Next Article in Journal
The Oral Health Status of Chinese Elderly People with and without Dementia: A Cross-Sectional Study
Next Article in Special Issue
Sitting Posture, Sagittal Spinal Curvatures and Back Pain in 8 to 12-Year-Old Children from the Region of Murcia (Spain): ISQUIOS Programme
Previous Article in Journal
Biomonitoring of Mercury in Hair among a Group of Eritreans (Africa)
Previous Article in Special Issue
An Observational Tool to Assess Activity Limitation in Ambulatory People with Cerebral Palsy When Performing Motor Skills
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Comparison between Two Different Device Models 18 Hz GPS Used for Time–Motion Analyses in Ecological Testing of Football

1
School of Sport Sciences, Universidad Europea de Madrid, 28670 Villaviciosa de Odón (Madrid), Spain
2
IGOID Research Group, Department of Physical Activity and Sport Sciences, University of Castilla-La Mancha, 45071 Toledo, Spain
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(6), 1912; https://doi.org/10.3390/ijerph17061912
Submission received: 11 February 2020 / Revised: 9 March 2020 / Accepted: 13 March 2020 / Published: 15 March 2020

Abstract

:
Background: The aim of this study was to compare the validity of two different GPS device models used for time–motion analyses in ecological testing of football. Methods: Ten healthy male players from a Spanish university football team participated in this study. The team sport simulation circuit (TSCC) used was based on previous research examining the validity and interunit reliability of different GPS systems. Participants were required to complete eight laps of the TSSC, resulting in a total distance of 1320 m. The GPS units used for the current study were the 18 Hz StatsSport Apex Pro and 18 Hz RealTrack WIMU Pro. Participants were required to wear either of the two GPS units during the test. To establish the construct validity of GPS as a measure of Vmax, timing lights were used as a gold standard. Results: The results clearly suggest that it is not possible to use the same 18 Hz GPS model or interchange it. The measurement can be considered precise when the noise is at least equal to or lower than the smallest worthwhile change. In this case, all standard deviation in measurement error was higher than the smallest worthwhile change. This is due to an inconsistency in the data processing of each trademark. Conclusions: It is important to prevent a club using different GPS trademarks at the same time, since it is not possible to compare in any case any type of result obtained between different trademarks.

1. Introduction

The global positioning system (GPS) is a satellite-based navigational technology originally devised for military purposes [1], which was first applied to the field of athlete tracking in 1997 [2] and team sports in 2006 [3]. The recent development of portable GPS units (approximately 8.0 × 4.0 × 1.5 cm and less than 10 g) has permitted a diary application of this technology in sport to provide scientists, coaches and trainers with comprehensive and real-time analysis of on-field player performance during competition or training [1], becoming a standard tool for determining movement patterns during matches and training sessions. Besides this use, GPS technologies have become a standard tool to provide an assessment of training and match load placed on players [4,5,6].
The use of GPS devices has been extended thanks to the fact that they have proved to be valid and reliable devices to describe player movement patterns, speed and distance travelled and number of accelerations and decelerations [7]. Player movement patterns and activity profiles (external loads) can be used in addition to tactical information (spatial x and y coordinates) and physiological responses (internal load) to characterize competitive match play [8]. This information could be a relevant performance indicator because it offers information about a teams’ overall space positioning and distribution principles and how the environmental functional constraints have an effect on the tactical positioning of the team [9]. Therefore, the quantification of different aspects of training and a match’s load is crucial to understand the training process in football [10]. However, the use of GPS during matches is related to establishing performance profiles and provide a framework to design training procedures; its use in training sessions permits a periodization on a daily basis [11].
The intensive daily use of GPS systems has caused multiple companies to develop this type of instrument [12]. On a scientific level, two generations of GPS have been developed. The first one, 1 Hz to 5 Hz systems, has had its reliability and accuracy analysed in previous studies [13,14]. A second generation, with frequencies above 10 Hz, was developed, and validated in previous research, which demonstrated better values of accuracy [15,16,17].
The accuracy of these systems is crucial in team sports such as football because the movement patterns of the players are intermittent in nature [18], with a huge high-intensity load that involves accelerations and decelerations [19], high-intensity runs (14–21 km·h−1), very high-intensity runs (21.1–24 km·h−1), and sprints (over 24 km·h−1) [20]. As a result, research in performance analysis of football suggests that the reaching of peak velocities and the distance covered at very high intensities are critical points [21].
Therefore, the validity and interunit reliability of 1 Hz and 5 Hz GPS units as measures of athlete movement demands have been established previously [22,23]. These studies have shown that the level of GPS interunit reliability decreased when relatively small distances were used for assessment, when sharp changes in direction occurred and when the speed of movement increased. In the same way, the validity and interunit reliability of the research 10 Hz and 15 Hz GPS units have been reported [24,25]. These studies have revealed more valid and reliable values, especially in high-intensity movement patterns, due to the increase in the sampling rate of these devices compared to their predecessors of 1 to 5 Hz.
Due to the high demand for this technology, different brands and suppliers offer GPS systems for team sports. In all cases, they have very similar functionalities, but the treatment of the information may be different. Currently, 10 Hz devices are frequently used in team sport practice and applied studies [26]. However, there are very few studies that analyze the new 18 Hz devices [6,16]. Previous studies have been developed, but there is insufficient evidence of the accuracy of the new 18 Hz devices recently introduced to the market [17,27]. Thus, the aim of this study was to compare the agreement between two different 18 Hz GPS devices used for time−motion analyses in ecological testing of football.

