Elite and expert athletes constantly push the boundary of human performance.   Regardless of their sport, balance is a fundamental skill that can make or break an athlete's success.

        Researchers set out to unpack the mystery of balance.  By closely studying balance abilities, they aimed to find a reliable way to measure and compare elite and expert athletes in a way that doesn't require expensive equipment and procedures easy to do anywhere.

        Researchers focused on four key factors:  Anterior-posterior (AP) displacement, medial-lateral displacement, length, and tilt angle.  These factors were carefully monitored during 30-second tests.  Each athlete had chances to stand on both soft and hard surfaces.  They also tried balancing with eyes open and eyes closed to mix things up.  This set-up gave scientists a wide range of data to work with.  Elite athletes tend to show more stable balance on uneven or hard surfaces to the extent that they do not register enough of a change in displacement.  Experts tend to need training to get results that elite athletes get without.

        The problem with this method is that it doesn't show the full picture or complexity of how a persons body and mind work together to make sure they stay balanced.  Researchers also calculated a metric called the Composite Multiscale Complexity Index but the tests and results did not show any improvement when used with one time domain or measured another time or mixed measurements.

        This study used data from 13 elite athletes and 12 expert athletes to test if using these metrics could tell the difference between the two.  Each test had weight changes to help see if these variables indicated balance.  This data was then processed through four different machine learning algorithms to see which one could best separate expert from elite athletes.

        Machine learning has become a valuable tool in sports science.  By analyzing vast amounts of data, these algorithms can identify patterns and make predictions that might not be obvious to humans.  But what does this mean for athletes?  What are the implications of being able to accurately assess balance abilities?  The importance of balance can not be overstated.

        Results show that traditional time-domain features were not enough to accurately assess the athletes' balance abilities.  Expectations were that CMCI coupled with Ranked Forests technology would work best.  Ranked Forests are a special type of decision tree that can handle nonlinear data.  And that is exactly what this study needed.  By processing the data in a way that would accept any possible number from 0 and up they were much more effective at being able to predict the balance ability of any athlete.

        Other things to consider are the range of sporting activities that involve balance, coordination, and agility.  The variations in balance between certain sports are significant.

        Another study used a different machine learning algorithm to predict the outcomes of various sports based on balance.  The outcomes in sports like snowboarding, ice skating, rock climbing and other sports have been difficult to predict.   These are complex sports and require a high level of balance and coordination in sport scientists.

        Ranked Forests and its ability to process non-Linear systems and data makes it a good fit to determine the balance ability of an athlete.

Navigating Balance: New Insights into Elite Athletes' Skills

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