Data Analytics in Sports

Transcript

1. DATA ANALYTICS IN SPORTS
2. WHAT IS THIS DOMAIN?  Data Analysis is the process of recording the exact motions of players and employing software to generate useful insights based on the collected data. This information can be used to improve team performance as well as player fitness and technique.  Sports Data Analysts spend their time gathering on-field and off-field data from a variety of sources, evaluating it and interpreting it for useful insights. Player performance and health as well as the viability of moves and tactics are all included in on-field data.  Off-field data can include fan behaviour, audience purchasing habits and social media interactions.
3. PREDICTIONS Predictions that can be made using Sports Data Analytics :- • INJURY PREDICTIONS • PLAYER VALUATIONS • TEAM STRATEGY • EVALUATING TICKET CHURN • TICKET PRICING • SPORTS BETTING
4. HOW DATA ANALYSIS IN SPORTS IS CHANGING THE GAME  HELPING TEAMS WIN  DRIVING CUSTOMER ENGAGEMENT  BENEFITING THE BROADER ECOSYSTEM  IMPROVING BACK-OFFICE INTELLIGENCE  EXPANDING PARTNERSHIPS
5. WHY IS DATA ANALYTICS SO IMPORTANT IN SPORTS?  The Sports industry uses sports analysis to increase revenue, improve player performance and a team’s quality of play, prevent injury and for many more enhancements.  Sports Analysts analyse statistics and use analytics for a competitive advantage  With the advancement in technology, some emerging developments include integrating data sources to advance competition, communicate why the data is useful and create a different fan experience.  Sports Analysts use current technology to analyse data and generate meaningful yet simple visuals to share with other important decision makers on a team.
6. TOP SPORTS LEAGUES USING DATA ANALYTICS  MAJOR LEAGUE BASEBALL (MLB)  NATIONAL BASKETBALL ASSOCIATION (NBA)  NATIONAL FOOTBALL LEAGUE (NFL)  NATIONAL HOCKEY LEAGUE (NHL)  PROFESSIONAL GOLF ASSOCIATION (PGA)
7. CASE STUDIES
8. SPORTS DATA ANALYTICS TO STUDY THE OFF-FIELD BEHAVIOUR OF PLAYERS  In this case, the off-field behaviour of players has been analysed using a statistical approach.  The focus of this study is to understand the nature of the data, explore the relation between the attributes of the data set and create a model to understand how the data relates to the underlying population using a real world data set.  It comprises motion sensor data of 19 activities among both male and female categories. This data is referred from the UCI repository.  The data set also clearly displays the three dimensions of big data namely volume, variety and veracity.
9. FOOTBALL CHAMPIONSHIP FANBASE COMPARISON: ALABAMA VS GEORGIA  The data was collected from social media from 1.74 million people that follow either Alabama Football or Georgia Football. The data collected was open source and publicly available.  A machine learning statistical tool was utilized coupled with artificial intelligence image recognition to identify fans that are highly identified with either Alabama Football or Georgia Football.  A statistical tool called Affinio was utilized to ascertain differences and similarities between the two fanbases on a variety of variables.
10. HOW GERMAN SOCCER TEAM FC BAYERN MUNICH GREW A U.S. FAN BASE  This study utilized the cutting edge social media analytics tools of Affinio to derive valuable insights about Bayern Munich’s U.S. based social media followers. It used machine learning to develop network graphs to understand the customers.  There appears to be a large base of FIFA 17 Gamers Cluster in the U.S. The Affinio analysis demonstrates that gamers are far more influential than any celebrities for this cluster.  Based on the Affinio analysis, Bayern Munich can increase its fanbase furthermore by partnering with the top media outlets which include Bleacher Report, ESPN and Complex.
11. THE FUTURE OF DATA ANALYTICS IN SPORTS  The World of Sports has been dramatically transformed by data analytics. The most well-known aspect of statistical analysis is the use of data to identify players who have been overlooked or undetected.  During game broadcasts, the world has grown accustomed to little boxes that notify viewers of a player’s field goal percentage or the opportunities they produce in a certain game.  The next step in this domain is to crunch the figures in real time and present more information to the viewer such as live win probability, average player position on the field and momentum tracking.  Over the course of this century, numbers have altered the fabric of sports and future advances will undoubtedly continue to do so.