Within-field variability of turfgrass surface properties and athlete performance: Modeling their relationship using GPS and GIS technologies

Abstract

Surface properties of turfgrass athletic fields exhibit within-field variability. Wearable global positioning system athlete performance tracking units allow for the investigation of field impact on athlete performance. The purpose of this technical note is to present a case study introducing a methodology to model the effect of within-field variability on athlete performance using global positioning system and geographic information system technologies. Fifteen male collegiate club rugby athletes wore a global positioning system unit (10 Hz frequency sampling locational data) during two home games. Only athlete speed (m/s) was considered because measurements were georeferenced. Soil moisture, soil compaction, turfgrass quality, and surface hardness measurements were taken and georeferenced from the field prior to the games. The field’s boundary was digitized in a geographic information system and divided into 3 m² grid cells. Georeferenced data were imported to the geographic information system and underwent processes to calculate the team’s weighted mean speed and surface property variability scores in each grid cell for both games. Linear regressions were conducted with the data sets to determine the effect of within-field variability on team mean speed. Depending on the game, within-field variability of each measured surface property, as well as a few interactions, did significantly influence team speed. Future larger-scale studies can build upon the reported methodology to further investigate and validate these types of relationships. Coaches, trainers, athletes, and field managers could use this information to prepare for, or manage, turfgrass athletic fields in a way that better meets expectations and maximizes performance.