2. Materials and Methods

2.1. Participants

The study comprised a convenience sample of 10 healthy male players from a Spanish university football team, all of whom volunteered to take part in this study. Players trained as a team 3 days per week for approximately 2 h per training session and competed in one weekly match in the Spanish University League, according to previous studies [6,16,17]. The power of the statistical results ranges from 0.84 to 0.99 for the selected sample. Players were recruited via the football coach, who explained the study to the whole team and that participation was voluntary. The mean (±SD) age, height, body mass and competitive playing age were 21.4 ± 1.1 years; 174.6 ± 3.8 cm; 74.5 ± 6.2 kg; and 10.4 ± 2.9 years, respectively. Each participant provided written informed consent before any testing began, based on the last version of the Helsinki Declaration. This study was approved by the Ethical Committee of the European University of Madrid (CIPI04/2019). To ensure the confidentiality of the players, all performance data were anonymized before analysis.

2.2. Procedures

The team sport simulation circuit (TSCC) used was based on previous research examining the validity and interunit reliability of different GPS systems in football [22,23,25]. The TSSC was designed to replicate the real movements and physiological requirements of football team patterns, and contained components of standing, walking, jogging, running and sprinting along with accelerations and decelerations. In addition, this protocol has been previously used in other sports such as field hockey [28] or Australian football [29]. Additionally, the TSSC was designed to assess the capabilities of GPS when measuring movement demands at speeds in excess of 20 km·h−1. The procedure of this test was described previously by Johnston et al. [25] (Figure 1). Participants were required to complete eight laps of the TSSC, resulting in a total distance of 1320 m, which is comparable with previous examinations into GPS and movement demands [5,30]. It was found that each lap was finished within approximately 1 min, with participants provided with a 15-s rest at the start of each subsequent lap. This meant that testing maintained a similar intensity to team sport matches [31].
The test was carried out on an artificial turf football field in the centre of Spain, on a sunny day, with a temperature of 22–24.5 °C and 12%–25% humidity, during April 2019.
The GPS units used for the current study were 18 Hz StatsSport Apex Pro (firmware: Apex Pro Series software v. 2.0.2.4, STATSports, Newry, N. Ireland) and 18 Hz RealTrack WIMU Pro (firmware: SPRO Software v. 948, Realtrack Systems SL, Almería, Spain). Participants were required to wear one of the GPS units during the test. These units were placed inside separate specially designed garments and positioned so that they sat between the participants’ scapulae, as indicated by Johston et al. [25].
The same variables, according to previous studies [20], were obtained from both systems: total distance (TD, distance covered by the player throughout the test independent from the speed at which players were moving); distance in zone 1 (DZ1, distance in metres covered by the player at a speed lower than 4.00 km·h−1); distance in zone 2 (DZ2, distance in metres covered by the player at a speed of between 4.00 and 7.99 km·h−1), distance in zone 3 (DZ3, distance in metres covered by the player at a speed of between 8.00 and 13.99 km·h−1) distance in zone 4 (DZ4, distance covered in metres by the player at a speed of between 14.00 and 20.99 km·h−1); distance in zone 5 (DZ5, distance in metres covered by the player at a speed of between 21.00 and 23.99 km·h−1); distance in zone 6 (DZ6, distance in metres covered by the player at a speed equal to or greater than 24.00 km·h−1), maximum speed (Vmax, peak height velocity achieved by the player, recorded in km·h−1), and average velocity (Vmean, average velocity by the player throughout the test, recorded in km·h−1).
To establish the construct validity of GPS as a measure of Vmax, timing lights (Microgate, Bolzano, Italy) were used as gold standard [32]. Timing lights were placed at the start and at 0, 10, 20, and 30 m marks along the 30 m sprint at the commencement of the TSSC. Average velocity from the 20–30 m split was used to calculate peak speed according to Johnston et al. [25].
Participants were required to complete a familiarity test. They were walked with the researcher through the circuit before testing. Cones and instructional markers were used throughout the TSSC to assist participants in understanding what was required of them and to ensure the circuit was correctly followed. The familiarity session was done on the same football pitch where one of the trial runs was done.

