Jaime Sampaio*，Rolan Leser，Arnol Baa，Julio Calleja-Gonzalez，Diogo CoutinhoBruno GonçalvesNuno Leite

aResearch Centre in Sports Sciences，Health Sciences and Human Development，CIDESD，CreativeLab Research Community，Vila Real 5000，Portugal

bSport Sciences Department，University of Trás-Os-Montes e Alto Douro，Vila Real 5000，Portugal

cCentre for Sport Science and University Sports，University of Vienna，Vienna 1150，Austria

dFaculty of Sport Sciences，University of the Basque Country，Vitoria 01007，Spain



Defensive pressure affects basketball technical actions but not the time-motion variables

Jaime Sampaioa，b，*，Roland Leserc，Arnold Bacac，Julio Calleja-Gonzalezd，Diogo Coutinhoa，b，Bruno Gonçalvesa，b，Nuno Leitea，b

aResearch Centre in Sports Sciences，Health Sciences and Human Development，CIDESD，CreativeLab Research Community，Vila Real 5000，Portugal

bSport Sciences Department，University of Trás-Os-Montes e Alto Douro，Vila Real 5000，Portugal

cCentre for Sport Science and University Sports，University of Vienna，Vienna 1150，Austria

dFaculty of Sport Sciences，University of the Basque Country，Vitoria 01007，Spain

Background：Novel player tracking technologies can change the understanding of performance determinants in team sports by allowing to accurately measuring the activity demands.The aim of this study was to identify how the defensive pressure affects the time-motion variables and the technical actions in basketball.

Methods：Twenty international male players（age：16.05±2.09 years，weight：73.13±8.10 kg，height：183.10±5.88 cm）played two 10 min basketball quarters，where they used a man-to-man 1/4-court defense until the 4th min（F1/4），changed to man-to-man full court（FULL）for 3 min and，from the 7th to the 10th min returned to 1/4-court defense（S1/4）.A computerized notational analysis was performed using Simi Scout and positional data were captured with the Ubisense Real Time Location System （mean sampling rate 3.74±0.45 Hz per transmitter/player）.

Results：The time-motion variables presented similar results between defensive conditions，showing a total distance covered around 90 m/min. However，results suggested possible vertical jump impairments in S1/4 periods.There was more distance covered while jogging in the offensive court（38.15±12.17 m/min offensive court vs.32.94±10.84 m/min defensive court，p＜0.05）and more distance covered while running in the defensive court（16.41±10.27 m/min offensive court vs.19.56±10.29 m/min defensive court，p＜0.05）.

Conclusion：These results suggest how to improve task representativeness during specific conditioning or game-based training situations and also to help coaches’strategic decisions during the games.

©2016 Production and hosting by Elsevier B.V.on behalf of Shanghai University of Sport.This is an open access article under the CC BY-NC-ND license（http：//creativecommons.org/licenses/by-nc-nd/4.0/）.

Activity demands；Performance analysis；Performance indicators；Team sports

1.Introduction

Performance analysis in team sports aims to measure and process data from training sessions and competitions in order to use the information to enhance future performances.1Therefore，important information may be provided about players and teams’strengths and weaknesses，2，3which allow coaches to improve their training sessions.4Novel player tracking technology is changing the way we understand team sports.5In fact，current radio-signals or camera-based systems track the movements of every player on the court，6，7measuring accurately，for example，speed and distance covered.8In basketball，some examples include how fast a player moves，how far he/she traveled during a game，and much more.Unfortunately，research using these systems is still very scarce.6，8

The available research is focused on performance described by the game-related statistics，as variables that capture the frequencies of technical actions.9-11Research using these variables allowed identifying the actions most related to successful performances.12For example，the defensive rebounds，2-pointfield goals and assists are considered as strong discriminants between winning and losing teams.2，13-15Overall，these variables appear to help coaches to develop the most appropriate training tasks and increase the teams’probability of winning.13The physiological demands of basketball games have been described earlier.16，17For instance，it was found that young players covered a total distance of 115 m/min during a game，18while in adult players values ranged from 130 to 133 m/min.19Apparently，maximum benefits are obtained when the training stimulus is closer to competition requirements.20Therefore，it is vital to provide valid information to coaches about the physical demands of the game so they can set up more specific and adequate conditioning programs.21

