What Has the Impact of Data Analytics Been on the World of Modern Sports?

The modern sports industry is one of the largest and most lucrative in the world, with major games like American football, soccer, baseball, and basketball attracting hardcore fans from all over.

With the rise of big data, there has been an integration of analytics into modern sports through a variety of means. The impact of this has been huge, and has changed the way that we play, view, bet on, and understand all major sports. In fact, research has shown that the global sports analytics market is growing exponentially, with a projected revenue growth of $3.78 billion in 2023 to $22.13 billion by 2030.

An Intro to Data Analysis

Data analysis is all about numbers. Experts will look at all statistics related to a certain aspect of a sport, such as specific players’ performance during practice or the number of points scored by an opposing team, and any other number of things. From examining this data, conclusions can be made about the health and fitness of players, the odds of a certain team winning, the strength of a team based on their history and predictions of their future, and so on.

The Link Between Data Analytics and Sport

The sole purpose of data analytics is not only to look at the numbers, it’s to apply them. Once information has been drawn from statistical evidence, the most popular modern sports from the industry use these figures for their own purposes.

Betting Odds

Sports betting operators use big data every single day, to provide the best dynamic odds for their customers. They will gather intel on team and player performance, weather conditions, historical game records, team line-ups, and more, to create betting options.

Through data analysis and the sheer amount of information available, oddsmakers can more accurately predict the outcome of future sports events and identify trends in the performance of specific players or teams. They can also analyse games as they are taking place, and then offer dynamic odds to bettors.

Bettors can also use sports data analytics to make their own predictions, even if they don’t agree with professional oddsmakers. This is done through platforms for betting exchange reviewed carefully by punters and experts, ensuring transparency and trustworthiness. At these sites, punters can bet against each other and offer their own odds on sporting events. These platforms have surged in popularity over recent years, with the biggest having millions of users and thousands of employees.

Fan Engagement

Fan engagement has been transformed for the better thanks to data analysis and visualisation techniques. In other words, vast amounts of statistical information can be formatted into visually appealing and easy-to-digest graphics, which allows fans to get deeper insight into the performance and predictions of a game, without getting overwhelmed by the numbers.

There are multiple platforms where sports fans can see mock-ups of games, find out a detailed history of a specific athlete’s performance, and read up on team stats, either before, after, or even while a match is taking place. Allowing viewers to dive into the world of modern sports more than ever before has enriched the experience and resulted in a huge upturn in engagement levels. In the world of fantasy sports, 75% of fans wanted more real-time data so that they could craft better fantasy teams.

Recruitment and Talent Scouting

Athletic recruitment and scouting has changed forever due to big data analytics. Scouts can now look at the history of a players’ performance, track their current performance, and make informed decisions on the future of their career based on this.

As well as being useful for transfers, analytics enables recruiters to discover up-and-coming talent, and work alongside these athletes to give them the best chance of success. Sports data analysts can determine which players are best suited to particular positions within a team, and which areas of the sport players need to improve in or focus on.

Performance Prediction

By tracking an athlete’s every move and looking into their performance history and the history of the team they play for, a lot can be predicted. For example, analysts can give advice on player positions, substitutions, training regimes, and more. This advice can then be applied to give a team the best odds of winning a match.

Of course, statistics are only one part of the story; there are other aspects to take into account when considering the outcome of a game. But being able to manipulate data to an advantage definitely gives a team a better chance of success.

Injury Prevention

Lots of sports teams focus on the health and fitness levels of their athletes, and for good reason. Their team will be required to wear trackers, such as smartwatches or fitbands, and attend medical physical check-ups regularly. The purpose of this is so that any quantitative data relating to their health can be analysed and used to gather information.

If any athlete is at risk of injury, or is recovering from an injury or health issue, they can be looked after and positioned in a way that will minimise their risk. This is not just to ensure the success of a match or league, but to protect the welfare of the team.

Injury prevention is an essential part of sports data analytics. Experts can examine at which points of a game or which parts of a sport are most likely to result in injuries for players, and relay this information to sports managers or coaches, who can then put steps in place to avoid this.