ABSTRACT Interpreting the physical qualities of youth athletes is complex due to the effects of growth, maturation and development. This study aimed to evaluate the effect of position, chronological age, relative age and maturation on the physical qualities of elite male academy rugby union players. 1,424 participants (n = 2,381 observations) from nine Rugby Football Union regional academies prospectively completed a physical testing battery at three time points, across three playing seasons. Anthropometrics, body composition, muscular power, muscular strength, speed, aerobic capacity and running momentum were assessed. Positional differences were identified for all physical qualities. The largest effect sizes were observed for the associations between chronological age (d = 0.65–0.73) and maturation (d = −0.77 to −0.69) and body mass related variables (i.e. body mass and running momentum). Relative strength, maximum velocity and aerobic capacity were the only models to include two fixed effects with all other models including at least three fixed effects (i.e. position and a combination of chronological age, relative age and maturation). These findings suggest a multidimensional approach considering position, chronological age, relative age and maturation is required to effectively assess the physical qualities of male age grade rugby union players. Therefore practitioners should use regression equations rather than traditional descriptive statistic tables to provide individualised normative comparisons thus enhancing the application of testing results for talent identification and player development. Highlights Practitioners should record and incorporate position, chronological age, relative age and maturation into the physical evaluation of elite academy rugby union players. The regression equations provided within this study offer highly generalisable comparative values that are specific to a players chronological and biological development. Through the use of enhanced player evaluation practitioners will be able to make more informed decisions surrounding talent identification and athlete development.

Purpose To assess indirect markers of intestinal endothelial cell damage and permeability in academy rugby players in response to rugby training at the beginning and end of preseason. Methods Blood and urinary measures (intestinal fatty acid binding protein and lactulose:rhamnose) as measures of gastrointestinal cell damage and permeability were taken at rest and after a standardised collision-based rugby training session in 19 elite male academy rugby players (age: 20 ± 1 years, backs: 89.3 ± 8.4 kg; forwards: 111.8 ± 7.6 kg) at the start of preseason. A subsample ( n = 5) repeated the protocol after six weeks of preseason training. Gastrointestinal symptoms (GIS; range of thirteen standard symptoms), aerobic capacity (30–15 intermittent fitness test), and strength (1 repetition maximum) were also measured. Results Following the rugby training session at the start of preseason, there was an increase (median; interquartile range) in intestinal fatty acid binding protein (2140; 1260–2730 to 3245; 1985–5143 pg/ml, p = 0.003) and lactulose:rhamnose (0.31; 0.26–0.34 to 0.97; 0.82–1.07, p < 0.001). After six weeks of preseason training players physical qualities improved, and the same trends in blood and urinary measures were observed within the subsample. Overall, the frequency and severity of GIS were low and not correlated to markers of endothelial damage. Conclusions Rugby training resulted in increased intestinal endothelial cell damage and permeability compared to rest. A similar magnitude of effect was observed after six weeks of pre-season training. This was not related to the experience of GIS.

The quantification and evaluation of training practices in youth rugby players, including exposure to competition and training loads, is important for supporting long-term athletic development. Training loads in youth rugby are highly variable within and between players, and the characteristics of training practices have been shown to differ by age category, playing standard and region. This chapter presents the research that has explored the training practices and training loads of youth rugby players, including periodisation frameworks with macro- and mesocycle overviews, weekly training loads, session characteristics and comparisons to match-play demands. The second part of the chapter provides a practical overview on methods of training load measurement, data analysis, visualisation and communication, as well as considerations on training to competition ratios in youth rugby. The chapter concludes with a range of recommendations to practically monitor training loads of youth rugby players, as well as practical implications regarding data interpretation, communication and additional considerations to improve decision-making for key stakeholders.

