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How Variability Within and Between Natural Turfgrass and Synthetic Athletic Fields Impacts Athlete Safety and Performance
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Welcome to The Turf Zone Podcast. This episode features the article “How Variability Within and Between Natural Turfgrass and Synthetic Athletic Fields Impacts Athlete Safety and Performance” written by Ava Veith, Dr. David McCall, Dr. Chase Straw, Dr. Daniel Sandor, Dr. Jay Williams, Elisabeth Kitchen, Kevin Hensler, Aaron Tucker and Dr. Caleb Henderson
Authors Note and Context
Ava Veith is a Ph.D. student in the Department of Plant Science at Penn State University under the advisement of Dr. Chase Straw, where her research focuses on studying within-field variability and athlete–surface interactions. However, the research presented in this article was conducted during her master’s program at Virginia Tech under Dr. David McCall.
This study served as a foundational investigation into how variability within and between natural turfgrass and synthetic turf athletic fields influences athletes. The findings from this work have shaped the direction of subsequent doctoral research. Building on this foundation, the planned Ph.D. project aims to examine athlete lower-limb joint biomechanics across natural turfgrass, synthetic turf, and hybrid (natural turfgrass reinforced with synthetic fibers) surfaces using multi-segment inertial measurement units.
At the conclusion of this article, the next phase of research will be briefly outlined to demonstrate how it has grown from the master’s study. In this way, the Virginia Tech study presented here represents both a completed project and the starting point for a broader, ongoing effort to better understand how the playing surface can affect athlete movement and injury-relevant mechanics.
Introduction
A safe playing surface is essential for athletic competition. Natural turfgrass and synthetic turf are common playing surfaces used for field sports, and extensive research has been conducted to compare these two surface types. However, limited attention has been given to within-field variability and its impact on athlete safety and performance. Studies often classify athletic fields broadly as synthetic or natural, overlooking critical surface metrics that fluctuate both within and between fields.
Key field characteristics such as surface hardness, rotational resistance, soil moisture, thatch depth, and infill depth (for synthetic fields) play a crucial role in assessing field quality. Variability in these factors can be influenced by environmental conditions, management practices, and field usage patterns. Despite the known importance of these factors, current research often fails to account for field-specific inconsistencies, limiting the effectiveness of broad comparisons between surfaces.
To improve field safety and optimize athlete performance, interdisciplinary collaboration among turfgrass scientists, sports scientists, and sports medicine professionals is necessary. Evidence-based field management strategies must be developed to ensure more consistent playing conditions, reducing the risk of injury. Wearable technologies such as STATSports GPS trackers (STATSports, 2025) and ankle inertial measurement units (IMUs) (IMeasureU, 2019) provide critical insights into athlete biomechanics, load monitoring, and more. These technologies allow researchers to quantify how different surface conditions influence athletes during performance, offering valuable data for injury prevention strategies.
Beyond data collected by wearable technologies, athlete perceptions of field conditions also play a role in performance and injury risk. Unpredictable surface variability can affect player confidence, movement efficiency, and risk-taking behaviors, making perception-based data collection essential. Understanding how athletes experience and perceive different playing surfaces can inform future improvements in field construction and maintenance.
The objective of this study is to quantify the impact of surface variability on athlete safety and performance, both within and between natural turfgrass and synthetic turf surfaces. This research will quantify how variations in key surface metrics, including surface hardness, rotational resistance, soil moisture, thatch depth, and infill depth, affect athletes utilizing data from wearable technologies, such as STATSports GPS trackers and ankle IMUs. Additionally, to further understand the influence of field surfaces, athletes will be surveyed before and after performing drills to gather insights into their perceptions of how surface variability impacts their performance.
Methodology
Athletic Fields Tested
This research was conducted in August of 2024, where four athletic fields on the Virginia Tech campus in Blacksburg, Virginia were studied. Two of these fields were natural turfgrass (bermudagrass), while the other two fields were synthetic turf. For both field types, one field was classified as ‘low usage’, while the other was classified as ‘high usage’. This was determined based on traffic frequency, field age, and management practices.
Preliminary Data Collection
Before live athletes were introduced, surface hardness was assessed on all four fields using a Clegg hammer, with 100 measurements collected per field. The data were then analyzed using ArcGIS Pro to generate surface hardness heatmaps, highlighting variability between and within each field. These maps allowed us to identify specific locations for the athletes to perform drills, where one selected area within each field was slightly harder than the rest of the field, and the other being slightly softer. Additionally, 20 measurements of rotational resistance (using Deltec’s rotational resistance tester), thatch depth (using a soil profile sampler), soil moisture (using a TDR 350 Soil Moisture Meter), and infill depth (using a Turf-Tec Professional Model Infill Depth Gauge) were taken in both the softer and harder areas to further characterize each field and understand the relationship between surface conditions and athlete performance.