Peer review under responsibility of Shanghai University of Sport.

http：//dx.doi.org/10.1016/j.jshs.2015.01.011

2095-2546/©2016 Production and hosting by Elsevier B.V.on behalf of Shanghai University of Sport.This is an open access article under the CC BY-NC-ND license（http：//creativecommons.org/licenses/by-nc-nd/4.0/）.

Despite the available research focused on performance analysis，information about the effects of manipulating defensive systems during the games is very scarce.Anecdotally，it is believed that defensive systems with constant pressure on the opponents are to be effective strategies to win a game.22，23For example，Wissel23stated that an optimal defensive system will increase the number of steals，interceptions and increase the number of missed shots in the opponents’team.

The most frequent level of defensive pressure is the man-toman 1/4-court defense.In this level of pressure，the players defend their direct opponent’only when he is positioned in the offensive 1/4-court.17，24During the game，coaches frequently ask the players to extend the man-to-man defense to a full court level，aiming to delay the ball transition from defense to offense and impair the opponents concentration in offense.24There is a coaching belief that changing the defensive pressure to full court increases energy demands and impairs offensive performances，however，there is no available research confirming this hypothesis.

In fact，research under this topic examined the efficacy of different defensive strategies and suggested that 1/4-court defense and full-court press showed efficacies above 50%.25There is also evidence that more points are scored against man-to-man defenses and that this defense generates more turnovers，while the zone defenses increase the number of committed faults.26From a physiological standpoint，the man-to-man and the zone defense seem very similar for game-activities changes（1.053 vs.1.056），frequency of high-intensity bouts（253 vs.224），or heart rate（93.3%±2.1%vs.92.8%±1.8% from the maximum heart rate）.17

Therefore，it might be expected to identify several differences in game-related statistics and time motion variables between these 2 levels of defensive strategies（1/4-court defense and a full court press）.These results would be very helpful to improve basketball practice specificity and coaches’strategic decisions during the games.Thus，the aim of the present study was to identify the effects of defensive pressure on technical actions and time-motion variables in basketball.

2.Methods

2.1.Experimental approach to the problem

A cross-sectional field study was used to identify the effects of defensive pressure（1/4-court defense and a full-court press） on technical actions and time-motion variables in basketball. The data were collected during a simulated basketball game，played in official court（28×15 m）with official rules and 3 referees.The players were rated by the national head coach according to their overall playing performances：passing ability，ball control，shooting，game perception，and decision.27Based on this classification，the players were assigned alternately into 4 balanced teams（A，B，C，and D）.Each team played two 10 min quarters（A vs.B played 1st and 3rd periods；C vs.D played 2nd and 4th periods）interspaced with 15 min of passive recovery.Two time-outs were conceded at the 4th and 7th min to allow changing the defensive system.The players were instructed to use 1/4-court defense until the 4th min（F1/4）. After that，the defense changed to man-to-man full court（FULL）and，from the 7th to the 10th min returned to 1/4-court defense（S1/4）.

2.2.Participants

Twenty semi-professional male basketball players volunteered to participate in this study（age：16.05±2.09 years；weight：73.13±8.10 kg；height：183.10±5.88 cm；weekly practice：10.9±1.94 h and playing experience：7.1±1.1 years）. All participants were part of a International Basketball Federation（FIBA）mid-level European national team and the players competed in their different clubs during the 2011/2012 season. The players，their parents and coaches agreed with the protocol description and were notified that they could withdraw from the study at any moment.An informed written consent was obtained from each participant’s parents.This study was conformed to the Declaration of Helsinki and was approved by both the Ethics Committee of the University ofVienna（Austria）and the Portuguese Foundation for Science and Technology（Portugal，PTDC/DES/098693/2008）.