Weakley, JJS, Till, K, Read, DB, Leduc, C, Roe, GAB, Phibbs, PJ, Darrall-Jones, J, and Jones, B. Jump training in rugby union players: barbell or hexagonal bar?. J Strength Cond Res 35(3): 754-761, 2021-The countermovement jump (CMJ) is an exercise that can develop athletic performance. Using the conventional barbell (BAR) and hexagonal barbell (HEX) while jumping, the intensity can be increased. However, the bar that provides greater adaptations is unknown. Therefore, this study aimed to assess changes in loaded and unloaded CMJ with either a BAR or HEX across a 4-week mesocycle in rugby union players. Twenty-nine subjects were strength-matched and randomized into 2 groups. Subjects completed 3 sets of CMJ at 20% of 1 repetition maximum back squat, 3 times per week for 4 weeks, using either a BAR or HEX. Subjects completed an unloaded CMJ on a force plate before and after, whereas the highest peak concentric velocity during the jump squat was recorded in the first and last training sessions using a linear position transducer. Magnitude-based inferences assessed meaningful changes within- and between-groups. Possibly greater improvements in unloaded CMJ were found in the HEX group in jump height (effect size +/- 90% confidence intervals: 0.27 +/- 0.27), relative peak (0.21 +/- 0.23), and mean power (0.32 +/- 0.36). In addition, likely to very likely greater improvements were observed in the HEX group in peak velocity (0.33 +/- 0.27), relative mean power (0.53 +/- 0.30), mean force (0.47 +/- 0.27), and 100-ms impulse (0.60 +/- 0.48). Similar raw changes in jump squat peak velocity occurred (0.20-0.25 m center dot s(-1)), despite the likely greater ES occurring with the BAR (0.32 +/- 0.26). These results indicate that training with the HEX leads to superior unloaded CMJ adaptations. In addition, practitioners should use either the HEX or BAR when aiming to enhance loaded jump ability.

Scantlebury, S, Till, K, Sawczuk, T, Dalton-Barron, N, Phibbs, P, and Jones, B. The frequency and intensity of representative and nonrepresentative late adolescent team-sport athletes' training schedules. J Strength Cond Res 35(12): 3400-3406, 2021-This study aimed to identify and compare the training frequency and intensity (via session rating of perceived exertion load [sRPE load]) of representative and nonrepresentative late adolescent athletes. Thirty-six team sport athletes completed a web-based questionnaire daily over an 8-month period, reporting their training/match activities from the previous day. Athletes were categorized as representative (academy/county/international) or nonrepresentative (club/school) depending on the highest level of their sport they participated. Mean weekly frequencies and sRPE load of different training/match activities were quantified for each athlete across 5 school terms. Mann-Whitney U tests established the significance of differences and effect sizes between playing standards for mean weekly frequencies and mean sRPE load. Within-athlete weekly sRPE loads were highly variable for both playing standards; however, representative level athletes participated in significantly more activity outside of school compared with nonrepresentative athletes during November-December (effect size; 0.43-club technical training; 0.36-club matches), January-February (effect size; 0.78-club technical training; 0.75-club matches), and February-March (effect size; 0.63-club technical training; 0.44-club matches). Therefore, club and school coaches must ensure that all elements of representative athletes training schedules are coordinated and flexible to promote positive adaptions to training such as skill and physical development and prevent maladaptive responses such as overuse injury and nonfunctional overreaching. A cooperative and malleable training schedule between club/school coaches and the athlete will allow the athlete to perform on multiple fronts while also being able to meet the demands of additional stressors such as schoolwork.

Weakley, JJS, Till, K, Read, DB, Phibbs, PJ, Roe, G, Darrall-Jones, J, and Jones, BL. The effects of superset configuration on kinetic, kinematic, and perceived exertion in the barbell bench press. J Strength Cond Res 34(1): 65-72, 2020-Training that is efficient and effective is of great importance to an athlete. One method of improving efficiency is by incorporating supersets into resistance training routines. However, the structuring of supersets is still unexplored. Therefore, the purpose of this study was to assess the effects of agonist-antagonist (A-A), alternate peripheral (A-P), and similar biomechanical (SB) superset configurations on rate of perceived exertion (RPE), kinetic and kinematic changes during the bench press. Ten subjects performed resistance training protocols in a randomized-crossover design, with magnitude-based inferences assessing changes/differences within and between protocols. Changes in rate of perceived exertion were very likely and almost certainly greater in the A-P and SB protocols when compared with the A-A, whereas all superset protocols had very likely to almost certain reductions in mean velocity and power from baseline. Reductions in mean velocity and power were almost certainly greater in the SB protocol, with differences between the A-A and A-P protocols being unclear. Decreases in peak force were likely and almost certain in the A-A and SB protocols respectively, with changes in A-P being unclear. Differences between these protocols showed likely greater decreases in SB peak forces when compared to A-A, with all other superset comparisons being unclear. This study demonstrates the importance of exercise selection when incorporating supersets into a training routine. It is suggested that the practitioner uses A-A supersets when aiming to improve training efficiency and minimize reductions in kinetic and kinematic output of the agonist musculature while completing the barbell bench press.

