Universidade de Trás-os-Montes e Alto Douro
Effect of match break, distance travelled, players’ age and time
played in high level basketball
Dissertação de Mestrado Internacional em Análise da Performance Desportiva
Ivan Jorge Miranda Torres
Orientador: Professor Doutor Nuno Miguel Correia Leite
II
Universidade de Trás-os-Montes e Alto Douro
Effect of match break, distance travelled, players’ age and time
played in high level basketball
Dissertação de Mestrado Internacional em Análise da Performance Desportiva
Ivan Jorge Miranda Torres
Orientador: Professor Doutor Nuno Miguel Correia Leite
Composição do Júri:
Professora Doutora Catarina Isabel Neto Gavião Abrantes
Professor Doutor António Jaime Eira Sampaio
Professor Doutor Nuno Miguel Correia Leite
III
Declaração
Nome: Ivan Jorge Miranda Torres C.C: 13756220
Telemóvel: (+351) 932564705
Correio Eletrónico: ivan.j.m.torres90@gmail.com
Designação do Mestrado: Mestrado Internacional em Análise da Performance Desportiva Título da dissertação: Effect of match break, distance travelled, players’ age and time played
in high level basketball
Orientador: Professor Doutor Nuno Miguel Correia Leite Ano de Conclusão: 2017
Declaro que esta dissertação de mestrado é o resultado de uma pesquisa e trabalho pessoal efetuada por mim e orientada pelo meu supervisor. O seu conteúdo é original e todas as fontes consultadas estão devidamente citadas no texto e mencionadas na bibliografia final. Declaro ainda que este trabalho não foi apresentado em nenhuma outra instituição para a obtenção de qualquer grau académico.
Vila Real, Outubro de 2017 Ivan Jorge Miranda Torres
IV
V
Agradecimentos
A realização deste trabalho foi conseguida com a colaboração, apoio e incentivo de diversas pessoas, às quais gostaria de manifestar o meu profundo agradecimento:
Ao Professor Doutor Nuno Leite, pela orientação e fundamental contributo na elaboração deste trabalho.
Ao aluno de doutoramento Nuno Mateus, pela preciosa ajuda e cooperação na realização deste trabalho.
À minha família, em especial, à minha Mãe, pela compreensão e encurajamento transmitido ao longo do meu percurso académico.
À minha companheira Mariana, pelo apoio, pela ajuda, pela compreensão em momentos mais complicados, pela confiança e pelo incentivo para a concretização do produto final.
Aos meus irmãos de viagem, Artur Júnior e Márcio Carvalho, pela amizade e principalmente, pelos momentos autênticos que marcarão para sempre esta etapa fundamental na minha vida.
A todos aqueles, que durante a minha vida contribuíram de alguma forma para a minha formação académica e pessoal.
VI
Abstract
The aim of this study was examine the variation of individual performance profiles in basketball, between national and international leagues and examine if the magnitude of this variation is affected by age and playing position. The players’ profiles, including age and playing position were collected from official league websites. A total of 1627 official boxscores from national and international leagues, from 2014-2015 and 2015-2016 seasons, were considered for this study. Besides the traditional notational analysis, we also computed the distance traveled and days of rest between national and international matches. Firstly, was realized a two-step cluster analysis to group the players into different groups. Posteriorly, one-way independent measure ANOVA was used to explore the difference of the within and between the obtained clusters; and finally, a descriptive discriminant analysis was conducted to identify which variables best discriminate the obtained clusters. The results showed that the number of clusters is different between both competitions, revealing a variation in the individual performance profiles. Factors such as distance traveled, time played and players' age, affected the performances in the international competition, however in the national competitions, distance traveled, match break and time played were the variables with a great discriminatory influence. This information can help in the development and optimization of performance specific profiles according with competitions, and can facilitate the detailed planning for specific groups of players and teams, contemplating the performance on the training session and on the game.
