Over the past decade sports have taken analytics to heart, using its tools to bring a more scientific approach to tactics, player management and fan engagement. The use of analytics is quickly becoming a prerequisite for success in the fiercely competitive world of professional sport, with teams as diverse as the St. Louis Cardinals baseball team and Chelsea football club working through tens of thousands of data points to help form their strategies.
As sports become more competitive, the difference between winning and losing can be measured in fractions and one area that has a significant impact is the product at the centre of the game. This can either be mechanical e.g. an F1 racing car or human i.e. the sports player themselves. Taking the former, the vehicle that arrives at the start grid is the output of years of development in wind tunnels, computer simulations and the test track itself where terabytes of information have to be analysed to establish the factors that give an edge.
Once on the track on-going real time monitoring of analytics ensure that edge can be maintained. An example on the human side is that NFL players are now wearing biometric clothing where every aspect of their game can be assessed to provide insight as to how they can change the way they play or provide input to optimise their training regimes i.e. improving the product.
Similarly at the heart of the business is the product or commodity it deals in and markets are now getting crowded and competition fierce. Those companies that can learn from the cutting edge analytics in sports to reduce time to market with shorter product cycles, react early to changes in market conditions or respond to telemetric or sensor feedback to improve operational efficiency will, like all successful teams, rise to the top of their game.
So, as rugby fans sit down to watch The Lions Tour, the question for many businesses and IT decision makers should be 'what can we learn from the use of analytics in sports to help enhance our own performance?'
In order to ensure a productive team, existing talent has to be nurtured and new talent has to be discovered to allow the continual replenishment of skill. Whilst the current players are a known entity and teams have the control over which metrics can be captured and the depth of analysis that can be undertaken, the identification of new players provides more of an analytical challenge.
Two options exist: the purchase of a known entity, which commands a high price and the purchase of a rookie with potential that comes with a lower price point. The role of analytics is to identify that potential, reduce the risk of a poor performer and give the benefit of a lower overhead. Nowhere is this better illustrated than the example of the Oakland A's or the Moneyball Team.
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