Engineering Physiological Fitness of Collegiate Field Hockey Players
2015 AIChE Annual Meeting
Margaret G. Graves, Jeremy A. Cook, Kathleen A. Bieryla and Ryan C. Snyder
Fitness tracking metrics and sports analytics have become commonplace in high profile professional and Olympic level sports; however, these methods are still in their infancy for collegiate level non-revenue sports such as field hockey. The National Collegiate Athletic Association has strict guidelines when it comes to the definition of “in-season” that forces players and coaches to pack fitness training and athletic competitions into a small window of time. During this time, some players have more field time during games than others players, amounting to up to four hours a week of additional cardiovascular and muscular exercise in comparison to other players. This work begins to look at the impact of differences in exercise load as well as other behaviors on players’ fitness.
The work presented here focuses on four areas of interest regarding player fitness and performance (1) a reactive strength index, (2) hear rate desaturation, (3) training impact (TRIMP) and perceived exertion as well as (4) sleep. The reactive strength index is calculated as the ratio of flight time to contact time when performing a drop jump technique. The subject steps from a box onto a force plate, jumps upwards as quickly as possible without bending the knees, and lands back on the force plate. Data on heart rate desaturation is collected using heart rate monitors worn around the torso. The subjects undergo cardiovascular activity meant to get the heart rate close to maximum and then all subjects stop to lower their heart rate. This data is fit to a first order differential equation and the parameters from the equation are used to assess cardiovascular fitness. TRIMP is calculated according to time in each heart rate zone (% of maximum heart rate) and is a metric of physical exertion. Perceived exertion is a self-reported measurement using the Borg Scale of Perceived Exertion and is used to measure how hard each subject believes they are working. Sleep quality and quantity, as well as approximate time to fall asleep are recorded daily by each subject. For each of these categories, both individual and aggregate data will be presented. At the conclusion of the season, this data will be combined with pre- and post-season fitness test data. Statistical analyses will be performed to draw more detailed conclusions from the data.
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