“You don’t put a team together with a computer” said Grady Fuson to Billy Beane in the 2011 movie Moneyball. “Baseball isn’t just numbers. It’s not science. If it was, anybody could do what we do, but they can’t because they don’t know what we know. They don’t have our experience and they don’t have our intuition.”
It may be a quote straight out of the Hollywood playbook, but Moneyball is based on author Michael Lewis’ non-fiction work Moneyball: The Art of Winning an Unfair Game, which tracks the Oakland Athletics baseball team’s ascent under general manager Billy Beane and his use of an analytical, evidence-based,sabermetric approach to assembling a competitive team.
The probability of success
Once a novel approach, data-led team selection is now a commonly deployed tactic in many sports. Similarly, technology and data analysis are being adopted by businesses to recruit personnel, select and form teams, and inform strategic thinking. So what are the advantages to using a data-centric approach in both disciplines?
Jonathan Leeder, physiologist at the English Institute of Sport explains that increasing the probability of success by finding areas for marginal gains is the whole point of data analysis. “You can’t guarantee success, but you can increase the odds of it,” he says.
In team sports in particular, technology and data is used to aid team selection, in addition to shaping training and tactics. Bill Gerrard, professor of business and sports analytics at Leeds University, is perhaps better known for his collaboration with Moneyball’s Beane around football, or what Beane might refer to as soccer. He explains how using technology and data helps to “establish a culture of evidence-based decisions which forces coaches to consider all of the factors involved in a decision on team selection, to clarify what the evidence is on each factor, and to make explicit the relative importance of different factors.”
So using data to inform team selection provides a more comprehensive view of likely outcomes. The more you can understand what a player’s peak performance levels are as a benchmark, the more you can assess their condition and readiness for a match. It stands to reason that if you aren’t using the data capabilities open to you, your competitors will be, so the likelihood is you will be behind before you’ve started.
Indeed, data-led team selection has become so integral to strategy and tactics in rugby that the RFU declined to comment for this article. Both the England and Ireland camps cited the “sensitive nature of the metrics” as the reason.
Data analysis, says Gerrard, has changed the tactics deployed in the Six Nations. “It has helped teams identify their strengths and weaknesses and clarify the critical success factors, some of which can be surprising.”
In the business world, data analysis and metrics-led recruitment is also gaining momentum. James Webb is the managing director at Propel London, a recruitment consultancy specialising in technology and digital. Performance data, he says, “plays a key role in a sales person, for example, getting through the door to an interview.”
Scott Ross, chief technology officer at global marketing and technology agency,DigitasLBi, explains that the recruitment market moves so fast that “the more we can pre-qualify our candidates, the more likely we are to find talent within their ever-shortening window of availability.”
People, not robots
Data analysis in sport has its limitations, as it does in recruitment. “Some key aspects of player performance are not particularly amenable to analysis,” says Gerrard. “How to blend together a group of individual players into a team so that the whole is more than the sum of the parts is more a matter of judgement than analysis,” he continues.
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