May 12, 2014

    Trajectory in action: How to put your data to use

    This case study comes to us from one of our our partners who uses SpartaTrac™ software along with a force plate to monitor and evaluate their athletes. In our post two weeks ago, we discussed the importance of collecting time series data so that the effects of an an athletes’ program can be evaluated as a trajectory. This post will show how useful that data can really be.

    The athlete is a collegiate female basketball player at a top 25 program. The Sparta Signature scans below are from late 2012, and as you can see are drastically different. Even more alarming is that if you play close attention to the dates, you can see that the scans are taken a little over a month apart! Any time we see a t score change more than 15 points from one scan to another it raises a red flag that this athlete is at serious risk for injury. In her case, the t score for LOAD had dropped 18 points. LOAD is a great indicator of an athletes ability to brace and create stiffness, so when we see a large drop in LOAD it tells us that athlete has lost her ability to stop her momentum. She tore her ACL on January 1st, 2013.

    college bball_2012

    Flash forward one year (after surgery and rehab) and lets look at her movement signature scans from late 2013 (below). Again we see a large drop in the t score for LOAD, this time 33 points in less than a month! First, we need to take a step back and think about why we are seeing a chronic loss is LOAD at that time of year. The college basketball season starts in early November. These two scans represent the first full month of the season. One of the great things about the Sparta Signature is that it really is a window into how each athletes’ nervous system is functioning. Because of that, it is great at showing how an athlete moves differently when they are fatigued. Ideally, an athletes’ signature doesn’t change from when they are rested and fresh to tired  and fatigued. In fact, we purposely scan our MLB starting pitchers when they are fatigued so that we can see if there are any changes in their movement signature, a great indicator of whether they are ready to pitch deep into games.

    college bball_2013bball feb scan

    Whether it is the challenge of going “game speed,” increased travel, stress of finals at the end of the year, or a combination of all those things, this female basketball player is just not handling the start of the season well. This season, the basketball coaching staff and strength team were able to work together and reduce her training volume by 50% during the second half of December and January. It is important to note that they did not change WHAT she was doing, just HOW MUCH. As we discussed in the post two weeks ago, you have to isolate a change (variable) to understand if that change had the desired effect. The result was that she stayed healthy and on the court. If we look at her signature from February of 2014 (on the right) we see that her LOAD returned to a normal level. In this case, having time series data at their disposal saved our partner and this athlete another potential injury.

    Tag(s): Sports , Monitoring

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