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Haydn Masters, Athletic Development Coach, NSW Waratahs Rugby

Haydn Masters, Athletic Development Coach, NSW Waratahs Rugby

By Haydn Masters

Rugby is a tough, physically demanding sport.  In one game each player makes 20-40 high-impact tackles. Typically one in four players is injured during the season. That is twenty-five percent of the NSW Waratahs thirty-five player squad not available to play. Last season players missed a total of 73 games.

As Athletic Development Coach, it’s my mission to protect and get the most from the club’s most vital assets – our players. It also is my job to improve their athletic performance, increase injury resilience, ensure we field the best team each game – giving the club the best chance at victory. With the 2014 Australian Rugby season fast approaching, what are we doing to manage this risk to the our business?

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In an Australian-first project, the NSW Waratahs are embracing the power of big data, and using IBM predictive analytics technology to provide answers and insights to questions that have previously been unanswered.Each week, we collect between 100-250 data variables per player.

NSW Waratahs Rugby's Tala Gray works out while coach Hadyn observes.

NSW Waratahs Rugby’s Tala Gray works out while coach Hadyn observes.

This data comes from multiple sources.  A GPS tracker is fitted to each player to measure and monitor intensity levels, collisions, and fatigue during training and matches, and is combined with data from medical screenings, wellness reports and player stats.

Analyzing this data allows us to build a clear picture of what’s really going on; finding patterns of where preventable injuries occurred; identifying the early warning signs; and how we could prevent them. These predictive insights give us a critical opportunity to anticipate an injury and change the variables, including modifying the training regime, or resting a player, to make the chance of injury much less likely.

SP Holloway, Alofa and Crawford

To test the accuracy of the model, IBM analyzed retrospective data from players.  The results were really very compelling. The analysis identified three players who went on to sustain an injury in the following weeks.  It was at this point that both parties were sure the right data points and model had been created to make a real difference to the Waratahs performance.

With predictive data we can now predict and act, rather than sense and respond to the requirements of players, we can train harder and smarter, with the intention of directly improving the Waratahs on-field performance in 2014.
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