Sports analytics is now big business. The industry has grown from a value of approximately $125 million in 2014 to an expected $4.7 billion by 2021. The reason for this meteoric rise in value is due to the inherent benefit that analytics offers to teams to gain a competitive edge both on and off the field. Whether it be the recruitment of players, the management of the team or developing young prospects, data analytics offers a base for understanding sports at a greater level.
What has made all of this possible is the development of new technologies such as the SportVU system set up in the NBA that tracks player and ball movement.
The origins of modern analytics
The use of analytics in the modern era was embraced in American sports, initially through Baseball. This was partly due to Bill James’ theory – and popular book – Moneyball which was used in a real season by Billy Beane, the GM of the Oakland Athletics in the early 2000s. Moneyball outlined the use of sabermetrics which effectively prioritised the use of data over the ‘gut-feeling’ philosophy that scouts had historically used to dismiss players based on biased reasoning. The genius of the Moneyball principle is that it’s very simple, it boils the game down to one statistic: ‘On-base %’ – the rate at which the player can get on base and into a scoring position.
This shift in mentality led the Oakland A’s to the Conference final in 2001 achieving a then record breaking 20-win winning streak on a budget of $39 million, compare this to the budget of the New York Yankees who enjoyed a budget of $139 million. Today nearly all sports have adopted analytics with the overarching objective of trying to simplify and understand sport at a more scientific and granular level.
There are two key areas of technology that have received a real focus from sports teams.
The first is virtual reality (VR). VR is a piece of technology that the large tech companies are making significant investments in, most notably Facebook buying Oculus for $2 billion in 2014. VR allows players to immerse themselves in the sport allowing players to better visualise the play more easily, assessing what they did and seeing the options again in real time. Watching game tape is already something that sports teams do, however, being able to visualise the game as it happened is a far more valuable tool as there is less of a disconnect between the player and the play. A company called Striver Labs are adapting VR to be used for this means and it is already being utilised by the Detroit Pistons, Dallas Cowboys and San Francisco 49ers.
The second key tech development is in wearable technology. These technologies track a number of different data points such as heart rate, sleep patterns and diet – these are integral to understanding how to drive improvement in the technical side of sport. As an example, by utilising this type of dataset and then using A.I to cluster and correlate the data, teams have been able to predict the likelihood of fractures and muscle injuries before they happen. Zepp has developed a sports variant of their wearable tech for Tennis, Baseball and Soccer – the baseball version attaches to the end of a bat and provides statistics on each swing; such as bat speed at impact and attack angle. This information is invaluable to coaches and players who can then make the necessary improvements when coaching.
While these technologies are helping coaches and owners understand their teams more, there is one area that isn’t currently being tracked because of the difficulty of doing so and that is the mental side of sport. It’s a common story across all sports where a new young talent emerges, gets the transfer to a big team and then never reaches their potential.
Research has suggested that mental health affects 35% of elite athletes. It therefore makes sense that having a better understanding of the mental health issues facing players should be a focus for both sports teams and sports analytics companies. Currently, data science is playing a limited role in this side of medicine, however, the use of machine learning and regressive analysis will eventually allow doctors to not only find issues before they develop but also, better understand the most effective treatments. AI practitioners also offer a level of anonymity making it easier for patients to open up.
Once perfected, data science will offer teams a much broader understanding of the health of their players; both physical and mental ensuring that they can extend both the playing time and the level reached of their players.
Data analytics has already had a significant impact on the way professional sports teams manage and operate and as technology evolves and new techniques are developed, this is set to improve further. It is likely that the best sports teams of the future are the ones that can pivot and successfully integrate these technologies as quickly as possible.