DSND-Write-A-Data-Science-Blog-Post

Football Events

Tools:

1. pandas
2. seaborn
3. numpy
4. matplotlib

Data source: https://www.kaggle.com/secareanualin/football-events

Introduction

This project explores sports data related to Football Events. The dataset comes from Kaggle, including a granular view of 9,074 living games, from the biggest 5 European soccer leagues: England, Spain, Germany, Italy, and France, for the 2011 to 2016 seasons.

File Descriptions

1. events.csv contains event data about each game. Text commentary was scraped from: bbc.com, espn.com and onefootball.com
2. ginf.csv - contains metadata and market odds about each game. Odds were collected from oddsportal.com
3. assist_method.csv, bodypart.csv, event_type.xlsx, event_type2.xlsx, location.csv, shot_outcome.csv, shot_place.csv, side.csv, and situation.csv contain dictionaries with the textual description of each categorical variable coded with integers
4. Write A Data Science Blog Project.ipynb contains detailed EDA steps

Summary

1. Spain has had the most goals over the term of this data.
2. For the statistical distribution of goals in all leagues for body part, right foot dominates over left foot and head.
3. Pie-chart shows Pass is the best assist method.

Acknowledgements

Kaggle

Here is the link to the corresponding Blog: https://medium.com/@jingxianlin/football-events-c2da9fc4a738