A Data Science Approach to Football Team Player Selection

Rajesh, P. K.; Bharadwaj,; Alam, Mansoor; Tahernezhadi, M.

doi:10.1109/eit48999.2020.9208331

Cited by 13 publications

(10 citation statements)

References 7 publications

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“…Ensemble Approach is a machine learning method to improve accuracy by studying a series of individual (base) classifier models and integrating them. The performance of the ensemble methods is compared with the base classifier models [23], [24]. To estimate the performance of the classifiers, evaluation metrics like accuracy score, f-measure, Jaccard similarity index, ROC curve are applied on the models developed to find the model with good accuracy [25].…”

Section: Experimental Methodologymentioning

confidence: 99%

Machine Learning and Statistical Analysis Techniques on Terrorism

Rajesh¹,

Donepudi²,

Alam³

et al. 2020

Fuzzy Systems and Data Mining VI

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Terrorism is a major issue facing the world today. It has negative impact on the economy of the nation suffering terrorist attacks from which it takes years to recover. Many developing countries are facing threats from terrorist groups and organizations. This paper examines various terrorist factors using data mining from the historical data to predict the terrorist groups most likely to attack a nation. In this paper we focus on sampled data primarily from India for the past two decades and also consider International database. To create meaningful insights, data mining, machine learning techniques and algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine, Ensemble methods, Random Forest Classification are implemented to analyze comparative based classification results. Patterns and predictions are represented in the form of visualizations with the help of Python and Jupyter Notebook. This analysis will help to take appropriate preventive measures to stop Terrorism attacks and to increase investments, to grow the economy and tourism.

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Section: Experimental Methodologymentioning

confidence: 99%

Machine Learning and Statistical Analysis Techniques on Terrorism

Rajesh¹,

Donepudi²,

Alam³

et al. 2020

Fuzzy Systems and Data Mining VI

Self Cite

View full text Add to dashboard Cite

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“…A Data Science Approach to Football Team Player Selection (Rajesh et al, 2020) 1 An option pricing framework for valuation of football players (Tunaru et al, 2005) 1 Hypernetworks reveal compound variables that capture cooperative and competitive interactions in a soccer match. (Ramos et al, 2017) 1 Win at home and draw away': Automatic formation analysis highlighting the differences in home and away team Proceedings of 8th annual MIT sloan sports analytics conference The foundations of tactics and strategy in team sports (Godbout & Bouthier, 1999) Presents approach to be taken by teachers introducing pupils to team sports.…”

Section: Potential Approaches To Character Trait Attributesmentioning

confidence: 99%

“…Visualizing and Analyzing Disputed Areas in Soccer (Allegre & Vuillemot, 2020) Presents a process to visualise and analyse disputed areas (cases where two or more footballers can reach a given location simultaneously) providing insights to understand assists and the ultimate pass that is critical for a team to score. A Data Science Approach to Football Team Player Selection (Rajesh et al, 2020) Considers the cost effective selection of players based upon player skills, performance, positions, ratings, market value and costs. Presents results showing that it leads to improved business profits through a systematic enhancement to football data sets.…”

Section: Theory To Practice Performance Preparation Models In Contemp...mentioning

confidence: 99%

The collection, analysis and exploitation of footballer attributes: A systematic review

Wakelam

Steuber

Wakelam³

2022

JSA

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There is growing on-going research into how footballer attributes, collected prior to, during and post-match, may address the demands of clubs, media pundits and gaming developers. Focusing upon individual player performance analysis and prediction, we examined the body of research which considers different player attributes. This resulted in the selection of 132 relevant papers published between 1999 and 2020. From these we have compiled a comprehensive list of player attributes, categorising them as static, such as age and height, or dynamic, such as pass completions and shots on target. To indicate their accuracy, we classified each attribute as objectively or subjectively derived, and finally by their implied accessibility and their likely personal and club sensitivity. We assigned these attributes to 25 logical groups such as passing, tackling and player demographics. We analysed the relative research focus on each group and noted the analytical methods deployed, identifying which statistical or machine learning techniques were used. We reviewed and considered the use of character trait attributes in the selected papers and discuss more formal approaches to their use. Based upon this we have made recommendations on how this work may be developed to support elite clubs in the consideration of transfer targets.

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“…In reality, it is difficult to assess the effectiveness of a football team composition result because it may require a considerable amount of money as well as being laborintensive. Fortunately, the emergence of a wide variety of football video games, such as Pro Evolution Soccer (PES) 1 , Electronic Arts Sports FC (also known as FIFA) 2 and Football Manager 3 , offers an opportunity to compose a team based on human preferences and evaluate outcomes efficiently. This opportunity exists not only because gamers can completely redo club designs as well as edit any player in the game but also because the platforms can fully simulate on-pitch football matches.…”

Section: Introductionmentioning

confidence: 99%

Discovering a cohesive football team through players’ attributed collaboration networks

Zeng

Pan

et al. 2022

Appl Intell

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The process of team composition in multiplayer sports such as football has been a main area of interest within the field of the science of teamwork, which is important for improving competition results and game experience. Recent algorithms for the football team composition problem take into account the skill proficiency of players but not the interactions between players that contribute to winning the championship. To automate the composition of a cohesive team, we consider the internal collaborations among football players. Specifically, we propose a Team Composition based on the Football Players’ Attributed Collaboration Network (TC-FPACN) model, aiming to identify a cohesive football team by maximizing football players’ capabilities and their collaborations via three network metrics, namely, network ability, network density and network heterogeneity&homogeneity. Solving the optimization problem is NP-hard; we develop an approximation method based on greedy algorithms and then improve the method through pruning strategies given a budget limit. We conduct experiments on two popular football simulation platforms. The experimental results show that our proposed approach can form effective teams that dominate others in the majority of simulated competitions.

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A Data Science Approach to Football Team Player Selection

Cited by 13 publications

References 7 publications

Machine Learning and Statistical Analysis Techniques on Terrorism

Machine Learning and Statistical Analysis Techniques on Terrorism

The collection, analysis and exploitation of footballer attributes: A systematic review

Discovering a cohesive football team through players’ attributed collaboration networks

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