VII
Resumo
O objetivo deste estudo foi examinar a variação dos perfis de desempenho individuais no basquete, entre as ligas nacionais e internacionais e examinar se a magnitude dessa variação é afetada pela idade e posição de jogo. Os perfis dos jogadores, incluindo idade e posição de jogo, foram coletados dos sites oficiais da liga. Um total de 1627 boletins oficiais das ligas nacionais e internacionais, das épocas 2014-2015 e 2015-2016, foram considerados para este estudo. Além da análise notacional tradicional, calculamos também a distância percorrida e os dias de descanso entre os jogos nacionais e internacionais. Em primeiro lugar, realizou-se uma análise de cluster de dois passos para agrupar os jogadores em diferentes grupos. Posteriormente, uma análise de variância ANOVA foi utilizada para explorar a diferença entre os clusters dos agrupamentos obtidos; e, finalmente, uma análise discriminante descritiva foi conduzida para identificar quais variáveis melhor discriminam os clusters obtidos. Os resultados mostraram que o número de clusters é diferente entre as duas competições, revelando uma variação nos perfis de desempenho individuais. Fatores como a distância percorrida, o tempo jogado e a idade dos jogadores afetaram os desempenhos na competição internacional, no entanto, nas competições nacionais, a distância percorrida, intervalo entre jogos e o tempo jogado foram as variáveis com a maior influência discriminatória. Esta informação pode ajudar no desenvolvimento e otimização de perfis específicos de desempenho, de acordo com as competições e pode facilitar o planeamento detalhado para grupos específicos de jogadores e equipes, contemplando o desempenho na sessão de treinamento e no jogo.
IX
Index
Introduction ... 1
Methods ... 3
Sample ... 3
Procedures and statistics analysis ... 3
Results ... 4
Discussion ... 8
Conclusions ... 12
X
Index Tables
Table 1. Means, standard deviations and structure coefficients from the obtained model of clusters for the Euroleague and the percentage in the distribution of the specific post in each of the clusters. .. Error! Bookmark not defined.
Table 2. Means, standard deviations and structure coefficients from the obtained model of clusters for the National competition and the percentage in the distribution of the specific post in each of the clusters. ... 7
Abbreviations List
SC – structure coefficients1
Introduction
The Euroleague is the highest competition in European basketball where we can find the most qualified athletes and the best performers in the game playing for the best teams that operate in the old continent. Previously, the competitive system included three different phases. In the first phase, all the teams were separated into four groups of six teams, playing against each other in a home-and-away format. Then, the best four teams of each group qualified for a second phase, where the teams were split in two new groups. Finally, the top four teams of each group advanced to playoffs. Nonetheless, from the 2016/17 season the competition format has changed, and now, only the 16 best European teams participate, in a single-phase tournament, in which all teams play against each other, in a home-and-away format. Being the Euroleague a very long and demanding competition eyeing the qualification to playoff and final four stages, success often depends on how coaches and coaches manage the participation of players in national league games, travel and rest between games. In this sense, these busy schedule often contribute to increase the players’ physical and physiological stress (Gonzalez et al, 2013). The specific characteristics of the different domestic competitions, suggest the possible emergence of different performance profiles and different competition patterns. In fact, game standards vary from country to country (Sampaio et al., 2006), with clear differences in game’ quality and speed, as in its defensive and offensive requirements. Sampaio and colleagues, showed that in the Portuguese basketball league, body characteristics as height and weight are highly relevant for the game outcome; and in the Spanish basketball league, the players are less specialized in their game roles; and finally, in the NBA, the differences between the playing positions is quite evident, possible due the different rules of the game (e.g. the 3-point line is one meter farther from the basket). The different competitive discrepancies across basketball leagues, contributed to identify diverse performance profiles, as a result of the different physical requirements of the competitions, affected by the constraints caused by opposing teams and the own competitive level of the teams (Rampinini et al., 2007).
In team sports, there are numerous factors that can influence individual and collective performances. In fact, directly or indirectly, each detail account and can influence the sequence of a game. Distance traveled (Sampaio et al., 2008), times zones (Nutting, 2009), fatigue (Montgomery et al., 2008), team standard (Ibáñez et al., 2009) and players’ age (Tanaka et al., 2008), are a few examples. The distance traveled between games, can also affect individual and collective performance, since can induce different consequences in the teams' behavior during
2
the game (Ibáñez et al., 2009). On the other hand, as age increases physical capacity decreases, resulting in poorest performances (Tanaka et al., 2003). Prior knowledge of competition patterns could help coaches to increase effectiveness in the recruitment of new players, using specific positional normative data, depending on the needs of the teams in the different competitions.
Assessing sports performance is accomplished through the use of match stats to evaluate the effectiveness of players and teams (Drinkwater et al., 2008). The match stats provide relevant information to plan specific tasks for the training helping in the optimization of individual and collective game’ behaviors (Duarte et al., 2009 and Pinder et al., 2011).