Data Collection During Athlete Involvement
Fourteen female athletes participated in the study, equipped with STATSports GPS devices (to measure running speed) and ankle IMUs (to measure lower limb impact intensity) to quantify their movements during drills. The athletes were each given new Nike cleats prior to participation to eliminate variation based on cleat configuration. They completed three drills, including a drop landing or drop jump drill, a T-drill, and a modified acceleration-deceleration drill, which were designed to replicate common athletic movements. Each drill was performed three times in both the softer and harder areas identified within each field. Additionally, each athlete completed pre- and post-performance surveys designed to capture their perceptions of field quality before and after completing the drills, providing insight into how different surfaces may have influenced their performance.
Results and Discussion
Surface Hardness Data
Heatmaps highlight surface hardness variability within each studied field. Surface hardness data (n = 100 per field) were analyzed using analysis of variance, and means were separated using Fisher’s protected least significant difference (LSD) test at α = 0.05 to evaluate statistical differences between locations.
Both synthetic turf fields had significantly harder surfaces than the natural turfgrass fields (p < 0.0001), and for both surface types, the high-usage field had a significantly harder surface than the low-usage field (p = 0.0029 for the natural turfgrass fields and p < 0.0001 for the synthetic turf fields). Both synthetic fields tested in this study were not constructed with a shock pad, which is typically placed beneath the layer of material that supports the synthetic fibers and utilized to help replicate the cushioning effect of natural turfgrass. The absence of a shock pad, along with the tendency of synthetic turf to harden over time due to infill material compaction from athlete foot traffic, may explain the harder surface values observed on the synthetic fields compared to the natural fields. Further, increased use or foot traffic on both natural turfgrass and synthetic turf leads to compaction, which causes the playing surface to harden over time. Therefore, it is anticipated that the high-usage fields exhibited higher surface hardness compared to the low-usage fields. Data Within Each Hard and Soft Area Resulting rotational resistance, thatch depth, soil moisture, and infill depth (synthetic fields only) measurements taken within each hard and soft area on all four fields are presented in Table 1 (available in the Spring 2026 issue of Pennsylvania Turfgrass magazine). These measurements (n = 20 per both hard and soft areas within each field) were analyzed using analysis of variance, and means were separated using Fisher’s protected least significant difference (LSD) test at α = 0.05 to evaluate statistical differences between locations.
Although the fields tested in this research were not professional-level fields, it is insightful to compare the results with the FIFA natural-pitch rating system (FIFA, 2022). All rotational resistance values fell within FIFA’s ‘excellent quality’ and ‘satisfactory quality’ thresholds, which is important because excessive rotational resistance has been linked to increased lower extremity injuries due to the foot becoming entrapped in the surface during pivoting movements, and too little resistance can increase the risk of slipping. However, soil moisture values exceed 35%, which FIFA classifies as ‘unacceptable quality’. This elevated moisture is likely the primary cause of the low surface hardness values observed on the natural turfgrass fields, which were lower than FIFA’s 70-85 Gmax ‘excellent quality’ range.
Additionally, FIFA considers thatch depths over 25 mm as unacceptable, and 10–15 mm satisfactory. Excessive thatch can cause athlete’s cleats to become caught within the surface, increasing knee ligament stress. The low-usage natural turfgrass field had more thatch despite regular maintenance, while the high-usage natural turfgrass field had less, likely due to recent sprigging the summer before. Soft areas in both natural turfgrass fields exhibited higher thatch levels than the hard areas, consistent with previous findings that core cultivation reduces both thatch and surface hardness (McCarty et al., 2007; Atkinson et al., 2012). This supports the understanding that increased thatch can act as a cushioning layer, absorbing impact and thereby reducing surface hardness.
The high-usage synthetic turf field exhibited significantly less infill and greater surface hardness compared to the low-usage synthetic turf field, and the soft areas within both synthetic fields had more infill than the hard areas. This aligns with previous research indicating that infill depth decreases with use, which in turn leads to higher surface hardness (Dickson et al., 2022). Additionally, the low-usage synthetic field exhibited greater variability in infill depth between the selected hard and soft areas, likely due to its relatively young age (only one year old at the time of the study). Compared to the older high-usage field, which was approximately ten years old, the infill in the low-usage synthetic field had less time to settle, making it more susceptible to displacement from foot traffic (Fleming et al., 2016).
