This dataset presents information pertaining to the spatiotemporal characteristics of Polo play from a 0 to 24-goal level. Data were collected by player worn GPS units, across a New Zealand Polo season. The dataset comprises observations from 466 chukkas of Polo, across mixed and women’s only Polo and is curated by cumulative player handicap. Data are presented for distance, speed and high intensity metrics, which have further categorised into five equine-based speed zones. The purpose of this dataset is to provide a detailed quantification of a range of Polo performance levels, advancing the scope and size of previous Polo literature that has employed GPS technology. This dataset may be of value to equine scientists and trainers, veterinary practitioners and sports scientists

Best, Russell

Standing, Regan J

English

Wintec Research Archive

dataData DescriptorDistance, Speed and High Intensity Characteristics of0 to 24-Goal, mixed and Women’s PoloRuss Best 1,2,* and Regan Standing 11 Centre for Sports Science and Human Performance, Wintec, Hamilton 3200, New Zealand2 School of Health and Social Care, Teesside University, middlesbrough TS1 3BX, UK* Correspondence: Russell.Best@wintec.ac.nzReceived: 27 June 2019; Accepted: 9 July 2019; Published: 10 July 2019Abstract: This dataset presents information pertaining to the spatiotemporal characteristics of Poloplay from a 0 to 24-goal level. Data were collected by player worn GPS units, across a New ZealandPolo season. The dataset comprises observations from 466 chukkas of Polo, across mixed andwomen’s only Polo and is curated by cumulative player handicap. Data are presented for distance,speed and high intensity metrics, which have further categorised into five equine-based speed zones.The purpose of this dataset is to provide a detailed quantification of a range of Polo performance levels,advancing the scope and size of previous Polo literature that has employed GPS technology. Thisdataset may be of value to equine scientists and trainers, veterinary practitioners and sports scientists.Dataset: available as the supplementary fileDataset License: CC-BY-NCKeywords: polo; GPS; horse welfare; speed; distance1. SummaryPolo is an equestrian based team sport, contested by two teams of four players on a 150 m × 275 mpitch. Play is divided into 7 min periods of play termed chukkas [1]. Dependent upon the level of Polobeing played, four or six chukkas are contested, with the objective being to score more goals than theopposing team. Players are awarded a handicap by the local Polo body between −2 and +10 goals [1].The cumulative team handicap dictates the level of play, e.g., 24 goals (for instance, a team made up offour 6-goal players).Global positioning systems (GPS) use in Polo is in its infancy, with previous literature utilizingGPS datasets to inform training programs [2] and assess one-off high goal match demands [3,4].Currently the largest dataset to have employed GPS within Polo comes from a reliability study [5],which assessed 37 unique horse-rider interactions. The current dataset was collected over the courseof a New Zealand Polo season, across a full spectrum of handicap levels and incorporates resultsfrom mixed and women’s only Polo. The purpose of this data collection was to curate a large andtransparent dataset to provide detailed quantification of each level of Polo played. The ability of thisdataset to differentiate the characteristics of play between mixed and women’s only Polo is a point ofdifference over previously published acute works [2–4].The intention of this dataset is to identify differences in spatiotemporal characteristics betweenlevels of Polo play, as ascertained by cumulative player handicap. This could be useful to inform notonly training demands and programs, but ultimately horse welfare and performance managementstrategies. This dataset may also be of interest to veterinary practitioners and researchers, and may piquethe interest of sports scientists, as Polo is currently an underexplored area within that discipline [5,6].Data 2019, 4, 95; doi:10.3390/data4030095 www.mdpi.com/journal/dataData 2019, 4, 95 2 of 4A further advantage of the public deposition of this dataset, is that we provide an anonymized repositoryfor information pertaining to the demands of 0 to 24-goal Polo that would not be created organicallywithin the global Polo community. This is likely due to the high level of financial investment in andthe competitive nature of the sport, which may foster an unwillingness to openly share information asthis may be perceived to provide a tactical advantage. However, we feel that by providing a large andanonymous dataset, these data may be of potential use in elevating the understanding and practicesemployed within New Zealand and global Polo, across all levels of play.2. Data DescriptionThis article describes data obtained via GPS from the Waikato and Auckland regions of the uppernorth island of New Zealand from Polo players mounted on ponies competing in 0–24 goal Polo. Thepitch sizes were 150m × 275m and are the biggest pitches used in professional sport. GPS data