GPS IN SPORT: ANALYSIS AND DETERMINATION OF FITNESS LEVELS
Final Year Thesis Submitted in partial fulfilment
of the requirements of GMAT4001
by
Daniel Abel Kurzawa
School of Surveying and Spatial Information Systems
University of New South Wales Kensington, NSW 2052
Submission: October, 2008
Thesis Committee:
Coordinator: Professor Chris Rizos .
Supervisor: ____________Dr Jinling Wang_____________
I declare that this assessment item is my own work, except where acknowledged, and has not been submitted for academic credit elsewhere, and acknowledge that the assessor of this item may, for the purpose of assessing this item: Reproduce this assessment item and provide a copy to another member of the University; and/or, Communicate a copy of this assessment item to a plagiarism checking service (which may then retain a copy of the assessment item on its database for the purpose of future plagiarism checking). I certify that I have read and understood the University Rules in respect of Student Academic Misconduct. Signed: ....................................................date:
Abstract The purpose of this thesis is to identify how various aspects of physical performance in
sport can be measured by tracking a subject in question using the Global Positioning
System as the primary tool for location determination. Some of these aspects of
performance include top speed, average speed, distance covered, and acceleration. The
equipment had to be tested first to determine its accuracy to ensure good results.
By researching how GPS can be augmented for better integrity, accuracy, availability and
continuity, it was identified how various methods are being used with standalone GPS to
enhance its value.
The thesis engaged both fieldwork and post-processing of several data-sets. The
fieldwork component involved collecting real match data using GPS technology in a
number of local touch football games, so as to achieve a good proportion of results for
comparison. Post-processing of the data included breaking down and summarising this
data to analyse for the various aspects of performance, identifying how powerful GPS
based technology is in the sports analysis industry.
Other data sets from a professional club were obtained and analysed, and an effort was
made to compare and contrast this data with the data personally collected. As a result of
the analysis of the various data sets, several graphs and tables were able to be determined.
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Table of Contents
ABSTRACT .................................................................................................................................................................. I TABLE OF CONTENTS ............................................................................................................................................II ACKNOWLEDGEMENTS ...................................................................................................................................... III 1. INTRODUCTION...............................................................................................................................................1 2. SCOPE OF PROJECT.......................................................................................................................................2 3. VALUE ASSESSMENT .....................................................................................................................................3 4. LITERATURE REVIEW...................................................................................................................................6
4.1 PROPERTIES OF GPS.....................................................................................................................................6 4.2 TYPES OF GPS TRACKERS............................................................................................................................6
4.2.1 Data Loggers ...............................................................................................................................................6 4.2.2 Data Pushers................................................................................................................................................8 4.2.3 Data Pullers ...............................................................................................................................................10
4.3 WHY GPS AUGMENTATION? .....................................................................................................................11 4.4 AUGMENTATION METHODS........................................................................................................................11
4.4.1 Receiver Algorithms...................................................................................................................................12 4.4.2 Global Navigation Satellite Systems ..........................................................................................................12 4.4.3 GPS Modernisation....................................................................................................................................13 4.4.4 Wide Area Augmentation Systems..............................................................................................................13 4.4.5 Local Area Augmentation Systems.............................................................................................................14
4.5 AUGMENTATION USING ADDITIONAL ON-BOARD SENSORS.......................................................................15 4.4.1 Altimeters ...................................................................................................................................................15 4.4.2 Compasses .................................................................................................................................................16 4.4.3 Accelerometers...........................................................................................................................................16 4.4.4 Gyroscopes.................................................................................................................................................17 4.4.5 Inertial Navigation Systems .......................................................................................................................18
5. EQUIPMENT TESTS ......................................................................................................................................20 5.1 SPEED DETERMINATION TEST ....................................................................................................................20
5.1.1 Speed Determination Test without Cruise Control ....................................................................................20 5.1.2 Speed Determination Test with Cruise Control .........................................................................................22
5.2 UNIT COMPARISON TEST............................................................................................................................25 5.3 POSITION TEST ...........................................................................................................................................27
6. METHOD OF DETERMINING RESULTS (TOUCH FOOTBALL) .........................................................32 7. SUMMARY OF RESULTS (TOUCH FOOTBALL)................................................................................39 8. SUMMARY OF RESULTS (1ST GRADE AFL)........................................................................................44 9. COMPARISON AND DISCUSSION OF RESULTS................................................................................47 10. CONCLUDING REMARKS.......................................................................................................................51 11. BIBLIOGRAPHY ........................................................................................................................................53 12. REFERENCES.............................................................................................................................................55 13. APPENDIX ...................................................................................................................................................57
13.1 SCIMS SSM 42318 ...................................................................................................................................57
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Acknowledgements
Many thanks go out to a number of people. Firstly thanks to Dr Jinling Wang for his
guidance & direction. Great thanks also to Songlai Han for his time and assistance in
downloading data and interpreting results.
A big thank you to Stuart Cormack and the West Coast Eagles football club, for being the
only professional football club willing to provide GPS match data from first grade
athletes.
Special thanks also to Ben Grice & Ric Elysee-Collen for collecting GPS data from a few
matches in the touch football season.
Thanks to Sasha Malinic for providing the use of the Mazda MX-6 for collection of data
at cruise control for the “speed determination test”. Thanks also to Mark from CAD
Consulting for providing SCIMS data for the “position determination test”
Gratitude also goes out to anyone that hasn’t been mentioned but provided some form of
assistance during the progression of this thesis.
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1. Introduction For the purpose of this thesis, it was determined that the SPI-Elite device available from
GPSports would be used to undertake all data collection in this project. This is because the SPI
Elite device is a small and robust unit tailored for use in many sporting applications.
The SPI Elite device not only houses the GPS unit itself, but in addition is coupled with a tri-
axial accelerometer, and also has the onboard capacity to collect in excess of 14,000 observations
(which translates to approximately four hours of data records captured at one second intervals,
but is claimed to log seven plus hours). These observations can then be downloaded via USB to
the computer, and analysed using the software available. The accelerometer assists in more
accurate distance, speed and acceleration results being obtained, and GPSports (2007) claim that
“the SPI Elite distance and speed results have been recently validated with less than a 1% error in
true outputs”.
Using the observations recorded by the unit, further analysis was undertaken to break-down the
raw data for fitness determination.
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2. Scope of Project
The scope of the project, as aforementioned in the Abstract, was primarily in the capture and
analysis of sports data using the GPS tracking technology to track and record player movements
in the real-match sporting situations of touch football. This data was analysed using several
methods, thus determining various aspects of player performance and match statistics. Data was
collected from eight separate touch football games, with a total of eleven data-sets being
collected from these matches.
Three separate equipment tests were determined, with the associated data-sets being processed
and analysed in determining the accuracy of the devices. Two data sets were also obtained from a
professional club, with these being analysed and compared with the data-sets collected.
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3. Value Assessment Advances in technologies available for on-field sports performance have increased immensely in
recent years. The use of motion analysis technology allows for individual monitoring and gives
useful feedback for coaches and fitness staff. GPS technologies are amongst those offering good
precision in performance analysis through position and velocity of motion. Many clubs already
use GPS coupled devices for tracking players and analysis of fitness performance. These clubs
use the SPI Elite device available from GPSports, and include 15 of the AFL teams, the Manly
Sea Eagles NRL team, the Melbourne Victory Football Club and both the Australian and New
Zealand Rugby Union sides.
It is interesting to note that of the few NRL clubs that use the GPSports technology, both the
Manly and Melbourne coaches have praised the effectiveness of the technology in providing
greater insight into player performance, with both of those teams making the Grand Final in
2008. Des Hasler, head coach of the Manly Sea Eagles, comments that “This insight has been
instrumental in the development and maintenance of our training protocols to ensure optimal
results in competition” (GPSports, 2008).
With the development of low-cost augmented GPS tracking devices, various other individuals
can now also access this same or similar technology for accurate determination of individual
sports performance. This includes anyone that may be a sports enthusiast, or may want to
undertake a fitness program to increase their physical endurance. The thesis looks at analysing
how such data captured can be analysed for sports performance, by utilising the SPI Elite GPS
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tracking unit in real match touch football games. This thesis will display how the GPS tracking
technologies can be used in performance analysis in sport by offering good precision in
positioning, distance derivation and velocity of motion.
Another value of the thesis topic is in the ability to establish interest in the field of surveying to
potential undergraduate students. This was evident during the engineering open day of this year,
where high school students came around during the day to the surveying table and witnessed a
short presentation showing the “iSPI” application that synchronises the data obtained from the
SPI Elite GPS unit and video footage of a hockey game, shown in Figure 1. The program also
displays graphically the position of the players on the field along with their movements.