2.3.Procedures

The identification of performance predictors in team sports should be preferentially done directly in game situations28and manipulating task constraints.29These research approaches allow overcoming several limitations from descriptive studies，which only provide statistics of performance.29，30From a research design perspective，this study could be counterbalanced in order to account for the order effect in the defensive situations；however，the FULL situation has no coaching interest，unless used in the course of the game.Consequently，we have only counterbalanced the man-to-man 1/4-court defense.

2.4.Instruments

The game was recorded with a standard digital camera located 5 m above the basketball court.The video files were downloaded to a computer and a notational analysis was performed using Simi Scout software（Version 2.0.0.174；Simi Reality Motion Systems GmbH，Unterschleissheim，Germany）. The following individual and team performance variables were registered：field goals，rebounds，steals and block shoots，freethrows，fouls，team number of ball possessions，team effective field goal percentage，team offensive rebounding percentage，and team free throw rate.11，15The individual performance indicators were normalized according to game ball possessions in order to account for differences in game pace.12，31In order to allow comparisons between the defensive strategies，all team variables were normalized per minute of play.An experienced performance analyst gathered the data and reliability was inspected by retesting 17.5%of the sample.The obtained intraclass correlation（ICC）coefficients were high（＞0.86）.32The time motion analysis was accomplished using the Ubisense Real Time Location System（Ubisense RTLS；Ubisense，Cambridge，UK），which allows to measure the distance covered in game sports activities with a mean deviation 0.09%±5.43%.33The Ubisense system is composed by 6 base stations mounted around the basketball field（outside of the court at each corner and in the middle of the long side lines）with a height of approximately 5 m，covering an area of about 33×18 m.The basketballplayers’positionswerecalculatedviatimedifference-of-arrival and angle-of-arrival measurements of ultra-wide band radio-signals that were sent by mobile transmitters（worn by the players in brackets atop their heads）to the base stations.33The mean sampling rate of the position measurements was 3.74±0.45 Hz per transmitter/player.The data obtained with the Ubisense software were processed through a combination of Kalman and low pass filtering.The position data for all transmitters were provided consecutively by used location system，meaning that only the values for 1 transmitter are available for each time instance of position measurement. Nevertheless，to obtain the pairs of x-/y-coordinates of each tag for all time instances the data were interpolated accordingly. The considered variables were the total distance covered and distances covered at different speed zones：34stand（0.0-0.1 m/s），walk（0.2-2.0 m/s），jog（2.1-3.7 m/s），run（3.8-6.0 m/s），and sprint（＞6.1 m/s）.

2.5.Data processing and analysis

All data sets were assessed for the identification of outliers and assumption of normality.Both individual and team gamerelated statistics were described using median and interquartile ranges（IQR）and compared between defensive situations using a Friedman nonparametric ANOVA.When appropriate，the pairwise differences were assessed using Wilcoxon’s matchedpair signed ranks test.A 2-way repeated measures ANOVA was performed to identify differences in distance covered at different speed zones.Also，repeated measures ANOVA was used to analyze the total distance covered，with pairwise differences assessed with Bonferroni post hoc test.Effect size was presented as η2and interpreted by the follow criteria：significant but weak（η2≤0.04），moderate（0.04＜η2≤0.36），and strong（η2＞0.36）.35All data sets were tested for each statistical technique corresponding assumptions and were analyzed with the statistical software IBM SPSS statistics for Windows（Version 19.0；IBM Corp.，Armonk，NY，USA）.The computed statistical power for this study was 0.96（G*Power，Version 3.1.9；Institutfür Experimentelle Psychologie，Düsseldorf，Germany）and the level of statistical significance was set at p＜0.05.