2.3. Statistical Analysis

Mean error (Diff) was calculated by deducting Wimu values from the Apex values. To establish the level of agreement between the two devices, the following tests were performed: mean absolute error (MAE), root mean square error (RMSE), product moment correlation (Corr), intraclass correlation coefficient (ICC) of the total agreement, and standard error of the estimate in standardized terms (SEE). Bland–Altman plots with mean error and 95% limits of agreement were also included. Vmax values estimated by Wimu and Apex devices were compared with those obtained by timing gates using Corr, ICC and SEE tests. Furthermore, standard deviation (SD) and the smallest worthwhile change (SWC) were included. The measurement can be considered as useful for precise measurements when the SD is at least equal to or lower than the SWC [33]. The SWC could be defined as 0.2 multiplied by the between-subject SD [34].

3. Results

Table 1 show the errors measured between Apex vs Wimu systems for the distance and velocity variables in semi-professional football players. The mean error (Diff), and the MAE show irregular values for the distance variables at all speeds, which is above 5% of the actual measured values. SWC is higher than SD of the measurement error (Figure 2). However, ICC values were from moderate and high in distance variables and very high in the high-intensity variable (DZ6 and Vmax).
Figure 3 shows the product−moment correlation between both systems and the gold standard measurement: timing gates in Vmax values. The Wimu (0.943) and Apex (0.971) systems present nearly perfect values, with high (Wimu = 0.865) and very high (Apex = 0.907) ICC values for Vmax.