Figure 1 “iSPI” application shown during the open day. Top section shows player stats, bottom right shows
movements depicted on the field, left shows video. Video image was lost during the print-screen.
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When talking to the young people it was evident some were quite interested in the application of
GPS in sport, with many stopping during the day to observe the “iSPI” application. By explaining
how aspects of performance can be measured and making connections between sport and
GPS/surveying, quite a few were interested in further information about the Surveying and SIS
degree.
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4. Literature Review
4.1 Properties of GPS
Standalone GPS receivers can only achieve accuracies of around 10m even with selective
availability switched off (Rupprecht, 2007). This might not be a factor when applying GPS
tracking to large scale navigation applications such as cargo-hauling on large ocean liners, where
measurements are recorded at greater time intervals such as every minute or so, and over large
distances. It may however be an issue when recording data at one second intervals and over a
small area. Luckily however, in terms of this thesis, position in itself is not of fundamental
importance, but speeds and distances observed by the SPI-Elite unit, with position only being
used as an aid in visual interpretation.
4.2 Types of GPS Trackers
Normally GPS trackers will fall into one of three categories. These categories include data
loggers, data pushers and data pullers.
4.2.1 Data Loggers
Data loggers simply record the position of the device at regular intervals onto the internal
memory of the unit (Wikipedia, 2008). Modern GPS data loggers have either a memory card slot
with data being recorded onto the memory card, or internal memory and a USB port which
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allows the data to be recorded on the internal memory then downloaded onto a computer for
further analysis and display of results. It is more advantageous to have a data logger which
records onto memory cards, as it is easier to purchase a larger card if requiring more data capture
than to change the devices whole internal memory.
Data loggers are mentioned by Wikipedia (2008) to be most suited for use in sport, and are
attached to the individual being monitored, with the data later downloaded and analysed for
factors such as the path taken, speeds, distances, and timing. These paths can also be
superimposed on maps with the aid of GIS software. Wikipedia (2008) gives an example of a
professional sport using GPS data loggers for the purpose of determining results as gliding,
where competitors are required to fly over circuits of hundreds of kilometres, and the GPS data
loggers are then used to prove that the participant has completed the course fairly. The data is
stored over many hours, and is then downloaded from the data logger to a computer and
calculated for the competitors’ time and hence determining a winner (Wikipedia, 2008). Other
team sports such as soccer, AFL, rugby league, rugby union and hockey can use data loggers
such as the SPI Elite unit to track athletes and evaluate their performances. The unit is shown in
Figure 2 below.
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Figure 2 The SPI Elite unit is an example of a “data logger (GPSports, 2007)”
It is also mentioned by Wikipedia (2008) that data loggers can also be correlated with photos
taken by digital cameras, where the camera saves the time a photo is taken, and provided the
camera clock is accurate enough, time can be combined with GPS log data to determine an
accurate location of the object. This application is known as geo-tagging.
4.2.2 Data Pushers
Data Pushers are GPS tracking units which are primarily used by the security industry, and send
the position of the unit at regular intervals to a server that has the ability to instantly update and
analyse location, direction, speed and distance and thus plotting the data (Tech-FAQ, 2008).
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TechFAQ (2008) states that data pushers are commonly used in fleet control to manage trucks
and various other vehicles, allowing for these vehicles to be instantly located and at the same
time monitoring their progress. Further applications of GPS data pushers, mentioned by
Wikipedia (2008) includes in search of stolen vehicles, animal control, race control, and in
espionage or surveillance. In fleet control, a delivery or taxi company for example can use such a
tracking unit on its vehicles to determine if the vehicle is on time or late, or doing its assigned
route (Wikipedia, 2008). Some owners may choose to put a tracker in the car that can be
activated if the car is car jacked or stolen, which is done by means of a command issued to the
tracker which then allows the tracker to observe where the car is located (Wikipedia 2008). This
can be done either directly (by pressing a button for example), or remotely (by a mobile phone
for example).
Figure 3 Example of how data pushers are incorporated in the in-vehicle monitoring component of fleet
management systems. (Image from http://www.alkantelecom.com/images/new_images/architecturesysteme_en.jpg)
9
Data pushers are also being applied in the fields of domestic animal and wildlife control.
Scientists can observe the wildlife activities and migration patterns to better understand certain
species of wildlife (Wikipedia, 2008). Furthermore, Wikipedia (2008) also mentions that animal
tracking collars can be placed on domestic animals to locate them in case they get lost or
runaway.
As mentioned before with data loggers, data pushers can also be implemented in the sport of
gliding to determine if the competitors are cheating, taking shortcuts, or for determining the gap
between the competitors (Wikipedia, 2008).
Data pushers can also commonly be used in espionage type tasks, where the GPS tracking unit is
placed on an individual or valuable asset to monitor the movements or habits of that subject
(Tech-FAQ, 2008). This sort of application is typically used by private investigators, or by some
parents that want to track their children, but raises an important issue due to its potential for
abuse (Tech-FAQ, 2008).
4.2.3 Data Pullers
Data Pullers are devices that are always on and can be queried whenever required for their
location (Tech-FAQ, 2008). These tracking devices can normally be used in situations where the
position of the tracker will only need to be known occasionally, for example when the tracker is
placed in property that may be stolen (Wikipedia, 2008). Furthermore, Wikipedia (2008)
mentions that data pullers are becoming more common in the form of devices containing a GPS
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receiver and a mobile phone which, when sent a special SMS message can reply to the message
with the location of the unit.
4.3 Why GPS Augmentation?
Standalone GPS may be inadequate for many applications in terms of its integrity, accuracy,
continuity and availability, and as a result many augmentation methods can be employed to assist
the standalone GPS. Petovello et al (1999) mentions that in terms of its integrity, standalone GPS
is unable to protect the user from inaccurate information in a timely manner and that by looking
at the accuracy, it can be noted that the difference between measured and true position can be
quite large on the large scale. Furthermore, Petovello et al (1999) says that in terms of its
continuity, standalone GPS may be unable to complete an operation without triggering an alarm,
and may not be available at all times when needed. For these reasons, standalone GPS is not
adequate for many applications, such as in aircraft landing and use in deep-pit mines.
4.4 Augmentation Methods
There are many ways in which standalone GPS can be augmented to achieve better results. Some
of these ways include through receiver algorithms, coupling with additional sensors, extra Global
Navigation Satellite Systems, GPS modernization, and various space-based and ground-based
augmentation systems. Some of these can be applied with GPS in sport, but others aren’t due to
the fact that it is may be too costly to implement certain augmentation methods, or the processing
necessary to achieve a better accuracy in position is not required.
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4.4.1 Receiver Algorithms
Petovello et al (1999) states that receiver algorithms can produce significant reliability
improvements than standalone GPS by detecting and excluding faults which are associated with
standalone GPS. This process requires at least five satellites, therefore is dependant on the
availability of the satellites in the visible sky.
4.4.2 Global Navigation Satellite Systems
Global Navigation Satellite Systems (GNSS) such as GLONASS, Galileo and Compass can be
combined with the conventional GPS system, which will assist in improved availability, and
hence coverage. To a small extent, accuracy of position will also be improved.
Wikipedia (2008) mentions that the features of the Russian GLONASS system will include
twenty four operational satellites in three orbital planes once complete, with the signal being
transmitted on two frequencies. There is no intentional degradation of the ranging signal. Space
and Tech (2001) mentions that GLONASS is the counterpart to the US GPS, and will have a
global coverage once complete. It is interesting to note that with both the Russian and US
systems in operation, double the satellites may be available at any given time in the sky.
Galileo is a new system conceived by the European Union, with plans of thirty satellites in
Medium Earth Orbit being operational by 2013 (Wikipedia, 2008). Furthermore Wikipedia
(2008) mentions that the system will contain three to four carrier frequencies, giving increased
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reliability and availability over the US GPS system. O’Neill (2001) says that Galileo will offer
integrity by providing alerts to the users via the satellites indicating whether the Galileo signals
are outside their specification. These guarantees will provide the require level of integrity for
safety-of-life applications such as aircraft approaches.
Compass is yet another player in the satellite navigation system market. Wikipedia (2008) says
that the compass system will contain thirty-five satellites and transmit on four bands.
It can be identified that each of these extra systems will assist in coverage, possibly integrity
based on the system, and to some degree accuracy will also be improved.
4.4.3 GPS Modernisation
The US is currently undergoing a program to Modernisation GPS which will provide better
accuracy and more powerful and secure signals from future satellites (Rizos, 20007).