Rugby union (RU) is a skill-collision team sport played at junior and senior levels worldwide. Within England, age-grade rugby governs the participation and talent development of youth players. The RU player development pathway has recently been questioned, regarding player performance and well-being, which sport science research can address. The purpose of this review was to summarise and critically appraise the literature in relation to the applied sport science of male age-grade RU players in England focussing upon (1) match-play characteristics, (2) training exposures, (3) physical qualities, (4) fatigue and recovery, (5) nutrition, (6) psychological challenges and development, and (7) injury. Current research evidence suggests that age, playing level and position influence the match-play characteristics of age-grade RU. Training exposures of players are described as ‘organised chaos’ due to the multiple environments and stakeholders involved in coordinating training schedules. Fatigue is apparent up to 72 h post match-play. Well-developed physical qualities are important for player development and injury risk reduction. The nutritional requirements are high due to the energetic costs of collisions. Concerns around the psychological characteristics have also been identified (e.g. perfectionism). Injury risk is an important consideration with prevention strategies available. This review highlights the important multi-disciplinary aspects of sport science for developing age-grade RU players for continued participation and player development. The review describes where some current practices may not be optimal, provides a framework to assist practitioners to effectively prepare age-grade players for the holistic demands of youth RU and considers areas for future research.

Background:Growing evidence highlights that elite rugby union players experience poor sleep quality and quantity which can be detrimental for performance.Objectives:This study aimed to i) compare objective sleep measures of rugby union players between age categories over a one week period, and ii) compare self-reported measures of sleep to wristwatch actigraphy as the criterion.Methods:Two hundred and fifty-three nights of sleep were recorded from 38 players representing four different age groups (i.e. under 16, under 18, senior academy, elite senior) in a professional rugby union club in the United Kingdom (UK). Linear mixed models and magnitude-based decisions were used for analysis.Results:The analysis of sleep schedules showed that U16 players went to bed and woke up later than their older counterparts (small differences). In general, players obtained seven hours of sleep per night, with trivial or unclear differences between age groups. The validity analysis highlighted a large relationship between objective and subjective sleep measures for bedtime (r = 0.56 [0.48 to 0.63]), and get up time (r = 0.70 [0.63 to 0.75]). A large standardised typical error (1.50 [1.23 to 1.88]) was observed for total sleep time.Conclusion:This study highlights that differences exist in sleep schedules between rugby union players in different age categories that should be considered when planning training. Additionally, self-reported measures overestimated sleep parameters. Coaches should consider these results to optimise sleep habits of their players and should be careful with self-reported sleep measures.

ABSTRACT The development of a youth team sport athlete is a complex process. This article outlines challenges that may restrict the optimal balance between training and recovery and provide solutions to help practitioners overcome these challenges. To facilitate positive youth athletic development, training aims must be aligned between stakeholders to synchronize periods of intensified training and recovery. Within- and between-athlete variations in weekly training load must be managed and practitioners should attempt to ensure the intended load of training equals the load perceived by the athlete. Furthermore, practitioners should be cognizant of the athletes' nonsport-related stressors to enable both academic and sporting pursuits. Although each of these challenges adds intricacy, they may be overcome through collaboration, monitoring, and, if necessary, the modification of the athletes' training load.

ABSTRACT To quantify and compare the maximum running intensities during rugby union match-play. Running intensity was quantified using micro-technology devices (S5 Optimeye, Catapult) from 202 players during 24 matches (472 observations). Instantaneous speed was used to calculate relative distance (m·min−1) using a 0.1 s rolling mean for different time durations (15 and 30 s and 1, 2, 2.5, 3, 4, 5, and 10 min). Data were analysed using a linear mixed-model and assessed with magnitude-based inferences and effect sizes (ES). Running intensity for consecutive durations (e.g., 15 s vs. 30 s, 30 s vs. 1 min, etc.) decreased as time increased (ES = 0.48–2.80). Running intensity was lower in forwards than backs during all durations (−0.74 ±0.21 to −1.19 ±0.21). Running intensity for the second row and back row positions was greater than the front row players at all durations (−0.58 ±0.38 to −1.18 ±0.29). Running intensity for scrum halves was greater (0.46 ±0.43 to 0.86 ±0.39) than inside and outside backs for all durations besides 15 and 30 s. Front rowers and scrum halves were markedly different from other sub-positional groups and should be conditioned appropriately. Coaches working in academy rugby can use this information to appropriately overload the intensity of running, specific to time durations and positions.

