The Telecom Football Field

The general who wins a battle makes many calculations before the battle is fought. The general who loses a battle makes but few calculations beforehand. Thus while a lot of calculations lead to victory,  too few lead to defeat: By attention to this point I can foresee who is likely to win or lose.”
– Sun Tzu, ancient Chinese philosopher

Imagine that you are watching an intense football match. Suddenly things start looking very different...

It is not a football field; it is a telecom ecosystem. It is not a team like Arsenal or Real Madrid; it is a telecom service provider like Airtel or Reliance. We aren’t talking of Mesut Ozil or Cristiano Ronaldo; we are talking of key telecom network elements. These are not football fans, these are telecom subscribers.

Like a telecom service provider, a football team too is a business, governed by rules of performance and profitability. With, however, one happy exception; fans hardly ever churn! An Arsenal fan would seldom commit the ultimate sin of becoming a Manchester United supporter.

Like telecom service provider managers, football coaches too have to define strategies that make sense from both a game performance and a business value perspective. The volume, variety, and complexity of elements to be taken into account while establishing game plans – whether in telecom or football – are similar to, what we call today, a Big Data problem.

While Germany wildly celebrated its World Cup victory, there was already serious discussion on what made this success possible – and many analysts believe that Big Data analytics made a big difference.

For far too long, football – unlike cricket – wasn’t considered to be a statistical beast.  The only numbers that mattered were something like: Germany 1, Argentina 0. But look at the plethora of numbers that swamp us now in a football match: Fabregas ran 11.7 km during his 80-minute stint on the football field; Barcelona enjoyed 69 per cent possession in their 2-1 defeat to Real Madrid (is there a counter-intuitive story here about possession being negatively correlated to victory?); or this current best-selling story that Germany won the World Cup because their passes were more rapid than those of other teams.

But what is really causing the Big Data wave in football is the use of video footage analytics. What is a player’s preference while taking penalties? Which player is capable of attaining the maximum height as he attempts to head from a corner kick? Is a team like France more vulnerable to a counter-attack via flanks than from the middle? Does van Persie have a higher probability of being caught offside than a Suarez? What body feints of a Robben or a Rooney are indicative of willful dives?

All these questions have been perennially discussed in pubs and in adda sessions – but analytics now allows us to quantify such observation and insights, it allows us to model, it allows us to do big data simulations of ‘what-if’ scenarios, and it allows us to compute probabilities. It also provides invaluable data to betting agencies – but that is another story!

Big Data brings in an exciting new dimension by using social network analytics. Who are the players who bring in the largest number of spectators, and maximize the number of TV eyeballs? Does advertising underwear or biting your opponent increase your personal valuation?

For a telecom service providers there is a strong sense of déjà vu. All telecom service providers (football teams) have access to the same network elements (players). Why then are some service providers so much more profitable than the others? (Why does Manchester United win cups so much more often than Arsenal?). How does a telecom service provider offer the best revenue or margin assurance (buy the best suited players for the team at the best price)? How does he optimise his call routing algorithms (how does a football team extract the best advantage from rapid and timely passing), how does he manage his network faults and performance (how does Barcelona ensure that Messi stays fit the whole year), how does he optimize his bandwidth and spectrum utilization (how does a team juggle its mid-fielders  to play both attacking and defensive roles), how does he negotiate offset terms (how does a football team offer his excess players on loan to other teams).

Some of these comparisons may seem tenuous, but surely readers are getting the drift. The insight that analytics offer is just the dribble needed to make telecom service providers (football teams) the leaders in their markets and playing fields.

The author, Srinivas Bhogle, is director and country head at TEOCO, a company with  $200 million turnover, which provides analytics solutions to 300 Communication Service Providers in more than 100 countries