Furthermore, Rizos (2007) mentions that the most notable improvements will be the extra signals
being broadcast, being the improved code on the L2 frequency, a new L5 frequency and a new
code on the L1 frequency which will allow for safety-of-life applications, and a reduction in the
ionospheric error.
4.4.4 Wide Area Augmentation Systems
Wide Area Augmentation Systems (WAAS) transmit corrections to one or more geostationary
satellites that have a wide footprint on earth (TechEncyclopedia, 2008). These services are
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mainly used in the aviation industry and are designed to allow the sole use of GPS for all phases
of flight through Category I precision approach (Petovello et al, 1999). Furthermore, Petovello et
al (1999) states that there are three basic functions of a WAAS, which includes providing
additional ranging signals to improve availability, providing transmission of GPS and integrity
data to navigators, and finally providing correction data for satellite orbit, clock error, differential
range and the ionosphere to improve accuracy. This is shown in Figure 4.
Figure 4 How a Wide Area Augmentation System works. (Image from
http://www.mitre.org/news/digest/images/ar_gps_waas.gif)
4.4.5 Local Area Augmentation Systems
Local Area Augmentation Systems are similar to WAAS but have a limited range and number of
base stations, have a single differential correction to account for all errors, and require smoothed-
code or carrier-phase approaches (Petovello et al, 1999)
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4.5 Augmentation using Additional On-Board Sensors
A number of sensors can also be used to complement GPS. These sensors include altimeters,
electronic (or digital) compasses, accelerometers, rate-gyros and Inertial Navigation Systems
which combine both accelerometers and gyroscopes.
4.5.1 Altimeters
It is quite well known that GPS-based altitude data is not as accurate as the latitude and longitude
data, and it is quite common to have a vertical error of two to three times that of the horizontal.
This is due to the geometry of satellites in the visible sky, and due to the fact that GPS ignores
signals from satellites at low elevations to the horizon. For these reasons, altimeters can simply
be combined with GPS units to provide far more accurate elevation readings.
This application of coupling altimeters to GPS units is particularly useful for bikers, hikers and
other athletes in training, and it can also help in determining an accurate location on a
topographic map (GPSTracklog, 2006). The altimeter is able to determine altitude above sea level
by determining the atmospheric pressure and is accurate to about ± three metres (MB Wiki,
2006). Furthermore, MB Wiki (2006) states that the altimeter must be calibrated every time an
activity is started otherwise inaccurate readings will suffice.
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4.5.2 Compasses
Compasses are useful in telling the user which direction they are facing even while standing still.
GPS can only point you in the right direction once you are moving, and it is for this reason that
when combined with GPS, compasses can make navigating and orienteering tasks much easier
(GPSTracklog, 2006). An electronic compass works by being able to electronically sense the
earth’s magnetic field and orientate itself to it (ASD, 2006). It is important to remember that there
may be foreign magnetic fields which will distort the true orientation to magnetic north, and the
user must be aware of this to ensure the electronic compass is used effectively.
4.5.3 Accelerometers
An accelerometer is an electromechanical device capable of measuring acceleration forces acting
on an object, with these forces being static or dynamic (Dimension Engineering, 2008). An
example of a static force is the constant pull of gravity downwards, and a dynamic force is caused
by movement or vibration of the device.
By measuring the amount of dynamic acceleration, a user is able to analyse the way that the
device is moving (Dimension Engineering, 2008).By undertaking a few calculations, speed of the
unit can be calculated, and hence distance can be derived as well. Triaxial accelerometers are
intended to measure simultaneous movement in the three perpendicular axes. Total acceleration
will be the sum of the dynamic acceleration in the three directions.
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For these reasons, tri-axial accelerometers have been used to determine human behaviour and
identify periods of movement (Mathie et al., 2003). Furthermore, Mathie et al. (2003) mentions
that these movements require a high sensitivity device for accurate determination of results. By
combining accelerometers with GPS, not only can movement be determined accurately, but
position can be applied for the ability to track the objects movements. For these reasons, GPS
coupled with accelerometers can be used as a useful tool in sports to determine player’s
movements and statistics.
4.5.4 Gyroscopes
A gyroscope is a device for measuring or maintaining orientation using the principles of angular
momentum (Wikipedia, 2008). Wikipedia (2008) mentions that this device contains a spinning
disk with an axle that is free to take any orientation. Gyroscopes are used in conjunction with
GPS systems to enhance the accuracy of GPS instruments in situations of lost GPS signal by
determining the attitude of the unit (Hintze, 2006). However, when combining gyroscopes with
GPS they are usually also integrated with accelerometers to enable more reliable attitude and
heading information, but may be used separately on low cost dead-reckoning systems in cars
where GPS is unavailable, for example in tunnels (Spirent, 2007).
Li et al. (2005) mentions that microelectromechanical sensors such as gyroscopes, accelerometers
and magnetometers are experiencing rapid growth in many applications, such as athletic training
monitoring, mainly due to their tiny size and low cost. These sensors make them ideal
components of a compact and affordable attitude and heading reference system (Li et al., 2005).
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However Li et al. (2005) points out that these sensors are still less accurate than the precise
inertial sensors required for use in inertial navigation systems.
4.5.5 Inertial Navigation Systems
Integration of accelerometers, gyroscopes and GPS can create an inertial navigation system, or
INS. An Inertial Navigation System has the ability to continually track position, orientation and
velocity of an object without the need for any external references (Wikipedia, 2008).
The inertial navigation system is initially provided with position typically from GPS, and from
then on is able to compute an updated position and velocity based on integrated information
received from the motion sensors (Wikipedia, 2008). This makes inertial navigation systems
particularly useful in places where GPS signal is attenuated or unavailable, such as in urban
canyons or places of small visible sky.
Gyroscopes measure the angular acceleration of the system in the inertial reference frame, and
accelerometers are used to measure the linear acceleration of the system in the inertial reference
frame. However Wikipedia (2008) mentions that small errors in acceleration and angular velocity
will become progressively larger, leading to larger errors in velocity. This in turn will lead to
greater errors in position, with a typical error of one kilometre per hour in position and tenths of a
degree in orientation (Wikipedia, 2008). This error can be reduced by continually updating
position through more frequent GPS measurements, thus calibrating the system more regularly.
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Current GPS/INS integrated systems are quite expensive if a high degree of accuracy is required.
For this reason, only low-cost sensors are implemented on GPS trackers in the application of
sports analysis. These augmented systems are quite acceptable in analysing human movement in
sport. Other augmentation methods such as Global Navigation Satellite Systems and GPS
modernisation may help in analysis of sports performance using GPS, but are not likely to
provide a necessary improvement.
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5. Equipment Tests
As the SPI-Elite device was the primary source of data for analysis purposes, it needed to go
through a number of tests to determine if it was suitable in achieving the required levels of
accuracy. These tests include the velocity test, the unit comparison test and the position test.
5.1 Speed Determination Test
As speed is one of the major aspects of performance being analysed for, it is important that the
device computes speed to an acceptable standard. For this reason, the speed determination test
was derived.
5.1.1 Speed Determination Test without Cruise Control
The first speed test was undertaken in a car without cruise control. This method involved holding
the SPI-Elite device in clear view of satellites next to the windscreen, and collecting data for a
short period of time. By observing the speedometer needle, a constant speed (as indicated on the
automobile instrument dials) was maintained by applying an increase in throttle when the needle
began to drop below the desired speed, and disengaging the throttle when the needle showed the
car travelling at greater speed than desired and applying a constant throttle otherwise.
The results taken by this method indicate that velocity remained fairly constant throughout the
period of data collection, as shown in Figure 5. An average speed of 98.1 km/hr was calculated,
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with a maximum reading of 100.4 km/hr and a minimum reading of 94.6 km/hr during the period
of analysis. This therefore gave a maximum deviation from the average being 3.5 km/hr. A
standard deviation of 0.96 was also calculated for the data set, indicating a fairly consistent
reading of speed over the time-frame of observation.
Observed Speed without Cruise Control at 100km/hr
919293949596979899
100101
1 31 61 91 121
Time (Seconds)
Spe
ed (K
m/h
r)
Figure 5 Graph showing speed observed without cruise control at one-second intervals at indicated 100 km/hr with
polynomial trendline fitted
A polynomial trendline of the 6th order was fitted to the graph to enhance interpretation of the
dataset. It can be seen that initially speed was constant for the first 30 or 40 seconds of collection,
before it started to vary by a larger amount. These variations may be due to the changing grade of
the road as it passes over differing terrain, and the inability to counteract these changes using the
‘throttle adjustment method’. The variations may also be due to wavering attention in
maintaining a constant speed for a long period. It is unlikely that the variations are due to
inaccurate readings by the SPI-Elite device.
