Purpose: Analyze European soccer game data to gain insights into team performance, player statistics, and predict match outcomes.

Description: If you have a passion for sports, this project is perfect for you. You will work with a dataset containing European soccer game details, including match results, team and player statistics. By leveraging SQL, you will analyze team performance, identify key players, calculate performance metrics, and even apply advanced techniques like regression analysis or machine learning algorithms to predict match outcomes. This project allows you to showcase your analytical skills in the realm of sports analysis.

Additional Tools: Python with libraries like pandas can be useful for data preprocessing, while machine learning libraries like scikit-learn can help you build models for match outcome prediction.

• Python: Utilized for data preprocessing, statistical analysis, and machine learning algorithms.
• Libraries like pandas, scikit-learn, and matplotlib: Used for data manipulation, modeling, and visualization.

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:

• http://football-data.mx-api.enetscores.com/ : scores, lineup, team formation and events

• http://www.football-data.co.uk/ : betting odds. Click here to understand the column naming system for betting odds:

• http://sofifa.com/ : players and teams attributes from EA Sports FIFA games. FIFA series and all FIFA assets property of EA Sports.

When you have a look at the database, you will notice foreign keys for
players and matches are the same as the original data sources. I have
called those foreign keys “api_id”.

Improving the dataset:

You will notice that some players are missing from the lineup (NULL values). This is because I have not been able to source their attributes from FIFA. This will be fixed overtime as the crawling algorithm is being improved.
The dataset will also be expanded to include international games, national cups, Champion’s League and Europa League. Please ask me if you’re after a specific tournament.

Please get in touch with me if you want to help improve this dataset.

CLICK HERE TO ACCESS THE PROJECT GITHUB

Important note for people interested in using the crawlers: since I first wrote the crawling scripts (in python), it appears sofifa.com has changed its design and with it comes new requirements for the scripts. The existing script to crawl players (‘Player Spider’) will not work until i’ve updated it.

Exploring the data:

Now that’s the fun part, there is a lot you can do with this dataset. I will be adding visuals and insights to this overview page but please have a look at the kernels and give it a try yourself ! Here are some ideas for you:

The Holy Grail…
… is obviously to predict the outcome of the game. The bookies use 3 classes (Home Win, Draw, Away Win). They get it right about 53% of the time. This is also what I’ve achieved so far using my own SVM. Though it may sound high for such a random sport game, you’ve got to know
that the home team wins about 46% of the time. So the base case (constantly predicting Home Win) has indeed 46% precision.

Probabilities vs Odds

When running a multi-class classifier like SVM you could also output a probability estimate and compare it to the betting odds. Have a look at your variance vs odds and see for what games you had very different predictions.

Explore and visualize features

With access to players and teams attributes, team formations and in-game events you should be able to produce some interesting insights into The Beautiful Game . Who knows, Guardiola himself may hire one of you some day!