Sports, Analytics and Their Manifold Permutations

We are bam in the middle of 2018 and technology is in every tissue and cell of the things we see around us. We all understand and appreciate this, don’t we? But besides technology, there’s another important entity that we often fail to notice. That’s analytics. Analytics empower technology and inform the development of newer products.

We might not always make a conscious observation about it but analytics plays a major role in every industry, including the sports industry. Everything about professional sports is heavily dependent on analytics. Football, baseball, basketball, soccer, cricket – all modern sports benefit greatly from analytics.  

Sports Analytics: Data Lives Around Us and Vice Versa

There’s no denying that analytics form a huge part of the sports ecosystem around us. But how much of a vital factor analytics really is in sports? Are we using it for just the heck of it? Or is there some categorical productivity that we draw from analysis and data in sports? Let’s try and solve this bit first.    

Besides the numbers that are flashed on the scoreboard, every match produces a ton of data. These sets of data relate to team detailing, player information, performance insight, fitness scores, injury charts, and what have you. Each section of the team management and members of the support staff work with their individual sets of data.

For example, the physio would work with the data set that relates to injuries and healing. Each row and column in their sheet represent a number that has something to do with keeping the team fit and advising players on their weaknesses in terms of physicality. From there, this data goes on to power a lot of the individual nutrition management charts of sportspersons.     

Market Analytics and Hype Generation

Sports Analytics 2

Another set of analytics pertains directly to the fan following of players and teams in the market. These data sets help in the tracking and analysis of places, prices, people, promotions, and products. The learning from these charts of data translates into higher merchandise sales, greater team popularity, and regular updates for the followers at large.  

Toward the collection, segregation, and selection of all the data involved in the extended analysis, there is a bunch of analytical platforms that come to aid. As and when these platforms are used efficiently by the management, the market value of teams goes up.

Above and beyond teams and players, fans of different sports also create a whole lot of data every day. To facilitate this, we can make extended use of blockchain technology. The energy you use at your home, the hours you spend watching sports, the extra gadgets you buy for sports alone – everything generates important data. If all these data packets are stored securely over a distributed ledger, you will have a large computer file of information per follower of the sport.   

Perhaps the most crucial part that analytics plays in sports is in luring in new investors. Seasoned investors work with a team of analysts who do extensive studies of teams and find out the new scope of investments. In effect, this allows investors to pick the right teams at right times.  

FIFA is Riding High on AI Based Sports Analytics

As we are grinding through a crunch FIFA World Cup 2018, there’s some pro-grade data analytics playing alongside the sportspersons. These analyses bear significant implications on in-game strategy, team and player evaluation, sports science, and even opposition scouting.

Add to this the German team’s partnership with SAP in the 2014 FIFA World Cup to create insights that helped create strategies and understand opponents. Moreover, they even used data analytics to time and evaluate practice sessions of their own players.

That was four years back. In FIFA World Cup Russia 2018, we are actually looking at a wholesome measure of AI and machine learning partnering with data analytics to create an immersive fan experience. The New Zealand cricket team uses SAS while all kinds of sports analyses make use of data mining software. 

Furthermore, technologies that find abundant utility in sports data analysis include SQL technologies, Data Mining, and Machine Learning.

Inferential statistics are getting bigger and we continue to improve upon computing of all types. As this happens, especially relevant devices are being employed to seize on player detailing. Hence, it is important that cloud services, powerful processors, and embedded technology must work together to make this happen.     

Today, the demand for professionals with skill and experience in data analytics and particularly analytics for sports. At Futran Solutions, we work with a pool of talented professionals with interdisciplinary experience in data analytics, machine learning, and data mining software. Contact us today to know more about our solutions for data analytics and sports data analytics.