Putting Predictive Analytics Into Play

By Samuel Greengard Print this article Print

The Leicester Tigers rugby team uses predictive analytics to leverage data about the physical condition of its players in order to prevent injuries and enhance performance.

By Samuel Greengard

The list of uses for predictive analytics seems to expand daily. Businesses and government are applying it in areas as diverse as health care, customer retention, energy conservation and crime prediction. Although sports organizations have tapped analytics for years—the book and film Moneyball highlights the use of analytics to make player decisions in major league baseball in the 1990s—software and applications are becoming more sophisticated and widely deployed.

Enter professional rugby, one of the fastest growing sports in the United States. It is known as a brutal contact sport with minimal protective gear. In fact, about one-quarter of all players wind up hurt during a typical season, and some suffer season-ending injuries. For these players, the injuries represent an incredibly frustrating situation. For the teams, the absence of key players on the field frequently results in lost games, as well as diminished ticket sales and attendance.

But teams such as the Leicester Tigers in the United Kingdom are now embracing predictive analytics in order to conduct deep analysis of raw injury data and statistics. "Sport is no longer just a game; it's becoming more and more a scientific undertaking that is driven by data and numbers," notes Jeremy Shaw, IBM's business analytics lead for Media and Entertainment. "Gone are the days of relying on raw talent and gut instinct to succeed."

IBM is working with the Leicaster Tigers team to develop more efficient ways to understand why injuries occur and how the organization can reduce them. Analysts are studying a variety of factors, including fatigue, threshold and game intensity levels to detect hidden patterns and anomalies that provide insights into who might wind up injured and what types of injuries could take place.

For example, if a player displays a statistically significant change in one or more of his fatigue parameters and the current intensity level of training is high, the data might indicate that there's an 80 percent greater chance of suffering an injury. Someone else on the team might register a 60 percent greater risk. This level of real-time information makes it possible for the team to alter a player's training regimen or substitution pattern in games in order to reduce the risk of injury.

But the analytics capabilities don't stop there. The technology also allows the Tigers to analyze psychological player data to reveal other key factors that could affect performance. This list includes the additional stress of playing on the road, as well as social or environmental elements that could effect the way players perform during a match. The end game is to create custom training programs tailored to each player's physical and psychological state.

The concept is scoring big points. "There is a tremendous value to be gained by retaining experienced players within the squad," explains Andrew Shelton, head of sports science for the Leicester Tigers. By adopting predictive analytics, "Our team, for the first time, will be able to leverage data about the physical condition of players and considerably enhance our performance."  

This article was originally published on 2012-05-02
Samuel Greengard is a freelance writer for Baseline.
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