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It is interesting to note that the average speed is 98.1 km/hr as opposed to the 100km/hr as
indicated by the speedometer needle. This may be due to parallax of the needle, making it appear
to be pointed at 100 km/hr when it is in fact less, or that indicated speed of the car is greater than
the true vehicle speed, which is quite common in automobile manufacturing processes.
One way in which this speed determination test could be improved to increase the accuracy of
results, includes taking more observations than the hundred and forty points observed. A larger
data set would give a better average and standard deviation. Apart from that, there wouldn’t be
any other way that this method could be improved to give a more accurate measure of the
velocity determined by the SPI-Elite device.
5.1.2 Speed Determination Test with Cruise Control
The second speed determination test was undertaken in a car with cruise control. This method
involved collecting data by placing the SPI-Elite device in clear view of satellites on the
dashboard of the vehicle. Cruise control was then switched on when the car reached the desired
speed of 100 km/hr as indicated on the speedometer.
The results taken by this method indicate that speed remained very constant throughout the
period of data collection. An average speed of 99.9 km/hr was calculated, with a maximum
reading of 101.5 km/hr and a minimum reading of 98.3 km/hr during the period of analysis. This
therefore gave a maximum deviation from the average being 1.6 km/hr. A standard deviation of
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0.41 was also calculated for the data set, which indicates that a fairly consistent reading of speed
was determined over the time-frame of observation.
Observed Speed at Cruise Control
98.0
98.5
99.0
99.5
100.0
100.5
101.0
101.5
102.0
1 61 121 181 241 301 361 421 481
Time (Seconds)
Spee
d (K
m/h
r)
Figure 6 Graph showing speed observed with cruise control at one-second intervals at indicated 100 km/hr with
polynomial trendline fitted
A polynomial trendline of the 6th order was also fitted to this set of results. The trendline is added
so as to fit a mathematical equation to the dataset, and enhance in interpreting the “trend’ of the
observations. It can be seen that there wasn’t much variation in speed at all, with really only a
slight notable variation towards the end of the dataset. This may be due to changes in terrain, and
the cruise control system may lag behind and be unable to fully maintain the constant vehicle
speed and required engine speed over these periods of changing grade of road.
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One way in which this particular speed determination test could be improved, would be to
increase the length of observation of the dataset. More observations allow for better analysis;
however it is unlikely that different results would be identified.
In comparing the two datasets, it can be identified that there is significantly less variation in
speed determined using a car with cruise control available than using the ‘throttle adjustment
method’, even though the cruise control dataset was captured over a longer period and the axes
also have a larger scale. This is to be expected, as a cruise control system can maintain speed
better than any human method, and from the results it is seen that the device is able to determine
speed to within ± 1 km/hr at 100 km/hr, which may translate to a 1% error in the speed value
recorded at any point in time.
It would be desirable to put the SPI-Elite unit through further tests, namely one that is able to
determine absolutely what speed the device should be travelling at against what is actually
observed by the device. Such a test would require a system that has the ability to determine speed
with no error, or insignificant error. It may have also been desirable to establish how accurately
the unit determines speed at a lower rate of travel, as it is just an educated guess that the unit
measures speed to 1% error across all ranges of speed.
However, from the results obtained a conclusion can be reached that the SPI-Elite unit determines
speed to a level that is acceptable for this project. The device itself only records speed to 0.1 m/s,
and any errors in speed will be averaged out over the length of the dataset and are therefore
become insignificant. In assuming that the device measures to 1% error, any top speed
24
determined in match analysis will be to ± 0.1 m/s, as maximum speed from running is no more
than 30km/hr which translates to ~ a 0.3 km/hr error or around 0.1 m/s.
5.2 Unit Comparison Test
The second test undertaken on the SPI-Elite units was designed to compare the results obtained
by the units in relation to each other. This test was performed by wearing both units at the same
time whilst cycling for approximately two hours and collecting a data-set for each unit, and then
comparing these data-sets with each other. One unit was worn above the other as can be seen in
Figure 7 below.
Figure 7 Method for collection of data for unit comparison
25
In theory the two units should collect similar data and have similar results for number of satellites
being observed, position, distance and speed. There will however be errors from a number of
different sources that will come into play.
The first process was to align the two data-sets by time of observation of each recorded
observation. Theoretically, both units should take the same recordings at the same time when
visible to the same satellites, and not have any observations when they are not visible. It was
identified however, that at some moments, one unit was able to obtain a recording whereas the
other one did not. These periods may have been due to the way the units were situated in relation
to one another, with certain satellites being blocked by one unit or the other during periods of low
numbers of satellites in the visible sky, either due to the time of day, or decreased visible sky
from the walls and various tunnels along the M7 cycle path.
GPS Speed (Unit 1) Speed (Unit 2) Distance (Unit 1) Distance (Unit 2) Satellites
Time Timer (m/s) (km/hr) (m/s) (km/hr) (m) * (m) (m) * (m) Unit 1 Unit 2
30:05.0 1:25:02 3 10.8 3 10.8 27536.6 27468.8 27561.9 27488.7 T 5 T 5
30:06.0 1:25:03 3 10.8 2.7 9.72 27539.7 27471.9 27564.7 27491.5 T 2 T 4
30:07.0 1:25:04 3 10.8 2.5 9 27542.7 27475 27567.3 27494.1 T 2 T 3
2.5 9 27569.8 27496.6 T 1
3.1 11.16 27572.6 27499.4 T 2
3.2 11.52 27578.8 27505.6 T 3
3.3 11.88 27582.2 27509 T 3
30:14.0 1:25:11 3.3 11.88 27563.9 27496.1 T 3
30:15.0 1:25:12 3.3 11.88 3.2 11.52 27567.1 27499.3 27592.2 27519.1 T 4 T 7
30:16.0 1:25:13 3.3 11.88 3.2 11.52 27570.4 27502.6 27595.6 27522.4 T 5 T 6
30:17.0 1:25:14 3.2 11.52 3.2 11.52 27573.3 27505.5 27598.8 27525.6 T 6 T 6
Table 1 Shows a period where both units did not obtain recordings at the same time
Over the period of data collection, an average speed of 4.74 m/s was determined by both units,
with a top speed of 10.7 m/s and 10.6 m/s respectively. This demonstrates that each unit is able to
26
determine speed very accurately, and backs up the results shown by the speed test in section 8.1.
There was a maximum difference of 1.4 m/s between the units at 12:52 pm and 28 seconds. A
possible reason for such a difference could be that at that particular moment, one of the units may
have been hand adjusted at the time, thus creating a dodgy reading for the true speed of travel.
The average difference in speed of the two units was insignificant due to its small size.
In terms of total distance travelled, both units physically travelled the same distance. There was a
slight difference between the two data-sets of approximately 32m, over the total distance
travelled of more or less 33.3 kilometres. This equated to an error of around 0.1%, which is by far
acceptable for the requirements of the thesis for fitness analysis.
5.3 Position Test
The position test was performed by collecting two separate data-sets with the SPI-Elite unit over
a known survey mark on two different days. The survey mark used was SSM 42318 outside No.
1 Park Ave, Kingswood. This mark was used as there is a relatively unobstructed view of the sky,
enabling maximum satellite readings for the unit, as can be seen in Figure 8 below.
27
Figure 8 Method for collecting data for the Position Test. Note the clear surrounds of the survey mark,
enabling maximum satellite coverage and visibility.
The first data-set was collected for duration of approximately seven minutes, with a minimum of
six, a maximum of nine, and an average of eight satellites in view over the period. Over this
period, recorded position varied by a maximum of approximately eight metres in easting and
seven metres in northing.
The average latitude and longitude were calculated from the observed readings, and the
maximum and minimum latitudes and longitudes were determined for the period of the data-set,
converted to MGA using the online calculator from Geoscience Australia, and graphed to show
their measured position in relation to the true position.
28
SSM 42318 True vs Measured
Min Longitude Position (290255.143, 6262352.100)
Position at Min Latitude and Max
Longitude (290263.190, 6262351.167)
TRUE POSITION (290260.296, 6262356.094)
AVERAGE MEASURED
(290259.281, 6262354.200)
Max Latitude Position (290257.651, 6262357.326)
6262350
6262351
6262352
6262353
6262354
6262355
6262356
6262357
6262358
6262359
6262360
290255 290257 290259 290261 290263 290265
MGA Easting
MG
A N
orth
ing
2.1m
2.9 m
6.5 m 5.7 m
Figure 9 Graph showing measured positions in relation to true survey mark position for the first data-set
As can be seen from the above graph, the average position measured by the device was
approximately two metres different from the true position, and there was a maximum measured
difference from true position of 6.5m recorded by the device at one particular observation.