It is unknown whether instantaneous visual feedback of resistance training outcomes can enhance barbell velocity in younger athletes. Therefore, the purpose of this study was to quantify the effects of visual feedback on mean concentric barbell velocity in the back squat, and to identify changes in motivation, competitiveness, and perceived workload. In a randomised-crossover design (Feedback vs. Control) feedback of mean concentric barbell velocity was or was not provided throughout a set of 10 repetitions in the barbell back squat. Magnitude-based inferences were used to assess changes between conditions, with almost certainly greater differences in mean concentric velocity between the Feedback (0.70 ±0.04 m·s) and Control (0.65 ±0.05 m·s) observed. Additionally, individual repetition mean concentric velocity ranged from possibly (repetition number two: 0.79 ±0.04 vs. 0.78 ±0.04 m·s) to almost certainly (repetition number 10: 0.58 ±0.05 vs. 0.49 ±0.05 m·s) greater when provided feedback, while almost certain differences were observed in motivation, competitiveness, and perceived workload, respectively. Providing adolescent male athletes with visual kinematic information while completing resistance training is beneficial for the maintenance of barbell velocity during a training set, potentially enhancing physical performance. Moreover, these improvements were observed alongside increases in motivation, competitiveness and perceived workload providing insight into the underlying mechanisms responsible for the performance gains observed. Given the observed maintenance of barbell velocity during a training set, practitioners can use this technique to manipulate training outcomes during resistance training.

Limited research has compared the physical qualities of adolescent rugby union (RU) players across differing playing standards. This study therefore compared the physical qualities of academy and school Under-18 RU players. One-hundred and eighty-four (professional regional academy,n=55 school,n=129) male RU players underwent a physical testing battery to quantify height, body mass, strength (bench press and pull-up), speed (10, 20 and 40m), 10m momentum (calculated; 10m velocity * body mass) and aproxymeasure of aerobic fitness (Yo-Yo Intermittent Recovery Test Level 1; IRTL1). The practical significance of differences between playing levels were assessed using magnitude-based inferences. Academy players were taller (very likely small), heavier (likely moderate) and stronger (bench presspossibly large; pull-up plus body masslikely small) than school players. Academy players were faster than school players over 20 and 40m (possiblyandlikely small), although differences in 10m speed were not apparent (possibly trivial). Academy players displayed greater 10m momentum (likely moderate)and greater IRTL1 performance (likely small) than school players. These findings suggest that body size, strength, running momentum, 40m speed and aerobic fitness contribute to a higher playing standard in adolescent rugby union.

The aim of this study was to quantify the mean weekly training load (TL) of elite adolescent rugby union players participating in multiple teams and examine the differences between playing positions. Twenty elite male adolescent rugby union players (17.4 ± 0.7 years) were recruited from a regional academy and categorized by playing position: forwards (n = 10) and backs (n = 10). Global positioning system and accelerometer microtechnology was used to quantify external TL, and session rating of perceived exertion (sRPE) was used to quantify internal TL during all sessions throughout a 10-week in-season period. A total of 97 complete observations (5 ± 3 weeks per participant) were analyzed, and differences between positions were assessed using Cohenu0027s d effect sizes (ES) and magnitude-based inferences. Mean weekly sRPE was 1,217 ± 364 arbitrary units (AU) (between-subject coefficient of variation [CV] = 30%), with a total distance (TD) of 11,629 ± 3,445 m (CV = 30%), and PlayerLoad (PL) of 1,124 ± 330 AU (CV = 29%). Within-subject CV ranged between 5 and 78% for sRPE, 24 and 82% for TD, and 19 and 84% for PL. Mean TD (13,063 ± 3,933 vs. 10,195 ± 2,242 m) and PL (1,246 ± 345 vs. 1,002 ± 279 AU) were both likely greater for backs compared with forwards (moderate ES); however, differences in sRPE were unclear (small ES). Although mean internal TLs and volumes were low, external TLs were higher than previously reported during preseason and in-season periods in senior professional players. Additionally, the large between-subject and within-subject variation in weekly TL suggests that players participate in a chaotic training system.

