There was a time not that long ago when there was still an aversion to using data in some sports. The practice of analytics was regarded as something almost too intellectual for sports – as if the two disciplines could not work together. The old-fashioned masculine view of sports did not trust allowing non-sports ideas equal footing with ingrained sports know-how.
But now it is rare that you will see any level of professional sports – and even further down the levels – not utilizing data and analytics to improve performance and results. Online betting sites online use the same information for their own ends, adding to the different groups that benefit from sports analytics. But how did we get to this point? And what is the potential for sports analytics in the future?
Becoming the Norm
Although some form of analyzing sports data can be traced back to the beginning of the 20th century, it was in the early years of the next century when it became popularized. The Moneyball book, later made into a film starring Brad Pitt, chronicled how Oakland A’s general manager Billy Beane used data to establish a winning baseball team.
Beane was able to deal with a smaller budget and use data and sports analytics to recruit overlooked players. By concentrating on the data rather than using traditional sports gut feeling or media hype, a new way of preparation was utilized by sports teams. One that was taken up by many others.
Analytical Models
Sports analytics is essentially the collecting and processing of data that can then be used in different ways and for different ends, depending on the sport. But the overriding reason for its popularity is that all the available models are proven to work and to improve performance.
Machine learning models are able to take in huge amounts of data and process it in a variety of ways. Statistical models can predict the movements of players and can be used for improving movement during games. Using a variety of models is key for teams to cover all bases, as it were, and reap the rewards.
Wearables
One of the most obvious and noticeable ways that sports data is collected for analysis is in the increased use of wearables. By incorporating GPS and other data collectors into the clothing worn by players, the analyzing teams can gather huge amounts of information.
Usually these will be inserted into the sportswear, under the official jerseys, and are lightweight enough to not affect the performances of the athletes. The data collected, on the other hand, is hugely beneficial to real-time tactics, preparation, post-game re-evaluation and future planning.
Figure 2 All kinds of data can be collected and used
Fan Engagement
But all this data is not solely for the benefit of the teams and players. There is so much information that can be collected these days that other business sectors are able to use it for their own ends. That can help with fan engagement and personalized content in an ever-developing sports media sector.
The rise in online sports betting in the US has also coincided with this surge in data collection. The information can be used to help sports fans with their betting and make more informed decisions. There is no guarantee of success, of course. But more exact odds and increased knowledge is a selling point for the sportsbooks.
Media Use and the Future
The potential for sports analytics and its use is huge. Broadcasters can use the information to enhance their output, allowing viewers to better understand what is going on at sports events in real-time. There has never been a lack of pundits keen to share their views – but now they have the data to back up their opinions.
Whole new careers and job opportunities have been created in sports and sports media thanks to analytics and the practice has revolutionized sports in general. There are still challenges for the sector to improve performance and possibly move into using data for emotional preparation thanks to AI. But it is obvious that sports analytics is here to stay – and that even more data will be able to be collected in the years to come.
