Stadium Data – International Rugby Union

10 Feb Stadium Data – International Rugby Union

INTRODUCTION

Global Navigation Satellite System (GNSS) technology utilised in the STATSports Apex device has previously demonstrated high levels of data quality when operating in the most challenging environments, including semi-enclosed stadia or high-rise residential/commercial complexes where signal reception may be significantly compromised.

GNSS technology can communicate with multiple satellite systems (GPS, GLONASS, Galileo, and BeiDou) simultaneously, thus maximising the accuracy of positional data through increased satellite availability.

Regional Satellite-Based Augmentation Systems (SBAS) has been shown to further improve the accuracy and reliability of GNSS information by correcting signal measurement errors, adjusting for atmospheric conditions, and providing information regarding satellite accuracy, integrity, and availability. SBAS utilises GNSS measurements derived from accurately located reference stations deployed across an entire continent [1].

For a variety of alternative commercially available GPS player tracking systems, stadium roof architecture in combination with the quantity and variety of electronic equipment operating in the area can present significant problems for the accuracy of data capture, in addition to the calculation and transmission of live data.

A study by Johnston et al., (2020) demonstrated significant discrepancies between GPS providers when examining the mean number of satellites obtained during match activities within challenging stadium environments.

This study highlighted that the STATSports Apex device, powered by augmented GNSS technology, connected to an average of 17.7 satellites during National Rugby League matches. In a similar study conducted within the same country and featuring comparable stadiums, an alternative player tracking technology provider, powered by single constellation GPS technology, was only able to record an average of 10.5 satellites during Australian Football League match-play [2].

This insight aimed to build on the findings of the English Premier League, Dutch Eredivisie, Austrian Bundesliga and Primeira Liga STATSports stadium data reports to identify the data quality of the STATSports Apex device in stadium environments throughout International Rugby Union from 2020-2022. Not only through average satellite counts, but also through Horizontal Dilution of Precision (HDOP) and Horizontal Accuracy (HACC). These measures aim to describe both precision and accuracy of the data from available satellites per stadia in International Rugby Union.

METHODOLOGY

Data was obtained and analysed from International Rugby Union matches, all held in national rugby union stadiums. In this case study, 9 International Stadia were analysed using the 2020-22 International Test block. This consisted of stadia across different continents and hemispheres, in the Guinness 6 Nations Championship (Europe) and International Tests held in New Zealand (Oceania).

A sample of varying positions was used (n=8) including Props, Hooker, Second Row, Back Row, Scrum Half, Fly Half, Centre and Back Three to include a holistic view of spatial positions per stadia. This was to ensure any potential variation between positions was accounted for in the data analysis.

Each player was assigned a specific STATSports Apex GNSS device which allows for continuity between players and positions. The dedicated sports scientist followed a strict protocol to ensure maximum accuracy was accounted for throughout the data collection phase. The devices were turned on approximately 20 minutes prior to kick off to allow for satellite signal acquisition.

The quality of collected data was determined using three variables (3)

1. Horizontal Dilution of Precision (HDOP)
2. Horizonal Accuracy (HACC)
3. Number of Satellites

Horizontal Dilution of Precision (HDOP)

HDOP is a term used to describe the strength of the current satellite configuration, or geometry, on the accuracy of the data collected by a GPS or GNSS receiver at the time of use.

HDOP is a factor in determining the relative accuracy of a horizontal position. With GPS receivers, when satellites are grouped together in the same general area of the sky, the satellite geometry is considered to be weak (higher DOP value). When satellites are evenly distributed throughout the sky, their geometry is considered strong (lower DOP values). This is interlinked with the number of satellites available, as the more satellites that are readily available and therefore evenly distributed throughout the sky, leads to better positional accuracy (lower the DOP value) (Figure 1 and 2) [6].

Figure 1. Good PDOP and visibility indicated by evenly spread satellites throughout the sky connected to a single receiver [1].

Figure 2. From the receiver’s point of view, if the satellites are spread apart in the sky, then the GPS receiver has a good dilution of precision [1].