Weakley, J, Wilson, K, Till, K, Banyard, H, Dyson, J, Phibbs, P, Read, D, and Jones, B. Show me, tell me, encourage me: The effect of different forms of feedback on resistance training performance. J Strength Cond Res 34(11): 3157-3163, 2020-When performing resistance training, verbal kinematic feedback and visual kinematic feedback are known to enhance performance. In addition, providing verbal encouragement can assist in the attenuation of fatigue. However, the effects of these forms of feedback have never been compared. Consequently, this study aimed to quantify the effects of verbal kinematic feedback and visual kinematic feedback, and verbal encouragement on barbell velocity during the back squat. Furthermore, changes in performance were related to individual-reported conscientiousness. Twelve semiprofessional rugby union players volunteered to participate in the study that consisted of the subjects completing a set of the barbell back squat across 4 conditions (i.e., no-feedback [control], verbal feedback of kinematic information [verbal], visual feedback of kinematic information [visual], and verbal encouragement [encouragement]). In addition, participants completed a questionnaire before the study to assess conscientiousness. Magnitude-based inferences were used to assess differences between conditions, whereas Spearman's rank correlation coefficient was used to assess relationships between conscientiousness and changes in barbell velocity. All 3 forms of feedback showed almost certain improvements in barbell velocity, while differences between interventions were likely to very likely trivial. Changes in barbell velocity showed small to large inverse relationships with conscientiousness. These findings suggest that practitioners should supply kinematic feedback (verbally or visually) or, when technology is not available, provide athletes with encouraging statements while resistance training. Verbal encouragement may be of greatest benefit for individuals who demonstrate low levels of conscientiousness. Given these findings, practitioners are advised to use either technology or verbal encouragement to manipulate acute training outcomes.

PURPOSETo identify which combination metrics of external and internal training load (TL) capture similar or unique information for individual professional players during skills training in rugby union using principal-component (PC) analysis.METHODSTL data were collected from 21 male professional rugby union players across a competitive season. This included PlayerLoad™, total distance, and individualized high-speed distance (>61% maximal velocity; all external TL) obtained from a microtechnology device (OptimEye X4; Catapult Innovations, Melbourne, Australia) that was worn by each player and the session rating of perceived exertion (RPE) (internal TL). PC analysis was conducted on each individual to extract the underlying combinations of the 4 TL measures that best describe the total information (variance) provided by the measures. TL measures with PC loadings (PCL) above 0.7 were deemed to possess well-defined relationships with the extracted PC.RESULTSThe findings show that from the 4 TL measures, the majority of an individual's TL information (first PC: 55-70%) during skills training can be explained by session RPE (PCL: 0.72-0.95), total distance (PCL: 0.86-0.98), or PlayerLoad (PCL: 0.71-0.98). High-speed distance was the only variable to relate to the second PC (PCL: 0.72-1.00), which captured additional TL information (+19-28%).CONCLUSIONSFindings suggest that practitioners could quantify the TL of rugby union skills training with one of PlayerLoad, total distance, or session RPE plus high-speed distance while limiting omitted information of the TL imposed during professional rugby union skills training.

Abstract Scantlebury, S, Till, K, Sawczuk, T, Phibbs, P, and Jones, B. Validity of retrospective session rating of perceived exertion to quantify training load in youth athletes. J Strength Cond Res 32(7): 1975–1980, 2018—Youth athletes frequently participate in multiple sports or for multiple teams within the same sport. To optimize player development and minimize undesirable training outcomes (e.g., overuse injuries), practitioners must be cognizant of an athlete's training load within and outside their practice. This study aimed to establish the validity of a 24-hour (s-RPE24) and 72-hour (s-RPE72) recall of session rating of perceived exertion (s-RPE) against the criterion measure of s-RPE collected 30 minutes' post training (s-RPE30). Thirty-eight adolescent athletes provided a s-RPE30 following the first field based training session of the week. Approximately 24 hours later subjects were asked to recall the intensity and duration of the previous days training. The following week subjects once again provided an s-RPE30 measure after training before recalling the intensity and duration of the session approximately 72 hours later. A nearly perfect correlation (0.98 [0.97–0.99]) was found between s-RPE30 and s-RPE24, with a small typical error of estimate (TEE; 8.3% [6.9–10.5]) and trivial mean bias (−1.1% [−2.8 to 0.6]). Despite a large correlation between s-RPE30 and s-RPE72 (0.73 [0.59–0.82]) and a trivial mean bias (−0.2% [−6.8 to 6.8]), there was a large TEE (35.3% [29.6–43.9]). s-RPE24 provides a valid measure of retrospectively quantifying s-RPE; however, the large error associated with s-RPE72 suggests that it is not a suitable method for monitoring training load in youth athletes.

