Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 25 Summary 26 Polo is an equestrian team sport, consisting of Open and Women’s only handicapping systems. As 27 cumulative player handicap increases in Open Polo, distance covered, average speeds and high 28 intensity work performed per chukka also increase. These activities may differ in terms of 29 distribution of, and their affect upon, match outcome in Women’s Polo, and thus have implications 30 for equine preparation and management. 31 To quantify spatiotemporal differences between Open and Women’s Polo when matched for 32 handicap and assess their affect upon chukka and match outcome using a prospective cohort 33 design. Distance, speed and high intensity activity data were collected via player worn global 34 positioning system (GPS) units during 16-goal Open and Women’s Polo tournaments. Notational 35 analysis quantified chukka duration and chukka and game outcomes. Between group differences 36 were assessed by independent samples t-tests, and two factor mixed effects ANOVA for within 37 group analyses. Between group differences were analysed using an independent samples t-test with 38 alpha defined a priori as p<0.05. 39 Open and Women’s Polo differed by a small to large extent (ES: 0.54 – 1.81) for all spatiotemporal 40 metrics. In Open Polo, players covered moderately more distance (429.0m; 238.9m to 619.0m), 41 with small to large increases in high intensity activities performed in games won. Whereas in 42 Women’s Polo, moderately higher maximum speeds were attained in games won (17.13 km/h; 43 11.86 km/h to 22.40 km/h) and a small increase in accelerations performed (5.1; 0.2 to 10.0). 44 Open and Women’s Polo, when matched for handicap, present with small to large spatiotemporal 45 differences that are likely of practical significance, and influence game outcome differently 46 between codes. These differences do not necessarily mean that Polo ponies need to be trained 47 differently for each code. 48 2 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 49 Introduction 50 Polo is an equestrian team sport contested by two teams of four players. Play is divided into seven- 51 minute chukkas, and a player must change horses between chukkas, to ensure adequate equine 52 physiological recovery [1-3]. Individual handicaps are awarded from –2 to +10 goals, with level 53 of play dictated by the cumulative handicap of each member of a team [3,4]. Female players can 54 hold parallel Open and Women’s handicaps, despite being scored on the same variables these 55 handicaps are weighted differently e.g. a female player may be an Open 4-goal player, but 10- 56 goals in Women’s Polo. The reason for implementing a parallel system is to account for 57 compression brought about by increased participation in women’s Polo internationally [5]. This 58 allows for greater differentiation between female players, with a similar Open handicap, with 59 Women’s handicaps usually higher than an equivalent open handicap [6]. 60 Previously, we have shown increases in average speed attained and distance covered per chukka 61 [7] as cumulative handicap increases in Open Polo; cumulative handicap may also affect high 62 intensity activities [7], imposing additional internal physiological loads upon horses and players 63 [8-12]. Thus, an understanding of the equine demands of Women’s Polo is required. At present 64 these demands are unknown and there may be important points of difference to Open Polo, that 65 may affect equine preparation for Polo participation, and in game horse management strategies. 66 Hence, the aim of this study is to explore the differences in spatiotemporal characteristics between 67 handicap-matched levels of Open and Women’s Polo, and to quantify the relationship between 68 spatiotemporal characteristics and match outcomes in Open and Women’s Polo. 3 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 69 Methods 70 All data collection took place over the 2018-2019 New Zealand Polo season, specifically at two 71 16-goal tournaments; one open and one women’s tournament, employing a cross sectional design. 72 Handicaps were as awarded by the New Zealand Polo Association. Women’s equivalent Open 73 handicaps were sourced from the New Zealand, Australian and Hurlingham Polo Associations. 