AxSys is pleased to announce that we have reduced the price of our AxSys GPS unit to $299.00.
This change reflects the access to an elite, highly accurate Sport GPS product for a consumer price.
Go to our website (axsysperformance.com) and see what you will be getting for your $299.00!!
University validated, easy to use, intuitive analytics dashboard.
Any questions? Don’t hesitate to contact us at firstname.lastname@example.org
GPS technology provides the user with a range of core outputs that can be utilised to generate a whole range of additional performance information that can be useful to the end user to modify their training and performance over time.
These core metrics captured include Time, Distance, Speed & Position and these metrics allow GPS providers to calculate a range of other metrics that can be used to evaluate the performance of an athlete.
Additional Primary Metrics can include:
- How much work the player is doing per minute of training – a very good measure of training/game intensity)
Number and distance of high-speed runs and sprints
- A key factor in team sport injury is doing too much high-speed work within any training week/period.
Number & volume of acceleration and deceleration efforts
- Both Acceleration and Deceleration efforts load up specific muscle groups (hip flexors, hamstrings) that can be easily exposed to injury if the volume of this work isn’t carefully monitored over time.
- Sports Load Rating (SLR)
- Game Day Loading (GDL)
- Metabolic Power
- Fatigue Index
- Dynamic Stress Load
- High-Metabolic Power Distance
Many of the above secondary metrics typically have not been validated and as such are often estimates, or representations of the true metric as calculated within an Exercise Physiology laboratory. Or they are a combination of metrics to create a new more meaningful metric (eg SLR/GDL)
For the majority of users of this technology – if you can gain a strong understanding of the Primary metrics and how these are used to maximise performance and minimise injury then you will be in the top percentile of users of this GPS technology in the sporting environment.
Core metrics captured using GPS include (Time, Distance, Speed & Position).
These core metrics allow GPS providers to calculate a range of other metrics that can be used to evaluate the performance of an athlete.
GPS signals are affected by several environmental factors leading to the absolute positioning error discussed in the last article.
The key factors affecting GPS accuracy include:
Atmospheric effects (the signal needs to pass through the entire atmosphere and this can result in errors in the signal timing and strength). This factor is the largest in creating the absolute errors that we see at the Earth surface.
- Cloud and Fog cover ‘DO NOT AFFECT GPS ACCURACY’.
- Multi-path – this is when the GPS is being used in a built-up environment (eg tall building areas). The GPS signal from the satellites can bounce off a building wall and be seen by the GPS antenna resulting in a timing error that results in positioning, distance and speed errors.
- Altitude spikes – Occasionally GPS will incorrectly calculate the altitude of the GPS device – this can have a major effect upon data accuracy and associated distance/speed calculations.
- Dilution of Precision – this is the geometric positioning of all the GPS satellites in range at the time of use of your GPS device. There is nothing you can do about this (except for maybe determining when the geometry is at its best and training then). If the geometry is poor this can have a negative impact upon your absolute positioning.
The key things you can do to maximise the quality of the data being captured by your GPS device include:
- Ensure you turn your GPS unit on outside so that the moment the G.PS units starts looking for satellites it can see their signals (some GPS devices if turned on inside will freeze as they cannot see any GPS signals and will then need to be reset).
- Ensure you are using your GPS device in an open field environment where possible.
- Ensure you give your GPS device a few seconds after it has acquired GPS satellites to pick up as many satellites as it can within range.
AxSys Performance has added additional software checks to ensure that any speed or altitude spike is identified and removed prior to generating final results for the client. This ensures high quality data being captured and exported from the AxSys GPS unit.
GPS technology will occasionally generate poor results – this can be caused by a range of issues – if you are aware of these you can typically avoid poor GPS performance by training in the right environment.
In the next article in this series we will explain what core and secondary metrics are able to be captured/calculated using GPS technology.
When discussion the accuracy of a GPS system it is important to distinguish b/w absolute positioning v’s relative positioning.