4. Discussion

The current study aimed to assess the intra-participant reliability of two different GPS device models (Apex and Wimu), both operating at a sampling rate of 18 Hz, respectively, in testing protocol TSSC, used in previous investigations [22,23,25], and the gold standard measurement photocells in a university football team. Both systems have been used and researched in elite football on a regular basis (e.g., Premier League, Serie A or La Liga) [17,35]. Our findings demonstrate that the GPS systems analysed—even in maximal intensity football-specific exercises—accurate position, average speed and peak speed measurements; whether the error margins are acceptable, showing consistent results, depends on the purpose of the analysis. However, neither of the GPS devices could be used within the same football game scenarios or training sessions interchangeably.
The main findings showed that both GPS systems used for the record of zone velocity and maximal running speed have high reliability values and did not present substantial variations in movement patterns. In fact, the validity and agreement showed moderate and very high ICCs for DZ6 and Vmax, respectively. This is an important finding, since it contrasts with previous studies [11,36,37] that have detected a low accuracy during high-intensity activities. This may be because these studies have used devices with a low sampling rate (5 Hz). Previous studies [5,6] have highlighted that the sampling rate is a crucial factor associated with the validity and reliability of this technology. Higher sampling frequency devices, such as those used in this study (18 Hz), are more accurate and reliable than previous devices (1–5 Hz and 10–15 Hz) [38]. Based on such evidence, it seems logical to assume that a large sampling rate, such as the one used in this study (18 Hz), resulted in an improvement in the validity of results, especially during the high-intensity values (e.g., Vmax or DZ6) as indicated in previous studies [6,39,40].
Both systems, but specially Apex Pro 18 Hz, demonstrated improved reliability vs. gold standard measurements in the ecological-specific football test. The data obtained confirm that, as the rules of the game are updated, the physical values detected in football players constantly increase [31]. Accordingly, when the Vmax is reached (>29 km·h−1) it is essential to have a technology accurate enough to measure high-intensity actions in both real games and training situations. It may be noted that high-intensity actions are clearly affected by the type or version of data analysis software of each device, and that each trademark has specific analysis algorithms. Buchheit et al. [36] highlighted that the measurement of accelerations and decelerations were large, with a variation of up to 50% by the type or update of the software, while Vmax actions were affected by 1% and actions >21 km·h−1 in a 6% maximum. Given that, our results did not substantially affect the distance covered at high-intensity speed or Vmax, which were designated as the key actions in the quantification of the external workload in elite football [5,41,42].
Thereby, this study demonstrates a high consistency of GPS devices in reliability football situations, especially between high-intensity actions. This demonstrated that both devices may be used by football clubs in routine training sessions and game settings effectively. This situation has been previously demonstrated [43]. However, previous research [44] has shown that the dispersion measures of these devices rise with increasing speed, reaching a 69% coefficient of variation in the high-intensity distance category (>21 km·h−1). Thus, one of the most relevant aspects of this research is that these types of devices, with a sampling frequency of 18 Hz, remain reliable even in these types of performance-determining situations in elite football. However, Randers et al. [44] revealed rather large between-system differences during an elite match situation in the determination of the absolute distances covered, implying that any comparison of results using different match analysis systems should be done with caution. Nevertheless, they confirm that different GPS systems were able to detect performance decrements during a football game and can be applied to study development and movement patterns in elite football.
Finally, as a proposal for a future research line, it would be interesting to monitor a professional football team with two devices of different trademarks at the same time in each player. This would provide clear evidence of the variations in results in real-game situations.

5. Conclusions

Football coaches and physical trainers should be aware that both 18 Hz GPS systems could be used in real training and competition situations with a high reliability for high-intensity football variables. However, the results clearly suggest that it is not possible to interchange different GPS models. The measurement can be considered precise when the noise is at least equal to or lower than the SWC [33]. In this case, all SDs of measurement errors were higher than the SWC. This is due to an inconsistency in the data processing by each trademark. Thereby, to eliminate inter-unit variation, GPS devices should not be used interchangeably.

Author Contributions

Conceptualization, J.L.F., D.V.-R. and J.S.-S.; methodology, J.G.-U and A.N formal analysis, J.G.-U. and J.S.-S.; investigation, A.H.-M., J.V.G. and D.V.-R.; resources, J.L.F. and L.G.; data curation, J.L.F. and J.G.-U.; writing—original draft preparation, J.L.F., J.V.G. and A.N.; writing—review and editing, J.S.-S. and L.G.; supervision, J.L.F.; project administration, D.V.-R.; funding acquisition, D.V.-R. and J.L.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the European University of Madrid, grant number 2019/UEM04.

