Redefining objective scoring chances: a comprehensive, unbiased machine learning approach to football expected goals
Marcoccia, Andrea (A.A. 2022/2023) Redefining objective scoring chances: a comprehensive, unbiased machine learning approach to football expected goals. Tesi di Laurea in Databases & big data, Luiss Guido Carli, relatore Blerina Sinaimeri, pp. 34. [Bachelor's Degree Thesis]
|
PDF (Full text)
Download (1MB) | Preview |
Abstract/Index
Football analytics: a game-changing revolution. Expected goals (xG): the cornerstone of modern football analytics. The limitations of conventional xG predictive models. The goal: creating an unbiased xG metric. The data. Source. Collection methodology. Dataset overview. Feature engineering. Exploratory data analysis (EDA). Data cleaning. EDA on categoricals. EDA on continuous variables. EDA on preceding event. Associative analysis of predictors. Predictive models. Metrics. Models. Benchmarking against literature.
References
Bibliografia: pp. 33-34.
| Thesis Type: | Bachelor's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Bachelor's Degree Programs > Bachelor's Degree Program in Management and Computer Science, English language (L-18) |
| Chair: | Databases & big data |
| Thesis Supervisor: | Sinaimeri, Blerina |
| Academic Year: | 2022/2023 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 01 Feb 2024 08:40 |
| Last Modified: | 01 Feb 2024 08:40 |
| URI: | https://tesi.luiss.it/id/eprint/37841 |
Downloads
Downloads per month over past year
Repository Staff Only
 |
View Item |