3.Results

Table 1 presents the results of the team performance variables for the different types of defensive strategies.There were only significant differences in ball possessions between F1/4 and S1/4，with higher values in the second situation.When considering the individual performances，the results obtained for each type of defensive situations showed differences mainly focused between the F1/4 and S1/4 periods for field goals，steals and blocked shoots，committed fouls and rebounds（Table 1）.The performances during the S1/4 were poorer，except for the fouls.

The total distance covered was similar between the defensive situations（F1/4=91.22±6.60 m/min，FULL=89.04± 11.27 m/min，S1/4=89.84±7.46 m/min）.The distances covered at different speed zones for the F1/4，S1/4，and FULL situations showed a significant interaction for the effect of the zone（F=1797.08，p＜0.001，strong；F=787.57，p＜0.001，strong，respectively），with the exception of standing and sprinting distances.Overall，the distance covered per minute was higher for the walk zone（ranging from 30 to 45 m/min）and decreased with an increase in speed.Fig.1 presents the results of distance covered at considered speed zones when in defensive and offensive court.No significant differences were found between the defensive situations.The distance covered in the offensive court indicated a significant effect for the different speed zones（F=73，787.02，p＜0.001，strong）with the exception of both the standing/sprinting and walking/jogging pairwise zones.There was more distance covered while jogging in the offensive court（38.15±12.17 m/min offensive court vs. 32.94±10.84 m/min defensive court，p＜0.05）and more distancecoveredwhilerunninginthedefensivecourt（16.41±10.27 m/min offensive court vs.19.56±10.29 m/min defensive court，p＜0.05）.

Table 1 Results of team and individual performance variables for each type of defensive situation.

4.Discussion

The aim of the present study was to identify the effects of defensive pressure（1/4-court defense and a full-court press）on technical actions and time-motion variables in basketball. Although there is a coaching belief that changing the defensive pressure to full court increases energy demands and impairs offensive performances，the obtained results only confirmed these hypotheses for some of the game-related statistics.The time-motion variables also presented similar results between defensive conditions，showing a total distance covered around 90 m/min.

When compared to full court，the 1/4-court defense is primarily used to restrict the available space for the attackers in the defensive court，keeping the defenders nearer to each other，and reducing slow defensive movements.26，36This coupled behavior among defenders may improve the ability to increase the opponents’unforced errors and missed shots and，afterward，allows triggering fast-break situations.13，26On the other hand，fullcourt defense is used as an important team strategy to affect the game pace，force the opponent to play uncomfortably with poorer shoot selection and perform a higher number of turnovers.37Therefore，it was expectable to find substantial differences between these conditions.

The team performance indicators showed that ball possessions increased along the quarter，and were significantly different between the F1/4 and S1/4 situation，suggesting an increase in game pace.31However，playing at these higher pacing did not affected the teams’efficacy in the analyzed variables.But from an individual perspective，player performance was particular impaired during the S1/4 situation，with poorer performances in 2-point field goals（both made and missed），blocked shoots and defensive rebounds.All these technical actions have a certain degree of dependence from the vertical jump performances，38，39probably due to the accumulated workload from F1/4 and FULL.40

Since the basketball activity patterns seem to depend on players’playing position and taking in consideration the individual role within team strategy，future investigations can also account for the specific playing positions.41

Interestingly，there were no differences in time motion variables between the 3 conditions，as measured by the positioning system.In fact，the system measures the time motion variables having a bi-dimensional perspective（using x-and y-coordinates）.Therefore，it seems that in S1/4 periods，the players were able to keep similar court positioning，however，they could be limited to perform actions that demand vertical jump.In fact，basketball time-motion analysis studies describe very well the energetic and muscular demands of the game situations，however，they are still limited to the use of bidimensional analysis，which might be an important limitation to analyze a sport like basketball.18，41，42Although the distances covered in the 3 periods were similar，high variability was identified in FULL and S1/4，probably showing inter-player differences in fitness conditions.The values of the total distance covered in both defensive situations were lower than those previous reported in junior elite male competition（90-92 m/ min vs.115 m/min）and in female basketball players（90-92 m/ min vs.127-136 m/min）.18，21Possibly，the players’expertise levels may account for some of these differences.The results from the distance covered in speed zones also showed that players presented stronger higher values in walking and jogging，in agreement with previous findings，showing that junior male basketball players spent 14.2%±1.3%in walking and 11.3%±1.6%in running，respectively.18

