Football data analytics: the science of talent: machine learning predictions for player scouting
Albertini, Domenico (A.A. 2023/2024) Football data analytics: the science of talent: machine learning predictions for player scouting. Tesi di Laurea in Data science in action, Luiss Guido Carli, relatore Alessio Martino, pp. 157. [Master's Degree Thesis]
|
PDF (Full text)
Download (11MB) | Preview |
Abstract/Index
Modern football scouting scenario: the need for a data-driven approach. Literature review. The role of machine learning and data analytics in sports. The growing role of data science in football. Machine learning in scouting and recruitment: a literature review. Bridging the gaps in football scouting: towards a more effective player identification. Data collection methodology and dataset structure. Data collection tool: the API. Dataset creation. Data preparation: cleaning, sorting and generation of derived variables. Data cleaning. Generation of additional variables. Feature engineering: construction of performance indices. Development of performance indices. In-depth performance indices. Performance indices validation. Introduction to validation. Classification models: implementation and results. Benchmarking against raw features: testing models without indices. Designing a practical and efficient scouting model. Evaluating model options for player scouting. From input to output: from user query to actionable model results. Final in-depth player comparison visualization. Real-world use cases in football scouting: testing the model and player analysis. Testing the model on attackers. Testing the model on midfielfers. Testing the model on defenders.
References
Bibliografia: pp. 137-140.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91) |
| Chair: | Data science in action |
| Thesis Supervisor: | Martino, Alessio |
| Thesis Co-Supervisor: | Spagnoletti, Paolo |
| Academic Year: | 2023/2024 |
| Session: | Extraordinary |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 10 Jul 2025 10:55 |
| Last Modified: | 10 Jul 2025 10:55 |
| URI: | https://tesi.luiss.it/id/eprint/42873 |
Downloads
Downloads per month over past year
Repository Staff Only
 |
View Item |