STATSports GPS Unit Data
In our study, STATSports GPS units were securely attached to each athlete’s upper back. These devices were used to determine if athlete running speed varied based on field type (natural turfgrass or synthetic turf), field usage level (high or low), or hardness (hard or soft areas within each field). However, no statistically significant differences were found. This consistency in speed across conditions is important because running speed can directly affect impact forces and biomechanical measurements. Prior studies have shown that faster running increases the ground reaction force and ultimately lower limb impact load (Leatham, 2004; Jiang et al., 2024). If athletes had run at different speeds on one field type compared to another, it could have affected the reliability of our ankle IMU data. However, since no significant speed differences were found across field types, usage, or hardness, we can confidently attribute the observed differences in the resulting ankle IMU data to the playing surface.
Ankle IMU Data
Ankle IMUs were utilized to record a metric called average intensity, which is defined as the mean impact intensity derived from every impact propagated into both limbs (IMeasureU, 2022). This metric is recorded in units of gravitational force (g). These devices were securely attached to each athlete’s ankle and recorded data as they performed drills on all four fields studied. After running statistical tests that accounted for individual differences between athletes, significant differences were found based on field, field usage, and hardness.
Across all three drills, field type had a noticeable impact (p < 0.0001) where athletes showed higher average intensity on synthetic turf fields compared to natural turfgrass. For the drop jump drill, the average intensity was 19.73 g [standard error (SE) ± 1.88] on natural turfgrass and 22.73 g (SE ± 1.82) on synthetic turf, placing the synthetic turf value within the IMU Step ‘high intensity’ foot strike range of 21.5–26.7 g (Wong and Finch, 2018). A similar trend was seen in the t-drill, with average intensities of 15.84 g (SE ± 1.20) on natural turfgrass and 18.07 g (SE ± 1.16) on synthetic turf. For the modified acceleration-deceleration drill, average intensity was 17.72 g (SE ± 1.15) on natural turfgrass and 21.35 g (SE ± 1.10) on synthetic turf. Field usage also made a difference in the t-drill (p < 0.0001), where the average intensity on high-usage fields was 18.14 g (SE ± 1.24), compared to 16.49 g (SE ± 1.24) on low-usage fields. Hardness played a role as well, especially in the t-drill (p = 0.0073) and the modified acceleration-deceleration drill (p < 0.0001). In the t-drill, hard areas resulted in an average intensity of 17.43 g (SE ± 1.22), slightly higher than the 17.05 g (SE ± 1.22) on soft areas. For the modified acceleration-deceleration drill, intensity averaged 20.38 g (SE ± 4.28) on hard areas and 18.85 g (SE ± 3.81) on soft areas. Overall, the synthetic turf fields, high-usage fields, and hard areas within fields exhibited higher average intensity values than the natural turfgrass fields, low-usage fields, and softer areas within fields. This aligns with our surface hardness findings, as synthetic turf fields were significantly harder than natural turfgrass fields on average. Additionally, hard areas within synthetic turf were harder than those on natural turf, and high-usage fields were harder than low-usage fields for both surface types. Thus, our data suggest that harder surfaces may explain the higher average intensity values recorded on the athlete’s lower limbs compared to softer surfaces. This trend has been heavily supported, as running on harder surfaces increases impact stress, which can ultimately contribute to lower limb injuries. However, all surface hardness values in this study were below 100 Gmax, which is the threshold deemed unsafe by the National Football League (NFL) guidelines (Sports Turf Managers Association, 2019) and unacceptable by FIFA. Yet, a potential positive correlation between surface hardness and impact was observed, as recorded by the ankle IMUs. While further research is needed, it is hypothesized that surface hardness exceeding 100 Gmax could significantly increase injury risk over time due to excessive impact on athletes’ lower limbs. Additionally, establishing threshold values for ankle IMU metrics is crucial to determine the point at which these values may lead to injury. Survey / Athlete Perception Data Athletes completed pre- and post-performance surveys to assess field quality and its impact on their performance. Individual responses were recorded and analyzed using one-way analysis of variance to assess statistical differences between fields. Post-hoc comparisons were conducted using Fisher’s protected least significant difference (LSD) test at α = 0.05. The low-usage natural turfgrass field received the highest quality rating for both pre- and post surveys, while the high-usage natural turfgrass field, hindered by weeds and poor maintenance, scored the lowest. Synthetic turf fields ranked in between the two natural fields (with the high usage synthetic turf field being ranked lower than the low-usage synthetic turf field), indicating a preference for synthetic surfaces over a poorly maintained natural field. Conclusions Considerable variation