Original Data Source: You can easily find data about soccer matches but they are usually scattered across different websites. A thorough data collection and processing has been done to make your life easier. I must insist that you do not make any commercial use of the data. The data was sourced from:
Analyzing Soccer Data Python notebook using data from European Soccer Database · 15,588 views · 4y ago · data visualization , exploratory data analysis , football 20
This dataset contains player, game, event, and table data from Major League Soccer (MLS). There is currently information on over 6000 matches and almost 420,000 events from those matches. For an introduction to the data, check out this kernel. For a quick overview of what data is available for what years, visit this spreadsheet.
We have a few soccer datasets that are already uploaded to the Kaggle platform. However, after playing with some hypothesis, we found that we need to have updates more often to complete started framework. Main difference between well-known European Soccer Database are: added not only odds per match, but Under Over, Asian Handicaps.
Explore and run machine learning code with Kaggle Notebooks | Using data from European Soccer Database
Analysis of the Soccer dataset from Kaggle. Contribute to AkashD19/Soccer-Database-Kaggle development by creating an account on GitHub.
Explore and run machine learning code with Kaggle Notebooks | Using data from FIFA worldcup 2018 Dataset ... Soccer World Cup 2018 Winner. Notebook. Data. Logs ...
We used a European Soccer Database from Kaggle.com to explore these hypotheses, given a sqlite3 database with 7 data tables covering over 25,000 historical soccer matches, 10,000 players, and teams (player & team ratings assessed by EA Sports) from 11 European countries from 2008–2016. To summarize our approach in terms of individual statistical hypotheses, we’re running four 2-tailed 2-sample T-tests, at a threshold for rejecting or not rejecting each null hypothesis (alpha=0.05).