Acknowledgments

The authors would like to thank the university football team for their support and collaboration, and also all those football players who contributed to this research. J.G.-U. acknowledges “Fondo Europeo de Desarrollo Regional, Programa Operativo de la Región de Castilla-La Mancha” (2018/11744) for funding the development of his research. A.H.-M. acknowledges the Spanish Ministry of Science, Innovation and Universities for funding the development of his Ph.D. (Grant Number: FPU18/03222).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cummins, C.; Orr, R.; O’Connor, H.; West, C. Global positioning systems (GPS) and microtechnology sensors in team sports: A systematic review. Sports Med. 2013, 43, 1025–1042. [Google Scholar] [CrossRef] [PubMed]
  2. Schutz, Y.; Chambaz, A. Could a satellite-based navigation system (GPS) be used to assess the physical activity of individuals on earth? Eur. J. Clin. Nutrition 1997, 51, 338–339. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Aughey, R.J. Applications of GPS technologies to field sports. Int. J. Sports Physiol. Perform. 2011, 6, 295–310. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Casamichana, D.; Castellano, J.; Calleja-Gonzalez, J.; San Román, J.; Castagna, C. Relationship between indicators of training load in soccer players. J. Strength Cond. Res. 2013, 27, 369–374. [Google Scholar] [CrossRef] [PubMed]
  5. Coutts, A.J.; Duffield, R. Validity and reliability of GPS devices for measuring movement demands of team sports. J. Sci. Med. Sport 2010, 13, 133–135. [Google Scholar] [CrossRef]
  6. Hoppe, M.W.; Baumgart, C.; Polglaze, T.; Freiwald, J. Validity and reliability of GPS and LPS for measuring distances covered and sprint mechanical properties in team sports. PLoS ONE 2018, 13, e0192708. [Google Scholar] [CrossRef] [Green Version]
  7. Malone, J.J.; Lovell, R.; Varley, M.C.; Coutts, A.J. Unpacking the black box: Applications and considerations for using GPS devices in sport. Int. J. Sports Physiol. Perform. 2017, 12, 18–26. [Google Scholar] [CrossRef] [Green Version]
  8. McLellan, C.P.; Lovell, D.I.; Gass, G.C. Performance analysis of elite rugby league match play using global positioning systems. J. Strength Cond. Res. 2011, 25, 1703–1710. [Google Scholar] [CrossRef]
  9. Sampaio, J.; Maçãs, V. Measuring tactical behaviour in football. Int. J. Sport Med. 2012, 33, 395–401. [Google Scholar] [CrossRef]
  10. Jara, D.; Ortega, E.; Gómez-Ruano, M.Á.; Weigelt, M.; Nikolic, B.; Sainz de Baranda, P. Physical and Tactical Demands of the Goalkeeper in Football in Different Small-Sided Games. Sensors 2019, 19, 3605. [Google Scholar] [CrossRef] [Green Version]
  11. Buchheit, M.; Allen, A.; Poon, T.K.; Modonutti, M.; Gregson, W.; Di Salvo, V. Integrating different tracking systems in football: Multiple camera semi-automatic system, local position measurement and GPS technologies. J. Sports Sci. 2014, 32, 1844–1857. [Google Scholar] [CrossRef] [PubMed]
  12. Gabbett, T.J. Sprinting patterns of national rugby league competition. J. Strenght Cond. Res. 2012, 26, 121–130. [Google Scholar] [CrossRef] [PubMed]
  13. Rampinini, E.; Alberti, G.; Fiorenza, M.; Riggio, M.; Sassi, R.; Borges, T.; Coutts, A. Accuracy of GPS devices for measuring high-intensity running in field-based team sports. Int. J. Sport Med. 2015, 36, 49–53. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Waldron, M.; Worsfold, P.; Twist, C.; Lamb, K. Concurrent validity and test–retest reliability of a global positioning system (GPS) and timing gates to assess sprint performance variables. J. Sports Sci. 2011, 29, 1613–1619. [Google Scholar] [CrossRef] [PubMed]
  15. Barr, M.; Beaver, T.; Turczyn, D.; Cornish, S. Validity and Reliability of 15 Hz Global Positioning System Units for Assessing the Activity Profiles of University Football Players. J. Strenght Cond. Res. 2019, 33, 1371–1379. [Google Scholar] [CrossRef] [PubMed]
  16. Beato, M.; Coratella, G.; Stiff, A.; Iacono, A.D. The validity and between-unit variability of GNSS units (STATSports Apex 10 and 18 Hz) for measuring distance and peak speed in team sports. Front. Physiol. 2018, 9, 1288. [Google Scholar] [CrossRef] [Green Version]
