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PLOS ONE logoLink to PLOS ONE
. 2021 Apr 23;16(4):e0250549. doi: 10.1371/journal.pone.0250549

How far from the gold standard? Comparing the accuracy of a Local Position Measurement (LPM) system and a 15 Hz GPS to a laser for measuring acceleration and running speed during team sports

Karin Fischer-Sonderegger 1,2,*, Wolfgang Taube 2, Martin Rumo 1, Markus Tschopp 1
Editor: Cristina Cortis3
PMCID: PMC8064559  PMID: 33891640

Abstract

Purpose

This study compared the validity and inter- and intra-unit reliability of local (LPM) and global (GPS) position measurement systems for measuring acceleration during team sports.

Methods

Devices were attached to a remote-controlled car and validated against a laser. Mean percentage biases (MPBs) of maximal acceleration (amax) and maximal running speed (vmax) were used to measure validity. Mean between-device and mean within-device standard deviations of the percentage biases (bd-SDs and wd-SDs) of amax and vmax were used to measure inter- and intra-unit reliability, respectively.

Results

Both systems tended to underestimate amax similarly (GPS: –61.8 to 3.5%; LPM: –53.9 to 9.6%). The MPBs of amax were lower in trials with unidirectional linear movements (GPS: –18.8 to 3.5%; LPM: −11.2 to 9.6%) than in trials with changes of direction (CODs; GPS: –61.8 to −21.1%; LPM: −53.9 to –35.3%). The MPBs of vmax (GPS: –3.3 to –1.0%; LPM: –12.4 to 1.5%) were lower than those of amax. The bd-SDs and the wd-SDs of amax were similar for both systems (bd-SDs: GPS: 2.8 to 12.0%; LPM 3.7 to 15.3%; wd-SDs: GPS: 3.7 to 28.4%; LPM: 5.3 to 27.2%), whereas GPS showed better bd-SDs of vmax than LPM.

Conclusion

The accuracy depended strongly on the type of action measured, with CODs displaying particularly poor validity, indicating a challenge for quantifying training loads in team sports.

Introduction

Team sports are characterized by frequent changes of direction (CODs) and many accelerations and decelerations [1, 2]. Since accelerating consumes more energy than maintaining a constant speed, measuring the distance achieved at different speeds does not completely reflect training or game loads [1, 35]. Therefore, in order to assess short, intense actions that are typical of team sports, position measurement systems that can accurately measure acceleration are needed [1]. Both global (GPS) and local (LPM) position measurement systems have been used to this end [68]. GPS uses satellites to determine the positions of players; it is therefore not locally bound and can be used flexibly across different sites (e.g., home and away games, training and match fields). However, the number of available satellites can influence GPS’s accuracy [9]. Satellites may also be hidden or have their signals reflected by physical obstacles such as buildings or trees, and can furthermore be affected by atmospheric conditions. In contrast, the LPM system uses base stations located around a specific site [7]. Its use is therefore limited to a fixed location, but is not affected by the satellite limitations of GPS and has a higher sampling frequency. We therefore hypothesized that the LPM measurement accuracy would be higher than GPS, particularly when measuring athletes’ movement patterns in team sports.

Despite the importance of measuring accelerations to assess physical load, most studies that have sought to validate position measurement systems in team sports have compared running distances [6, 7, 1012] or averaged speed [8, 11, 13, 14] to a gold standard. For total distance, the measurement error of three different 1 Hz GPS devices was established to be < 5% [6] and the coefficient of variation (CV) for 10 Hz and 18 Hz GPS ranged from 2.5 to 13.0% and 1.1 to 5.1%, respectively [10]. Similarly, Frencken et al. [7] used total distance in a team sport context to validate an LPM system’s accuracy and found that it generally underestimated actual distance by up to –1.6% and mean speed by –0.1 to −0.6 km·h−1. However despite fairly low error indicators for both systems, the key movements of team sports, such as accelerations and changes of direction, cannot be captured by such measurements [15]. Previous studies have accounted for accelerations when validating the accuracy of position measurement systems. Akenhead et al. [16] demonstrated that the validity and reliability of speeds measured with a 10 Hz GPS were inversely correlated to acceleration—i.e., the higher the acceleration, the lower the validity and reliability of speed measurements during the acceleration. Furthermore, Buchheit et al. [17] compared three different GPS models and showed a between-unit variation in peak acceleration of 10%; and the CV for maximal speed and maximal acceleration in Lacome et al. [18] was 0.5% and 6.4% respectively, as measured during a 40 m sprint test. However, to our knowledge, no studies have investigated accelerations during changes of direction.