74 Ethical approval for this investigation was provided by Waikato Institute of Technology’s 75 (Wintec) ethics committee (Approval code: WTFE2601102018), and as per the International 76 Guiding Principles for Biomedical Research Involving Animals as issued by the Council for the 77 International Organizations of Medical Sciences. Data for the present study are freely available 78 online [13]. 79 80 Sample population 81 This study comprised observations from two distinct playing groups: two open teams and three 82 women’s teams – both groups played in the 16-goal sections of their respective tournaments. Open 83 participants consisted of eight Polo players (7 males and 1 female; Handicap range 0–7 goals), 84 whereas women’s participants consisted of 12 female Polo players (Handicap range 0–10 goals). 85 Handicaps of individual players are listed in Table 1. Prior to study involvement, informed consent 86 was obtained from players and owners. 87 Players selected their own strings of ponies, with ponies stabled either truck-side or in open air 88 yards prior to playing. Warm up and feeding protocols were at the players’ and grooms’ discretion. 89 Playing distribution and strategy of Polo ponies within a player’s string was also at the discretion 90 of each player. 91 4 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 92 Data collection procedures 93 Data were collected from a total of 258 chukkas across both Open and Women’s Polo tournaments 94 (n = 130 and n = 128, respectively; no a priori sample size calculations were performed but this 95 represents two entire tournaments across multiple teams) using player worn GPS monitors (VX 96 Sport) set to equestrian mode with a sampling frequency of 10 Hz and a speed range of 0 – 60 97 km/h. We have previously shown this method to produce reliable results for the metrics assessed 98 in the present investigation [10], when mounted either between the players’ shoulders or worn on 99 players’ belts. 100 GPS units were turned upon arrival at the playing venues to obtain an initial satellite lock and were 101 then turned on again 30 min prior to the start of games, to ensure a secure connection to multiple 102 satellites was established. All players opted to wear GPS units in a pouch fixed to their belts. The 103 belt pouch was secured with insulation tape to minimise oscillation of the unit during games. Upon 104 game completion, units were turned off and data downloaded using specialist software as provided 105 by the manufacturer (VX Sport). The initial satellite lock period was trimmed from the data, and 106 the game period was divided into chukkas as per an accompanying notational analysis to normalise 107 data for between and within groups analyses. Speed zones using in-built software thresholds were 108 derived 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 109 4: 28.2–47.4 km/h; and Zone 5: 47.4–60 km/h. Total distance (m), distance covered (m) in each 110 speed zone, the number of accelerations, decelerations, impacts and sprints were selected as 111 dependent variables from the GPS output (metrics defined as per [13]), with chukka duration 112 (min:s) reported from the notational analysis. Data were then exported to Microsoft Excel for 113 further analysis as detailed below. Players were provided with a brief data analysis and feedback 114 following each tournament. 115 5 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 116 Statistical Analyses 117 Data were considered normally distributed if they passed the mean and SD test (2xSD>mean), or 118 if the mean and median were within 10% of each other. Following these tests, homogeneity and 119 sphericity between group differences were analysed using an independent samples t-test with alpha 120 defined a priori as p<0.05. A two factor mixed effects ANOVA was used to assess the effect of 121 chukka (win/loss) and game outcomes (win/loss) upon spatiotemporal characteristics, at the same 122 alpha level. It should be noted that the absence of statistical significance does not signify lack of 123 practical importance, with respect to Polo performance. All analytical procedures were computed 124 using SPSS (v24). Effect sizes for between group comparisons (Cohen’s d) and accompanying 125 95% confidence intervals (C.I.) were calculated using a customised spreadsheet. Magnitudes of 126 effect were interpreted using the descriptors suggested by Hopkins et al., [14]. An effect was 127 deemed unclear if its confidence interval crossed zero and the threshold for a small effect [15]. For 128 within group comparisons (chukka and game win loss outcomes) data are reported as raw 129 differences between outcomes with accompanying 95% confidence intervals, effect sizes (Cohen’s 130 d) and magnitude-based descriptors. 