There was more distance covered while jogging in the offensive court and more distance covered while running in the defensive court.The available literature has shown that most of the offensive situations in basketball emerge based on controlled offensive sets.43Otherwise，the defensive pressure and the use of positional fluctuations（defensive help）have been identified as key-performance indicators.10，25Most likely，thismotion behavior elicits a high number of accelerations and decelerations（turning，shuffling，and changes of direction）.In fact，this strategic and tactical behavior helps explaining why the defensive court holds higher intensity activity profiles.

These results indicate how to improve task representativeness during specific conditioning or game-based training situations and also to help coaches’strategic decisions during the games.For example，the man-to-man defenses were the most used during game situations，26，44therefore，changing to fullcourt defense has the consequence of forcing the opponents to change the offensive strategy.45The defense-based tasks used in training seem more demanding and probably best suited for the end of the practice session.During the game-based situations，manipulating the defense allows to develop different technical abilities and perception-action cycles，apparently without increasing the external workload.In this sense，players will benefit from performing tasks with different speed，space，and pressure requirements.Moreover，several psychological-related abilities could also be fostered with the manipulation of the defensive strategies during training sessions and game-based situations.In fact，these constraints may increase the teams’cohesion，communication，responsibility，and self-confidence.46

5.Conclusion

Overall，this exploratory study presents new insights about the effects of defensive pressure constraint on basketball players’technical actions，decision-making and time-motion variables.The obtained results can also contribute to enhance the coaches’understanding of individual and collective performance indicators in game context.Anecdotally，basketball coaches use FULL tactical constraint as an efficient defensive strategy，however，current results show much less differences than the expected.Nevertheless，the FULL can be a useful tactical solution in critical moments of the game to benefit from states of psychological crisis.47，48

Acknowledgment

This study was supported by the project“Evaluating training and competition in team sports.Aggregating tactical analysis，external and internal workload”financed by the Portuguese Foundation for Science and Technology（No. PTDC/DES/098693/2008）.

JS，RL，JCG，and NL carried out the design and data collection.DC and BG processed the data.JS，RL，AB，JCG，DC，BG，and NL drafted the manuscript.All authors have read and approved the final version of the manuscript，and agree with the order of presentation of the authors.

Competing interests

None of the authors declare competing financial interests.

1.Hughes M，Franks I.The essentials of performance analysis：an introduction.London：Taylor&Francis；2007.

2.Ibanez SJ，Garcia J，Feu S，Lorenzo A，Sampaio J.Effects of consecutive basketball games on the game-related statistics that discriminate winner and losing teams.J Sport Sci Med 2009；8：458-62.

3.Lago-Penas C，Lago-Ballesteros J，Dellal A，Gomez M.Game-related statistics that discriminated winning，drawing and losing teams from the Spanish soccer league.J Sport Sci Med 2010；9：288-93.

4.Hughes M，Franks I.Notational analysis of sport.2nd ed.London：Routledge；2004.

5.Bourbousson J，Seve C，McGarry T.Space-time coordination dynamics in basketball：part 2.The interaction between the two teams.J Sport Sci 2010；28：349-58.

6.Leite NM，Leser R，Gonçalves B，Calleja-Gonzalez J，Baca A，Sampaio J. Effect of defensive pressure on movement behaviour during an under-18 basketball game.Int J Sports Med 2014；35：743-8.

7.Bourbousson J，Seve C，McGarry T.Space-time coordination dynamics in basketball：part 1.Intra-and inter-couplings among player dyads.J Sport Sci 2010；28：339-47.