While distances can be validated with a trundle wheel or measuring tape and averaged speeds can be measured with timing gates [6, 19, 20], there is no “gold standard” to assess acceleration in team sport-specific actions. Ideally, such a system should be able to measure both speed and acceleration, since the ability to accelerate depends on the speed at baseline. More specifically, maximal voluntary acceleration has been shown to decrease with increased initial running speed [21]. Lasers and radar beams are therefore more suitable measurement tools than timing gates [19, 2224]. When used with adequate signal filtering, lasers are understood to accurately measure speed and acceleration [25], with an average speed error of < 2%, as reported by Tuerk-Noack and Schmalz [26]. Another study found that the typical error when using lasers to measure speed in a repeated running trial was very small (0.05 m·s–1) and that the intra-class correlation was high (r = 0.98) [27]. However, it is also known that laser measurements’ accuracy is limited during the first acceleration phase of a sprint due to shifts in the human body’s center of gravity [28]. Furthermore, an athlete’s upper-body movements during a sprint can negatively influence the accuracy of the laser [16, 29, 30]. These limitations can be circumvented if the movement of the human body is simulated by a rigid body, such as a vehicle. Additionally, a vehicle offers the opportunity to wear several devices at the time. However, to date, only three GPS accuracy studies have analyzed acceleration using an object other than a person as a device carrier [1618].

To determine the inter-unit reliability of several GPS and LPM devices, and less so the intra-unit reliability, a number of devices should be attached to the same person or object. Since there is limited space to adequately position multiple measurement devices on the back of a human, most studies to date have only used two to four devices to avoid encountering problems (e.g., discomfort, movement restrictions, additional weight) [11, 24, 31]. Other studies have approached this problem by attaching only one device at a time to an athlete’s back and then instructing the athlete to complete a course several times [13, 32, 33]; however, this approach cannot differentiate between measurement errors of the different devices versus changes in the athlete’s movement execution.

So far, no studies have assessed positioning system accuracy using a non-human object as a device carrier, while simultaneously comparing the accuracy of two different systems (i.e., global and local positioning systems) to a gold standard in different team sport-specific actions. Thus, the aim of this study was to determine and compare the validity of commercially available GPS and LPM systems in assessing acceleration and speed during forward and backward and single directional actions, using a laser measurement system as gold standard. Furthermore, this study aimed to assess the inter-unit and intra-unit reliability of GPS and LPM speed and acceleration measurements in different team sport-specific actions.

Materials and methods

Testing procedures

To examine the validity and the inter- and intra-unit reliability of a 15 Hz GPS and an LPM system, a remote-controlled car (RCC) (Traxxas Rally rushless 4WD 1/10 RTR, Model 70, Plano, Texas), steered by an operator, was used to carry the position measurement devices and simulate movement patterns common to team sports. One team sport-specific movement pattern can provide several team sport-specific actions (e.g. a movement pattern with two accelerations interspersed with an abrupt deceleration can be accounted for both low acceleration from standstill [LA] and acceleration after an abrupt deceleration [A-D]). Team sport-specific actions differing in acceleration capacity, initial speed, maximal speed, number of CODs, and were divided into seven subcategories. In order to present a number of trials within each subcategory (Table 1), sport-specific movement patterns were carried out several times. Throughout, the operator attempted to achieve speeds and accelerations that mimicked team sport-specific movement patterns as closely as possible. Accelerations occurred between 0 and 7.8 m·s−2, and were classed as low acceleration if < 3.5 m·s−2 or high acceleration if > 3.5 m·s−2. Flying start accelerations were initialized from speeds between 6 and 15 km·h−1. This approach allowed us to identify differences in validity and inter- and intra-unit reliability of maximal speed (vmax) and maximal acceleration (amax) during different types of actions.

Table 1. Subcategories of different team sport-specific actions and the number of trials within the subcategory.

Subcategory # of trials
low acceleration from standstill (LA) 8
high acceleration from standstill (HA) 6
high acceleration from a flying start (HA-flyingS) 6
acceleration after an abrupt deceleration (A-D) 4
acceleration after a 180° change of direction (A-COD) 4
repetitive high acceleration, shuttle runs 4 x 5m (RA-5m) 6
repetitive high acceleration, shuttle run 4 x 10m (RA-10m) 6

Six GPS devices (15 Hz GPS, SPI HPU, GPSports Pty Ltd, Canberra, Australia) and six LPM devices (Inmotiotec GmbH, Regau, Austria) were simultaneously mounted in an upright position on a platform installed on top of the RCC. Care was taken to ensure that the LPM systems’ antennas were horizontal, as they are when attached to an athlete’s shoulder, and that all antennas were free from obstructions. The RCC’s wheels were modified to allow it to drive on two steel ropes stretched 40 cm apart and 1.50 m above the ground, simulating the height at which position measurement devices are normally worn when attached to athletes (Fig 1). The steel ropes were stretched across the entire length of the soccer field through the middle of the field. The actions were carried out in the middle of the field.

Fig 1. Remote-controlled car (RCC) driving on two steel ropes.

Fig 1

Six LPM and six GPS devices were attached on the top of the RCC. At the back of the RCC was a reflective, white panel used to detect the laser beam.

The raw GPS data was interpolated by the device’s software from 5 Hz to 15 Hz, as in Nagahara et al. [34]. An LPM system can record position data up to 1,000 Hz, but the effective frequency for each device is divided by the number of devices that are used. To reach the same frequency as in official games, 16 additional devices were randomly placed across the field, for a total of 22 devices. Thus, each LPM device recorded at a sampling frequency of 45 Hz (1,000 / 22). No trees or buildings were positioned around the measurement site, and testing was conducted under a clear blue sky with no cloud cover.

All collected GPS and LPM data were downloaded using customized software (SPI: Team AMS51; GPSports Systems, Canberra, Australia; LPM: Inmotio software, Inmotiotec GmbH, Regau, Austria) and then exported to Microsoft Excel (2016). The GPS and LPM customized software programs both provided speed and acceleration data by default. Validity and inter- and intra-unit reliability of speed and acceleration were tested by concentrating on vmax and amax from each trial within a subcategory, which were manually extracted in a post-processing step.

A laser (LDM301, Jenoptic, Jena, Germany) with a sampling frequency of 100 Hz was used as the criterion to measure speed and acceleration of the RCC during both forward and backward and single directional movement patterns. The use of the RCC favored to ensure a complete laser signal, as a player’s upper body can quickly shift outside the laser’s range during a sprint compared to the car, which drives in a straight line with no lateral deviations. As a laser measures the time delay of a reflected pulsed infrared light, the sighting beam was focused on the center of a 0.3 m x 0.5 m white reflective panel secured on the RCC; the laser was located on a tripod 3m behind the start. Raw data was collected using the respective manufacturer software and then exported to Microsoft Excel (2016). During data post-processing, the first and second derivatives of the position data from the laser were calculated to obtain the speed and acceleration data. Subsequently, a moving average filter was used over 20 data points (forward and backward) to smooth the speed and acceleration data.

Statistical analysis

The vmax and amax means and between-device standard deviations (SDs) measured by GPS and LPM devices, as well as the vmax and amax means measured by the laser, were assessed for each subcategory of the team sport-specific actions (Table 1). To evaluate the validity of the two position measurement systems, the mean percentage biases (MPBs) of amax and vmax for GPS and LPM were expressed relative to the laser (gold standard), using the following formula: (100 × GPS parameter / laser parameter– 100) and (100 × LPM parameter / laser parameter– 100) (similar to Nagahara et al. [34]). The MPBs were calculated for each trial and averaged for each subcategory.

Similarly, to assess inter-unit reliability, between-device SDs of the percentage biases of amax and vmax for all six devices were calculated for each trial and averaged for each subcategory. Additionally, the typical error was calculated and expressed as a CV.

For intra-unit reliability, the within-device SD of the percentage biases of amax and vmax for each individual device was calculated over all trials and averaged for each subcategory. Both the between- and within-device SDs of the percentage biases, representing the inter- and intra-unit reliability, respectively, were expressed as a percentage relative to the laser-derived values. This approach was chosen because of the inevitable variations in the levels of acceleration and maximum speed between different trials within each subcategory. Using the SD of the absolute value instead of the SD of the percentage bias—especially when determining the intra-unit reliability—would have led to a misinterpretation of the reliability, due to the RCC’s varying acceleration and speed curves from trial to trial.

Results

Validity of maximal acceleration

Table 2 shows the means and between-device SDs of amax during different team sport-specific actions as measured by the GPS and LPM systems, and the mean of the laser measurement. Fig 2A shows the MPB of amax as an indicator of validity. Both the GPS and the LPM devices tended to underestimate amax, especially for actions that included CODs. The exceptions were high acceleration from standstill (HA) and A-D, which were slightly overestimated by GPS (MPB: 3.5% and 1.1%, respectively) and high acceleration from a flying start (HA-flyingS), which was overestimated by LPM (MPB: 9.6%). Moreover, the MPBs for amax were lower for both systems when measuring linear movements (LA, HA, HA-flyingS, and A-D) as compared to actions with CODs (acceleration after a 180° change of direction [A-COD], repetitive high acceleration, shuttle runs 4 x 5m [RA-5m], and repetitive high acceleration, shuttle runs 4 x 10m [RA-10m]) and were higher for RA-5m than for RA-10m. The highest MPB was in A-COD (GPS: –61.8%; LPM: –53.9%).

Table 2. Means and between-device standard deviations (SDs) of maximal acceleration (amax) during different team sport-specific actions measured with a GPS and an LPM system and the mean of amax measured with laser.

Subcategory Laser (m·s−2) GPS LPM
Mean (m·s−2) SD (m·s−2) CV (%) Mean (m·s−2) SD (m·s−2) CV (%)
low acceleration from standstill (LA) 1.76 1.32 0.22 16.1 1.49 0.22 14.7
high acceleration from standstill (HA) 6.25 6.55 0.79 10.3 4.90 0.24 4.8
high acceleration from a flying start (HA-flyingS) 4.62 4.54 0.13 2.8 5.06 0.32 6.3
acceleration after an abrupt deceleration (A-D) 2.85 2.85 0.13 5.0 2.99 0.25 11.1
acceleration after a 180° change of direction (A-COD) 3.46 1.33 0.18 12.8 1.65 0.14 8.7
repetitive high acceleration, shuttle runs 4 x 5m (RA-5m) 5.08 3.04 0.29 9.8 2.94 0.32 10.7
repetitive high acceleration, shuttle runs 4 x 10m (RA-10m) 5.16 4.25 0.16 4.4 3.44 0.27 9.3

The Coefficient of Variation (CV) is an indicator of the inter-unit reliability.

Fig 2. Validity, inter- and intra-unit reliability of maximal acceleration (amax; A) and maximal speed (vmax; B).

Fig 2

Mean percentage bias (MPB; relative bias of the GPS and LPM devices compared to the laser) of amax and vmax of 7 different team sport-specific actions indicates the validity of amax and vmax. The first whisker of each subcategory within the black and grey squares is the between-device SD of the percentage biases of amax and vmax, and represents the inter-unit reliability of amax and vmax. The second whisker is the within-device SD of the percentage biases of amax and vmax, and represents the intra-unit reliability of amax and vmax. The third whisker of each subcategory is the combination of the between- and within-device SD of the percentage biases of amax and vmax, which indicates the inter- and intra-unit reliability of amax and vmax. If a GPS or LPM device is randomly chosen for repeated measures, the third whisker illustrates the percentage measurement error.

Validity of maximal running speed

Table 3 shows the means and between-device SDs of amax during different team sport-specific actions as measured by the GPS and LPM systems, and the mean of the laser measurement. The MPB of vmax is presented in Fig 2B as an indicator of validity. In general, the MPBs of vmax were markedly lower than of amax; similar to amax, vmax tended to be systematically underestimated by GPS (−3.3 to −1.0%), whereas both negative and positive biases occurred with LPM (–12.4 to 1.5%). The largest differences between the GPS and LPM measurements were in RA-5m (GPS: –3.3%; LPM: –12.4%) and RA-10m (GPS: –1.9%; LPM: –8.0%). As shown in Fig 3, the LPM speed curve was clearly time-delayed during these repetitive accelerations, and its maximum recorded speed was considerably lower than the speed measured with the laser and GPS (with the exception of one LPM device in the second and fourth CODs).

Table 3. Means and between-device standard deviations (SDs) of maximal running speed (vmax) during different team sport-specific actions measured with a GPS and an LPM system and the mean of amax measured with laser.

Subcategory Laser (m·s−1) GPS LPM
Mean (m·s−1) SD (m·s−1) CV (%) Mean (m·s−1) SD (m·s−1) CV (%)
low acceleration from standstill (LA) 6.06 5.95 0.02 0.36 5.86 0.08 1.48
high acceleration from standstill (HA) 10.05 9.95 0.01 0.63 10.04 0.19 1.78
high acceleration from a flying start (HA-flyingS) 11.04 10.92 0.01 0.11 11.21 0.20 1.76
acceleration after an abrupt deceleration (A-D) 7.21 7.13 0.02 0.36 7.31 0.19 2.62
acceleration after a 180° change of direction (A-COD) 5.42 5.34 0.01 0.30 5.33 0.06 1.15
repetitive high acceleration, shuttle runs 4 x 5m (RA-5m) 4.79 4.63 0.02 0.51 4.20 0.09 5.44
repetitive high acceleration, shuttle runs 4 x 10m (RA-10m) 7.05 6.91 0.03 0.47 6.46 0.12 3.25

The Coefficient of Variation (CV) is an indicator of the inter-unit reliability.

Fig 3. Speed curve of the laser signal, 6 GPS and 6 LPM signals during repetitive high-accelerated shuttle runs 4 x 5m (RA-5m).

Fig 3

Inter- and intra-unit reliability of maximal acceleration

The inter-unit and intra-unit reliability of amax, as measured by both GPS and LPM, is shown in Fig 2A as the between-device and within-device SDs of the percentage biases. Table 2 shows the CV of the GPS and LPM measurements, which can be also interpreted as an indicator of inter-unit reliability.

The inter-unit reliability of amax, as measured by GPS, ranged from 2.8 to 5.6% (expressed as the between-device SDs) for most of the tested actions, with the exceptions of LA (11.8%) and HA (12.0%). For LPM, the inter-unit reliability of amax ranged from 4.0 to 7.0%, with the exceptions of A-D (10.7%) and LA (14.6%).

The intra-unit reliability of amax, as measured by GPS, ranged from 3.7 to 28.4% (expressed as the within-device SDs). Accelerations initiated from a standstill (HA and LA) had the lowest intra-unit reliability (26.6% and 28.4%, respectively), while HA-flyingS and A-D had the highest (3.7% and 4.4%, respectively). For LPM, the intra-unit reliability of amax ranged from 5.3 to 18.2%, with the exception of LA (28.3%); HA and HA-flyingS had the highest intra-unit reliability (5.3% and 9.8%, respectively).

For GPS, the highest inter- and intra-unit reliability of amax—i.e., its summarized reliability—was found for HA-flyingS and A-D (6.5% and 9.5%, respectively), while HA and LA (38.6% and 40.3%, respectively) displayed the lowest summarized reliability. For LPM, HA generated the highest summarized reliability (9.0%) and LA the lowest (43.0%). The summarized reliability illustrates the percentage measurement error if a GPS or LPM device is randomly chosen for repeated measures.

Inter- and intra-unit reliability of maximal running speed

Fig 2B shows the inter- and intra-unit reliability of vmax measured by both GPS and LPM as between-device and within-device SDs of the percentage biases.

The overall inter- and intra-unit reliability of vmax was considerably higher than that of amax. The inter-unit reliability values for vmax were all smaller than 0.5% when measured by GPS and smaller than 2.6% when measured by LPM, across all subcategories.

Furthermore, the intra-unit reliability of vmax was lower than the inter-unit reliability for both systems, with the sole exception of A-D when measured by LPM. The intra-unit reliability was lower using LPM than GPS (GPS: between 0.2% and 1.0% and LPM: between 1.8% and 3.7%). Moreover, the GPS summarized reliability values for vmax were below 1.3% across all subcategories, while the LPM summarized values for vmax ranged from 2.9 to 5.6%.

Discussion

The present study examined validity and inter- and intra-unit reliability of two different position measurement systems (local and global), when evaluating the accuracy of acceleration measurements in different team sport-specific actions. In contrast to our hypothesis, the current data revealed that in most cases the validity of amax in team sport-specific actions was not lower when measured with the 15 Hz GPS than with the LPM system. However, both systems’ amax results demonstrated surprisingly large discrepancies compared to the “gold standard” laser measurements. On the other hand, the validity of vmax was generally good and comparable to the “gold standard” in both systems.

Inter-unit reliability of amax was similar across both systems, while inter-unit reliability of vmax was noticeably better with the GPS than with the LPM system. The inter-unit reliability of amax and vmax was generally better than the intra-unit reliability using both measurement systems.

Validity of the acceleration assessment

Overall, the results show that the validity for assessing amax using both systems is lower than the validity for assessing vmax. Although acceleration is the derivative of speed over time and it could be assumed that the amax biases would correlate to the vmax biases, it must be noted that the amax values were attained earlier than the vmax values in every action [21]. For this reason, the amax measurement errors did not correlate with the vmax ones. Furthermore, validity of amax (and vmax) not only depended on the type of measurement system, but also on the type of action being evaluated: both systems demonstrated the lowest validity of amax during actions with one COD (A-COD), and validity remained low in actions with multiple CODs (RA-5m, RA-10m). Furthermore, validity of amax was higher when high-accelerated actions are initiated from a flying start (HA-flyingS) than from standstill (LA, HA). The reason for the great difference in validity of amax between accelerations from standstill and from a flying start may be due to manufacturer’s filter settings which smoothed the raw data [35, 36]. The Kalman filter explicitly relies on predictions of the next measurement and therefore assumes predictable trajectories. However, keeping trajectories unpredictable is an important factor in team sports. As shown in Fischer-Sonderegger et al. [37], soccer players are often already in motion before high acceleration occurs, but also accelerate from standstill. Consequently, the MPBs of amax can vary greatly depending on the type of action or type of exercise. For example, MPBs are higher in small-sided games, where unpredictable CODs often occur, and lower during constant running drills that lack abrupt changes in direction or speed. Nonetheless, it is worth noting that validity of amax for actions with high accelerations, both from a standstill and from a flying start, were noticeably better when measured with the GPS than with the LPM system.

As recognized above, both measurement systems had the lowest validity of amax when accelerations occurred after an abrupt 180° COD (A-COD). During the trial with multiple CODs within 10 m (RA-10m), LPM had higher MPBs in amax than GPS. When the running distance between the CODs was reduced to 5 m (RA-5m), the validity of amax decreased further and was equally poor for both systems (–39.9% for GPS and –42.3% for LPM). As shown in Fig 3, LPM’s poor ability to assess abrupt CODs is illustrated by its incomplete deceleration curves prior to the next acceleration: even if the effective speed was 0 for a few tenths of a second during the COD, the measured speed, and therefore also the measured acceleration, never reached 0. This led to a considerably lower validity for LPM amax values when several CODs occurred within short time intervals and without long standstill phases. As mentioned previously, we assume that these incomplete deceleration curves were due to the use of Kalman filters with inadequate predictions [35, 36].

In addition, when acceleration occurred after an immediate deceleration but without a COD (A-D), the validity of amax was noticeably better than in trials with one or more CODs (A-COD, RA-5m, and RA-10m) and was relatively strong for both systems (GPS: 1.1%; LPM: –0.1%). One possible explanation for the large difference in validity of amax between A-D and A-COD, RA-5m, and RA-10m could be due to the Kalman filter settings. In contrast to A-COD, RA-5m, and RA-10m, the direction of A-D is already correctly predicted by the Kalman filter. This leads to the difference in validity between a stop-and-go with COD (A-COD, RA-5m and RA-10m) compared to without COD (A-D). However, this can only be speculated and would have to be verified in further studies with different Kalman filter settings. The chosen test setting allowed only the testing of the two extremes of a stop-and-go action, represented in the subcategories A-D versus A-COD, RA-5m, and RA-10m.

Validity of speed assessment

We found that the GPS systematically underestimated vmax; the MPBs of vmax ranged from –3.3 to −1.0%. These results are comparable to those of Lacome et al. [18], who found an overall bias of –3.0%. Buchheit et al. [32] observed a positive mean bias of vmax of 0.3% for 5 Hz GPS measurements. However, no abrupt 180° CODs were performed which produced the highest negative biases in our study. In contrast, the biases of vmax measured by the LPM system were unsystematic and ranged from –12.4 to 1.5%. Again, the results of this study are poorer than the results of Buchheit et al. [32], who measured a mean bias of vmax of 0.4% for LPM measurements. Similar to amax, validity of vmax was markedly lower in actions with multiple CODs than in linear movements: for GPS, the highest bias was –3.3% (RA-5m), while for the LPM system, the bias reached as high as –12.4%. (RA-5m). Again, the main reason for the LPM systems’ poor performance in these actions was likely due to the configuration of the Kalman filter, as noted previously [35, 36].

Inter- and intra-unit reliability

The inter-unit reliability (as measured by CV) for amax was in approximately the same range for both systems (GPS: CV 2.8 to 16.1%; LPM: CV 4.8 to 14.7%). The CV of amax depended on the type of action and was highest for both systems during low accelerations from a standstill. Buchheit et al. [32] reported comparable results for the LPM system (CV ± 10.0% depending on the type of action) but worse results for the GPS (CV >10.0%). The GPS results of inter-unit reliability of this study are similar to those found by Lacome et al. [18], who calculated a CV of 6.4% for amax during a 40 m sprint with a 16 Hz GPS.

The inter-unit reliabilities for both measurement systems were clearly better for vmax than for amax and the CVs for vmax in every subcategory were within the recommended 5% from Hopkins [38]. The only exception was the RA-5m measured with the LPM system (CV of 5.4%). In contrast, Buchheit et al. [32] found that CV values of vmax measured by GPS tend to be higher than those of the LPM system, and are considerably higher than the ones measured in this study. As with amax, inter-unit CV measures of vmax for GPS are similar to those found by Lacome et al. [18] (CV of 0.5%), but better than those reported in studies by Coutts and Duffield [6] (CV 2.3 to 5.8% for different types of GPS devices) and by Johnston et al. [11] (CV of 8.1%; 15Hz GPS).

Interestingly, inter-unit reliability of amax and vmax was generally better than intra-unit reliability for both measurement systems; the only exception to this was amax in A-D with GPS. A possible explanation is that inter-unit reliability was determined simultaneously within the same trial (the RCC carried six devices for each system), whereas the intra-unit reliability was determined by reproducing the same type of action several times throughout the day. Furthermore, satellite availability for the GPS measurements most likely did not remain the same. However, the reason(s) for the low LPM intra-unit reliability are not clear. Different battery charging status is plausible but unlikely. Further research is thus needed to elucidate the factors that influence LPM systems’ intra-unit reliability.

Comparison of local versus global positioning systems

Since LPM systems record with higher frequencies and their base stations are placed around a single site, it was assumed that measurements with the LPM systems would be more accurate than those with GPS. However, this hypothesis has not been confirmed. The validity of vmax and amax depended strongly on the types of action and were similar for both the GPS and the LPM system in most of the team sport-specific actions that were analyzed. The only exceptions were found in the HA and RA-10m trials for vmax, and RA-5m and RA-10m for amax respectively, where GPS surprisingly outperformed the LPM system. The LPM system has significant potential to produce more accurate measurements of speed and acceleration. To benefit from the high potential of the LPM system, it is proposed that sport-specific Kalman filter configurations need to be integrated. Likewise, integrating inertial sensors, such as accelerometers, could allow for further improvements to both GPS and LPM system measurements.

Study strengths and weaknesses

Due to the use of an RCC, the validity and inter- and intra-unit reliability of amax and vmax could be determined without any interference from upper body movements, which have previously been shown to negatively affect the accuracy of laser measurements [16, 2830]. Additionally, using a vehicle as a device carrier guaranteed laser measurements over a full soccer field without risking a loss of signal. Since the RCC runs on the steel ropes and a platform is attached on top of the RCC, six GPS and six LPM devices can record position measurements simultaneously and without obstructions in a horizontal position at shoulder height, simulating a player wearing the devices. This allowed us to compare the inter-unit reliability of multiple devices in the same trial. However, since the RCC mimicked team sport-specific actions on tensioned steel ropes, not all possible team sport-specific actions could be validated (e.g., acceleration after a COD with an angle of < 180°). However, all actions included in this study were very well recorded by a laser measurement setup.

The GPS and LPM systems’ biases were assessed in comparison to the laser system for each trial, and the results were then averaged to calculate the percentage bias. Thus, the laser system’s measurements were compared to GPS and the LPM system measurements for each trial, in order to avoid differences between trials caused by variation in the RCC’s operation, rather than the different measurement systems.

We are aware that the technology of position measurement systems is developing extremely fast and the recording frequency of GPS devices has been significantly increased in recent years (from 1 Hz to currently 18 Hz). Although position measurement systems with higher recording frequencies than the ones we have tested now exist, we suppose that the main results of this study can be nevertheless transferred to these systems. This is due to previous results showing that an increase in recording frequency did not automatically result in better data quality [11]. Measurement inaccuracies are likely due to the (wrong) filter configurations, rather than low recording frequencies. Furthermore, the LPM system does not make use of their integrated inertial sensors, although that might greatly improve the measurement accuracy, as they facilitate the differentiation and interpretation of various motor actions. We are convinced that these changes (appropriate filter settings, use of inertial sensors) would improve measurement accuracy to a greater extent than simply increasing the recording frequency, which currently seems to be the main target of the manufacturers.

Conclusion

Although validity of vmax for both systems was generally good (except for LPM measurements during trials with CODs), validity of amax was very low when measured by GPS, and, surprisingly, not higher when measured by the LPM system. The accuracy of amax depended on the type of action, and was considerably lower when actions included any CODs. The Kalman filter configurations are the most likely explanation for this inaccuracy. Given our current knowledge of tracking system accuracy, it is debatable whether acceleration measurements should be included in game or training analyses.

However, acceleration measurements are fundamental to adequately describe physical loads in team sports, and improvements to Kalman filter properties are therefore necessary if team sports with different accelerations and CODs are to benefit from position-tracking systems. In our opinion, using the tested systems in team sports without filter improvements to measure accelerations during CODs is worthless due to the large measurement errors. Nevertheless, if teams do choose to include acceleration as an indicator of physical load, despite the recognized limitations in measuring it, it is essential that they interpret their results with caution.

Supporting information

S1 Data

(XLSX)

Acknowledgments

The authors would like to thank Andreas Christen for remodeling the RCC so that it could be used in this study and for serving as the RCC’s driver. We would also like to thank Severin Trösch for his valuable support with statistics.

Data Availability

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

The authors received no specific funding for this work.

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Decision Letter 0

Cristina Cortis

19 Aug 2020

PONE-D-20-09964

How far from the gold standard? Comparing the accuracy of a local position measurement (LPM) system and a 15 Hz GPS to a laser for measuring acceleration and running speed during team sports

PLOS ONE

Dear Dr. Fischer-Sonderegger,

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PLOS ONE

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1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: Partly

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Introduction

The introduction is quite long and needs some adjustment. Overall, authors should consider that introduction should lead the reader to understand the current literature on the selected topic and the rationale for the study. In this section as it stands there are many indications about the methods used in the study as well as the what it has been done in this study. Please re-consider this part taking also into considerations the following suggestions:

Line 49: it is necessary referencing the first sentence

Line 51-62: Also here are present too many general information that should be sustained by the use of referenes

Line 74: I would suggest avoiding the use of questions which is more colloquial and not fully suitable in a scientific paper

Line 89-95: this part should be deleted from introduction since it applies to method section

Line 177: “Our investigation is the first to…” This applies to a possible discussion section rather than introduction.

Methods

Line 138: why different number of trials were assessed for each sub-category? Please specify.

Table 1: how the categories were identified? For instance, based on what an acceleration was considered low medium or high? Much more information is required about this categories’ selection and in general about the methods applied. For instance, on which distances were measured, which accelerations and decelerations were adopted etc.

Results

Line 210: abbreviations should be expressed in the text and not with reference to table. Please change accordingly.

Discussion

This section is well-written and easy to follow. I would just suggest to include the limitations of the study and some practical applications for the use of GPS and LPS.

Reviewer #2: General Comments:

The current paper investigated the validity, inter and intra-unit reliability of Local and Global position measurement systems compared to laser measurements. The paper is generally, well written with few editorial concerns (see Specific Comments). However, there is a major problem in why the authors used a filtering technique that they acknowledge was most likely inappropriate for the data (see Specific Comments). Furthermore, in this reviewer’s opinion, they should have been more forceful in their Conclusion by stating the unsuitability of using these devices for determining acceleration during change of direction. As this type of movement is extremely common in sport (as noted by the authors) and the error so large, the methodology is of virtually no use.

Specific Comments:

Line 31-35: Somewhat a run on sentence, which I would suggest breaking apart as follows: "Mean percentage biases (MPBs) of maximal acceleration (amax) and maximal running speed (vmax) were used to measure validity, and mean between-device. Mean within-device standard deviations of the percentage biases (bd-SD and wd-SD) of amax and vmax were used to measure inter- and intra-unit reliability, respectively."

Lines 92-95: What about making the car turn? Although it would not be an exact diagonal movement, it could simulate that sort of change. Even if this is an inappropriate type of movement (i.e. rounding off the direction), the authors should probably address it.

Line 111: Suggest inserting "as a device carrier."

Line 208: The term "MPB" is not defined until Line 225-226. While "MPBs" is defined in the abstract, it should be here as well. Please correct.

Line 210: Please capitalize "Table".

Table 2: Nearly all of the CVs are above 5%, a level that Hopkins suggests renders the performance near useless when considering repeatability (http://www.sportsci.org/resource/stats/index.html). Therefore how practical is it to use these devices for determining acceleration? As that is one of the objectives of the current study, how can the authors suggest the Methods are valid/reliable? (Please see comment in the Conclusion)

Line 222: Is this sentence supposed to be a footnote to the Table? If so, I'd suggest indenting it. Suggest doing the same for Line 254.

Lines 232-234: This reviewer would suggest a bit more explanation as to the "combination" referred to here. I understand that it appears to just be summing them, but why is this useful. An explanation would make it more clear to the reader.

Lines 246-247: Do the authors have any explanation for this variation? There doesn't appear to be anything noted in the Discussion.

Lines 312-316: As the authors were aware of this limitation, why not try a different type of filter? Would it not be possible to use the raw data and do the filtering outside of the device if the filter system was within the device? If this was impossible, then the authors should state why this approach was not used.

Lines 339-340: Please provide some explanation as to why you would make this assumption.

Lines 365-367: While the GPS CVs were almost 10 times lower than LPM, is this really a major issue? Both are below the values recommended by Hopkins for CV (except for RA-5, which is just over 5%), so maybe it doesn't really matter...

Lines 435-436: I would go even further than this caveat. The method should NOT be used for COD, where the error was so high as well as lagging behind.

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Decision Letter 1

Cristina Cortis

2 Mar 2021

PONE-D-20-09964R1

How far from the gold standard? Comparing the accuracy of a local position measurement (LPM) system and a 15 Hz GPS to a laser for measuring acceleration and running speed during team sports

PLOS ONE

Dear Dr. Fischer-Sonderegger,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

As you can see from the reviews' comments, the manuscript vastly improved. There are only few minor comments to be addressed before accepting the paper for publication.

Please submit your revised manuscript by Apr 16 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Cristina Cortis, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors addressed all my previous comments improving the quality of the manuscript. I would just suggest the addition of this reference, which would be fully suitable in their manuscript:

Conte, D. (2020). Validity of local positioning systems to measure external load in sport settings: a brief review. Human Movement, 21(1).

Reviewer #2: General Comments:

The current version of the manuscript is much improved. There remain a few editorial issues (see Specific Comments), but otherwise only one area of content that should be addressed. I would encourage the authors to provide some explanation for why the validity of acceleration was so different when there were multiple accelerations (see Specific Comments). While they have previously stated their reluctance to speculate on outcomes (an admirable trait), they could still provide a possible explanation, which would provide readers with a basis for what should be examined further.

Specific Comments:

Lines 112-113: Single paragraph sentences should be avoided, if possible. That could be accomplished easily by just combining these two paragraphs. The second is really just carrying on from the first anyway.

Line 127: Suggest replacing "differed" with "differing".

Line 154: Maybe insert "by the device's software". This would assist in indicating that you used the software of the device manufacturer, and didn't filter the data any further.

Lines 169-170: I still have a problem with "linear and multi-directional" being used to describe the movements. Although the direction changed 180 degrees, it was still linear to my way of thinking; as opposed to the "turning" aspect mentioned in the first review. Can the authors come up with a different word for "multi-directional"? I'd encourage that if it were possible. Maybe "forward and backward" or something like that...

Lines 223-229: It is not clear why there are bolded phrases in these lines. If they are to indicate where the figures go, those lines should be separated out from the text, as was done for Figure 1. Otherwise, this section is awkward and difficult to follow what the authors are getting at here.

Lines 233-236: These lines appear to be combined sentences that don't match up. The version in the tracked changes seems to read correct, but this one does not. I think the words as follows need to be removed: "The summarized reliability illustrates the percentage measurement error if a GPS or LPM device is randomly chosen for repeated measures."

Line 318: Typo "smoothed"

Lines 342 & 344: Suggest inserting a comma after "RA-5m". It helps to clarify that you are referring to how A-D differs from all three of the other conditions "A-COD, RA-5m, and RA-10m".

Lines 344-346: Could the authors suggest a possible reason as to why this difference may have occurred? Perhaps it was due to the multiple accelerations that took place in the A-COD, RA-5m, and RA-10m. This is somewhat alluded to in lines 340-342, where the authors state "In addition, when acceleration occurred after an immediate deceleration but without a COD (A-D), the validity of amax was noticeably better than in trials with one or more CODs (A-COD, RA-5m and RA-10m)". Is it possible that the multiple accelerations were "averaged" or the effect was additive? While the authors may not have data to explicitly state this, they could note that additional work could be done to examine this possibility.

Line 406: Suggest replacing "on" with "at".

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Decision Letter 2

Cristina Cortis

12 Apr 2021

How far from the gold standard? Comparing the accuracy of a local position measurement (LPM) system and a 15 Hz GPS to a laser for measuring acceleration and running speed during team sports

PONE-D-20-09964R2

Dear Dr. Fischer-Sonderegger,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Additional Editor Comments:

The authors succesfully addressed all the comments and suggestions from the reviewer, and I think the paper can be now accepted for publication.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No further comments. The authors addressed all the comments and the manuscript reached a level suitable for publication.

Reviewer #2: (No Response)

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

Acceptance letter

Cristina Cortis

15 Apr 2021

PONE-D-20-09964R2

How far from the gold standard? Comparing the accuracy of a local position measurement (LPM) system and a 15 Hz GPS to a laser for measuring acceleration and running speed during team sports

Dear Dr. Fischer-Sonderegger:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

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on behalf of

Prof. Dr. Cristina Cortis

Academic Editor

PLOS ONE

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