131 6 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 132 Results 133 Significant differences between Open and Women’s Polo were found for all spatiotemporal 134 characteristics assessed, although these differences varied in terms of magnitude (Small to Very 135 Large); as presented in Table 2, with differences per speed zone between Open and Women’s play 136 shown in Figure 1. Significant results of two factor mixed effects ANOVAs are grouped by metrics 137 and reported for Open and Women’s play in the subsections below. Complete results can be found 138 in supplementary material Tables 1 and 2 for Open and Women’s Polo, respectively. 139 140 Distance metrics 141 There were large differences (ES: 1.54; 95% CI: 1.26 to 1.81) in total distance covered per chukka 142 between Open and Women’s Polo. Between groups differences for independent speed zones 1 – 5 143 are presented in Figure 1. In Open Polo, distance per chukka was significantly influenced by both 144 chukka (F (1,126) = 5.80; p = 0.018) and game (F (1,126) = 19.95; p < 0.001) outcomes, with 145 winning chukkas showing a small reduction in distance covered (-231.2m; -421.3m to -41.2m) but 146 moderately more distance covered in games won (429.0m; 238.9m to 619.0m). Whereas, in 147 women’s Polo neither chukka nor game outcome significantly affected total distance per chukka, 148 but there was a significant interaction between chukka and game outcome with respect to total 149 distance. More specifically, distance covered in speeds zones 1 (F (1,126) = 28.47; p < 0.001), 2 150 (F (1,126) = 4.29; p < 0.041) and 5 (F (1,126) = 5.18; p < 0.025) in Open Polo were significantly 151 affected by game outcome, whereas in Women’s Polo only distance covered in speed zone 4 152 showed a chukka by game interaction (F (1,124) = 2.01; p = 0.017). 153 154 Speed metrics 155 Absolute maximum speeds for Open and Women’s play were 61.5 and 59 km/h respectively, with 156 large differences in average maximum speeds (p<0.001, Table 2) between groups but only small 7 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 157 differences in average playing speed (p = 0.019; Table 2). Maximum speed data for each category 158 of play are shown in Figure 2 to demonstrate the distribution of maximal speeds between groups. 159 A small reduction in average speed (-1.37 km/h; -2.33 km/h to -0.40 km/h) was seen in winning 160 games in Open Polo (F (1,126) = 7.91; p = 0.006), whereas in Women’s Polo maximum speed was 161 moderately higher (17.13 km/h; 11.86 km/h to 22.40 km/h; F (1,124) = 41.40; p < 0.001). 162 163 High intensity metrics 164 Small to Large differences between Open and Women’s Polo were found for all high intensity 165 activities (all p ≤ 0.001; Table 2). Within Open Polo, more sprints (8.3; 5.9 to 10.7), accelerations 166 (7.6; 2.4 to 12.9) and decelerations (7.0; 2.0 to 11.9) were performed in games won (all p ≤ 0.006), 167 but their effect upon chukka outcome was unclear. Conversely, in Women’s Polo a small increase 168 in accelerations (5.1; 0.2 to 10.0) were performed in games won (p = 0.041). Despite differing 169 between groups (Table 2), the role of impacts in chukka or game outcome was either trivial or 170 unclear. 