Absolute positioning is your exact location at any given time on Earth (indicated by a Latitude and Longitude).
Relative positioning is the actual distance travelled as indicated by the GPS from a start point to a known end point.
The image to the left shows that on any given day your absolute positioning will be +/- 2-5m of the true location BUT if you travel a known distance (regardless of your starting point), the accuracy of a system like the AxSys system will be around 99% of true distance.
It is for this reason that being able to accurately display player to player positioning is difficult due to the absolute positioning error, but the distances covered by the athlete (relative positioning) are very accurate and reliable.
There are methods to improve on the absolute accuracy of a GPS system, but this typically involves additional technologies and increased cost.
If you are interested in a reasonable estimation of the positioning of your player – then the absolute positioning error of systems like the AxSys system is likely to be acceptable.
As with all things GPS, the clearer the area that the data is being collected (eg open field), the better chance of capturing accurate performance data.
We are regularly asked “How accurate is the GPS product we have” – Before we can answer this question appropriately we need to explain the difference between Absolute and Relative Positioning and the associated accuracies when applied to a sporting environment.
In the next article in this series we will explain why GPS is not cm level accurate and highlight how you can use the technology to ensure the most accurate signal possible.
There are many myths around the use of GPS in sport – This series aims to highlight the strengths and weaknesses of GPS technology and associated sensors (Accelerometers/Gyroscopes) for use in a sport environment.
Firstly, there is no doubt that Sport GPS units have changed the team sport landscape for ever. Gone are the days of guestimating how much work has been completed by each individual player. With the advent of accurate, small GPS units, coaching staff now have access to all key metrics needed to make informed decisions on training loads and intensities.
GPS is a free technology (No fees to be paid to access the signal).
GPS does not need to be calibrated, it self-calibrates.
GPS only works outdoors (requires a direct line of sight from the satellite to the GPS receiver in your device/phone).
GPS has a position error (where you are located on the Earth) of between 2-10m depending on the environment and quality of GPS module.
GPS doesn’t like built up environments (city centres, forests) – the accuracy will decrease in these environments.
GPS uses two methods to calculate distance/speed of a user
Doppler (rate of change of the GPS signal reaching a GPS module).
There are 20+ GPS satellites in the sky in most regions of the world.
There are multiple GPS companies with GPS satellites in the sky – Most modern GPS units will have GPS modules that can see 2 or more of these systems (ensuring very good satellite coverage anywhere on Earth).
Only 4 satellites are required to generate an accurate and reliable position.
GPS devices have different update rates – Typically your phone or GPS watch will only sample at 1Hz (once per second) – this is not suitable for rapid change of direction activities as would happen in a team sport environment.
The team sport specific GPS devices have sampling rates 10Hz or more.
AxSys GPS has a sampling rate of 18hz – leading to a very high accuracy of all GPS metrics.
GPS can be used to capture the following performance metrics from an athlete:
Summary Video: How Does GPS Work? by sciBRIGHT - https://www.youtube.com/watch?v=FU_pY2sTwTA
GPS technology is a free platform that can allow for accurate measurement of sport performance.
There are some limitations to the technology, that if you understand and avoid will maximise your use of GPS technology in your training.
In the next article in this series we will explain the difference in accuracy using GPS (Absolute versus Relative positioning accuracy).
As mentioned in our previous blog post ‘Speed Zones in Sport’, when talking GPS technology, there are a multitude of different speed zones and accompanying activity descriptors utilised both within and between sports. This post serves the purpose of providing you with brief examples of speed zones used within a small sample of team sports.
