Data Analysis a game-changer in major sports

Data Analysis a game-changer in major sports

Data analysis is a term that gets thrown around quite a lot these days, however, it is not a new term. As a matter of fact, it has been around since ancient Egyptians were taking a periodic census to build the pyramids. Since then, data analysis has grown a lot. Especially due to the advancement of computers. Today, most business firms use data analysis to stay ahead of the competition, and it’s no different for the sports industry.

To put it simply, data analysis is a process that involves inspecting, cleansing, transforming, and modeling a dataset so as to extract useful information that helps in the decision making process. Data analysis in sports usually referred to as sports analytics, has been in use for many decades. But as mentioned earlier, the advancement in data collection, management, and other technologies has helped data analysis become more entrenched in sports. 

Sports analytics uses data such as weather conditions, a player's fitness/training stats, the team's recent performance, and so on. By using these sorts of data, predictive machine learning models can be made which ultimately is used to make game-changing decisions. Sports teams function as a business, and at the end of the day, they want to maintain a competitive advantage over other firms. That’s where data analysis helps them. It is used not only to improve a team’s win percentage but also helps the team better engage with their fans and also bring in operational improvements. To understand these claims in detail, this article will dive into the use-cases of data analytics in sports along with some famous success stories related to sports analytics.

The Winning Formula

The primary objective of any sports team or sportsperson is to try to win the matches they participate in. Through predictive analysis, coaches can derive better insights on how their team should go about in particular matches and try to increase their win probability.

Analytics is used to determine which position is best suited for a particular player or set up a specific training and dietary routine for players. Most of us probably know or might have heard about one of the biggest upsets in football history. That is, Leicester City winning the English Premier League title of the 2015/16 season.

Leicester City has always been on the forefront when it comes to integrating data analytic tools, and that is exactly what aided them in reaching the pinnacle of English football. At that time, they used the Prozone3 tracking data in order to support an enhanced assessment of the players. They had collected different metrics such as how much distance does a player cover, the number of sprints, and many more. Assessment of these metrics helped the coaching staff to tailor different training regimens as per the need of the individual. This helped the team suffer fewer injuries and perform better on the pitch.

Of course, keeping injuries to a minimum is not the only area in which sports teams use data analytics. Automated video analysis has been around for quite some time, and they have only improved over time. Teams use video analysis to figure out the best possible way to counter the opposition's game plan and also improve their own overall gameplay. Opta Pro for instance provides teams with match footage along with subjective comments and statistics. These tools are then used to identify weaknesses and areas of improvement. The best example of using video analysis to come up with a winning strategy is most probably that of Billy Beane’s Oakland Athletics. After all, it did inspire a book and the subsequent movie- Moneyball.

Team statistics can be used to build learning models such as deep neural networks and SVMs. These models can help in figuring out the best possible tactics for a team to win against various opposition. Other statistics such as player behavior and interaction in sports such as F1 also enables the team to gain a competitive advantage. 

Operational Management

Sports teams are a business entity and therefore they look to cut their operational cost and increase revenue. Teams now have access to detailed data regarding marketing, sale of tickets, merchandise, and so on. Detailed operational reports help to improve procurement and supply-chain management. 

Advanced analytics technologies help companies to improve their existing customer relationship and also human resource practices. Information gathered through the use of CRM and other apps help sports teams make key decisions about their core products. For instance, in the NBA, an intricate web of decision points affects the playoff structure, draft lotteries, and much more.

Understand & Engage With Fans

Fans are an integral part of a sports team. They leave no stones unturned when it comes to supporting their team and are a major source of revenue. Thus, understanding what fans want and need is of utmost importance for any sporting institute. 

During the current pandemic, major football clubs and other sports team have sorely missed their fans. Playing in an empty stadium does not provide any sort of home advantage to a team and the teams do not generate any revenue through ticket sales. This situation demands the use of data analytics. In order to connect with the passionate fan base, sporting teams often come up with promotional campaigns and gather data from those events. The data is then used to identify what their fans like and work more on that. 

By engaging with their fan base through social media, YouTube videos, and more, teams are often seen promoting their merchandise. Data analytics helps to identify what sort of content is gaining more views and work on producing similar content. Not only that, but the data from customer engagement also helps sports teams to extend their services in the stadium. For instance, the New England Patriots track what their fans purchase at the pro shop. By analyzing those data and coming up with suitable decisions, they can improve their revenue.

Data analysis also plays a huge role in gaining new audiences and retaining old ones. We have seen the use of data-driven insights and AI services in Wimbledon being used to improve fan engagement. They use cameras and data from multiple sensors to create an effective highlight reel that shows each bit of exciting action, crowd cheers, and more. They even monitor and rank the sound of the racket. An engaging highlight reel goes a long way in attracting new fans for the sport.

So, there’s no doubt that the use of data analytics in sports will continue to grow in the future. With the amount of data that is available, the potential for sports teams to grow both on the field and outside of it is huge.