Modeling within-field variability of turfgrass surface properties and athlete performance

Published: 09-07-2019| Version 1 | DOI: 10.17632/2j366rhvtc.1
Contributors:
Chase Straw,
Emily Kurtz

Description

Surface properties of turfgrass sports fields exhibit within-field variability. Wearable global positioning system (GPS) athlete performance tracking units offer the possibility to investigate its impact on performance. These data and R code are used as an example to model the relationship between within-field variability and athlete performance; specifically, speed. Data were collected on one home field from two games involving a collegiate club rugby team. Athlete speed data were collected from GPS athlete performance tracking units worn by the participating athletes during the two games. Soil moisture, soil compaction, surface hardness, and turfgrass quality were measured with GPS-equipped sampling devices from the field prior to each game. Using a geographic information system (GIS), the field’s boundary was digitized and divided into 3x3 m grid cells that were each assigned an ID number. Athlete speed and field data were further manipulated in the GIS to calculate average athlete speed and field property variability scores (-3 to 3, where -3 indicates the lowest valued areas in the field and 3 indicates the highest valued areas in the field) in each grid cell both games. The end result were spreadsheets from each game that contain columns with grid cell ID numbers, data point counts and mean speed of each athlete, and variability scores for each surface property for all cells each game. Data point counts indicated athletes’ time spent within a cell. Counts were used to determine weights for calculating the team’s weighted mean speed in each cell both games, where those who spent the most time in a cell had more weight in the cell’s mean speed. The field was also further divided into larger sections to account for areas that may receive varying amounts of play. Linear regressions were conducted to analyze the data. In the models, a 1-unit increase in field property variability score will correspond to an increase (positive coefficient) or decrease (negative coefficient) in team speed. Depending on the game, within-field variability of each measured surface property, as well as a few interactions, did significantly influence team speed. Athletes location on the field also influenced team speed. This is information is useful for understanding the impact of field conditions on athlete performance. It can be used by coaches, trainers, athletes, and field managers to better prepare for, or manage, turfgrass sports fields.

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