6 min read

The rise of Big Data and Analytics is drastically changing the landscape of many businesses – and the sports industry is one of them. In today’s age of cut-throat competition, data-based strategies are slowly taking the front seat when it comes to crucial decision making – helping teams gain that decisive edge over their competition.Sports Analytics is slowly becoming the next big thing!

In the past, many believed that the key to conquering the opponent in any professional sport is to make the player or the team better – be it making them stronger, faster, or more intelligent.  ‘Analysis’ then was limited to mere ‘clipboard statistics’ and the intuition built by coaches on the basis of raw video footage of games. This is not the case anymore. From handling media contracts and merchandising to evaluating individual or team performance on matchday, analytics is slowly changing the landscape of sports.

The explosion of data in sports

The amount and quality of information available to decision-makers within the sports organization have increased exponentially over the last two decades. There are several factors contributing to this:

  1. Innovation in sports science over the last decade, which has been incredible, to say the least.
  2. In-depth records maintained by trainers, coaches, medical staff, nutritionists and even the sales and marketing departments
  3. Improved processing power and lower cost of storage allowing for maintaining large amounts of historical data.

Of late, the adoption of motion capture technology and wearable devices has proved to be a real game-changer in sports, where every movement on the field can be tracked and recorded.

Today, many teams in a variety of sports such as Boston Red Sox and Houston Astros in Major League Baseball (MLB), San Antonio Spurs in NBA and teams like Arsenal, Manchester City and Liverpool FC in football (soccer) are adopting analytics in different capacities.

Turning sports data into insights

Needless to say, all the crucial sports data being generated today need equally good analytics techniques to extract the most value out of it. This is where Sports Analytics comes into the picture.

Sports analytics is defined as the use of analytics on current as well as historical sport-related data to identify useful patterns, which can be used to gain a competitive advantage on the field of play.

There are several techniques and algorithms which fall under the umbrella of Sports Analytics. Machine learning, among them, is a widely used set of techniques that sports analysts use to derive insights. It is a popular form of Artificial Intelligence where systems are trained using large datasets to give reliable predictions on random data. With the help of a variety of classification and recommendation algorithms, analysts are now able to identify patterns within the existing attributes of a player, and how they can be best optimized to improve his performance. Using cross-validation techniques, the machine learning models then ensure there is no degree of bias involved, and the predictions can be generalized even in cases of unknown datasets.

Analytics is being put to use by a lot of sports teams today, in many different ways. Here are some key use-cases of sports analytics:

Pushing the limit: Optimizing player performance

Right from tracking an athlete’s heartbeats per minute to finding injury patterns, analytics can play a crucial role in understanding how an individual performs on the field. With the help of video, wearables and sensor data, it is possible to identify exactly when an athlete’s performance drops and corrective steps can be taken accordingly. It is now possible to assess a player’s physiological and technical attributes and work on specific drills in training to push them to an optimal level.

Developing search-powered data intelligence platforms seems to be the way forward. The best example for this is Tellius, a search-based data intelligence tool which allows you to determine a player’s efficiency in terms of fitness and performance through search-powered analytics.

Smells like team spirit: Better team and athlete management

Analytics also helps the coaches manage their team better. For example, Adidas has developed a system called miCoach which works by having the players use wearables during the games and training sessions. The data obtained from the devices highlights the top performers and the ones who need rest. It is also possible to identify and improve patterns in a team’s playing styles, and developing a ‘system’ to improve the efficiency in gameplay.

For individual athletes, real-time stats such as speed, heart rate, and acceleration could help the trainers plan the training and conditioning sessions accordingly.

Getting intelligent responses regarding player and team performances and real-time in-game tactics is something that will make the coaches’ and management’s life a lot easier, going forward.

All in the game: Improving game-day strategy

By analyzing the real-time training data, it is possible to identify the fitter, in-form players to be picked for the game. Not just that, analyzing opposition and picking the right strategy to beat them becomes easier once you have the relevant data insights with you. Different data visualization techniques can be used not just with historical data but also with real-time data, when the game is in progress.

Splashing the cash: Boosting merchandising

What are fans buying once they’re inside the stadium? Is it the home team’s shirt, or is it their scarfs and posters? What food are they eating in the stadium eateries? By analyzing all this data, retailers and club merchandise stores can store the fan-favorite merchandise and other items in adequate quantities, so that they never run out of stock.

Analyzing sales via online portals and e-stores also help the teams identify the countries or areas where the buyers live. This is a good indicator for them to concentrate sales and marketing efforts in those regions.

Analytics also plays a key role in product endorsements and sponsorships. Determining which brands to endorse, identifying the best possible sponsor, the ideal duration of sponsorship and the sponsorship fee – these are some key decisions that can be taken by analyzing current trends along with the historical data.

Challenges in sports analytics

Although the advantages offered by analytics are there for all to see, many sports teams have still not incorporated analytics into their day-to-day operations. Lack of awareness seems to be the biggest factor here. Many teams underestimate or still don’t understand, the power of analytics.

Choosing the right Big Data and analytics tool is another challenge. When it comes to the humongous amounts of data, especially, the time investment needed to clean and format the data for effective analysis is problematic and is something many teams aren’t interested in.

Another challenge is the rising demand for analytics and a sharp deficit when it comes to supply, driving higher salaries. Add to that the need to have a thorough understanding of the sport to find effective insights from data – and it becomes even more difficult to get the right data experts.

What next for sports analytics?

Understanding data and how it can be used in sports – to improve performance and maximize profits – is now deemed by many teams to be the key differentiator between success and failure. And it’s not just success that teams are after – it’s sustained success, and analytics goes a long way in helping teams achieve that. Gone are the days when traditional ways of finding insights were enough. Sports have evolved, and teams are now digging harder into data to get that slightest edge over the competition, which can prove to be massive in the long run.

If you found the article to be insightful, make sure you check out our interview on sports analytics with ESPN Senior Stats Analyst Gaurav Sundararaman.

Data Science Enthusiast. A massive science fiction and Manchester United fan. Loves to read, write and listen to music.


    • Hi Aniket,

      Thanks for reading our article.

      A lot of sports are already using analytics to a very large extent – sports like football, cricket, basketball, baseball and athletics are some of the popular application areas. Going forward, it is expected that analytics will play an even more important role in deciding how players’ as well as the team’s performance can be optimized, and how the athletes can be made stronger, faster and more agile.


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