23 JUNE 2014

How is football an analytics problem?

A football field is a telecom eco-system meme. With teams to be managed like networks, players as key elements, and fan bases as subscribers. A team is in all aspect a business, governed by rules of performance and profitability – with the notable exception that their fans hardly ever churn.

Coaches and managers 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, make it a big data problem. Strategic decisions are based on metrics that are generated from statistics derived on historical activities: how many passes, goals, tackles did a player achieve in one game? What tactic brought success in front of which team? What were the social media reactions to which game? And how did that affect sales of derived products and impact the team’s bottom line?

Applying analytics to player selection, for instance, which is key to the success and market value of the team, coaches are able to predict the performance of players, and make strategic decision so as to which athlete they should hire, and what/whom they should give up on to do so. Performance, team cohesion, values to the fans, costs, assets of the opponents, these are factors that can be computed and analyzed. Based on the processing of this data, the value of players and the strategies of coaches can be measured, while patterns for success can be discovered: how to move on the field?, who to select?, at which position?, how to play against whom?, what player has the most value to the fans, and brings the most revenues? Thanks to these reports, coaches can optimize selections, trainings, tactics, and performance.

All these questions are a paradigm away from those that network engineering and financial officers ask themselves. Like in all analytics problem, the success of the project depends on the contextualization of the information, and the depth of the subject matter expertise needed to draw well informed and valuable conclusions. For CSPs, the challenge is to couple big data processing capabilities and telecom expertise so as to extract the maximum amount of information.

Making the best decisions to retain customers, maintain cash flows, and optimize network just in-time at the most cost-effective expense, can only be achieved by attributing the right scores and metrics to the parameters that populate our ecosystem. Analytics is not a strategy, but an enabler for financial, technical and customer optimization.

With CSPs facing daily the dynamism, challenges and fierce competition that world class teams meet every four years at the world cup, the insight power brought by analytics is the dribble needed to make operators leaders in their market.