Horizontal Accuracy (HACC)

Horizontal Accuracy (HACC) represents the error of measured position compared to the absolute position of the receiver projected in the horizontal plane. In STATSports APEX, HACC is represented by a value ranging from 0-7.

Number of satellites

The total number of satellites used in the calculation of the unit’s position.

The data used for analysis accounts only for players who participated in the full game, first half only and/or second half only. The drills were created on an individual basis to account for maximal accuracy in the data analysis. Data generated in the half-time period was excluded.

Figure 3. This figure displays three options using precision and accuracy measures mentioned previously, accuracy (HACC) and precision (HDOP).

As a result of using the three measurements of accuracy (HDOP, HACC and Number of Satellites) we can attribute these to Figure 3 in a real-life scenario. All 3 measures are of high importance, as only accounting for individual measurements in isolation could result in high precision but low accuracy or high accuracy but low precision. Where a strong relationship between the measurements is present in an ideal scenario of high accuracy and high precision, you can be confident in the quality and accuracy of the data.

RESULTS

Table 1. Descriptive statistics illustrating the average satellite count, Average HDOP and HACC (± Standard Deviation), per stadia.

Table 2. Descriptive statistics illustrating the percentage breakdown of GNSS satellite quality of HACC and HDOP, per stadia. Values less than 1 (HACC) are ‘Excellent’. Values less than 1 (HDOP) are displayed as “Ideal”. Values less than 2 (HDOP) are displayed as “Very Good” (4).

CONCLUSION

STATSports are, and will continue to be, committed to data transparency with all data collected using the STATSports Apex system. The Sonra performance analysis platform enables users to export all raw data collected by Apex devices; providing users with the opportunity to analyse their stadium data in whatever way they choose without restriction. As can be seen from the results of this report, the STATSports Apex device provided extremely accurate and reliable data, as determined by the average number of satellites, HDOP, and HACC values throughout all International Rugby Stadia.

With specific reference to the upcoming Six Nations tournament, teams can be fully confident in the quality of data that will be collected in their matches due to the high, proven levels of data quality in stadia. As shown in Figure 4, all STATSports devices in these stadia acquire data of the highest quality, with HDOP values all less than 1.35, and HACC values less than 0.09, all values which are deemed to be excellent & very good in data quality ratings.

Figure 4. Average HDOP & HACC values obtained within international rugby union stadia during official test matches.

*Note – The Principality Stadium in Wales is not included as matches were played under a closed roof.

REFERENCES

1. GPS Accuracy: HDOP, PDOP, GDOP, Multipath & the Atmosphere. Retrieved June 17, 2020, from Remote Sensing website: https://gisgeography.com/gps-accuracy-hdop-pdop-gdop-multipath/
2. Johnston, R. D., Thornton, H. R., Wade, J. A., Devlin, P., & Duthie, G. M. (2020). The Distribution of Match Activities Relative to the Maximal Mean Intensities in Professional Rugby League and Australian Football. Journal of Strength and Conditioning Research, 1. https://doi.org/10.1519/jsc.0000000000003613
3. Galileo and EGNOS featured at InterGeo 2019. Retrieved June 17, 2020, from Newsroom website: https://www.gsa.europa.eu/newsroom/news/galileo-and-egnos-featured-intergeo-2019
4. Isik, Oguz Kagan; Hong, Juhyeon; Petrunin, Ivan; Tsourdos, Antonios (25 August 2020). “Integrity Analysis for GPS-Based Navigation of UAVs in Urban Environment”. Robotics. 9 (3): 66. doi:10.3390/robotics9030066.
5. Malone, J. J., Lovell, R., Varley, M. C., & Coutts, A. J. (2017). Unpacking the black box: applications and considerations for using GPS devices in sport. International journal of sports physiology and performance, 12(s2), S2-18.
6. Specht, M., 2022. Experimental Studies on the Relationship between HDOP and Position Error in the GPS System. Metrology and Measurement Systems, pp.17-36.