The second data-set was collected for duration of approximately twenty minutes, with a
minimum of four, a maximum of six, and an average of five satellites in view over the period.
29
Over this period, recorded position varied by a maximum of approximately fourteen metres in
easting and twelve metres in northing.
Similarly to the first data-set, the average latitude and longitude were calculated from the
observed readings, and the maximum and minimum latitudes and longitudes were determined for
the period of the data-set, converted to MGA using the online calculator from Geoscience
Australia, and graphed to show their measured position in relation to the true position.
SSM 42318 True vs Measured
Min Latitude Position (290264.177, 6262348.415)
Max Longitude Position (290265.949, 6262352.149)
AVERAGE POSITION (290259.175, 6262354.813)
TRUE POSITION (290260.296, 6262356.094)
Max Latitude Position (290257.900, 6262359.928)
Min Longitude Position (290252.399, 6262357.210)
6262345
6262350
6262355
6262360
290250 290255 290260 290265 290270
MGA Easting
MG
A No
rthin
g
1.7m
8.0m
4.5m
8.6m
6.9m
Figure 10 Graph showing measured positions in relation to true survey mark position for the second data-set
30
As can be seen from this second graph, the average position measured by the device was also
approximately 1.7 metres different from the true position, and there was a maximum measured
difference from true position of around 8.6m recorded by the device at one particular observation.
From the two data-sets collected, it can be seen that the position obtained by the unit is far from
being super accurate. This was to be expected, as the SPI-Elite is a stand-alone GPS unit, and
only coupled with a tri-axial accelerometer which does not enhance position determination.
However, positional accuracy to deci-metre level is not fundamentally important for the
application to be used of the device. It only assists in the tracking analysis of the subject in
question. It was interesting to note also that there was a smaller maximum error of measured
position in the first data-set. This may quite likely be due to the increased number of available
satellites at the time.
Ways in which positional accuracy can be improved is by utilising some of the augmentation
methods discussed in the literature review above. Most of these methods will be impractical for
such a unit that is required for many sporting applications.
31
6. Method of Determining Results (Touch Football) Data was collected using the SPI Elite device on several members of the “No Touch” touch
football team, which participates in the Westmead Touch Association mixed division. These
games were played against other mixed sides of varied skill at Ollie Webb Reserve, Parramatta.
Figure 11 The SPI Elite device was worn underneath the jersey during data collection from matches
The device was worn throughout the whole of each match, and kept on through all break periods,
including pre-game, half-time, post-fulltime and any associated periods on the interchange bench.
For this reason, these periods had to be removed before analysing the results further.
32
Off-field/bench periods
Figure 12 TeamAMS “map” showing some of the periods from a sample match that must be removed before further
detailed and more accurate analysis can be undertaken
After the data was collected by the SPI Elite unit, it was downloaded onto the computer and
viewed with the “Team AMS” software provided with the units. From there it was exported into
Microsoft Excel so that further and more game-accurate analysis could be undertaken.
As the playing fields at Ollie Webb Reserve run very close to true north and south, any break or
off-field periods could be identified as being any position to the east or west of five metres in
from the touch line (depending on where the “bench” was in relation to the field), while also
having little or no speed being recorded by the unit. This meant that coordinates had to be derived
for the field corners before determining the critical longitude. As the games were only played on
one of two fields, only two critical longitudes were determined. Other break periods such as pre-
33
match and post-fulltime could be indicated by long periods of little movement or low speeds
recorded in the dataset, usually at the start and end of the period of the dataset.
Figure 13 Drawing indicating how break periods were defined
After data was collected for the corners of the fields using the SPI Elite, it was averaged for each
corner, giving a latitude and longitude of each point. The data isn’t super-accurate but is enough
to get a rough idea of the position of the sidelines for analysis. To make it easier to calculate the
critical longitude, the latitude and longitude of each point was converted to MGA using the
online WGS-MGA calculator from Geoscience Australia. From these values we could determine
that the fields did in fact run very close to true north and south. A critical MGA easting was
determined by subtracting five metres from the average MGA easting values of the North-East
and South-East corners for “Field 4”, and adding five metres to the average MGA easting values
of the North-West and South-West corners for “Field 2”. Once this was completed, the two
34
critical MGA eastings were each converted to a critical Longitude by using the MGA-WGS
calculator also from Geoscience Australia. Five metres in from the sideline was used as the
players collecting the data hardly ever played on the wing during the match, and if they did the
“off-field” position was ignored. The critical Longitudes were then used to create an “If” function
in Excel to determine for each of the matches where each individual recorded data point was in
relation to the critical longitude, i.e. “on-field or off-field”.
Ollie Webb fields
6255700
6255750
6255800
6255850
314150 314200 314250 314300 314350 314400 314450
MGA Easting
MG
A N
orth
ing
Field 4Field 2
Off-field Off-field
Critical Longitudes
Figure 14 Graph showing the observed MGA position of the fields, and the critical longitudes of the two fields
It can be seen that the observed field values are not too accurate, but using the critical Longitude,
position in relation to on-field can be determined and is satisfactory for initial analysis.
The next step was to break each match up into periods. These periods include attack, defence,
off-field (bench) and neutral periods. It was noted after each match the direction in which attack
and defence was being travelled in each half. As the fields run North-South, direction of travel
35
could be determined by looking at the local Y-values of the dataset from one second to the next.
Using this information in combination with the observed “off-field” periods, a colour was
assigned to each record that determined whether the point was recorded during an attacking,
defensive, off-field, or transition period.
Time Spd Dis Δ dis
X (m)
Y (m)
Latitude Longitude location
09:26.0 0.1 68.2 0 34.8 51.2 -33.8219567 150.994335 ONFIELD 09:27.0 0.2 68.2 0.5 34.8 51.4 -33.821955 150.994335 ONFIELD 09:28.0 0.9 68.7 0.8 34.8 50.8 -33.82196 150.994335 ONFIELD 09:29.0 2 69.5 2.8 35.1 51.6 -33.8219533 150.9943383 ONFIELD 09:30.0 3.2 72.3 3.5 35.4 54.4 -33.8219283 150.9943417 ONFIELD 09:31.0 3.8 75.8 3.9 35.3 57.9 -33.8218967 150.99434 ONFIELD 09:32.0 3.8 79.7 3.5 35.3 61.7 -33.8218617 150.99434 ONFIELD 09:33.0 3 83.2 3.2 35.1 65.3 -33.82183 150.9943383 ONFIELD 09:34.0 3.5 86.4 3.9 34.6 68.4 -33.8218017 150.9943333 ONFIELD 09:35.0 4 90.3 3.4 34.2 72.3 -33.8217667 150.9943283 ONFIELD 09:36.0 2.8 93.7 1.8 34.2 75.6 -33.8217367 150.9943283 ONFIELD 09:37.0 0.9 95.5 0.8 34.3 77.5 -33.82172 150.99433 ONFIELD 09:38.0 0.7 96.3 1.1 34.2 78.2 -33.8217133 150.9943283 ONFIELD 09:39.0 1.4 97.4 0.3 33.6 79.1 -33.821705 150.9943217 ONFIELD 09:40.0 1.3 97.7 1.6 33.3 78.9 -33.8217067 150.9943183 ONFIELD 09:41.0 1.7 99.3 1.8 33.6 77.5 -33.82172 150.9943217 ONFIELD 09:42.0 1.9 101.1 1.9 33.6 75.6 -33.8217367 150.9943217 ONFIELD 09:43.0 1.5 103 0.9 33.4 73.8 -33.8217533 150.99432 ONFIELD 09:44.0 0.7 103.9 1 33.1 72.8 -33.8217617 150.9943167 ONFIELD 09:45.0 0.9 104.9 1.5 33.1 71.9 -33.82177 150.9943167 ONFIELD 09:46.0 1.7 106.4 2.6 33.9 70.6 -33.8217817 150.994325 ONFIELD 09:47.0 2.9 109 4.1 34.5 68 -33.821805 150.9943317 ONFIELD 09:48.0 4.6 113.1 4.4 34.8 64 -33.8218417 150.994335 ONFIELD 09:49.0 3.7 117.5 3.6 34.8 59.5 -33.8218817 150.994335 ONFIELD 09:50.0 2.8 121.1 3.3 34.5 56 -33.8219133 150.9943317 ONFIELD 09:51.0 3.8 124.4 4 34.9 52.7 -33.8219433 150.9943367 ONFIELD 09:52.0 4 128.4 3.5 35.7 48.8 -33.8219783 150.994345 ONFIELD 09:53.0 3 131.9 4 36.8 45.5 -33.8220083 150.9943567 ONFIELD 09:54.0 4.7 135.9 4.7 40 43.1 -33.82203 150.9943917 ONFIELD 09:55.0 4.7 140.6 3 43.7 40.1 -33.8220567 150.9944317 ONFIELD 09:56.0 0.8 143.6 0.2 45.7 37.9 -33.8220767 150.9944533 ONFIELD
Decreasing position indicating attacking period
Getting into position after coming from the bench (neutral period)
Increasing position indicating defensive period
Table 2 Example of a period collected from a match showing how periods can be defined.