In Rugby, players are naturally required to perform intermittent bouts of moderate-to-high intensity efforts interspersed with periods of low intensity and rest.1
After exploring existing literature on GPS use in Rugby (15’s & 7’s), it has become apparent that there is no commonly used or standardised set of speed zones in research or otherwise. However, a number of studies 2,3,4 have used zones and activity descriptors along the lines of the following approximations;
Standing & Walking: 0-6 km.h-1
Jogging: 6-12 km.h-1
Cruising: 12-14 km.h-1
Striding: 14-18 km.h-1
High-intensity Running: 18-20 km.h-1
Sprinting: >20 km.h-1
FYI: Speed zones in sport are often expressed as km.h-1 but are also expressed as m.s-1, both in research and practice – the conversion is quite simple; multiply km.h-1 figure by 5 then divide by 18 (1km/1 hour = 1000m/3600 seconds, which simplifies to 5m/18seconds).
When considering Rugby League, research involving the inclusion of speed zones is somewhat limited. Many of the existing literature have simply been concerned with Low-Speed (LSR) and High-Speed Running (HSR) thresholds (i.e. LSR: 0-18 km.h-1, HSR: >18 km.h-1).6,7
Similar to rugby, Australian Rules Football (AFL) is a fast-paced, multifaceted game requiring repeated high intensity efforts combined with low intensity efforts and active recovery. Again, there is little to no consistency in AFL research when defining speed zones and activity descriptors. To provide you with an example however, one of the most commonly cited papers5 uses the following zones;
Standing: 0-0.7 km.h-1
Walking: 0.7-7 km.h-1
Jogging: 7-14.4 km.h-1
Running: 14.4-20 km.h-1
Higher-Speed Running: 20-23 km.h-1
Sprinting: >23 km.h-1
The game of Soccer also involves intermittent, varying efforts that are characterized by unpredictable changes in intensity, direction and mode of movement.8 As you probably would have noticed, there is a running theme of inconsistent, arbitrary speed zones being used in current research, and soccer is no exception. For the purpose of this blog post and general guidance, the following sample of speed zones have been defined in existing literature; 8,9
Standing: 0-0.7 km.h-1
Walking: 0.7-7.2 km.h-1
Jogging: 7.2-14.4 km.h-1
Running: 14.4-19.8 km.h-1
High Speed Running: 19.8-25.2 km.h-1
Sprinting: >25.2 km.h-1
Lastly, Field Hockey, like the aforementioned codes of ‘football’ also consists of intermittent bouts of high and low intensity efforts. Unfortunately, field hockey is largely untouched when speaking of GPS research and as a result there is very limited information on speed zones utilized. One study however used the following; 10
Standing: 0-0.6 km.h-1
Walking: 0.7-6.1 km.h-1
Jogging: 6.1-11 km.h-1
Running: 11.1-15 km.h-1
Fast Running: 15.1-19 km.h-1
Sprinting: >19 km.h-1
Absolute v Relative Speed Zones
There is currently an overwhelming use (across all sports, ages, and genders) of default, arbitrary speed zones for all players involved in a given team. And, as you can already see through the examples provided, there are evidently varying conceptions of athlete physical capabilities (i.e. Max Velocity) between sports (and rightly so). Continuing on with that notion, individual athletes within the same sport/team will also obviously possess different capabilities – particularly when looking at different positions (i.e. Rugby forwards v backs).
Although the adoption of a common set of ‘absolute’ speed zones allows standardization and simple player comparison, it may not in fact provide a valid representation of players’ individual locomotive demands.
A recent study exploring the concept of individualised speed thresholds in rugby1, compared the locomotive demands of players using both absolute and relative speed zones and the differences between the two. It was found that the use of a single set of absolute speed zones can lead to both overestimation and/or underestimation (position specific) of variables such as HSR metres. The biggest practical implication of such results is the eventual prescribing of incorrect or inaccurate training workloads. 1
With no research-based, standardised speed zones defined, and no good reasoning behind zones used (other than that prior research used similar), there are questions surrounding the use of these arbitrary zones and the accompanying descriptors.