8.Erculj F，Dezman B，Vuckovic G，Pers J，Perse M，Kristan M.An analysis of basketball players’movements in the Slovenian Basketball League play-offs using the Sagit tracking system.Phys Educ Sport 2008；6：75-84.

9.Sampaio J，Janeira M，Ibanez S，Lorenzo A.Discriminant analysis of game-related statistics between basketball guards，forwards and centres in three professional leagues.Eur J Sport Sci 2006；6：173-8.

10.Ibanez SJ，Sampaio J，Feu S，Lorenzo A，Gomez MA，Ortega E.Basketball game-related statistics that discriminate between teams’season-long success.Eur J Sport Sci 2008；8：369-72.

11.Sampaio J，Drinkwater EJ，Leite NM.Effects of season period，team quality，and playing time on basketball players’game-related statistics.Eur J Sport Sci 2010；10：141-9.

12.Sampaio J，Janeira M.Statistical analyses of basketball team performance：understanding team’s wins and losses according to a different index of ball possessions.Int J Perform Anal Sport 2003；3：40-9.

13.Gomez A，Lorenzo A，Sampaio J，Ibanez S，Ortega E.Game-related statistics that discriminated winning and losing teams from the Spanish men’s professional basketball teams.Collegium Antropol 2008；32：451-6.

14.Sampaio J，Ibanez SJ，Feu S，Lorenzo A，Gomez MA，Ortega E.Basketball game-related statistics that discriminate between teams’season-long success.Eur J Sport Sci 2008；8：369-72.

15.Lorenzo A，Gomez MA，Ortega E，Ibanez SJ，Sampaio J.Game related statistics which discriminate between winning and losing under-16 male basketball games.J Sport Sci Med 2010；9：664-8.

16.Matthew D，Delextrat A.Heart rate，blood lactate concentration，and time-motion analysis of female basketball players during competition.J Sports Sci 2009；27：813-21.

17.Ben Abdelkrim N，Castagna C，El Fazaa S，El Ati J.The effect of players’standard and tactical strategy on game demands in men’s basketball.J Strength Cond Res 2010；24：2652-62.

18.Ben Abdelkrim N，Castagna C，Jabri I，Battikh T，El Fazaa S，El Ati J. Activity profile and physiological requirements of junior elite basketball players in relation to aerobic-anaerobic fitness.J Strength Cond Res 2010；24：2330-42.

19.Scanlan A，Dascombe B，Reaburn P.A comparison of the activity demands of elite and sub-eliteAustralian men’s basketball competition.J Sports Sci 2011；29：1153-60.

20.BompaTO.Theoryandmethodologyoftraining.Dubusque：Kendall/Hunt；1983.

21.Scanlan T，Dascombe BJ，Reaburn P，Dalbo VJ.The physiological and activity demands experienced by Australian female basketball players during competition.J Sci Med Sport 2012；15：341-7.

22.Brown H.Let's talk defense：tips，skills&drills for better defensive basketball.1st ed.New York，NY：McGraw-Hill；2005.

23.Wissel H.Baloncesto.Aprender y progresar.3rd ed.Barcelona：Paidotribo；2008.

24.Gomez A，Evangelos T，Alberto L.Defensive systems in basketball ball possessions.Int J Perform Anal Sport 2006；6：98-107.

25.Alvarez A，Ortega E，Gomez MA，Salado J.Study of the defensive performance indicators in peak performance basketball.Rev Psicol Deporte 2009；18：379-84.

26.Gomez A，Lorenzo A，Ibanez S，Ortega E，Leite N，Sampaio J.An analysis of defensive strategies used by home and away basketball teams.Percept Motor Skill 2010；110：159-66.

27.Sampaio J，Lago C，Gonçalves B，Macãs V，Leite N.Effects of pacing，status and unbalance in time motion variables，heart rate and tactical behaviour when playing 5-a-side football small-sided games.J Sci Med Sport 2014；17：229-33.

