How many yards will an NFL player gain after receiving a handoff?
Sport data always contains a lot of very interesting insights. Having been inspired by Sheldon Cooper we decided to find out what helps professional sport players win. American football is a complex sport and defintely contains many secrets to discover for data scientists. Deeper insight about what is important and what is not will help teams, media, and fans better understand the game, and for coaches to find better strategies.
As good data scientists we are going to start discovering the data with nice visualizations. Looking at players positions, understanding coach strategies and team statistics will hopefully give us surprising ideas. As a model task we will use our insights to predict how many yards will an NFL player gain after receiving a handoff.
- Which parameters of team, tactics and environment really affect the result of the game?
- How these parameters are connected with each other? For example, do the temperature and wind speed affect player speed?
- How to build perfect American Football team?
- What is the best data representation of in-game situation to analyze and build ML models upon it?
- Which model is the best to predict the result of the play?
- Was Sheldon Cooper right? ;)
The data is provided by the running Kaggle competition and presented in tabular data containing 509762 rows and 49 columns. Each line is a time-stamp of one of the players for one of the 512 games. For each time-stamp we know player position and even orientation he looks at, we also know what wether it was, so there is a really huge space for interesting correlations!