Theoretically, the use of individualised speed zones based on percentages of athletes’ determined max velocity (Vmax) make much more sense. However, there are some uncertainties surrounding true Vmax vs. in-game Vmax, and which is the appropriate measure to use. Nevertheless, it seems that individualised, relative speed zones may in fact provide a more accurate depiction of player locomotive demands – and therefore allow more appropriate and valid monitoring and prescription of workloads.
What are we doing to help?
At AxSys, we are working towards providing users with the ability to use a default set of absolute speed zones or define their own parameters and/or activity descriptors to use.
Not only will we allow the customisation of absolute speed zones, but we will also provide the ability to switch from absolute to relative, individualised speed zones for each player on your team. Provided you have determined your players’ Vmax, you will be able to either use our default set of percentage-based speed zones, or define your own.
We understand that all of our clients have different needs and ideas of their own, so we are trying our utmost to accommodate those and provide you with as much freedom and customizability as possible.
1. Reardon, C., Tobin, D. P., & Delahunt, E. (2015). Application of individualized speed thresholds to interpret position specific running demands in elite professional rugby union: a GPS study. PloS one, 10(7), e0133410.
2. Cunniffe, B., Proctor, W., Baker, J. S., & Davies, B. (2009). An evaluation of the physiological demands of elite rugby union using global positioning system tracking software. The Journal of Strength & Conditioning Research, 23(4), 1195-1203.
3. Suarez-Arrones, L., Arenas, C., López, G., Requena, B., Terrill, O., & Mendez-Villanueva, A. (2014). Positional differences in match running performance and physical collisions in men rugby sevens. International journal of sports physiology and performance, 9(2), 316-323.
4. Suarez-Arrones, L. J., Nuñez, F. J., Portillo, J., & Mendez-Villanueva, A. (2012). Running demands and heart rate responses in men rugby sevens. The Journal of Strength & Conditioning Research, 26(11), 3155-3159.
5. Coutts, A. J., Quinn, J., Hocking, J., Castagna, C., & Rampinini, E. (2010). Match running performance in elite Australian Rules Football. Journal of Science and Medicine in Sport, 13(5), 543-548.
6. Gabbett, T. J., Jenkins, D. G., & Abernethy, B. (2012). Physical demands of professional rugby league training and competition using microtechnology. Journal of Science and Medicine in Sport, 15(1), 80-86.
7. Twist, C., Highton, J., Waldron, M., Edwards, E., Austin, D., & Gabbett, T. J. (2014). Movement demands of elite rugby league players during Australian National Rugby League and European Super League matches. International journal of sports physiology and performance, 9(6), 925-930.
8. Di Salvo, V., Gregson, W., Atkinson, G., Tordoff, P., & Drust, B. (2009). Analysis of high intensity activity in Premier League soccer. International journal of sports medicine, 30(03), 205-212.
9. Rampinini, E., Coutts, A. J., Castagna, C., Sassi, R., & Impellizzeri, F. M. (2007). Variation in top level soccer match performance. International journal of sports medicine, 28(12), 1018-1024.
10. Macutkiewicz, D., & Sunderland, C. (2011). The use of GPS to evaluate activity profiles of elite women hockey players during match-play. Journal of Sports Sciences, 29(9), 967-973.
With the use of Global Positioning Systems (GPS) in sport becoming a commonality, data collected and analysed is now more important than ever. GPS companies like AxSys allow coaches and athletes alike to measure variables such as; player position, speed/velocity, and movement patterns in attempts to further understand physical demands and workloads.
When thinking about speed, it is common place to divide efforts into a number of speed zones (or bands) for analysis. However, there is much debate and inconsistency when defining these speed zones and associated activity descriptors (i.e. standing/walking, jogging etc.) within and across sports.
In a systematic review conducted by Cummins et al., 2013, the greatest variations in speed zones were found between sports. For example, a single speed zone (Zone 4) varied from 13 - 14km/h (cricket) through to 14-20km/h (AFL), with the accompanying descriptors ranging from jog through to high intensity run.
