With UEFA EURO 2024 approaching, the potential to harness advanced analytics in sports is immense, particularly with the increasing availability of comprehensive datasets encompassing various aspects of football. In recent years, the integration of data science and machine learning in sports has revolutionized how teams prepare, strategize, and compete. These technologies offer profound insights into player performance, tactical decisions, and overall team effectiveness. Furthermore, as a major international event, EURO 2024 also significantly impacts the tourism industry of the host nations, making it a critical area of interest for economic analysis.
Despite the wealth of data available, several challenges hinder the effective use of these resources in enhancing football analytics. Data fragmentation and accessibility issues arise as information is often scattered across various platforms without a standardized format for easy analysis. The complexity of the data, which ranges from structured data like player statistics to unstructured data such as tactical formations and real-time game developments, adds another layer of difficulty. Moreover, current analyses frequently fail to fully leverage advanced machine learning techniques that could provide deeper insights into complex dynamics like in-game decision-making and player performance under various conditions. The challenge of integrating multiple data types, including real-time data and historical performance metrics, is crucial for obtaining comprehensive insights but presents significant analytical hurdles. Additionally, there is a need to assess the socio-economic impact of UEFA EURO 2024 on tourism, requiring the integration and analysis of tourism-related data, an area less explored in the context of sports events.
Utilize machine learning to provide detailed insights into the playing styles, strengths, and weaknesses of participating teams based on historical and current data.
Assess the influence of EURO 2024 on tourism, analyzing data related to host cities and their readiness and appeal as tourist destinations.
Demonstrate the capability of machine learning and data analytics tools to interpret vast datasets, providing stakeholders with actionable insights into both sports and economic aspects.
- Detailed analysis of player statistics such as goals, assists, and playing time.
- Identification of key players and their impact on team performance.
- Visualization of tactical formations and in-game strategies.
- Discovery of trends and shifts in team tactics over recent tournaments.
- Application of decision trees, gradient boosting classifiers, linear and Lasso regression models, clustering, and neural networks for various analytical tasks.
- Successful model training and evaluation using appropriate metrics such as accuracy, precision, recall, and AUC.
The brainstorming file contains the major questions we have responded to during the project, providing a comprehensive overview of our analytical approach and key focus areas.
The dataset Excel file contains detailed information about the relevant datasets used in the project, including data sources, descriptions, and any preprocessing steps undertaken.