Data analytics and data science in sport (Blog 2)
13 September 2022 | Written by Andy Nutting
How data science is used in sport: Andy Nutting’s second blog
This second blog looks at the use of data analytics and data science in sport.
Remember the 2011 film Moneyball?
You may recall this film for the performance of Brad Pitt, who played Billy Beane the Oakland Athletics baseball team’s general manager.
But the really interesting fact is that the film was based on actual events in 2002 and popularised the term ‘sports analytics’.
Billy Beane relies heavily on the use of analytics to build a competitive team on a minimal budget. This system helped the team become the first in a 100 years of American League baseball to win 20 consecutive games.
As technology has advanced over the last few years, data collection has become more in-depth and can be conducted relatively easily.
This has enabled sports analytics to grow as well, leading to the development of advanced statistics and machine learning.
What’s often overlooked is that – along with the advantage of using big data and analytics – comes the lawful requirement to protect personal data and comply with UK GDPR and Data Protection Act 2018.
Increasingly sport and associated clubs are using data science and analytics before making decisions or developing strategies to win games.
Sports such as basketball and tennis have a long history of using data, and football has increasingly become more data driven over the last decade.
For example, data driven scouting is on the rise in football by collecting various data points to scout for new potential signings:
- Arsenal FC paid over £2million in 2018 for the US company StatDNA, whose data has since been used to advise on their signings.
- Leeds United signed up to Zone7, an American company that uses an artificial intelligence driven human performance platform, designed to help athletes reduce the risk of injury and to help avoid lengthy spells on the side lines.
Data analytics have come to play an important role in the football industry today, with teams of backroom staff using data to advise the coach and/or director of football.
Clubs look to gain a competitive edge on and off the pitch, and big data is allowing them to extract insights to improve player performance, prevent injuries and increase commercial efficiency.
Indeed, the BBC ran a headline on its website last year: ‘Data experts are becoming football’s best signings‘ alluding to the fact that data and technical analysis has become essential for today’s football clubs.
But it’s not just professional association football clubs who are utilising technology and data scientists to manipulate data. Athletes rely on data to provide clear insights on how to aid improvement on individual performance.
Tennis, cricket and golf use wearable sensors and data analytics to analyse a players swing of a bat, racquet or club.
Sports are beginning to use big data to not just analyse the past, but to predict the future, and this will be dramatically affected by the application of artificial intelligence.
The next blog will look at how technology is being used to manage and utilise data on sport clubs’ member databases.