SciSports is one of the world's leading companies in providing actionable insights from football data through its online player recruitment platform, as well as a wide range of services. To obtain these insights, our Data Analytics team is continuously developing cutting-edge football analytics metrics leveraging state-of-the-art machine learning and data science techniques. These metrics help football organizations, among others, to gain valuable insights in the overall abilities of players, the playing styles of players and the impact of the actions of players on their team's performance.
In our first Master Class on Sports Analytics, we introduced the participants to the different types of football data and football metrics that are currently available on the market before inviting them to build their own expected-goals and expected-assists models under the supervision of our Data Analytics team. We exposed the participants to the state-of-the-art tools that our Data Analytics team also uses to develop their football analytics metrics, including Jupyter Notebook, Python, pandas, scikit-learn, seaborn, XGBoost and CatBoost.