Each team sport requires a unique set of physiological characteristics and demands – so, a uniform set of speed zones seems inappropriate and could potentially lead to less than ideal data collected. There is potential however, for the development of standardised speed zones within sports, which would allow easy and consistent data comparison for coaches and athletes.
In the meantime, whilst there is no standardised speed zones and/or activity descriptors for team sports, we at AxSys provide users with a default set of 4 speed zones (Walking: 3-6km/h, Jogging: 6-12km/h, Running: 12-18km/h, Sprinting: >18km/h) while also providing the freedom to modify and customize both speed zones and descriptors to suit indiviuals and teams. We are also currently working on the inclusion of a research-based speed zone archive for specific sports.
What sport are you involved in? What are the speed zones you use for analysis?
In the coming weeks, the theme will continue to be speed zones, with posts on specific sports and the most common speed zones used for each - stay tuned!
Be sure to follow us on our social media pages to keep up to date with AxSys news and updates!
Cummins, C., Orr, R., O'connor, H., & West, C. (2013). Global positioning systems (GPS) and microtechnology sensors in team sports: a systematic review. Sports medicine, 43(10), 1025.
After 15-years of elite sport embracing GPS tracking systems, it is evident that there has one limitation to achieving the levels of distance and speed accuracy that most programs desire from such products: that of GPS sampling rates.
AxSys Performance, the latest company to enter this market has effectively solved this dilemma by integrating a high sampling rate GPS module (18Hz) to bring a new level of accuracy to the much-needed sports performance tracking market.
Several validation studies completed in recent years have clearly stated the issues with lower sampling rate systems when it comes to distance and speed accuracy, including:
1. Accuracy and reliability of GPS devices for measurement of sports-specific movement patterns related to cricket, tennis, and field-based team sports.
- Vickery WM1, Dascombe BJ, Baker JD, Higham DG, Spratford WA, Duffield R.
- J Strength Cond Res. 2014 Jun; 28(6):1697-705. doi: 10.1519/JSC.0000000000000285.
“Based on these results, practitioners of these devices should be aware that measurements of distance and speed may be consistently underestimated, regardless of the movements performed.”
2. Accuracy and reliability of GPS devices for measurement of movement patterns in confined spaces for court-based sports.
- Rob Duffield a, Machar Reid b, John Baker c, Wayne Spratford c, a School of Human Movement Studies, Charles Sturt University, Australia b Tennis Australia, Australia c Australian Institute of Sport, Biomechanics & Performance Analysis Department,
- Received 18 February 2009; received in revised form 23 June 2009; accepted 21 July 2009
“In conclusion, for court-based sports or movements in confined spaces, GPS technology under reports distance covered and both mean and peak speed of movement.”
3. The validity and reliability of GPS units for measuring distance in team sport specific running patterns.
- Int J Sports Physiol Perform. 2010 Sep;5(3):328-41.
- Jennings D1, Cormack S, Coutts AJ, Boyd L, Aughey RJ.
“Current GPS systems may be limited for assessment of short, high speed straight line running and efforts involving change of direction. An increased sample rate improves validity and reliability of GPS devices.”
AxSys GPS Validation Results:
AxSys Performance recently completed our first validation study based on our new AxSys GPS (18Hz) GPS unit.
The study, completed by Jocelyn Mara from the Sports Studies Department at the University of Canberra, indicates that the integration of a higher sampling rate GPS module (and associated architecture to deal with higher sampling rates) has effectively removed this accuracy limitation.
To determine the inter-unit reliability of the GPS for distance and maximum speed measurements, the typical error (TE), typical error as a percent of the mean (TE%) and the Intraclass Correlation Coefficient (ICC) were calculated (Hopkins, 2000). The typical error was calculated as the standard deviation of the difference (between GPS 1 and GPS 2), divided by the square-root of 2. The ICC measured the level of agreement between the values derived from GPS 1 and GPS 2. A weighted Kappa correlation coefficient was used to determine the inter-unit agreement for detecting accelerations >2.5m/s2 (Altman, 1999).