171 172 Duration 173 Chukka durations differed significantly (p < 0.001) between Open and Women’s Polo by a large 174 extent. In Open Polo, chukkas won were significantly (p = 0.017) shorter by a small extent (-01:06; 175 95% C.I. -02:00 to -00:11), despite games won being moderately longer than games lost (02:45; 176 01:51 to 03:39; p < 0.001). In Women’s Polo, however, the difference in duration between games 177 won and lost was small (00:40; 00:02 to 01:17; p = 0.037), with no statistically significant 178 difference between chukkas won or lost. 179 8 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 180 Discussion 181 This investigation aimed to assess the differences in spatiotemporal characteristics between 182 handicap-matched levels of Open and Women’s Polo. With a secondary aim of assessing the effect 183 of chukka and game outcome upon spatiotemporal characteristics in Open and Women’s Polo. 184 Between group comparisons (Table 2) showed statistically significant differences between Open 185 and Women’s Polo for all spatiotemporal characteristics (all p ≤ 0.001), with differences ranging 186 in magnitude from small to large. Of importance are the large differences in chukka duration 187 between groups and the nearly 700m discrepancy in total distance covered per chukka when 188 Women’s Polo is compared to Open play. Whilst distance covered only differed by a trivial extent 189 in games won and lost in Women’s Polo, distance covered was moderately greater in games won 190 (429.0; 238.9 to 619.0) and reported a significant chukka by game interaction (p = 0.049) 191 suggesting that covering more ground than one’s opponents in at least one chukka resulted in a 192 greater win rate. The same interaction effect is seen in Women’s Polo, but the magnitude of this 193 interaction is small, this is likely driven by the lesser distance covered per chukka in Women’s 194 Polo, and the bidirectional nature of confidence limits for chukka and game outcomes. The 195 implications of these findings upon Polo horse preparation and management during games are 196 explored throughout this discussion. 197 The differences in distance between groups are further emphasised by Figure 1. Women’s Polo 198 displays a U-like distribution with broad error bars especially in speed zone 4 (0–1622m), whereas 199 Open Polo represents an inverted-U with greater consistency within the velocities attained. 200 Practically, this indicates very different rhythms of play; Open Polo is characterised by a 201 maintenance of a cruising velocity with relatively little distance accumulated at low or near 202 maximal speeds. Most accelerations and decelerations may also occur within this speed zone, 203 hence its emphasis. High speeds are still consistently attained though (Figures 1 and 2), suggesting 204 these maximal efforts may take place with a shorter lead in (i.e. greater rates of acceleration) and 9 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 205 serve a different tactical purpose in comparison to Women’s Polo. Speed shows a more polarised 206 distribution (Fig.1) of a seemingly stochastic nature in Women’s Polo; accompanying error 207 margins (Fig.1 and 2) highlight that whilst players may be physically and technically proficient 208 [16], their ponies must also be physically conditioned to cope with a slow/fast playing style. Such 209 conditioning may take the form of high intensity interval training [13,17,18], although this has 210 been noted to be potentially injurious in thoroughbreds [18]. Injury may also occur in s if the 211 relationship between speed and limb force exceeds a critical limit during turns [19] but Polo ponies 212 typically display a greater tolerance to this and can turn in tighter circles than race horses [19]. 213 Irrespective of the source, injury risk must be minimised by appropriate loading of ponies [20,21] 214 playing in either Open or Women’s Polo, due to the relatively high acceleration, deceleration and 215 sprint counts sustained per chukka (Table 2). 216 Maximum speeds significantly differed (p < 0.001) between groups (Large; 1.39; 1.22 to 1.69), 217 also showing markedly different distributions and ranges (Figure 2). Higher maximum speeds may 218 still be of practical or tactical importance in Women’s Polo despite higher speeds being attained 219 more frequently and consistently in Open Polo. Hence, training for both Open and Women’s Polo 220 should expose ponies to near maximal velocities, to ensure adequate speed capacity, condition 221 ponies to game demands and minimise the risk of injury [20,21]. By extension, Polo ponies should 222 also be conditioned to perform high intensity activities as more sprints, accelerations and 223 decelerations were performed in games won than in games lost, despite differing by a small to 224 large extent between Open and Women’s Polo (p ≤ 0.001). Indeed, such movements likely impact 225 upon the health of the horse’s lower limb, with tendon injuries frequently reported in Polo 226 [7,22]. Such injury is likely due to repetitive eccentric loading across multiple joints [23] brought 227 about by simultaneous braking and turning forces [24,25], attention should also be paid to the 228 speed at which these movements are trained [19] to minimise injury risk, regardless of code of 229 Polo played. 