An Introduction to Big Data And Analytics In Sports

Ever wondered how digital platforms are influencing the sports viewership and engagement across the globe? Read the post to know more about big data and analytics in sports.


No matter how thirsty you are at the ocean; it could never quench your thirst. The concept of “Big data” is similar, somewhat! You may have large amount of data at disposal but what’s the use if you cannot make sense out of it. Such humongous data could provide insights, only if converted to meaningful format.

Here’s the most authoritative definition recently updated on Oxford English Dictionary (OED);
Data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges.  However, I would like to define big data using four Vs of big data as follows;

Big data can be considered as a collection of large (volume) of streaming data sets that may not be accurate (veracity) produced at tremendous speed (velocity) originating from different (varied) structured and unstructured data sources.

Too complex to understand? Lets make it simple.

Big data is considered to be the large amount of information on internet that we generate and consume daily in form of text, video, images, live data streams via websites, mobile apps, email, tweets, status updates etc.

What makes this daily information so special to be called as big data; is it’s size, form and tremendous speed by which it is being generated.

What does OED mean by saying “logistical challenge”?

Database may refer to physical location where all our “digital assets” are stored in “digital data” format. Most of our daily transactional data is generally stored neatly in these database systems. Here, transactional data refers to records of our online transactions like shopping transactions, bank transactions, social media updates, emails etc.

Big data was never a buzz word until the world entered in digital arena with advanced mobile devices, high speed internet and technology advancements. Demand for rich media content including videos, images and online streaming data has increased tremendously over past few years.

For instance, the FIFA World Cup 2014 witnessed 250% increase in the live streaming. It was also estimated that 87% of the traffic for online viewership came from the mobile devices. This shift of sports viewership on mobile devices has increased the demand for digital content; thereby providing impetus to big data and analytics in sports.

In a typical sports tournament the big data is supposed to be generated by the sports associations, sports organizers, event sponsors, broadcasters, online publishers, teams, leagues, players, viewers and last but not the least you!

I am sure, if you are real sports fan, nothing could stop you from “tweeting” your emotions on social media sites using photos or videos. It was observed that around 32 million tweets were generated during the FIFA world cup 2014 final match. For more information, read my previous post on social media statistics for world cup final 2014.

Even the recent ICC Cricket world cup 2015 has proved that twitter is the second screen for many cricket enthusiasts. It was observed that, IBM’s ScoreWithData campaign on Twitter was able to increase the level of excitement and engagement with fans. After analyzing millions of tweets and hastags on Twitter, IBM derived some insights on social sentiments, win-loss probabilities and team / player metrics.

Such analytics in sports could help build winning strategies; just how SAP and the German Football association did to improve player performance at the World Cup in Brazil.

According to German national football team manager,
In just 10 minutes, 10 players with three balls can produce over 7 million data points. 
The data points are collected with respect to players performance to measure movement, distance and speed involved in the game. Collective data points of all the players gives individual performances which resonates the team performance. This data accumulated via advanced capturing technology is analysed to reveal patterns, trends and associations.

Importance of big data and analytics in sports is perfectly epitomized by German team in the recent FIFA 2014 world cup matches.

The German team used SAP Match Insights tool powered with the SAP HANA platform. It enabled team to analyze video footage of several matches and thereby develop a customized training for “informed decisions” in all matches.

Using Match Insights, the team was able to analyze that “speed” was the key to win over opponents. After data analysis the team succeeded in cutting down the the average possession time from 3.4 seconds to about 1.1 seconds.

I hope at this point, you must have captured a gist on big data analytics in sports. Any information on big data is too small to explain how big it is. Stay tuned for my next post to understand how big is big data and how much data we produce, everyday.

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