  17. Beato, M.; de Keijzer, K.L. The inter-unit and inter-model reliability of GNSS STATSports Apex and Viper units in measuring peak speed over 5, 10, 15, 20 and 30 meters. Biol. Sport 2019, 36, 317–321. [Google Scholar] [CrossRef]
  18. Carling, C.; Le Gall, F.; Dupont, G. Analysis of repeated high-intensity running performance in professional soccer. J. Sport Sci. 2012, 30, 325–336. [Google Scholar] [CrossRef]
  19. Rampinini, E.; Sassi, A.; Morelli, A.; Mazzoni, S.; Fanchini, M.; Coutts, A.J. Repeated-sprint ability in professional and amateur soccer players. Appl. Physiol. Nutr. Metab. 2009, 34, 1048–1054. [Google Scholar] [CrossRef]
  20. Felipe, J.L.; Garcia-Unanue, J.; Viejo-Romero, D.; Navandar, A.; Sánchez-Sánchez, J. Validation of a Video-Based Performance Analysis System (Mediacoach®) to Analyze the Physical Demands during Matches in LaLiga. Sensors 2019, 19, 4113. [Google Scholar] [CrossRef] [Green Version]
  21. Andrzejewski, M.; Chmura, J.; Pluta, B.; Konarski, J.M. Sprinting activities and distance covered by top level Europa league soccer players. Int. J. Sports Sci. Coach. 2015, 10, 39–50. [Google Scholar] [CrossRef]
  22. Jennings, D.; Cormack, S.; Coutts, A.J.; Boyd, L.; Aughey, R.J. The validity and reliability of GPS units for measuring distance in team sport specific running patterns. Int. J. Sports Physiol. Perform. 2010, 5, 328–341. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Johnston, R.J.; Watsford, M.L.; Pine, M.J.; Spurrs, R.W.; Murphy, A.; Pruyn, E.C. Movement demands and match performance in professional Australian football. Int. J. Sport Med. 2012, 33, 89–93. [Google Scholar] [CrossRef] [PubMed]
  24. Castellano, J.; Casamichana, D.; Calleja-González, J.; San Román, J.; Ostojic, S.M. Reliability and accuracy of 10 Hz GPS devices for short-distance exercise. J. Sports Sci. Med. 2011, 10, 233. [Google Scholar] [PubMed]
  25. Johnston, R.J.; Watsford, M.L.; Kelly, S.J.; Pine, M.J.; Spurrs, R.W. Validity and interunit reliability of 10 Hz and 15 Hz GPS units for assessing athlete movement demands. J. Strenght Cond. Res. 2014, 28, 1649–1655. [Google Scholar] [CrossRef] [PubMed]
  26. Dellaserra, C.L.; Gao, Y.; Ransdell, L. Use of integrated technology in team sports: A review of opportunities, challenges, and future directions for athletes. J. Strenght Cond. Res. 2014, 28, 556–573. [Google Scholar] [CrossRef]
  27. Bastida Castillo, A.; Gómez Carmona, C.D.; De la Cruz Sánchez, E.; Pino Ortega, J. Accuracy, intra-and inter-unit reliability, and comparison between GPS and UWB-based position-tracking systems used for time–motion analyses in soccer. Eur. J. Sport Sci. 2018, 18, 450–457. [Google Scholar] [CrossRef]
  28. Weir, G.; Alderson, J.; Hiscock, D.; Smith, M.; Alicea, A.; Donnelly, C.J. The effect of fatigue induced from a simulated hockey match on biomechanical ACL injury risk factors in elite female field hockey players. In Proceedings of the 35th Conference of the International Society of Biomechanics in Sports, Cologne, Germany, 14–18 June 2017; Volume 35, pp. 468–471. [Google Scholar]
  29. Johnston, R.; Watsford, M.; Austin, D.; Pine, M.; Spurrs, R. Player acceleration and deceleration profiles in professional Australian football. J. Sports Med. Phys. Fit. 2015, 55, 931–939. [Google Scholar]
  30. Edgecomb, S.; Norton, K. Comparison of global positioning and computer-based tracking systems for measuring player movement distance during Australian football. J. Sci. Med. Sport 2006, 9, 25–32. [Google Scholar] [CrossRef]
  31. Wisbey, B.; Montgomery, P.G.; Pyne, D.B.; Rattray, B. Quantifying movement demands of AFL football using GPS tracking. J. Sci. Med. Sport 2010, 13, 531–536. [Google Scholar] [CrossRef]
  32. Barbero-Álvarez, J.C.; Coutts, A.; Granda, J.; Barbero-Álvarez, V.; Castagna, C. The validity and reliability of a global positioning satellite system device to assess speed and repeated sprint ability (RSA) in athletes. J. Sci. Med. Sport 2010, 13, 232–235. [Google Scholar] [CrossRef] [PubMed]
  33. Haugen, T.A.; Tønnessen, E.; Svendsen, I.S.; Seiler, S. Sprint time differences between single-and dual-beam timing systems. J. Strength Cond. Res. 2014, 28, 2376–2379. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Hopkins, W.G.; Marshall, S.W.; Batterham, A.M.; Hanin, J. Progressive statistics for studies in sports medicine and exercise science. Med. Sci. Sports Exerc. 2009, 41, 3–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Pons, E.; Garcia-Calvo, T.; Resta, R.; Blanco, H.; del Campo, R.L.; Garcia, J.D.; Pulido, J.J. A comparison of a GPS device and a multi-camera video technology during official soccer matches: Agreement between systems. PLoS ONE 2019, 14, e0220729. [Google Scholar] [CrossRef] [Green Version]
