Changing Face Of Cricket, Experienced Through Social Data 02-17

Changing Face Of Cricket, Experienced Through Social Data

Sports is a passion for many across the globe. However, for Indians there is one sport in particular that is more than passion. It’s a religion that trumps all others and it’s called ‘Cricket’ – it’s a unifying force like no other. So why am I talking about Cricket given there’s so much information about this beautiful game already out there.

 Well, to start with the World Cup is currently on and the excitement all around is palpable. Of late, I’ve been intrigued about how Cricket is evolving for the viewer, organizer, players, advertisers, ground staff and everyone involved in the game. Yes, Cricket, Social Data and Analytics are coming together leading to possibilities never before imagined.

Cricket has always been a breeding ground for data buffs with the multitude of statistics discussed and debated before, during and after a cricket match. This traditionally has been done using static data to come up with interesting infographics, blogs, charts. Today, we live in a social world like never before with the power and magnitude of social displayed through our networks, impact and influence. With the advent of social networks and social media feeds, we have unstructured data sources which combined with analytics opens a whole new window of possibilities.

My social data gives me information on my favourite cricketers regularly based on what is said about them in the social world. What if this real time data is combined with sentiment analytics to help advertisers determine if a particular cricketer is marketable to a certain demography, segment, age group of viewers tuning in at a certain time and those are sure to lap up the advertisement? Advertisers would then know when a particular kind of viewer would most likely watch a particular advertisement and make changes real time to positively impact viewership.

What if social data combined with information from IBM CrowdTracker together helps the World Cup tournament organizers understand where are the long queues to enter the stadium. Once they have this insight, they can immediately open additional gates, ticket scanners, crowd control management personnel, merchandise selling personnel to ensure long queues are a thing of history through real time data. Additionally, using social media information on best routes can be shared with fans who are yet to arrive at the stadium thereby ensuring they avoid crowds.

IBM being a leader in the space of Social and Analytics is right in the thick of things having been associated with major sporting events such as ‘The Masters’, US Open, Wimbledon to engage the audience, helping organizers build momentum coming into a tournament. In fact at the ‘Australian Open’ this year, the IBM CrowdTracker on IBM Analytics provided an enhanced fan experience and helped the organizers improve their operations and business success.

With Watson analytics, IBM can now provide the cognitive analytics by understanding human interaction over social media to make informed suggestions based on unstructured social data. This also means fans can interact by asking questions to the Watson engine regarding the game, facilities, tournament. Organizers can prepare in advance, or make quick adjustments based on information coming out real time and lots more.

This year, IBM India has tied up with Wisden where IBM’s cutting edge Analytics capabilities combined with Wisden’s proprietary ‘Impact Index’ is providing unique insights & experiences during this World Cup for the fans and organizers. There is a lot for us as fans and IBMers to contribute here.

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What Social Data Can Tell You (And Why) 02-17

What Social Data Can Tell You (And Why)

Pretty much everything. In a nutshell.

Take this fascinating piece of research: Facebook's data scientists were able to determine a very particular pattern when couples were courting, and when they had started a relationship simply. How? Simply by looking at the frequency of status updates between two people.

In essence:

During the 100 days before the relationship starts, we observe a slow but steady increase in the number of timeline posts shared between the future couple. When the relationship starts ("day 0"), posts begin to decrease. We observe a peak of 1.67 posts per day 12 days before the relationship begins, and a lowest point of 1.53 posts per day 85 days into the relationship. Presumably, couples decide to spend more time together, courtship is off, and online interactions give way to more interactions in the physical world.

Social data are powerful

These predictions comes from a new class of data called "social data". Broadly speaking, social data is the data that people create when they use social platforms like Facebook, Pinterest or LinkedIn. It is our likes, pins, favourites, retweets, status messages, content of those messages, people we are friends with.

Social data is normally voluntarily and informally created by the individual during the act of using a social platform. It is voluntarily made public (or semi-public)on the platform in question. It reflects their ordinary course of business, the stuff we care about. It provides a picture which is explicitly incomplete. It can be viewed in aggregate or at an individual (although not necessarily prima facie personally identified).

Social data is new. No more than 10 or 15 years old, running back to the older social platforms (although arguably it stretches back to the earliest social protocols like Usenet).

It is also very powerful. And can predict much more about individuals and groups that the non-specialist can imagine.

Birds of a feather, that's why

Take the pattern of courtship to relationship discovered by Facebook's data scientists. This pattern emerges because of similarities in the way people behave as they go through similar life stages. We can all be different, but in aggregate we might be able to evince some underlying pattern. It is these underlying patterns which allow us to predict things which seem unlikely - like whether a couple of entering a phase of courtship.

I write 'unlikely' but it's really only 'unlikely' if you don't work with social data, or other behaviour data, and don't hang around with data scientists and other statistical types.

In truth, that we can predict courtship from signals like this is hardly at all surprising. We can predict other things too. These include wealth, gender, sexual orientation, even whether ones parent's divorced before you were an adult. All of those predictions were based on Facebook Like data - commonly acknowledged as quite noisy. (You can read an earlier blog post I wrote:Three Insights from Social Data which covers some of these predictions.)

For a moment think about that.

It's a brave new world

For companies, social data allows them to know who they are dealing with, well before that person becomes a customer. If a potential customer follows you on Twitter, you can build a pretty full picture of who they are, what they might like and what you might want them as a customer. Well before you decide to despatch advertising or a sales person towards them. For example, at PeerIndex we've helped companies modify their product offerings in near real-time on the strength of behaviours we've picked up in social data. IBM is helping companies identify customer's personality types based on social data - for better customer service.

For governments, social data allows you to understand the over-arching trends and themes in your jurisdiction. It allows for the early detection of themes that you might need to respond to. (Disease outbreaks? Or sociological changes in household behaviour?)

With the increase in people sharing data with companies - a subject I touched on a few weeks ago - such predictions will become more precise, more accurate and more relevant.

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