Articles
ANALYSIS OF SOCCER FIELD SURFACE HARDNESS
Article number
661_36
Pages
287 – 294
Language
English
Abstract
Soccer ball reactions and player performance are dependent on an athletic fields surface hardness, uniformity, and smoothness.
Spatial variation due to wear patterns can be a confounding factor for experimental designs on athletic fields.
This research was undertaken to spatially provide objective quantitative values of athletic field hardness.
A Clegg Impact Soil Tester was used to evaluate surface hardness at 80 specific locations on two US soccer fields on a regular basis over a two year period.
Significant differences were noted within and between fields.
Uniformity on the sand-based field was high (average difference = 30%) but hardness in areas of high traffic could be distinguished and spatially mapped using these methods.
Hardness differences within the native soil based field ranged from 51 to 96% depending on month sampled.
Geostatistical estimates indicated spatial relatedness can bridge the entire field.
Spatial analysis and graphical evaluations indicated that extrinsic factors played a significant role in field variability.
All three analysis techniques proved to be useful for classifying areas based on surface response as well as determining field consistency.
To a certain extent, the methods complement each other in providing different kinds of inferences from the same dataset.
Spatial variation due to wear patterns can be a confounding factor for experimental designs on athletic fields.
This research was undertaken to spatially provide objective quantitative values of athletic field hardness.
A Clegg Impact Soil Tester was used to evaluate surface hardness at 80 specific locations on two US soccer fields on a regular basis over a two year period.
Significant differences were noted within and between fields.
Uniformity on the sand-based field was high (average difference = 30%) but hardness in areas of high traffic could be distinguished and spatially mapped using these methods.
Hardness differences within the native soil based field ranged from 51 to 96% depending on month sampled.
Geostatistical estimates indicated spatial relatedness can bridge the entire field.
Spatial analysis and graphical evaluations indicated that extrinsic factors played a significant role in field variability.
All three analysis techniques proved to be useful for classifying areas based on surface response as well as determining field consistency.
To a certain extent, the methods complement each other in providing different kinds of inferences from the same dataset.
Authors
G.L. Miller
Keywords
spatial analysis, uniformity, statistics, geostatistics, semivariance, kriging
Online Articles (78)