Roe, G, Shaw, W, Darrall-Jones, J, Phibbs, PJ, Read, D, Weakley, JJ, Till, K, and Jones, B. Reliability and validity of a medicine ball-contained accelerometer for measuring upper-body neuromuscular performance. J Strength Cond Res 32(7): 1915-1918, 2018-The aim of the study was to assess the between-day reliability and validity of a medicine ball-contained accelerometer (MBA) for assessing upper-body neuromuscular performance during a throwing task. Ten professional rugby union players partook in the study. Between-day reliability was assessed from the best score attained during 2 sets of 3 throws, on 2 testing occasions separated by 7 days. Validity was assessed against a criterion measure (Optioelectronic system) during 75 throws from a subgroup of 3 participants. The MBA exhibited a small between-day error of 2.2% (90% confidence intervals; 2.0-4.6%) and an almost perfect relationship with a criterion measure (r = 0.91 [90% CIs; 0.87-0.94]). However, the mean bias and standard error were moderate (7.9% [90% CIs; 6.6-9.2%] and 4.9% [90% CIs; 4.2-5.7%], respectively). Practitioners using an MBA to assess neuromuscular performance of the upper body must take into account the overestimation and error associated with such assessment with respect to a criterion measure. However, as the error associated with between-day testing was small and testing is easy to implement in applied practice, an MBA may provide a useful tool for monitoring upper-body neuromuscular performance over time.

The aims of this study were to determine the variability of weekly match and training loads in adolescent rugby union players across a competitive season, and to investigate the effect of match frequency on load distribution across different activities. Internal match and training load data (i.e. session-rating of perceived exertion (sRPE)) were collected daily from 20 players from a regional academy across a 14-week season. Data were analysed using a mixed-effects linear model, and variability was reported as a coefficient of variation (CV). Differences between 0-, 1-, 2-, and 3-match weeks were assessed using Cohen's d effect sizes and magnitude-based inferences. Mean weekly total match and training sRPE load was 1425 ± 545 arbitrary units (AU), with a between-player CV of 10 ± 6% and within-player CV of 37 ± 3%. Mean week-to-week change in total sRPE load was 497 ± 423 AU (35%), and 40% of weekly observations were outside the suggested acute:chronic workload ratio 'safe zone'. Total weekly sRPE loads increased substantially with match frequency (1210 ± 571, 1511 ± 489, and 1692 ± 517 AU, for 0-, 1-, and 2-match weeks, respectively), except for 3-match weeks (1520 ± 442 AU). Weekly match and training loads were highly variable for adolescent rugby players during the competitive season, and match frequency has a substantial effect on the distribution of loads. Therefore, match and training loads should be coordinated, monitored, and managed on an individual basis to protect players from negative training consequences, and to promote long-term athlete development.

PURPOSE To quantify the frequencies and timings of rugby union match-play phases (ie, attacking, defending, ball in play [BIP], and ball out of play [BOP]) and then compare the physical characteristics of attacking, defending, and BOP between forwards and backs. METHODS Data were analyzed from 59 male rugby union academy players (259 observations). Each player wore a microtechnology device (OptimEye S5; Catapult, Melbourne, Australia) with video footage analyzed for phase timings and frequencies. Dependent variables were analyzed using a linear mixed-effects model and assessed with magnitude-based inferences and Cohen d effect sizes (ES). RESULTS Attack, defense, BIP, and BOP times were 12.7 (3.1), 14.7 (2.5), 27.4 (2.9), and 47.4 (4.1) min, respectively. Mean attack (26 [17] s), defense (26 [18] s), and BIP (33 [24] s) phases were shorter than BOP phases (59 [33] s). The relative distance in attacking phases was similar (112.2 [48.4] vs 114.6 [52.3] m·min-1, ES = 0.00 ± 0.23) between forwards and backs but greater in forwards (114.5 [52.7] vs 109.0 [54.8] m·min-1, ES = 0.32 ± 0.23) during defense and greater in backs during BOP (ES = -0.66 ± 0.23). CONCLUSIONS Total time in attack, defense, and therefore BIP was less than BOP. Relative distance was greater in forwards during defense, whereas it was greater in backs during BOP and similar between positions during attack. Players should be exposed to training intensities from in-play phases (ie, attack and defense) rather than whole-match data and practice technical skills during these intensities.