Results: The difference between GPS and criterion values for distance and maximum speed measurements
|Mean ± SD||-1.56 ± 2.70||-0.02 ± 0.14|
|Abs. Mean ± SD||2.35 ± 2.04||0.10 ± 0.09|
|Range (min – max)||-10 to 4||-0.3 to 0.3|
|Abs. % Difference||2.37||1.38|
% Difference = the difference expressed as a percentage of mean values (GPS and Criterion); Abs. = Absolute values reportedData are expressed as the difference between GPS and Criterion values (GPS minus Criterion);
The above table shows the difference between the GPS measurements and criterion values for distance and maximum speed. On average, the GPS device underestimated distance by only 1.57% and underestimated maximum speed by only 0.35%, resulting in very acceptable levels of accuracy for these measures.
This early research has demonstrated that Sport GPS systems should embrace higher sampling rate GPS modules such as that used in the AxSys GPS product, otherwise risk under reporting of true distances and speeds achieved by the athletes using these products.
AxSys All Areas provides additional insight into the AxSys technology that is unique to the market and sets us ahead of the competition.
AxSys GPS units track and collect large amounts of data during training sessions and games. However, traditional wireless methods of downloading (Bluetooth or Infrared) are ineffective to download the large amounts of data captured. Instead, the AxSys GPS units use high speed Wi-Fi to download the data from the unit to the App quickly and seamlessly. No cords or wires are needed to connect the GPS device before you can access your data.
Additionally, we have built our android Team App to automatically download data from multiple units at once. There is no limit to the number of units to be downloaded and no need for a docking station or cradle to download the data and generate reports.
This seamless integration from data capture to data download is unique to the AxSys products allowing athletes and coaches to focus on what really matters.
Summary of Adrian Faccioni’s Keynote Presentation: Utilising Wearable Tech for Improved Outcomes – 2017 ESSA Business Review
With the recent boom in wearable technology, there has been an influx of cheap and easy to use products (such as the Fitbit and Garmin ranges) and a subsequent demand for them. Predominately taking watch form, current wearables are taking advantage of the ‘timeless’, well-worn watch culture (pun intended) to provide convenient and fashionable activity monitoring. However, with wrist based wearables often comes significant accuracy/reliability issues that are now seeing a plateau in product uptake.
Firstly, collecting data based on wrist movement often does not provide an accurate or reliable indication of what the body has been expending or what activity it has performed as a systematically functioning unit.
Secondly, wrist wearables currently place emphasis on numbers rather than answering crucial questions about performance or activity. Capturing a range of biometric data such as steps, heart rate, stairs climbed, and time spent sitting, standing and walking are not necessarily the most accurate indicators of one’s activity or workload.
Lastly, activity measurement is dependent upon a field of computing known as ‘Pattern Recognition’; a technique focused on evaluating patterns in data and trying to match unknown patterns with known activities. Although a revolutionary concept in itself, the patterns that are evaluated vary greatly between individuals and activities, which gives rise to a number of reliability/validity issues. Additionally, a major flaw of wrist wearables is the generation of steps or ‘activity’ with a simple swing of the arm – leading to inaccurate data collection and load monitoring.
So, let’s assume we can progress wearable technology to provide reliable and valid data. Apart from counting steps, how can we quantify an individual’s workload/activity to provide useful information that informs decisions about future exercise routines?
Basically, the question we are trying to answer is;
How do we get wearables to a point where they are customised to YOU; the weekend warrior, the aspiring athlete, the proven champion?
At AxSys Performance we possess deep pride in our product’s ability to provide simple and personalised analytics alongside an expansive mobile app that serves as an Athlete Management System.
With near-future plans to incorporate additional biometrics (i.e. heart rate) into our technology, AxSys Performance will open doors to both individualised athlete monitoring and remote coaching abilities.