AxSys Blog

Speed Zones in Sport (Part II)

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 one10(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 Research23(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 performance9(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 Research26(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 Sport13(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 Sport15(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 performance9(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 medicine30(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 medicine28(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 Sciences29(9), 967-973.

Speed Zones in Sport

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!

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Cummins, C., Orr, R., O'connor, H., & West, C. (2013). Global positioning systems (GPS) and microtechnology sensors in team sports: a systematic review. Sports medicine43(10), 1025.

AxSys GPS Validation June 2017

 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.

Reliability Analysis

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

Difference

Distance (m)

Maximal Speed(m/s)

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
% Difference -1.57 -0.35
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.

Conclusion:

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.