Continuous planning and monitoring of players performance, both in training and in game, is fundamental to optimize the decisions about the individualized training loads that will be taken by the technical staff (Sampaio, J. et al, 2015), being a process of high complexity. According to Gamble (2006) and Pyne et al. (2000), more effective training periodization strategies can ensure that players are better prepared to operate optimally on a weekly basis. For example, coaches may seek to vary volume and intensity training on a week-to-week basis in order to optimize the athletes’ preparation for the next game. Every detail is taken into account, from training loads (Lambert & Borresen, 2008), distances covered in the training sessions and during the games (Wallace, Slattery, & Coutts, 2009), recovery time between games (McLean, Coutts, Kelly, McGuigan, & Cormack, 2010), game location, players’ physical or mental fatigue (Gonzalez et al, 2013 and Thomson et al., 2009), and difference between time-zones (Nutting et al., 2015). Being the main objective to facilitate the planning of a season, increasing the quality of the weekly periodization, will improve the strategy of game-to-game. Such a priori knowledge, may offer to coaches the opportunity to strategically manipulate training loads, as anticipation of the difficulty of a specific game.
Thus, this study aims to examine the variation of individual performance profiles in basketball, between national and international leagues. Moreover, the aim is to examine if the magnitude of this variation is affected by age and playing position.
3
Methods
SampleThe archival data were obtained from official records of Euroleague boxscores and their respective national leagues from the Top-16 male teams, from 2014-2015 and 2015-2016 seasons (available atwww.euroleague.net and www.basketball.realgm.com). A total of 1627 boxscores were considered to this study and correspond to a period of time between the national league match before the Euroleague beginning, until the national league match immediately after the end of Euroleague regular season. The database collected has recorded 16268 game performances, such us full verification both national and international leagues, also included players details, age, playing position, match break, distance traveled among others. The players who played in any game, less than 5 minutes were excluded from the analysis (Sampaio et al., 2006). Several game performance statistics and external factors were included: i) offensive variables - points, two-point field goals made and missed, three-point field goals made and missed, free-throws made and missed, offensive rebounds, assists and turnovers; ii) defensive variables - defensive rebounds, steals, blocks, and personal fouls; and iii) external variables - players’ age, time-played, distance travelled (kilometers) and match-break (days).
Procedures and statistics analysis
The database were screened for univariate outliers (cases outside the range Mean ± 3SD) and distribution tested in order to make the following inferential analysis (Kerlinger & Pedhazur, 1973). Firstly, a two-step cluster with log-likelihood as the distances measure and Schwartz’s Bayesian criterion was carried out to classify basketball players into the different groups (Gómez et al., 2017; Gómez, Lorenzo, Ibanez, & Sampaio, 2013; Mateus et al., 2015; Sampaio, Drinkwater, & Leite, 2010), and the variables used for the calculation were: distance traveled, match break, players’ age and time played. Secondly, one-way independent measure ANOVA was used to explore the difference of the within and between groups. Bonferroni homogeneous subsets were used to describe post-hoc results. Statistical significance was set at 0.05. Finally, a descriptive discriminant analysis was conducted to identify which variables best discriminate the obtained clusters. Discriminant analysis is robust for these derived rate variables (Norusis & Inc, 2004). Interpretation of the obtained discriminant function was based on examination of structure coefficients greater than |0.30|, which means that variables with higher absolute values
4
were best placed to discriminate between groups (Tabachnick & Fidell, 2007). Validation of discriminant models conducted using the leave-one-out method of cross-validation (Norusis & Inc, 2004). Cross-validation analysis evaluated the usefulness of discriminant functions when classifying new data. This method involved generating the discriminant function on all. Moreover, the distribution of all performances, national and international from different competitions, obtained cluster groups were performed by separately setting up custom and cross tables using IBM SPSS Statistics for Windows (Armonk, NY: IBM Corp).
Results
The means and standard deviations of the variables present in this study in the different groupings of players, such as the distribution by the specific position are presented in Tables 1 and 2.
The coefficients of structure in the first function corresponding to the Euroleague, reflected a greater emphasis on points (SC = 0.713), field goal made (SC = 0.658), field goal missed (SC = 0.566), assists (SC = 0.491) and less emphasis, but not least on defensive rebound (SC = 0.444), total rebounds (SC = 0.418), 3-point missed (SC = 0.399), free-throw made (SC = 0.347) and turnovers (SC = 0.328).
5
Table 1. Means, standard deviations and structure coefficients from the obtained model of clusters for the
Euroleague and the percentage in the distribution of the specific post in each of the clusters.
