting a player for a team by considering the cost and player's skills as constraints. Such an analysis will help an owner to maximize the profit and popularity of an existing club or to create a new club. We present statistical analysis of player performance based on abilities and skills for a new team using powerBI and Python Pandas by minimizing the cost. The results show that it leads to improved business profits through a systematic enhancement to football data sets. These kind of approaches and analytical results can be useful to franchisor of proprietary knowledge to form group of selected players as team.read more
Citations
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Secure Communication across the Internet by Encrypting the Data using Cryptography and Image Steganography
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The collection, analysis and exploitation of footballer attributes: A systematic review
TL;DR: Recommendations are made on how the use of character trait attributes in the selected papers may be developed to support elite clubs in the consideration of transfer targets.
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Discovering a cohesive football team through players’ attributed collaboration networks
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Analysis and Comparison of Business Intelligence Tools Most Preferred by Companies in Turkey
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An Intelligent Decision Support System for Bid Prediction of Undervalued Football Players
Manaswita Datta,Bhawana Rudra +1 more
TL;DR: In this article , the authors employ novelty detection methods to find out the undervalued players from the data and investigate their method by using five machine learning models: support vector machine, random forest, decision tree, linear regression and XGBoost.
References
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Proceedings ArticleDOI
The harsh rule of the goals: Data-driven performance indicators for football teams
TL;DR: A data-driven approach is proposed and it is shown that there is a large potential to boost the understanding of football team performance and that a complex systems' view on football data has the potential of revealing hidden patterns and behavior of superior quality.
Journal ArticleDOI
Visual analysis of pressure in football
Gennady Andrienko,Gennady Andrienko,Natalia Andrienko,Natalia Andrienko,Guido Budziak,Jason Dykes,Georg Fuchs,Tatiana von Landesberger,Hendrik Weber +8 more
TL;DR: A computational approach to detecting and quantifying the relationships of pressure emerging during a game and a novel interactive visual tool “time mask” to support examination of team tactics in different situations are proposed.
Journal ArticleDOI
How to make sense of team sport data: From acquisition to data modeling and research aspects
Manuel Stein,Halldór Janetzko,Daniel Seebacher,Alexander Jäger,Manuel Nagel,Jürgen Hölsch,Sven Kosub,Tobias Schreck,Daniel A. Keim,Michael Grossniklaus +9 more
TL;DR: This work considers team sport as group movement including collaboration and competition of individuals following specific rule sets, and identifies important components of team sport data, exemplified by the soccer case, and explains how to analyzeteam sport data in general.
Proceedings ArticleDOI
Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data
TL;DR: A data-driven approach for identifying patterns of movement that account for both spatial and temporal information which represent potential offensive tactics is described, which is able to identify interesting strategies per team related to goal kicks, corners and set pieces.
Proceedings ArticleDOI
Discerning Tactical Patterns for Professional Soccer Teams: An Enhanced Topic Model with Applications
TL;DR: This paper develops a novel model named Team Tactic Topic Model (T3M) for learning the latent tactical patterns, which can model the locations and passing relations of players simultaneously and demonstrates several potential applications enabled by the proposed T3M, such as automatic tactical pattern discovery, pass segment annotation, and spatial analysis of player roles.