10 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 230 Collectively, these data support the use of a parallel handicap system for Women’s Polo due to 231 differences observed in distribution of playing speeds (Fig. 1), typical distances covered per 232 chukka (Table 2) and the greater variability within these characteristics (Fig.1 and 2). These 233 spatiotemporal differences are likely accompanied by differences in technical proficiency and 234 tactical behaviours, evidenced in part by differences in Open handicap (Table 1), which likely 235 contribute to chukka and game outcomes alongside the differences in spatiotemporal 236 characteristics identified in the present study. Concomitant measures of internal load such as horse 237 heart rate would also be of value in assessing the physiological consequences of distances covered 238 per speed zone. It is unclear whether spatiotemporal differences of the present magnitudes signify 239 a genuine need to prepare ponies differently for Open and Women’s Polo, or more likely that 240 ponies should be managed differently in games e.g. opting to half chukka ponies in Open Polo. 241 A possible limitation is that some of these differences may be perceived as occurring simply due 242 to differences in average chukka length. Whilst some influence cannot be ruled out, it is unlikely 243 the sole explanatory factor as the most likely explanation for longer chukkas would either be due 244 to the ball going out of play more frequently, conceding of more penalties by either team or injuries 245 sustained by a player or horse. These incidents all slow down Polo play, therefore fewer metres 246 are accrued in higher speed zones, so the differences between Open and Women’s play have 247 occurred in spite of longer chukka lengths in Open Polo. A further limitation of this study is the 248 use of player worn GPS, whilst this is the most feasible strategy for Polo due to multiple horse 249 changes [10], it means braking and turning forces cannot be calculated at the joint and thus our 250 work does not directly support that of Tan and Wilson [19] who calculated the forces experienced 251 by turning Polo ponies. However, due to the high volume of turning and braking movements 252 performed per chukka, and games played per season, we recommend prudent preparation of ponies 253 within a periodised Polo training programme that progressively exposes ponies to the intensities 254 and movement requirements of in-season play. 11 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 255 In conclusion, Open and Women’s Polo, when matched for their cumulative handicaps, present 256 with small to large spatiotemporal differences that may be of practical and statistical significance. 257 Within Polo codes, a greater number of variables were affected by game and chukka outcome in 258 Open Polo, whereas in Women’s Polo fewer variables were associated with chukka or game 259 outcome. A further point of difference was the distribution of distance covered within playing 260 speed zones (Figure 1) and maximal speeds attained (Figure 2). These differences, whilst likely of 261 practical importance on the Polo pitch and further influenced by players’ technical proficiency, do 262 not necessarily mean that Polo ponies need to be trained differently for each code. We recommend 263 the incorporation of sufficient aerobic development to cover between 2500 – 3000m per chukka, 264 and progressive exposure to high speeds and braking and turning forces during preparation for 265 Polo, irrespective of whether one is playing Open or Women’s Polo. 266 267 Manufacturers details: 268 SPSS: (v24, IBM, United States) 269 VX Sport: (350, Lower Hutt, New Zealand) 270 271 Supplementary legends: 272 Supplementary 1: Results of factorial ANOVA for