36
However, it is important to note that at times during the match the observer would not have been
running North-South but may have been running East-West to make a touch in defence, look for
a gap, or go into the dummy-half position in attack. There were also periods of little running on
the field due to a penalty or stoppage, which required looking back at which team had the
possession before or after, and therefore determining whether to assign a green attacking phase or
a yellow defensive phase for the duration of that period. This stage of breaking down the data
into periods for each match was quite time consuming as there was a great deal of data to sort
through and assign a period to. Approximately thirty thousand individual records needed to be
assigned with either an attacking, defensive, off-field or transition value.
There is no doubt there will be some error in assigning the wrong period to some records, but it is
unlikely to be more than two percent, as the periods were thoroughly determined using the
methods discussed above. One way in which the periods can be better defined, would be to
integrate video analysis to observe player movements during the match and therefore assigning
the right period to the right record with almost no error.
After each match data-set was divided into periods, the attacking and defensive periods were
isolated from the total data-set, and analysed for factors such as distance covered, maximum
speed, average speed and percentages of the on-field time spent in attack or defence. These were
also split by 1st half and 2nd half, so as to determine whether there is any notable difference in
physical performance earlier or later on in the match.
37
Each of the files from each game can be found on the CD at the appendix of the thesis. The match
Excel files contain four tabs at the bottom, with the first tab being the original data-set captured
by the SPI Elite device and exported into Excel through the “TeamAMS” software, the second
tab showing each period derived for the whole match and a few basic match stats, the third
showing the attacking periods and statistics, and the fourth showing the defensive periods and
statistics.
38
7. Summary of Results (Touch Football)
Using the “TeamAMS” software, an initial overview of the data-sets could be determined. The
TeamAMS software produced several graphs automatically based on the raw-data that was
collected in the field, such as Figure 15 and 16 below.
Figure 15 Graph of speed collected over the duration of an example data-set. Note the periods less than one
metre/second, these may indicate breaks and thus had to removed before further analysis could be undertaken
39
Figure 16 Graph of acceleration collected over the duration of an example data-set from the on-board triaxial
accelerometer.
Using the accelerometer data collected from the games, the “TeamAMS” software processed it to
identify impacts and body load. Impacts indicate force from sudden changes of direction due to
accelerations and decelerations, and body loads portray the amount of physiological stress placed
on the body due to impacts for the duration of a game. From this data supplied by “TeamAMS”,
Table 3 was created showing a comparison of impacts and body load from each match, and is
shown below.
40
Game 1 2 3 4 5 6 7 8
Date 24-Jun 1-Jul 8-Jul 15-Jul 22-Jul 5-Aug
12-Aug 19-Aug
Opponent I.L.B IV
Big Fish W. B.
Autotech
Coulda..
A-Team
Frankles N. N.
Score 10-1 7-1 3-2 7-1 13-0 3-5 8-0 18-1
Position of Opponent 12th 9th 7th 5th 8th 2nd 6th 12th
Observor D.K. D.K. D.K. B.G. D.K. R.E-C D.K. D.K. B.G. D.K. B.G.
Time On Field (mins) 35.5 31 29.5 22 22.5 26 21.5 23.5 13.5 29 27
Impacts zone 1 (5-5.5g) 56 77 68 73 6 36 41 47 3 97 13
zone 2 (5.5-6g) 11 11 14 25 1 10 10 4 1 32 2
zone 3 (6-7g) 6 7 9 21 1 8 14 3 0 22 0 zone 4 (7-8g) 1 2 3 7 2 0 1 0 0 13 0
zone 5 (8-10.5g) 1 0 0 1 0 1 0 0 0 0 0
zone 6 (>10.5g) 0 0 0 0 0 0 0 0 0 0 0
Total No. of impacts 75 97 94 127 10 55 66 54 4 164 15
Max Impact (g) 10.09 8.73 9.51 10.39 9.85 10.39 9.33 8.81 7.27 9.87 7.45
Body Load Total (units)
67695.4
88194.4
90499.2
88571.5
25891.7
63797.7
60745.6
46363.8
6890.0
120696.2
24235.4
Table 3 Impacts and Body Loads associated with each game observed
By analysing each data-set collected in the touch football games using the methods discussed in
chapter 9, table 4 was created and is shown below. This table lists some of the aspects of physical
performance and match stats derived from further analysis.
41
Game 1 2 3 4 5 6 7 8
Date 24-Jun 1-Jul 8-Jul 15-Jul 22-Jul 5-Aug
12-Aug
19-Aug
Opponent I.L.B IV
Big Fish
W. B.
Autotech
Coulda..
A-Team
Frankles N. N.
Score 10-1 7-1 3-2 7-1 13-0 3-5 8-0 18-1
Position of Opponent 12th 9th 7th 5th 8th 2nd 6th 12th
Observer D.K. D.K. D.K. B.G. D.K. R.E-C D.K. D.K. B.G. D.K. B.G.
Time On Field (mins) 35.5 31 29.5 22 22.5 26 21.5 23.5 13.5 29 27
1st Half Attack Distance (m) 686.2 858.0
717.2 567.4 514.0
584.9 540.9 719.5 -------
915.8
620.8
Avg Speed (km/hr) 7.5 7.3 8.8 7.0 6.8 7.3 7.7 7.5 ------- 6.4 6.0
Top Speed (km/hr) 27.7 25.0 25.9 22.2 27.0 24.8 21.2 26.3 ------- 22.0 25.6 1st Half Defense Distance (m)
1006.3 875.7
797.7 530.5 657.5
661.2 662.2 839.9 -------
722.0
596.4
Avg Speed (km/hr) 6.1 6.2 6.1 5.3 6.2 5.8 7.1 6.0 ------- 6.8 6.8
Top Speed (km/hr) 21.6 19.8 17.0 16.1 18.4 16.9 23.0 16.6 ------- 20.5 16.6 2nd Half Attack Distance (m) 967.2 812.2
963.9 578.9 611.6
648.3 629.5 416.7
609.0
854.2
755.8
Avg Speed (km/hr) 6.0 8.0 7.0 7.2 8.6 7.0 7.2 8.1 7.2 7.8 6.6
Top Speed (km/hr) 23.0 26.9 26.3 25.9 27.0 25.9 24.8 25.9 24.5 25.9 22.3 2nd Half Defense Distance (m) 911.6 878.1
977.7 558.5 687.2
852.8 750.1 547.8
654.0
899.1
887.0
Avg Speed (km/hr) 5.3 5.8 6.7 5.3 5.7 5.8 7.1 5.8 4.6 6.7 6.1
Top Speed (km/hr) 16.6 24.1 24.8 16.9 25.2 17.6 20.9 20.5 14.4 21.2 17.6 Total Match Distance (m)
3571.3
3424.0
3456.5
2235.3
2470.3
2747.2
2582.7
2523.9
1263.0
3391.1
2860.0
Avg Speed (km/hr) 6.0 6.6 7.0 6.1 6.6 6.3 7.3 6.5 5.6 6.9 6.4
Top Speed (km/hr) 27.7 26.9 26.3 25.9 27.0 25.9 24.8 26.3 24.5 25.9 25.6
Attack 42% 42% 44% 43% 38% 39% 44% 37% 38% 51% 48% Defense 58% 58% 56% 57% 62% 61% 56% 63% 62% 49% 52%
Table 4 Summary of results from each touch football match collected, determined using the methods discussed in
Chapter 9
From this table, it can be identified that the average and top speeds during each match were all
encountered in attack. This indicates that in all games speed of play was always quicker when the
opposition didn’t have possession. This indicates that the quality of the opposition teams was
predominantly of a lower standard. This is also indicated by the possession, as the ball was turned
over quicker as the sets ended faster.
