hannahigboke / epl-soccer-data--an-exploratory-data-analysis-using-python

This is an exploratory analysis of the EPL soccer data for the 2018–2019 season. Using Python, I identified interesting trends to answer specific questions from the data.

EPL Soccer⚽ Data: An exploratory data analysis using python

The English Premier League (EPL) is the top professional football (soccer) in England. It is one of the most important and widely watched football leagues in the world. The EPL consists of 20 teams that compete against each other in a round-robin format.

• Each team plays every other team exactly once, home and away (two matches per team).
• The total number of matches in a season is calculated by the formula [teams * (teams - 1)]. In this case, with 20 teams, there are 20 * 19 = 380 matches in a season.
• Teams receive points for each match based on the outcome: 3 points for a win, 1 point for a draw, and 0 points for a loss.
• The team with the most points at the end of the season is crowned the EPL champion.

This dataset contains data of every game from the 2018-2019 season in the English Premier League. In this project I perform exploratory analysis and identify interesting trends to answer specific questions from the data using python.

Project tasks

1. What Team committed the most fouls?
2. What is the distribution of the features of the game? Are there outliers?
3. For a team winning at half time, how does it change at full time?
4. Does the number of red cards a team receives have an effect on its probability of winning a game?

Solutions

Solutions to the project tasks can be found in my jupyter notebook here.