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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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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