in surface hardness was observed both within and between fields, with synthetic turf fields generally being harder than natural turfgrass fields. High-usage fields, regardless of type, were significantly harder than low-usage fields. Other metrics, such as rotational resistance, soil moisture, thatch depth, and infill depth, also showed variability. For natural turfgrass fields, higher soil moisture led to lower surface hardness, while synthetic turf fields exhibited a negative relationship between field usage and infill depth, where frequent foot traffic reduced infill and increased surface hardness. Although achieving perfect field uniformity is not possible, these findings emphasize how field usage and maintenance impact surface variability. Additionally, our data suggest a potential link between surface hardness and the mechanical load on athletes’ lower limbs. While this trend was observed, further research is needed to investigate its long-term effects on athlete health, particularly on surfaces that exceed acceptable hardness thresholds. Survey data revealed athletes rated the quality of the low-usage natural turfgrass field the highest, likely due to its softer surface and better aesthetics. In contrast, the high-usage natural turfgrass field, which suffered from poor maintenance and weed pressure, received the lowest ratings, underlining the importance of field condition in shaping athlete perceptions. These results highlight the role of field management and athlete feedback in optimizing field quality. Overall, this study offers valuable insights into how different sports surfaces impact athletes. Our findings suggest that harder surfaces, such as synthetic turf or high-traffic areas, can increase impact and loading on the lower limbs. These results highlight the critical importance of effective field management, maintenance, and consideration of field conditions prior to athletic competition. Next Phase of Research: Ph.D. Project Overview Building on the findings of the Virginia Tech study, this doctoral research at Penn State expands the investigation from impact loading to full lower-limb joint biomechanics during sport-specific movements. While the Virginia Tech study demonstrated that harder surfaces were associated with increased lower-limb impact intensity, the next question is whether different playing surfaces subtly alter how athletes move at the joint level during high-risk tasks such as cutting and decelerating. The planned Ph.D. project uses a multi-segment inertial measurement unit (IMU) configuration placed on the athlete’s dominant limb, including sensors at the foot, shank, thigh, and pelvis. Positioning sensors closer to the ground improves sensitivity to surface-related differences, allowing evaluation of not only impact but also ankle, knee, and hip joint kinematics derived through inverse kinematics workflows. Female athletes will perform sport-specific movements, including a single-leg drop-landing followed by a 90° cut, as well as an acceleration to deceleration drill, on four playing surface types: natural turfgrass, synthetic turf, carpet-type hybrid reinforced turfgrass, and stitched fiber hybrid reinforced turfgrass. Each athlete will complete multiple trials on each surface in a within-subject, repeated-measures design, allowing direct biomechanical comparisons across surface types. Female athletes are of particular interest given they experience substantially higher rates of non-contact ACL injury compared to their male counterparts, highlighting the importance of understanding how the playing surface may influence movement. Joint angles of interest include knee flexion and frontal-plane knee motion (dynamic valgus), as well as hip and foot orientation variables commonly discussed in the context of non-contact ACL injury mechanisms. Because hybrid systems are increasingly used in elite stadium environments and are required for upcoming international competitions (e.g., the FIFA World Cup), understanding how live athletes respond biomechanically to these surfaces is of particular interest. To date, most hybrid research has relied primarily on mechanical testing devices rather than human movement data. An additional component of the project involves comparing human biomechanical responses to mechanical surface testing metrics, including measurements from the fLEX testing device (Dickson and Sorochan, 2022; SGL System, n.d.). If consistent relationships are identified between device measurements and athlete joint mechanics, field managers may ultimately be able to more confidently use standardized mechanical testing tools as practical indicators of athlete–surface interactions. Collectively, this progression advances a more comprehensive framework that integrates both the playing surface and athlete biomechanics. By focusing on human movement responses within real field environments, this work strengthens interdisciplinary collaboration across field management, kinesiology, and sports medicine. Ultimately, it aims to generate practical knowledge that supports both performance and safety in sport. A full list of references as well as accompanying figures, photos and tables are available with this article in the Spring 2026 issue of Pennsylvania Turfgrass magazine available on www.TheTurfZone.com.
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