Machine Learning in Sports Industry - Discover promising athletes for the future of Handball

Aug 5, 2026

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Technology and data-driven methods increasingly shape modern sports industries through systematic collection and processing. Visual and analytical data interpretation supports coaches, managers, and scouting decisions across disciplines, including handball. This project work introduces predictive analytics to identify promising handball athletes using collected variables, despite limited data from the Federação de Andebol de Portugal. It includes data collection and pre-preparation, visual presentations (bar charts and maps), and machine learning models to forecast player potential.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages101

Reading Time4 h 15 min

AccessFree to download

1. Introduction

1.1. Context

1.2. Motivation

1.3. Objectives

1.4. Study importance and relevance

2. Literature review

2.1. Handball

2.2. Study tools and concepts

2.3. Predictive analytics a

Master Degree Program in Data Science and Advanced Analytics MDSAA Machine Learning in Sports Industry Discover promising athletes for the future of Handball Miguel Coutinho Nunes Project Work presented as partial requirement for obtaining the Master Degree Program in Data Science and Advanced Analytics NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa MACHINE LEARNING IN SPORTS INDUSTRY: DISCOVER PROMISING ATHLETES FOR THE FUTURE OF HANDBALL by Miguel Coutinho Nunes Project Work presented as partial requirement for obtaining the Master’s degree in Advanced Analytics, with a Specialization in Business Analytics Supervisor: Vítor Manuel Pereira Duarte dos Santos, PhD Advisor: Ricardo Andorinho November 2022 DEDICATION Dedico inteiramente esta tese à minha família e namorada que sempre me apoiaram econtribuíram para que conseguisse atingir esta etapa de minha vida, sendo uma das mais importantes. Obrigado a todos! AKNOWLEDGMENT Gostaria de começar por agradecer o apoio e colaboração do Prof. Dr. Vítor Duarte dos Santos na orientação e disponibilidade prestada ao longo do desenvolvimento da tese. A suainfluência e encorajamento foram essenciais para que este projeto fosse finalizado. Quero também agradecer à Federação Portuguesa de Andebol e seus representantes, Ricardo Andorinho e João Sousa, por providenciarem os dados, esclarecerem dúvidas e contribuírem com ideias para que conseguisse avançar com o estudo. Não poderia deixar de mencionar o agradecimento pela ajuda do meu amigo Rui Sousa na criação do programa em VBA para pré-preparação dos dados e não menos importante, um reconhecimento especial pelo apoio da minha namorada, Madalena Cabaço, ao incentivarme nos momentos finais de entrega. ABSTRACT Nowadays, technology applied in industries has increased day by day. As a result, different ways of collecting and processing data have been investigated. Machine Learning is one of those ways where, in addition to its applicability in all sectors, it has been increasingly explored in different sports. Furthermore, the analysis of data at a visual level helps in the interpretation and understanding of them. These types of procedures always seek to support in the decision-making work done by coaches, managers and scouting. It can be inherent to any sport and handball is obviously included. This specific investigation addresses methods to create advantages for Handball, introducing predictive analytics. The discovery of promising athletes based on collected variables is oneof the biggest challenges in this sport and although the data provided by the Federação de Andebol de Portugal are limited, this study demonstrates a 'direction' of how it can be done based on a single variable. In addition to working in the collection and pre-preparation of sports data, examples of visual presentations such as vertical/horizontal bar graphs and maps are exposed. Finally, Machine Learning algorithms with and without default parameters are used to predict if the player is promising. From this perspective, it can be concluded that, based on formation years, models score is slightly better for Support Vector Machines algorithms despite the proximity of the results. It is important to point out that relevant conclusions were also drawn from the graphs. KEYWORDS Machine Learning; Promising forecasting; Predictive Analysis; Sports industry; Performance metrics; Handball INDEX 1. Introduction ........................................................................................................................ 1 1.1. Context......................................................................................................................... 1 1.2. Motivation .......................................

FAQs

What is the main objective of the handball athlete discovery study?

To use predictive analytics and machine learning to identify promising handball athletes based on collected variables, supporting decision-making in coaching and scouting.

What data preparation and visualization steps are included?

The work covers sports data collection and pre-preparation, and presents visual outputs such as vertical/horizontal bar graphs and maps to help interpretation.

Which machine learning approach is used to predict player potential, and what is the outcome?

Machine learning algorithms with and without default parameters are used to predict whether players are promising; results indicate slightly better scoring for Support Vector Machines using formation years, with supporting insights from the graphs.

Aug 5, 2026

Upload by Patrick