42
Individual graphs plotting paths taken during the match could also be plotted in excel, to show
how the player was being tracked and monitored for any period during the match. Such graphs
help to determine player movements during significant stages in a game, such as a try being
scored. An example of such a graph is in Figure 17 below.
Try-time (B.G.), 2nd Half v Autotech
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60
Local Coords (easting)
Loca
l Coo
rds
(nor
thin
g)
Position
.
Figure 17 Path plotted in Excel showing a try being scored from one of the data-sets collected. Note the swerve at
half way to create the line-break.
43
8. Summary of Results (1st Grade AFL)
It was difficult to obtain any GPS data-sets from professional teams, as most clubs have a policy
to not hand out any GPS data collected with their SPI Elite devices. Of all the AFL clubs
contacted, only two data-sets from a first grade match were provided by the West Coast Eagles
AFL club. Detailed processing and analysis could not be undertaken using GPS position and
movements on these data-sets as information from the matches to do with direction of travel were
not known, therefore it was difficult to break down the results into periods, as was able with the
data collected personally. However, some analysis was undertaken to separate off-field periods
by observing periods where speed was minimal or non-existent. From these results, Table 5 was
created.
Game 1 Date 24-May Opponent Adelaide Crows Time On Field (mins) 105 114 Player 1 2 1st Half Distance (m) 7132.4 6958.5 Avg Speed (km/hr) 7.3 7.1 Top Speed (km/hr) 29.2 31.0 2nd Half Distance (m) 5057.0 6613.8 Avg Speed (km/hr) 6.2 7.0 Top Speed (km/hr) 27.7 30.0 Total Match Distance (m) 12189.4 13572.3 Avg Speed (km/hr) 6.8 7.1 Top Speed (km/hr) 29.2 31.0
Table 5 Match Statistics derived for on-field performance
44
The “TeamAMS” software produced a few graphs that related to the data collected by the SPI
Elite unit directly from the match. The following graph shows the path plotted throughout the
period of the match, and shows the coverage of the field by that player.
Figure 18 Coverage of the AFL ground showing how the observor was tracked and monitored throughout the match
45
Similarly to the results derived in chapter 10 for touch football data-sets, using the accelerometer
data collected from the AFL games the “TeamAMS” software processed it to identify impacts
and body load. From this data, the following table was produced showing a comparison of
impacts and body load between the players for the match.
Game 1 Date 24-May Opponent Adelaide Crows Time On Field (mins) 105 114 Player 1 2 Impacts zone 1 (5-5.5g) 109 253 zone 2 (5.5-6g) 23 75 zone 3 (6-7g) 22 88 zone 4 (7-8g) 10 13 zone 5 (8-10.5g) 4 1 zone 6 (>10.5g) 0 0 Total No. of impacts 168 430 Max Impact (g) 10.39 10.39 Body Load Total (units) 235085.5 529887.6
Table 6 Body Loads and Impacts from “TeamAMS” for the match between the two data-sets given
46
9. Comparison and Discussion of Results
From the results obtained, it can be seen that the average speed from some of the touch football
matches was similar to the average speed of the AFL players for their match. In fact the
maximum on field average speed of a data-set came from a touch football game, having an
average of 7.3 km/hr. This data-set was from a game against one of the top sides in the
competition, which made for a faster game. However, in saying this it is important to remember
that the AFL players need to sustain this speed for 3-5 times as long as the data-sets collected
from touch football, as during touch football games there is a great deal more substituting and
therefore more breaks during general play, allowing the players to catch their breaths and lower
their heart-rates after intense periods of play.
In comparing top speed between the touch football data collected and the AFL data obtained, it is
evident that the two AFL players could reach a speed during the match that could not be reached
in any of the data-sets in touch football. The average top speed of the two AFL players was 13%
greater than the average top speed of all the data-sets from touch football.
47
Comparison (Top and Average Speeds)
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
1 2 3 4 5 6 7 8 9 10 11 12 13
Data-sets 1-11 = touch football, 12-13 = AFL
Spee
d (k
m/h
r)
Figure 19 Graph showing the comparison of top speed and average speed between the touch football and
professional AFL data-sets
The distances covered in the AFL matches were quite large as expected. This is due to AFL
matches being played for much longer periods than touch football, the size of the field in
comparison to a touch football field, and the nature of the game. As the field is much larger, and
the ball is kicked around, more running has to be undertaken to get to loose balls, whereas with
touch football, the ball is only passed by hand, therefore less distance has to be covered when
following the ball.
By using the accelerometer data collected by the unit in all the data-sets and the way it was
analysed by “TeamAMS”, body load and impacts were calculated to determine whether touch
football or AFL is more strenuous on the body. Due to there being a difference in time periods
between all data-sets, the body loads and impacts had to be converted to a “per hour” likely
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value, based on the values that were collected in each data-set for the period of that data-set. This
yielded Tables 7 and 8 below.
Game 1 2 3 4 5 6 7 8 Date 24-Jun 1-Jul 8-Jul 15-Jul 22-Jul 5-Aug 12-Aug 19-Aug
Opponent I.L.B IV Big Fish W. B.
Autotech
Coulda..
A-Team
Frankles N. N.
Score 10-1 7-1 3-2 7-1 13-0 3-5 8-0 18-1
Position of Opponent 12th 9th 7th 5th 8th 2nd 6th 12th
Observor D.K. D.K. D.K. B.G. D.K. R.E-C D.K. D.K. B.G. D.K. B.G.
Time On Field (mins) 35.5 31 29.5 22 22.5 26 21.5 23.5 13.5 29 27
Impacts zone 1 (5-5.5g) 95 149 138 199 16 83 114 120 13 201 29 (per hour) zone 2 (5.5-6g) 19 21 28 68 3 23 28 10 4 66 4 zone 3 (6-7g) 10 14 18 57 3 18 39 8 0 46 0 zone 4 (7-8g) 2 4 6 19 5 0 3 0 0 27 0 zone 5 (8-10.5g) 2 0 0 3 0 2 0 0 0 0 0
zone 6 (>10.5g) 0 0 0 0 0 0 0 0 0 0 0
Total No. of impacts 127 188 191 346 27 127 184 138 18 339 33 Max Impact (g) 10.09 8.73 9.51 10.39 9.85 10.39 9.33 8.81 7.27 9.87 7.45 Body Load Total (units)
114414.8
170698.8
184066.2
241558.6
69044.5
147225.5
169522.6
118375.7
30622.2
249716.3
53856.4
(per hour)
Table 7 Deduced Body load and Impacts per hour for the data-sets collected in touch football
Game 1 Date 24-May Opponent Adelaide Crows Time On Field (mins) 105 114
Player 1 2
Impacts zone 1 (5-5.5g) 62 133 (per hour) zone 2 (5.5-6g) 13 39 zone 3 (6-7g) 13 46 zone 4 (7-8g) 6 7 zone 5 (8-10.5g) 2 1
zone 6 (>10.5g) 0 0
Total No. of impacts 96 226 Max Impact (g) 10.39 10.39 Body Load Total (units) 134334.6 278888.2 (per hour)
Table 8 Deduced Body load and Impacts per hour for the data-sets collected in AFL
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From the reduced results, it can be noted that body loads and impacts change dramatically from
game to game and player to player. The variation in total body load and number of impacts in the
touch football data-sets may have been a reflection of the quality of the opponent, or the physical
demands for the game. An explanation of why total body load and number of impacts varied
between the two AFL players so greatly may have been a result of the position they play on the
field, which due to the trend of the match resulted in more work required of certain players than
others (i.e. the team may have been attacking more than defending). It was therefore difficult in
drawing a conclusion between the intensity of touch football and AFL in terms of which of the
two sports physically put more requirement on the human body.
There are a number of ways in which better and more accurate results could be obtained. The
integration of video would greatly enhance the ability to split up the match into attacking and
defensive periods more accurately than the methods adopted in Chapter 9. Video analysis would
also be helpful in determining where the maximum accelerations and decelerations occur during
the match. The most obvious way in which results could be improved is by collecting a great deal
more data-sets. This will give a more accurate depiction on the average values of physical
performance being measured, and applies to both the data collected from touch football and any
professional sport, and would therefore provide a better comparison between the physical
demands of both non-professional and professional sports.
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10. Concluding Remarks
It can be seen that GPS-based tracking technologies for sporting analysis can be of great value to
both professional sporting bodies that strive for continual improvement in all aspects of their
game, and the everyday athlete that just wants the ability to identify how they are performing and
what sort of output they are achieving during their exercise.
