Endurance Training/Cardiorespiratory
Max R. DiPierro
Master's Student
Kent State University
Lyndhurst, Ohio, United States
Ryan W. Gant, MS, CSCS
Doctoral Student
Kent State University
Kent, Ohio, United States
Clayton Lavigne
Assistant Strength and Conditioning Coach
Kent State University
Kent, Ohio, United States
Adam R. Jajtner, PhD
Associate Professor
Kent State University
Kent, Ohio, United States
Meghan K. Magee, PhD, CSCS
Assistant Professor
Kent State University
Kent, Ohio, United States
Soccer fields vary in composition, with different surfaces purported to influence player performance.
Purpose: To compare various measurements of performance metrics in collegiate female soccer players during competitions on natural grass and artificial turf surfaces.
Methods: Global Positioning Devices (GPS) were worn by players on a D-I Midwestern Women’s Soccer Team during 18 matches throughout the 2023 season. Match data were taken from 10 away matches and segmented into competitions on natural grass (n = 5) or artificial turf (n=5). Data were then further split into first and second halves of matches. GPS data were assessed for the total number of sprints (# sprints), sprint distance (in meters), time spent in low (LSR; < 15km/hr.) and high-speed (HSR; ≥ 15 km/hr.) running zones, low (LHR; < 80% HRR) and high (HHR; ≥ 80% HRR) heart rate zones, total distance (in meters), and training load (TL). Measurements were adjusted to be relative to the number of minutes played, with only players averaging more than 20 min per game throughout the season included in the analysis. A 2x2 (surface x half) repeated measures analysis of variance was employed to assess differences within matches. Additionally, significant interactions were further teased apart by using least significant differences (LSD). Alpha (ɑ) was set to 0.05.
Results: Data indicate an interaction for total distance (F = 21.887, p < 0.001) showing less distance covered on natural grass during the 1st half (p = 0.008), while reductions in total distance from the 1st to 2nd half on natural grass (p = 0.043) and turf (p < 0.001) were also observed. Differences were observed for time spent in LHR (F = 26.019, p < 0.001). with more time was spent in LHR zones during natural grass matches than turf during the 1st half (p < 0.001). Differences were also observed for time spent in HHR zones (F = 7.416, p = 0.021) with more time spent in HHR zones on matches played on artificial turf compared to grass during the 1st half (p = 0.012). Comparatively, more time spent in HHR zones was observed during grass matches compared to artificial turf during the 2nd half of games (p = 0.033). An interaction was observed for time spent in LSR zones (F = 14.536, p = 0.002), which revealed more time spent in LSR zones during grass matches compared to artificial turf matches during the 1st half (p = 0.041), with a significant difference in time spent in LSR zones during grass matches compared to artificial turf matches during the 2nd half (p = 0.034). Differences were also observed for time spent in HSR zones (F = 11.775, p = 0.005) with matches on artificial turf resulting in more time spent in HSR zones in the 1st half (p = 0.004).
Conclusion: Data suggest increased workload during matches played on natural grass when compared to artificial turf. PRACTICAL APPLICATION: Coaches can use these data to adjust training loads prior to matches as match surface can influence workloads, and thereby influence performance.
Acknowledgements: None