28.Atkinson G，Batterham AM，Hopkins WG.Sports performance research under the spotlight.Int J Sports Med 2012；33：949.doi：10.1055/s-0032-1327755

29.Vilar L，Araújo D，Davids K，Button C.The role of ecological dynamics in analysing performance in team sports.Sports Med 2012；42：1-10.

30.Davids K，Araújo D，Correia V，Vilar L.How small-sided and conditioned games enhance acquisition of movement and decision-making skills.Exerc Sport Sci Rev 2013；41：154-61.

31.IKubatko J，Oliver D，Pelton K，Rosenbaum D.A starting point for analyzing basketball statistics.J Quant Anal Sports 2007；3：1-22.

32.O’Donoghue P.Research methods for sports performance analysis. London：Routledge；2010.

33.Leser R.Accuracy test of a wireless position tracking system for measuring sportsactivities.In：Pre-OlympicCongressoftheInternational Association of Computer Science in Sport 2012.Liverpool，UK.July 24-25，2012.

34.Dwyer DB，Gabbett TJ.Global positioning system data analysis：velocity ranges and a new definition of sprinting for field sport athletes.J Strength Cond Res 2012；26：818-24.

35.Tabachnick BG，Fidell LS.Using multivariate statistics.5th ed.Boston，MA：18 Allyn and Bacon；2007.

36.Sivils K.Game strategies and tactics for basketball：bench coaching for success.Indianapolis，IN：Dog Ear Publishing；2009.

37.Sampaio J，Lago C，Drinkwater EJ.Explanations for the United States of America’s dominance in basketball at the Beijing Olympic Games（2008）. J Sports Sci 2010；28：147-52.

38.Ziv G，Lidor R.Vertical jump in female and male basketball players-a review of observational and experimental studies.J Sport Sci Med 2010；13：332-9.

39.Balciunas M，Stonkus S，Abrantes C，Sampaio J.Long term effects of different training modalities on power，speed，skill and anaerobic capacity in young male basketball players.J Sport Sci Med 2006；5：163-70.

40.Knicker AJ，Renshaw I，Oldham ARH，Cairns SP.Interactive processes link the multiple symptoms of fatigue in sport competition.Sports Med 2011；41：307-28.

41.Ben Abdelkrim N，Chaouachi A，Chamari K，Chtara M，Castagna C. Positional role and competitive-level differences in elite-level men’s basketball players.J Strength Cond Res 2010；24：1346-55.

42.Ben Abdelkrim N，El Fazaa S，El Ati J.Time-motion analysis and physiological data of elite under 19-year-old basketball players during competition.Br J Sport Med 2007；41：69-75.

43.Bazanov B，Vohandu P，Haljand R.Trends in offensive team activity in basketball.Educ Phys Train Sport 2006；61：5-11.

44.Mexas K，Tsitskaris G，Kyriakou D，Garefis A.Comparison of effectiveness of organized offences between two different championships in high level basketball.Int J Perf Anal Sport 2005；5：72-82.

45.Mikes J.Computer breakdown of percentage basketball.Scholastic Coach 1987；57：52-4.

46.Otto K.Defense wins！Coach Athl Director 1998；67：24-5.

47.Bar-Eli M，Tractinsky N.Criticality of game situations and decision making in basketball：an application of performance crisis perspective. Psychol Sport Exerc 2000；1：27-39.

48.Uphill M，Groom R，Jones M.The influence of in-game emotions on basketball performance.Eur J Sport Sci 2014；14：76-83.

.Research Centre in Sports Sciences，Health Sciences and Human Development，CIDESD，CreativeLab Research Community，Vila Real 5000，Portugal.

E-mail address：ajaime@utad.pt（J.Sampaio）

s’contributions

14 April 2014；revised 11 November 2014；accepted 26 January 2015 Available online 30 May 2015