Sports data
- wearable technology for movement patterns / injury patterns
- predicting results based on angle, velocity of ball
- quality of player based on their ability to make hard / easy shots
- text data on fans (reactions, interest)
- basic player stats
- shot frequency divided up by particular location
What do we do with data?
- make predictions about outcome of a game
- predications about results from combined data of different players
- understand player effectiveness - eg football yards
- impacts of rule changes on scores
- undestand how good a player is under specific circumstances
- how good a football player is on one side of the line
- predict yards based on which side
- predict future success based on model (hockey ELO)
- based on wins / loses
- interaction of win/loss with home/away
- not just additive
- compare players
- determine correlations between types of success
- calculate average stats - compare player to average
- likelihood of scoring in particular circumstances
- probability of winning given how much time has passed, data, and time left
- understand consistancy of player performance
- predict play for a opponent based on history
- match offense to defense
- predict successful plays in certain circumstances
- performance of players from different countries / physiology