wererecorded per game and split into separate periods of play (chukkas) for analysis purposes, with chukkalength ranging from 6:23 to 19:27 (min:sec). The data included within this article describe the distance,speeds, and high intensity activities performed by each player per chukka, as per Table 1. There wereno constraints or pre-configured playing requirements of players, as an authentic and habitual playingprofile was sought.Data are presented in sheets pertaining to the separate key variables of distance, speed and highintensity activity, with gender, date and chukka length information provided per recorded sub-variable.Data should only be interpreted via cumulative player handicap or gender, as mixed methods of playcan impact on the speeds, distances and high intensity activities performed by riders. Speed zones (1–5)(as described in methods) were then used to separate data into relevant portions and are presentedalongside the variables outlined below.Table 1. A description of variables derived from GPS recording providing a quantitative description ofplayers’ movements. Sub-variable names and descriptors were retrieved from VX Sport [7].Variable Sub-Variable DescriptionDistance (G)Distance Total (m) Total Distance of all the data collected. Can be changeddependent on Trim periods selected.Distance Rate (m/min) The rate that gives us distance travelled per unit of time.Distance Speed Hi-Inten (m) Total distance that is covered at a specified High Intensitythreshold.Speed (G)Speed Avg (km/h) Calculates the Average Speed of the session.Duration Speed Hi-Inten (min:sec) Total duration that is gathered from above the HighIntensity Speed BenchmarkSpeed max (km/h) Returns the maximum instantaneous speed that the athletereached during the sessionHigh intensity (A)Sprints Total (num) Total amount of Sprints that fall in the sprint equationSprints Hi-Inten (num) Total amount of sprints that are above the High IntensitySprint BenchmarkBody Impacts (num) The number of Impacts within the sessionHi Intensity Effort The total number of efforts reported across fivespecific metrics (Hi-int Sprints, Hi-int Accel, Hi-int Decel,Body impacts & Jumps).AccelerationsAcceleration Zone Total (num) Total number of sprints within each acceleration zoneDistance AvgAcceleration Zone Total (m) Average distance covered within each acceleration zone.Distance maxAcceleration Zone Total (m) maximum distance covered within each acceleration zoneDistance TotalAcceleration Zone Total (m) Total distance covered in each acceleration zone.DecelerationsDeceleration Zone Total (num) Total number of decelerations completed in each zoneHi Int Deceleration The total amount of deceleration’s that are above the HighIntensity Deceleration BenchmarkDistance AvgDeceleration Zone Total (m) Average distance covered within each deceleration zoneDistance maxDeceleration Zone Total (m) maximum distance covered within each deceleration zoneDistance TotalDeceleration Zone Total (m) Total distance covered in each deceleration zone.Variables assessed via GPS are denoted with a (G); data captured from accelerometers are noted by (A).Data 2019, 4, 95 3 of 43. MethodsThe investigation was carried out following the rules of the Declaration of Helsinki and inaccordance with the International Guiding Principles for Biomedical Research Involving Animals asissued by the Council for the International Organizations of medical Sciences. Approval from theWaikato Institute of Technology ethics committee was obtained in October 2018 prior to undertaking thisresearch. Project identification codes are not current institutional procedure. Players who participatedin this investigation were all in possession of a current New Zealand Polo Association handicap (−2to +7 goals mixed; −2 to +10 goals women) and provided voluntary informed consent for them andthe performance of their ponies to contribute to this dataset. Data relating to the Polo handicaps ofindividual participants have been removed to preserve athlete anonymity; such identifiable materialsare not of relevance to the dataset and may have breached participant consent if included.Data were collected using player mounted GPS units (VX Sport 350, Wellington, New Zealand) setin equine mode, to enhance the tolerable upper limit of data presented when extracted and analysedsubsequently. These devices are equipped with 8th Generation 10Hz Swiss high precision chips,that support multiple satellite systems, 104Hz tri-axial accelerometry and 18Hz tri-axial gyroscopeand magnetometers. Raw accelerometer and magnetometer data were excluded from the present datasetto minimise error due to individual rider and horse gait and playing profiles. Whilst such movementsignatures are likely interesting, work independent of this investigation is required to fully understandthese factors. We have previously shown that the data processed by the unit manufacturer’s software toproduce reliable spatiotemporal data whether mounted in a specialist vest or mounted on the players’belts [5], hence this processed data is preferred. All players in the present dataset opted to wear the uniton their