How is data being used in professional sports

We all love to play and watch sports and the global sports industry is worth up to $620 billion today.

Innovations in sports medicine and science have seen the level of competition in sports leagues internationally increase exponentially. The internet age has also ushered in an explosion in viewership and exposure for athletes. The latest step in sports is the use of data to make informed decisions.

Big data has taken the sports industry by storm and has replaced traditional methods of analysis and prediction. Coaches, clubs, and players rely more on data analytics these days rather than intuition or gut feeling. In this post we will discuss examples of big data analytics in sports, and the overall positive impact big data has created in sports. 

Create better Training and Sporting strategies 

Advances in technology such as small, wearable tracking devices that can collect biometric data are a valuable source of data for clubs. The data from such devices can record an athlete's unique physical and behavioural characteristics. This data can then be analysed with the conclusions being used to alter the programs for athlete.

For example,sleep patterns and anxiety levels can indicate overexertion, whereas data gathered about mitochondrial respiration/glutathione levels/reactive oxygen species could indicate susceptibility to muscle injury (hence requiring rest).

These devices also record the extent to which the athlete has followed the training instructions, and his/her physical parameters of importance during a strenuous workout.This combined with specific sporting knowledge can streamline training methodologies for maximum benefit. 

Big data analytics can help the coaches know if the forward combination is gaining in manoeuvrability or if a striker is losing his agility.     

Create and nurture a Sports contingent 

Big data helps sports administrators to collect and process knowledge about players' natural abilities and inherent strengths. Then, they match these with different sports disciplines, and select the best possible talent to be groomed as future athletes. 

Data about recovery and training can be analysed to optimise players’ schedules. This may vary across sports and future events, and could include aspects such as diet, equipment, and at-home exercises

One can estimate the rate of exertion of players and replenishments that might be needed, given a set of probable outcomes. This helps teams and players to better manage the physical and mental strain related to competitive sports, and aids in obtaining peak performance and effective recovery. Big data analytics also helps in predicting, and thereby preventing, sport related injuries and burn outs.

Big data analytics helps broadcasters create a better viewing experience 

Data is not just beneficial to clubs; telecasters are using data to provide the best possible viewing experience. Channels may choose to provide the viewers interesting facts, trivia, and figures, as well as exploring new areas of competition and comparison. 

Predictive analytics also enhance the viewing experience. Software uses data points about teams or players on top of many other pieces of data, ranging from the time of day to past performance to produce a ‘win predictor’. This is like the calculations completed by betting companies when setting the odds. This prediction is broadcast to the viewers. This prediction may change over the course of a match, providing expense to viewers.

Big data analytics helps sports casters know their viewership better, e.g., what is the age demographic and location of their viewership?This helps them shape the broadcast to be more entertaining.


Big data analytics in Sports is crucial in Fan analysis and management 

Using Big Data analytics in Sports, one can quickly analyse data pertaining to social media activity of fans, match attendance (including people viewing live streaming) and merchandise sales to determine the expectations of fans. 

Clubs can also design and develop merchandise and memorabilia that will be popular amongst fans, based on the analysis of what was popular previously and the expectations of fans. Data can also be used to optimise marketing campaigns to ensure that sales are maximised.

Targeted marketing strategies and advertising campaigns can be launched and tracked to keep the fans engaged with their favourite teams. This way, Sports related clubs/management can focus their efforts and use their effectively. 

Aids in streamlining and optimising supportive activities 

Yet another use of big data is to aid in support staff selection/rotation, team roster management, staff management and training schedule management. By analysing past data, clubs can see where there is tangible value in employing new staff or reallocating funds to different departments.

Helps people who do not play sports themselves 

Even for those who do not play professional sports, the data created by sports clubs can benefit them. When professionals have access to how diet or training can change a person, they can apply that knowledge in their jobs. For example, a sports injury specialist can treat amateurs better because of the data gathered in professional sports.

AI & predictive analytics

Artificial intelligence and machine learning can create real life scenarios based on augmented reality, which in turn can be used for specific practice and training. 

Also, data analytics helps in knowing how a team can qualify for the playoffs or move ahead in the group, based on the current group standing and future matches. 

Leagues and broadcasters will often use predictive analytics (like sportsbetting) to choose the strongest line-up or to predict the outcome of other games.

Maximise Revenue

The budgets of sporting clubs are driven by a variety of factors, including payments for TV rights (usually managed by the league, with clubs receiving payments from them), ticket sales, and membership sales. Data analytics can help maximise each of them.

The popular platforms and tools for data analytics are: 

  • PowerBI for analysis and visualisations
  • R (as a mathematical and statistical analysis language), 
  • Python (for general purpose and sport specific programming), 
  • SQL (to fetch relevant data and analyse it)       

Sports is yet another field where data analytics is gaining popularity and it's slated to be used more and more in future. This can be a viable career and business option for people who have an inclination for Sports as well as Technology.