Open Polo; Significant p values are presented 273 in bold. All raw differences are calculated as WIN-LOSS. Raw differences are not provided for 274 interaction effects. Magnitudes of effect sizes are denoted by the following symbols: *: Small; # 275 Moderate; †: Large; ‡: Very Large 276 Supplementary 2: Results of factorial ANOVA for Women's Polo; Significant p values are 277 presented in bold. All raw differences are calculated as WIN-LOSS. Raw differences are not 278 provided for interaction effects. Magnitudes of effect sizes are denoted by the following symbols: 279 *: Small; # Moderate; †: Large; ‡: Very Large 12 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 280 References 281 1. Fiander, A. and Williams, J. (2014) The Impact of Playing Strategies on Recovery in 282 Low‐Goal Polo‐Ponies. Equine Vet. J. 46, 12–12. 283 2. Williams, J.M. and Fiander, A. (2014) The impact of full vs. half chukka playing 284 strategies on recovery in low goal polo ponies. Comparative Exercise Physiology 285 10, 139–145. 286 3. 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(1998) Association between long periods without high-speed workouts 339 and risk of complete humeral or pelvic fracture in thoroughbred racehorses: 54 cases 340 (1991-1994). J Am Vet Med Assoc 212, 1582–1587. 341 22. Inness, C.M. and Morgan, K.L. (2015) Polo pony injuries: player-owner reported 342 risk, perception, mitigation and risk factors. Equine Vet. J. 47, 422–427. 343 23. Butler, D., Valenchon, M., Annan, R., Whay, H.R. and Mullan, S. (2019) Living the 344 'Best Life' or “One Size Fits All-”Stakeholder Perceptions of Racehorse Welfare. 345 Animals 9, 134. 346 24. Chateau, H., Camus, M., Holden-Douilly, L., Falala, S., Ravary, B., Vergari, C., 347 Lepley, J., Denoix, J.-M., Pourcelot, P. and Crevier-Denoix, N. (2013) Kinetics of 348 the forelimb in horses circling on different ground surfaces at the trot. The 349 Veterinary Journal 198, e20–e26. 350 25. Brocklehurst, C., Weller, R. and Pfau, T. (2014) Effect of turn direction on body 351 lean angle in the horse in trot and canter. The Veterinary Journal 199, 258–262. 352 353 14 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 354 Table 1 Player Handicaps (goals) for Open and Women’s handicaps. Male Open players are not eligible for a 355 Women’s handicap but all female players have both an Open and Women’s handicap. Team Player # Open handicap Women’s handicap Open 1 1 0 N/A 2 4 10 3 5 N/A 4 7 N/A Open 2 1 2 N/A 2 3 N/A 3 6 N/A 4 5 N/A Women’s 1 1 -2 0 2 -1 0 3 1 5 4 4 10 Women’s 2 1 -1 1 2 0 3 3 1 5 4 1 6 Women’s 3 1 -1 1 2 0 2 3 0 3 4 2 10 356 357 15 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 Table 2 Comparison between spatiotemporal characteristics of Open and Women's Polo. Raw values are presented as means ± standard deviations, with accompanying p values, effect sizes and C.I. and magnitude descriptors Variable Open Women’s p value ES Confidence Interval Descriptor Duration (min:s) 11:54 ± 02:26 09:09 ± 01:14 <0.001 1.42 1.14 to 1.69 Large Distance (m) 3138.89 ± 491.62 2452.73 ± 394.27 <0.001 1.54 1.26 to 1.81 Large Average Speed (km/h) 16.60 ± 2.35 15.90 ± 2.41 0.019 0.30 0.05 to 0.54 Small Average Maximum Speed (km/h) 54.81 ± 3.55 39.07 ± 15.66 <0.001 1.39 1.12 to 1.66 Large Sprints 38.11 ± 6.80 35.27 ± 6.86 0.001 0.42 0.17 to 0.66 Small Impacts 1.72 ± 1.77 0.72 ± 1.84 <0.001 0.56 0.30 to 0.80 Small Accelerations 74.08 ± 12.94 63.05 ± 12.94 <0.001 0.85 0.60 to 1.10 Moderate Decelerations 68.58 ± 12.02 52.61 ± 13.55 <0.001 1.25 0.98 to 1.51 Large 16 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 Figure legends Figure 1: Box and whisker plot showing the distribution of playing speeds (by speed zones) in Open (green boxes) and Women’s Polo (purple boxes). Data are presented as medians (change of colour tone) with first and third quartiles; error bars denote minimum and maximum values. Magnitudes of effect sizes are denoted by the following symbols: *: Small; # Moderate; †: Large; ‡: Very Large. 17 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 August 2019 doi:10.20944/preprints201908.0308.v1 Figure 2: Maximum speeds attained in Open and Women’s Polo. Individual data points are represented by open circles and solid black bars represent the mean value for each group. 18