  36. Buchheit, M.; Al Haddad, H.; Simpson, B.M.; Palazzi, D.; Bourdon, P.C.; Di Salvo, V.; Mendez-Villanueva, A. Monitoring accelerations with GPS in football: Time to slow down? Int. J. Sports Physiol. Perform. 2014, 9, 442–445. [Google Scholar] [CrossRef]
  37. Stevens, T.G.; de Ruiter, C.J.; van Niel, C.; van de Rhee, R.; Beek, P.J.; Savelsbergh, G.J. Measuring acceleration and deceleration in soccer-specific movements using a local position measurement (LPM) system. Int. J. Sports Physiol. Perform. 2014, 9, 446–456. [Google Scholar] [CrossRef]
  38. Scott, M.T.; Scott, T.J.; Kelly, V.G. The validity and reliability of global positioning systems in team sport: A brief review. J. Strenght Cond. Res. 2016, 30, 1470–1490. [Google Scholar] [CrossRef]
  39. Beato, M.; Bartolini, D.; Ghia, G.; Zamparo, P. Accuracy of a 10 Hz GPS unit in measuring shuttle velocity performed at different speeds and distances (5–20 m). J. Hum. Kinetics 2016, 54, 15–22. [Google Scholar] [CrossRef] [Green Version]
  40. Christopher, J.; Beato, M.; Hulton, A.T. Manipulation of exercise to rest ratio within set duration on physical and technical outcomes during small-sided games in elite youth soccer players. Hum. Mov. Sci. 2016, 48, 1–6. [Google Scholar] [CrossRef]
  41. Mendez-Villanueva, A.; Buchheit, M.; Simpson, B.; Peltola, E.; Bourdon, P. Does on-field sprinting performance in young soccer players depend on how fast they can run or how fast they do run? J. Strenght Cond. Res. 2011, 25, 2634–2638. [Google Scholar] [CrossRef]
  42. Varley, M.C.; Fairweather, I.H.; Aughey, R.J. Validity and reliability of GPS for measuring instantaneous velocity during acceleration, deceleration, and constant motion. J. Sport Sci. 2012, 30, 121–127. [Google Scholar] [CrossRef] [PubMed]
  43. Pino, J.; Martinez-Santos, R.; Moreno, M.I.; Padilla, C. Automatic analysis of football games using GPS on real time. J. Sport Sci. Med. 2007, 6, 9. [Google Scholar]
  44. Randers, M.B.; Mujika, I.; Hewitt, A.; Santisteban, J.; Bischoff, R.; Solano, R.; Zubillaga, A.; Peltola, E.; Krustrup, P.; Mohr, M. Application of four different football match analysis systems: A comparative study. J. Sports Sci. 2010, 28, 171–182. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Team sport simulation circuit (TSSC) [25].
Figure 1. Team sport simulation circuit (TSSC) [25].
Ijerph 17 01912 g001
Figure 2. Bland−Altman analysis between Apex and Wimu system in every variable.
Figure 2. Bland−Altman analysis between Apex and Wimu system in every variable.
Ijerph 17 01912 g002
Figure 3. Product–moment Correlation for Vmax for both systems and the gold standard timing gates.
Figure 3. Product–moment Correlation for Vmax for both systems and the gold standard timing gates.
Ijerph 17 01912 g003
Table 1. Validity and agreement of the distance and velocity variable.
Table 1. Validity and agreement of the distance and velocity variable.
VariablesApexWimu
MSDMSDDiffSDMAERMSESWCCorrICCSEE
Total Distance (m)1197.8530.231258.5142.12−60.6628.1760.9466.289.470.74 *0.30 *0.90
DistanceZ1 (m)205.1636.86213.9229.19−8.7624.4518.2924.796.530.75 *0.72 *0.88
DistanceZ2 (m)224.2450.74223.7042.520.5437.3027.5235.399.110.69 *0.71 *0.69
DistanceZ3 (m)157.1643.59154.4935.562.6720.4917.2919.627.750.89 *0.88 *0.53
DistanceZ4 (m)295.7674.30299.7180.85−3.9563.1152.9260.0115.120.67 *0.69 *1.10
DistanceZ5 (m)183.4668.74191.6666.74−8.2169.8851.2166.813.220.470.491.89
DistanceZ6 (m)132.0892.98127.5882.854.5034.5324.0833.0617.150.93 *0.93 *0.40
Vmax (km·h−1)29.192.9529.852.94−0.661.620.891.670.580.85 *0.84 *0.62
Vmean (km·h−1)5.640.218.230.56−2.590.472.592.630.280.580.021.41
* p < 0.05. M = Mean; SD = standard deviation; Diff = difference between means; MAE = mean absolute error; RMSE = root–mean–square deviation; SWC = smallest worthwhile change; Corr = product−moment correlation; ICC = intraclass correlation coefficient; EES = standard error of the estimate.