belt in a pouch, secured by tape. This position is preferred as it does not impair rider comfortor performance, and is positioned closest to the saddle. We assigned each player the same GPS unit forrepeat observations to minimize error due to differences in inter-unit reliability. Device dimensions(74mm × 47mm × 17mm) and weight (50 g) are also discrete so as not to impair sport performance.Prior to use GPS units were issued and the prospective location of data collection named.This identifies the location for analysis purposes and is associated with longitude and latitudecoordinates. On game days GPS units were turned on at least 30 min prior to game start to ensuresufficient time to acquire a ‘satellite lock’ from multiple sources. Upon game completion units wereturned off and data were extracted using specialist software provided by the unit manufacturer(VX Sport Software 5.3.1, Wellington, New Zealand). Data were then trimmed to remove the initialsatellite lock and warm-up periods, and splits were inserted to match chukka durations (min:sec)as per accompanying notational analysis. All trims (chukka durations) were the same per player(identified as Athlete in the dataset), per team, per game. Using the same software, distance, speedand high intensity variables of interest were selected (Table 1). These included maximum, mean andcount values, with speed zones derived from an upper limit of 60 km/h, to provide a more detailedquantification of distance and speed parameters. Speed zones were as follows: Zone 1: 0–19.2 km/h;Zone 2: 19.2–23.4 km/h; Zone 3: 23.4–28.2 km/h; Zone 4: 28.2–47.4 km/h; Zone 5: 47.4–60 km/h.Following extraction of variables of interest, data were exported to microsoft Excel to facilitate datacuration. Data are provided as Supplementary material and are categorised per variable (distance,speed, high intensity), with level of play stated under Goal.Supplementary Materials: The following are available online at http://www.mdpi.com/2306-5729/4/3/95/s1.Author Contributions: Conceptualization, R.B. and R.S.; methodology, R.B. and R.S.; formal analysis, R.B.;investigation, R.B. and R.S.; resources, R.B. and R.S.; data curation, R.B.; writing—original draft preparation, R.B.and R.S.; writing—review and editing, R.B. and R.S.; visualization, R.B. and R.S.Funding: This research received no external funding.Acknowledgments: We would like to thank Adrian Pooley, and the team at VX Sport for their technical supportand enthusiasm throughout this project.Conflicts of Interest: The authors declare no conflict of interest.Data 2019, 4, 95 4 of 4References1. Hurlingham Polo Association. Outdoor Rule and Regulations 2018; Hurlingham Polo Association: Faringdon,UK, 2018; pp. 1–83.2. Chanda, M.; Srikuea, R.; Cherdchutam, W.; Chairoungdua, A.; Piyachaturawat, P. modulating effects ofexercise training regimen on skeletal muscle properties in female polo ponies. BMC Vet. Res. 2016, 12, 245.[CrossRef] [PubMed]3. Gondin, M.R.; Foz, N.S.B.; Pereira, M.C.; Flagliari, J.J.; Orozco, C.A.G.; D’Angelis, F.H.F.; Queiroz-Neto, A.;Ferraz, G.C. Acute Phase Responses of Different Positions of High-Goal (Elite) Polo Ponies. J. Equine Vet. Sci.2013, 33, 956–961. [CrossRef]4. Ferraz, G.C.; Soares, O.A.B.; Foz, N.S.B.; Pereira, M.C.; Queiroz-Neto, A. The workload and plasma ionconcentration in a training match session of high-goal (elite) polo ponies. Equine Vet. J. 2010, 42, 191–195.[CrossRef] [PubMed]5. Best, R.; Standing, R. Feasibility of a Global Positioning System to Assess the Spatiotemporal Characteristicsof Polo Performance. J. Equine Vet. Sci. 2019, 79, 59–62. [CrossRef]6. Best, R.; Standing, R. Performance Characteristics of a Winning Polo Team. New Zealand J. Sport Exerc. Sci.2019, 2, 1–11.7. VX Sport metric Glossary. Available online: https://support.vxsport.com/hc/en-us/articles/115006458148-VX-Sport-Metric-Glossary. (accessed on 27 June 2019).© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Distance, speed and high intensity characteristics of 0 to 24-goal, mixed and women’s polo

This dataset presents information pertaining to the spatiotemporal characteristics of Polo play from a 0 to 24-goal level. Data were collected by player worn GPS units, across a New Zealand Polo season. The dataset comprises observations from 466 chukkas of Polo, across mixed and women’s only Polo and is curated by cumulative player handicap. Data are presented for distance, speed and high intensity metrics, which have further ategorised into five equine-based speed zones. The purpose of this dataset is to provide a detailed quantification of a range of Polo performance levels, advancing the scope and size of previous Polo literature that has employed GPS technology. This dataset may be of value to equine scientists and trainers, veterinary practitioners and sports scientists

Distance, speed and high-intensity characteristics of 0 to 24-goal, mixed and women’s polo

http://researcharchive.wintec.ac.nz/7118/1/data%2D04%2D00095.pdf

Distance, speed and high intensity characteristics of 0 to 24-goal, mixed and women’s polo

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