The SPI Elite device from GPSports is just one example of a technology that provides to the
coaches invaluable information about player movements during both training and off-field
situations. This is evident as all the AFL clubs use the SPI Elite unit in some aspect their player
analysis, and several NRL clubs have adopted the technology, including Manly and Melbourne.
From personal data collection, it can be concluded that the SPI Elite device was successful in its
ability to accurately track and monitor player movements, and by using the positioning capability
of GPS further analysis could be undertaken on each of the data-sets collected.
It can also be identified from the summary of results that professional AFL and touch football are
not that dissimilar in terms of average speeds of the matches and the stresses placed on the body
from impacts and body load. However, it is also important to mention that the professional AFL
players sustain this level for periods up to five times as long and do not have the benefits of
unlimited substitutions during the game. They therefore cover greater distances on the field, and
also have to contend more with physical impacts (as opposed to the “impacts” due to
accelerations and decelerations of the unit, as mentioned in previous sections of the thesis) as it is
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a contact sport. It is also important to note that these results obtained are hinged on only a
handful of data-sets, and a great deal more data-sets, up to hundreds more, would need to be
analysed to get a true, unbiased comparison.
In final conclusion, it is more important to reflect on how GPS-based technology is such an
invaluable tool in sports performance analysis, rather than the results which have been
determined, as the results merely represent the ability of GPS-based technology to deliver them.
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11. Bibliography
Das, A. (2007). "GPS Augmentation System." Retrieved 15th September, 2008, from http://www.location.net.in/magazine/2007/jan-feb/20.htm. Defense. (2005). "GPS / INS Precision Guidance System." Retrieved 17th September, 2008, from http://www.defense-update.com/products/g/gps-guidance.htm. Dimoff, G. (2007). "GPS Receivers Guide and Explanation." Retrieved 12th August, 2008, from http://www.incar-navigation.com/car-navigation-systems-3.htm. Gizmag. (2008). "Real-time athlete monitoring - the future of sport." Retrieved 28th September, 2008, from http://www.gizmag.com/go/7254/. GPSports. (2007). "GPSports The World Leader in Sport GPS Solutions." Retrieved 16th October, 2008, from http://www.gpsports.com/. Herper, M. (2004). "What's The Human Speed Limit?" Retrieved 26th September, 2008, from http://www.forbes.com/2004/05/14/cx_mh_0514running.html. Kumar, V. (2004). "Integration of Inertial Navigation System and Global Positioning System Using Kalman Filtering." Retrieved 19th September, 2008, from http://www.casde.iitb.ac.in/Publications/pdfdoc-2004/vikas-ddp.pdf. Larrson, P. (2003). " Global Positioning System and Sport-Specific Testing." Retrieved 12th August, 2008, from http://www.ingentaconnect.com/content/adis/smd/2003/00000033/00000015/art00001. Mehaffey, J. (2001). "GPS Altitude Readout > How Accurate?" Retrieved 28th September, 2008, from http://gpsinformation.net/main/altitude.htm. Modular-Mining. (1998). "Accuracy." Retrieved 13th September, 2008, from http://www.mmsi.com/gps/accuracy.htm. NASA. (2001). "GPS Augmentation & Other Networks." Retrieved 15th September, 2008, from http://gpshome.ssc.nasa.gov/content.aspx?s=aug. Powerhouse. (2003). "Wearable GPS tracking device made by GPSports Systems Pty Ltd, 2003." Retrieved 16th September, 2008, from http://www.powerhousemuseum.com/collection/database/?irn=352413. Saveabuck. (2007). "GlobalSat Personal GPS Sport Watch With Heart Monitor." Retrieved 17th September, 2008, from http://www.saveabuck.com.au/Product/GlobalSat-Personal-GPS-Sport-Watch-With-Heart-Monitor.aspx.
53
Thompson, G. L. (unknown). "GPS Tracking and its Applications." Retrieved 13th September, 2008, from http://www.gmat.unsw.edu.au/snap/gps/pdf/gps-article3.pdf. unknown. (unknown). "Lightweight, Low Cost Integrated GPS/INS Navigation." Retrieved 18th September, 2008, from http://gps.csr.utexas.edu/GPS-INS/.
54
12. References
ASD. (2008). "How Do Electronic Compasses Work?" Retrieved 20th September, 2008, from http://www.safety-devices.com/how_compass_works.htm. DimensionEngineering. (2008). " A beginner’s guide to accelerometers." Retrieved 22nd September, 2008, from http://www.dimensionengineering.com/accelerometers.htm. GPSports. (2008). "globally positioning sport Wi SPI/SPI Elite." Retrieved 13th October, 2008, from http://www.sdam.it/immagini/docs/GPS_Wi%20SPI_SPI%20elite_Elec%20V_AUS.pdf. GPSTracklog. (2006). "Why have a barometric altimeter?" Retrieved 13th September, 2008, from http://gpstracklog.typepad.com/gps_tracklog/2006/06/why_have_a_baro.html. GPSTracklog. (2006). "Why have an electronic compass?" Retrieved 13th September, 2008, from http://gpstracklog.typepad.com/gps_tracklog/2006/03/why_have_an_ele.html. Hintze, C. (2006). "Compact Gyroscope Enhances GPS Accuracy." Retrieved 23rd September, 2008. Li Y., Dempster A., Li B., Wang J., Rizos C. (2005). "A low-cost attitude heading reference system by combination of GPS and magnetometers and MEMS inertial sensors for mobile applications." Retrieved 29th September, 2008, from http://www.gmat.unsw.edu.au/snap/publications/liy_etal2005d.pdf. M.G. Petovello, G. L. (1999). "An Overview of GPS Augmentation Systems." Retrieved 15th September, 2008, from http://www.pttimeeting.org/archivemeetings/1999papers/paper9.pdf. M.J. Mathie, N. H. L., A.C.F.Coster, B.G. Celler (2002). "DETERMINING ACTIVITY USING A TRIAXIAL ACCELEROMETER." Retrieved 22nd September, 2008, from http://www.bsl.unsw.edu.au/docs/2002/EMBS2002_TRIAX.pdf. O'Neil, K. (2001). "Galileo - European Satellite Navigation System." Retrieved 19th September, 2008, from http://www.aatl.net/publications/galileo.htm. Rizos, C. (2007). "The Future of Global Navigation Satellite Systems." Retrieved 13th October, 2008, from http://www.gmat.unsw.edu.au/snap/publications/rizos_2007a.pdf. Rupprecht, W. (2007). " Post SA GPS Accuracy Measurements." Retrieved 19th September, 2008, from http://www.wsrcc.com/wolfgang/gps/accuracy.html. Space&Tech. (2001). "GLONASS - Summary." Retrieved 14th September, 2008, from http://www.spaceandtech.com/spacedata/constellations/glonass_consum.shtml.
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Spirent. (2007). "Principles and Terms of GPS/Inertial Navigation." Retrieved 29th September, 2008, from http://www.spirent.com/documents/4846.pdf. TechFAQ. (2008). "How does GPS Tracking work?" Retrieved 29th September, 2008, from http://www.tech-faq.com/gps-tracking.shtml. TechWeb. (2007). "GPS augmentation." Retrieved 20th September, 2008, from http://www.techweb.com/encyclopedia/defineterm.jhtml?term=GPSaugmentationsystem. Wiki, M.-b. (2006). "Barometric Altimeter." Retrieved 20th September, 2008, from http://wiki.motionbased.com/mb/GPS_Barometric_Altimeter. Wikipedia. (2008). "Accelerometer." Retrieved 16th September, 2008, from http://en.wikipedia.org/wiki/Accelerometer. Wikipedia. (2008). "Compass navigation system." Retrieved 20th September, 2008, from http://en.wikipedia.org/wiki/COMPASS_navigation_system. Wikipedia. (2008). "Galileo (satellite navigation)." Retrieved 15th September, 2008, from http://en.wikipedia.org/wiki/Galileo_positioning_system. Wikipedia. (2008). "GLONASS." Retrieved 14th September, 2008, from http://en.wikipedia.org/wiki/GLONASS. Wikipedia. (2008). "GPS tracking." Retrieved 15th September, 2008, from http://en.wikipedia.org/wiki/GPS_tracking. Wikipedia. (2008). "GPS/INS." Retrieved 15th september, 2008, from http://en.wikipedia.org/wiki/GPS/INS. Wikipedia. (2008). "Gyroscope." Retrieved 16th September, 2008, from http://en.wikipedia.org/wiki/Gyroscope. Wikipedia. (2008). "Inertial navigation system." Retrieved 15th September, 2008, from http://en.wikipedia.org/wiki/Inertial_navigation_system.
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13. Appendix 13.1 SCIMS SSM 42318
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