Share and Cite

MDPI and ACS Style

Gimenez, J.V.; Garcia-Unanue, J.; Navandar, A.; Viejo-Romero, D.; Sanchez-Sanchez, J.; Gallardo, L.; Hernandez-Martin, A.; Felipe, J.L. Comparison between Two Different Device Models 18 Hz GPS Used for Time–Motion Analyses in Ecological Testing of Football. Int. J. Environ. Res. Public Health 2020, 17, 1912. https://doi.org/10.3390/ijerph17061912

AMA Style

Gimenez JV, Garcia-Unanue J, Navandar A, Viejo-Romero D, Sanchez-Sanchez J, Gallardo L, Hernandez-Martin A, Felipe JL. Comparison between Two Different Device Models 18 Hz GPS Used for Time–Motion Analyses in Ecological Testing of Football. International Journal of Environmental Research and Public Health. 2020; 17(6):1912. https://doi.org/10.3390/ijerph17061912

Chicago/Turabian Style

Gimenez, Jesus Vicente, Jorge Garcia-Unanue, Archit Navandar, David Viejo-Romero, Javier Sanchez-Sanchez, Leonor Gallardo, Antonio Hernandez-Martin, and Jose Luis Felipe. 2020. "Comparison between Two Different Device Models 18 Hz GPS Used for Time–Motion Analyses in Ecological Testing of Football" International Journal of Environmental Research and Public Health 17, no. 6: 1912. https://doi.org/10.3390/ijerph17061912

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop