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Real-time versus post-game GPS data in team sports
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Journal of Science and Medicine in Sport 13 (2010) 348–349
Original paper
Real-time versus post-game GPS data in team sports Robert J. Aughey a,b,∗ , Cameron Falloon b a
Centre for Ageing, Rehabilitation, Exercise & Sport Science, School of Sport and Exercise Science, Victoria University, Australia b Western Bulldogs Football Club, Australia Received 14 August 2008; received in revised form 16 December 2008; accepted 16 January 2009
Abstract Real-time (RT) GPS is used to monitor performance during Australian Football matches. Typically athlete targets monitored in RT are set from post-game (PG) data. The validity of RT compared to PG data is not yet known. This study compared RT data for key parameters to those obtained PG, using MinimaxX GPS. RT was different to PG for jog 4.2–5.0, run 5.0–6.9, sprint 6.9–10.0 m s−1 , and total distance (CV = 6.4–19.6%). The signal to noise ratio was low for jog and run distance. For sprint noise exceeded the signal, with the opposite true for total distance. Caution must be applied if using RT data to monitor performance, especially if targets are set for players using PG data. © 2009 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved. Keywords: Sports performance; Global positioning systems
1. Introduction The analysis of player performance during Australian Football (AF) provides information on physiological requirements of games that can subsequently be used to enhance the specificity of training to better prepare athletes for competition. Time motion analysis (TMA) has been used to analyse player performance.1,2 This process has a number of limitations that include: the time taken to complete analyses; the definition of movement categories; parallax error; and lack of reliability.3 The global positioning system (GPS) has been applied to team sports, in an attempt to overcome some of the problems with TMA. There are several advantages of using the GPS for tracking AF athletes including: the ability to monitor multiple players at once; the time effectiveness of analysis; and the ability to receive information in real-time (RT) during matches. Information regarding the accuracy, reliability and validity of GPS for AF player tracking is, however, sparse. The error with 1 Hz GPS (GPSports, Spi10) was approxi∗ Corresponding author at: School of Human Movement, Recreation and Performance, Victoria University, Australia. E-mail address: [email protected] (R.J. Aughey).
mately 5% when compared to distance measured by a trundle wheel,4 which may be too large to detect subtle changes in player performance.5 Despite the lack of supporting literature, many AF teams have commenced using RT GPS data to make decisions on player performance and interchange strategies during matches, with targets set based on historical data, usually obtained with post-game (PG) analysis. The validity of this data compared to PG has not yet been established, and this was therefore the aim of this study.
2. Methods Twelve elite AF athletes from one Australian Football League (AFL) club participated in this study. This study conformed to the National Health and Medical Research Council’s Statement on Human Experimentation. All athletes gave informed consent. Players wore GPS units (MinimaXX, Team Sport 2.0, Catapult Innovations) in a pocket sewn in the playing jersey during two elite AFL matches during the 2008 season. Data were analysed per quarter of match played, with no data recorded for analysis when players were interchanged off the field of play.
1440-2440/$ – see front matter © 2009 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.jsams.2009.01.006
R.J. Aughey, C. Falloon / Journal of Science and Medicine in Sport 13 (2010) 348–349
349
Table 1 Differences between real-time (RT) and post-game (PG) running data per quarter. Data expressed as mean ± S.D. RT PG Mean difference (%) Range (%) Typical error (m) Smallest meaningful difference (m) Coefficient of variation (%) Pearson’s R
Jog (m)
Run (m)
Sprint (m)
Total distance (m)
367 ± 144 440 ± 198 −1 ± 9 −14 to 9 30.1 33.5 8.2 0.97
488 ± 193 450 ± 194 9±9 −10 to 35 31.3 31.9 6.4 0.96
121 ± 110 98 ± 105 24 ± 35 −85 to 100 23.7 17.3 19.6 0.93
3378 ± 702 3223 ± 798 5 ± 11% −7 to 36 55.8 134.6 9.6 0.97
Distance covered in a quarter was compared for RT and PG for our custom speed zones jog 4.2–5.0, run 5.0–6.9, sprint 6.9–10.0 m s−1 , and total distance. Importantly, an upper limit for sprint was unable to be set in RT. Total distance covered included these bands and any movement <4.2 m s−1 . For each variable, the mean ± S.D., absolute and percentage difference were calculated for both RT and PG. The standard error of the mean which in plain language is the typical error of measurement6 as an absolute (TE) and percentage of the mean (CV)6 was calculated, and Pearson’s correlations performed. The utility of RT data was established by comparing the calculated smallest meaningful difference (SMD)7 in each variable (signal) for PG to the TE (noise). All data was analysed in a custom spreadsheet.8
3. Results The distance and player load data for both RT and PG are presented in Table 1. Correlations between RT and PG were strong for all parameters, but difference in the mean and total error (TE) was large with a wide range of scores (Table 1). The signal (SMD) was similar to the noise (TE) for jog and run distance (Table 1). Sprint noise exceeded the signal strength, with the opposite true for total distance covered (Table 1).
4. Discussion The RT data collected in this study showed a surprisingly low concurrent validity against PG data. Moreover, the range of error casts serious doubt over the utility of RT data for making decisions on player performance during AF, if compared to PG data. AF involves repeated bouts of short-duration high intensity activity, with periods of moderate running1 and high player fatigue. Strategies to overcome fatigue during matches include regular interchange of players for periods of rest (rotations). Increasingly in AF, coaches are utilising RT data to support decisions rotations during matches. In order to utilise performance data, the signal (SMD) must be greater than the noise (TE) in the test. For jog and run distance, the TE was only slightly less that the SMD, whilst for
sprint TE was greater. Only total distance showed an acceptable signal: noise ratio. More alarming is that the actual sprint distance could be between 85% lower, and 100% higher than shown in RT. Given the magnitude of error present, targets set for players to be monitored in RT must take into account the large error present. Sport scientists should also be aware that the error is greatest for higher velocity running. It is unclear as to the source of error between RT and PG data. Possibly the algorithms for calculating distance in speed zones were different in RT compared to PG, although this does not explain differences for total distance covered. For sprint running, it may be due to not being able to set an upper velocity limit for this speed zone. This does not, however account for differences in other locomotor bands. It is possible that interference in the RT signal may be a partial cause of error. If the RT signal was weak, not all data would be accounted for in RT. This does not explain the range of the differences between RT and PG data. Caution is required when using RT data to monitor performance during AF matches. Coaches should be aware of the magnitude and likely range of error, and account for this when setting targets for players to be monitored by RT data.
References 1. Dawson B, Hopkinson R, Appleby B, Stewart G, Roberts C. Player movement patterns and game activities in the Australian Football League. J Sci Med Sport 2004;7:278–91. 2. Dawson B, Hopkinson R, Appleby B, Stewart G, Roberts C. Comparison of training activities and game demands in the Australian Football League. J Sci Med Sport 2004;7:292–301. 3. Dobson B, Keogh J. Methodological issues for the application of timemotion analysis research. Strength Cond J 2007;29:48–55. 4. Edgecomb SJ, Norton KI. 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. 5. Hopkins WG, Hawley JA, Burke LM. Design and analysis of research on sport performance enhancement. Med Sci Sports Exerc 1999;31:472–85. 6. Hopkins WG. Measures of reliability in sports medicine and science. Sports Med 2000;30:1–15. 7. Batterham A, Hopkins WG. Making meaningful inferences about magnitudes. Int J Sports Physiol Perform 2006;1:50–7. 8. Hopkins WG. Reliability from consecutive pairs of trials (Excel spreadsheet). Sportscience: A new view of statistics. Sportsci.org. Internet Society for Sport Science; 2000.
Journal of Science and Medicine in Sport 13 (2010) 348–349
Original paper
Real-time versus post-game GPS data in team sports Robert J. Aughey a,b,∗ , Cameron Falloon b a
Centre for Ageing, Rehabilitation, Exercise & Sport Science, School of Sport and Exercise Science, Victoria University, Australia b Western Bulldogs Football Club, Australia Received 14 August 2008; received in revised form 16 December 2008; accepted 16 January 2009
Abstract Real-time (RT) GPS is used to monitor performance during Australian Football matches. Typically athlete targets monitored in RT are set from post-game (PG) data. The validity of RT compared to PG data is not yet known. This study compared RT data for key parameters to those obtained PG, using MinimaxX GPS. RT was different to PG for jog 4.2–5.0, run 5.0–6.9, sprint 6.9–10.0 m s−1 , and total distance (CV = 6.4–19.6%). The signal to noise ratio was low for jog and run distance. For sprint noise exceeded the signal, with the opposite true for total distance. Caution must be applied if using RT data to monitor performance, especially if targets are set for players using PG data. © 2009 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved. Keywords: Sports performance; Global positioning systems
1. Introduction The analysis of player performance during Australian Football (AF) provides information on physiological requirements of games that can subsequently be used to enhance the specificity of training to better prepare athletes for competition. Time motion analysis (TMA) has been used to analyse player performance.1,2 This process has a number of limitations that include: the time taken to complete analyses; the definition of movement categories; parallax error; and lack of reliability.3 The global positioning system (GPS) has been applied to team sports, in an attempt to overcome some of the problems with TMA. There are several advantages of using the GPS for tracking AF athletes including: the ability to monitor multiple players at once; the time effectiveness of analysis; and the ability to receive information in real-time (RT) during matches. Information regarding the accuracy, reliability and validity of GPS for AF player tracking is, however, sparse. The error with 1 Hz GPS (GPSports, Spi10) was approxi∗ Corresponding author at: School of Human Movement, Recreation and Performance, Victoria University, Australia. E-mail address: [email protected] (R.J. Aughey).
mately 5% when compared to distance measured by a trundle wheel,4 which may be too large to detect subtle changes in player performance.5 Despite the lack of supporting literature, many AF teams have commenced using RT GPS data to make decisions on player performance and interchange strategies during matches, with targets set based on historical data, usually obtained with post-game (PG) analysis. The validity of this data compared to PG has not yet been established, and this was therefore the aim of this study.
2. Methods Twelve elite AF athletes from one Australian Football League (AFL) club participated in this study. This study conformed to the National Health and Medical Research Council’s Statement on Human Experimentation. All athletes gave informed consent. Players wore GPS units (MinimaXX, Team Sport 2.0, Catapult Innovations) in a pocket sewn in the playing jersey during two elite AFL matches during the 2008 season. Data were analysed per quarter of match played, with no data recorded for analysis when players were interchanged off the field of play.
1440-2440/$ – see front matter © 2009 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.jsams.2009.01.006
R.J. Aughey, C. Falloon / Journal of Science and Medicine in Sport 13 (2010) 348–349
349
Table 1 Differences between real-time (RT) and post-game (PG) running data per quarter. Data expressed as mean ± S.D. RT PG Mean difference (%) Range (%) Typical error (m) Smallest meaningful difference (m) Coefficient of variation (%) Pearson’s R
Jog (m)
Run (m)
Sprint (m)
Total distance (m)
367 ± 144 440 ± 198 −1 ± 9 −14 to 9 30.1 33.5 8.2 0.97
488 ± 193 450 ± 194 9±9 −10 to 35 31.3 31.9 6.4 0.96
121 ± 110 98 ± 105 24 ± 35 −85 to 100 23.7 17.3 19.6 0.93
3378 ± 702 3223 ± 798 5 ± 11% −7 to 36 55.8 134.6 9.6 0.97
Distance covered in a quarter was compared for RT and PG for our custom speed zones jog 4.2–5.0, run 5.0–6.9, sprint 6.9–10.0 m s−1 , and total distance. Importantly, an upper limit for sprint was unable to be set in RT. Total distance covered included these bands and any movement <4.2 m s−1 . For each variable, the mean ± S.D., absolute and percentage difference were calculated for both RT and PG. The standard error of the mean which in plain language is the typical error of measurement6 as an absolute (TE) and percentage of the mean (CV)6 was calculated, and Pearson’s correlations performed. The utility of RT data was established by comparing the calculated smallest meaningful difference (SMD)7 in each variable (signal) for PG to the TE (noise). All data was analysed in a custom spreadsheet.8
3. Results The distance and player load data for both RT and PG are presented in Table 1. Correlations between RT and PG were strong for all parameters, but difference in the mean and total error (TE) was large with a wide range of scores (Table 1). The signal (SMD) was similar to the noise (TE) for jog and run distance (Table 1). Sprint noise exceeded the signal strength, with the opposite true for total distance covered (Table 1).
4. Discussion The RT data collected in this study showed a surprisingly low concurrent validity against PG data. Moreover, the range of error casts serious doubt over the utility of RT data for making decisions on player performance during AF, if compared to PG data. AF involves repeated bouts of short-duration high intensity activity, with periods of moderate running1 and high player fatigue. Strategies to overcome fatigue during matches include regular interchange of players for periods of rest (rotations). Increasingly in AF, coaches are utilising RT data to support decisions rotations during matches. In order to utilise performance data, the signal (SMD) must be greater than the noise (TE) in the test. For jog and run distance, the TE was only slightly less that the SMD, whilst for
sprint TE was greater. Only total distance showed an acceptable signal: noise ratio. More alarming is that the actual sprint distance could be between 85% lower, and 100% higher than shown in RT. Given the magnitude of error present, targets set for players to be monitored in RT must take into account the large error present. Sport scientists should also be aware that the error is greatest for higher velocity running. It is unclear as to the source of error between RT and PG data. Possibly the algorithms for calculating distance in speed zones were different in RT compared to PG, although this does not explain differences for total distance covered. For sprint running, it may be due to not being able to set an upper velocity limit for this speed zone. This does not, however account for differences in other locomotor bands. It is possible that interference in the RT signal may be a partial cause of error. If the RT signal was weak, not all data would be accounted for in RT. This does not explain the range of the differences between RT and PG data. Caution is required when using RT data to monitor performance during AF matches. Coaches should be aware of the magnitude and likely range of error, and account for this when setting targets for players to be monitored by RT data.
References 1. Dawson B, Hopkinson R, Appleby B, Stewart G, Roberts C. Player movement patterns and game activities in the Australian Football League. J Sci Med Sport 2004;7:278–91. 2. Dawson B, Hopkinson R, Appleby B, Stewart G, Roberts C. Comparison of training activities and game demands in the Australian Football League. J Sci Med Sport 2004;7:292–301. 3. Dobson B, Keogh J. Methodological issues for the application of timemotion analysis research. Strength Cond J 2007;29:48–55. 4. Edgecomb SJ, Norton KI. 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. 5. Hopkins WG, Hawley JA, Burke LM. Design and analysis of research on sport performance enhancement. Med Sci Sports Exerc 1999;31:472–85. 6. Hopkins WG. Measures of reliability in sports medicine and science. Sports Med 2000;30:1–15. 7. Batterham A, Hopkins WG. Making meaningful inferences about magnitudes. Int J Sports Physiol Perform 2006;1:50–7. 8. Hopkins WG. Reliability from consecutive pairs of trials (Excel spreadsheet). Sportscience: A new view of statistics. Sportsci.org. Internet Society for Sport Science; 2000.