Cluster/Variable Cluster 1 Cluster 2 Cluster 3
Function 1
(99.2%)
Field goal missed 2,35±1,81 3,10±2,01 4,68±2,48 0,566
Field goal made 1,75±1,56 2,67±1,93 4,24±2,22 0,658
3-point missed 1,00±1,18 1,28±1,40 2,08±1,71 0,389 3-point made 0,46±0,79 0,69±1,03 1,30±1,36 0,399 Free-throw missed 2,43±3,73 2,45±3,25 1,81±2,06 -0,109 Free-throw made 0,85±1,315 1,36±1,76 2,07±2,15 0,347 Defensive rebound 1,51±1,46 2,25±1,88 3,18±2,22 0,444 Offensive rebound 0,70±1,00 0,91±1,20 1,08±1,32 0,156 Total rebounds 2,21±1,92 3,16±2,47 4,26±2,83 0,418 Assists 1,00±1,31 1,53±1,78 2,81±2,48 0,491 Personal fouls 1,97±1,32 2,06±1,24 2,30±1,23 0,137 Turnovers 0,91±1,03 1,15±1,14 1,67±1,36 0,328 Steals 0,44±0,71 0,57±0,80 0,92±1,02 0,290 Blocks 0,21±0,53 0,29±0,62 0,37±0,75 0,118 Points 4,83±4,080 7,41±5,04 11,90±5,85 0,713 Age 23,92±2,84 30,80±2,09 26,33±2,16 Match Break 3,44±1,41 3,44±1,33 3,45±1,44 Distance 863,65±1077,85 944,30±1154,12 889,18±1111,51 Time played 14,38±4,97 19,02±5,85 28,01±4,12 Point Guard 28,5% 30,4% 41,1% Shooting Guard 35,3% 33,4% 31,3% Small Forward 39,7% 27,4% 32,9% Power Forward 32,3% 39,3% 28,4% Center 27,8% 52,9% 19,3%
6
In Euroleague, all the cases grouped in cluster 1 correspond to the younger players (23,92 ± 2,84) and that they had the smallest number of minutes played, being this cluster composed mainly by small forwards (39.7%) and shooting guards (35.3%), cluster 2 grouped the older players (30.80 ± 2.09) and the time played was intermediate (19.02 ± 5.85), composed the same by centers (52.9 %), in turn, cluster 3 grouped the players with intermediate age (26.33 ± 2.16) and who performed the largest number of played time (28.01 ± 4.12), this cluster consisting of point guards (41%). The match break did not have great variability between the three clusters.
7
Table 2. Means, standard deviations and structure coefficients from the obtained model of clusters for the
National competition and the percentage in the distribution of the specific post in each of the clusters.
In national leagues competitions, the coefficients of structure of the first function, reflected a greater emphasis on points (SC = 0.628), field goal missed (SC = 0.546), field goal made (SC = 0.564), and a lower emphasis on defensive rebound (SC = 0.406), total rebounds (SC = 0.394), 3-point missed (SC = 0.377) and 3-point made (SC = = 0.335). Cluster 1 grouped the younger players (21.78 ± 2.56) and with the smallest number of minutes played. This cluster has the particularity of being the most heterogeneous, not highlighting a specific position that discriminates. Cluster 2 and 3 grouped players with intermediate ages (25.40 ± 2.64) and (25.55 ± 1.67), with being cluster 2 is composed of players whose specific position is point guards (26.3%), with being the highest number of time played (28.89 ± 3.44) and cluster 3 is composed
Cluster
/ Variable Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5
Function 1 (98,7%)
Field goal missed 1,83±1,48 4,64±2,30 3,09±1,83 2,06±1,53 3,64±1,99 0,546
Field goal made 1,58±1,40 4,53±2,28 3,06±1,88 1,96±1,61 3,66±2,06 0,564
3-point missed 0,81±1,01 2,19±1,69 1,40±1,33 0,80±1,11 1,62±1,51 0,377 3-point made 0,40±0,71 1,42±1,43 0,85±1,09 0,48±0,86 1,08±1,26 0,335 Free-throw missed 2,77±4,26 1,82±1,88 2,63±2,96 3,06±4,26 2,27±2,52 -0,120 Free-throw made 0,78±1,27 2,29±2,24 1,51±1,79 0,99±1,43 1,89±2,08 0,302 Defensive rebound 1,43±1,37 3,47±2,26 2,33±1,78 1,67±1,54 2,90±1,99 0,406 Offensive rebound 0,63±0,91 1,11±1,34 0,93±1,21 0,68±0,97 1,09±1,32 0,165 Total rebounds 2,07±1,80 4,60±2,88 3,25±2,38 2,35±1,95 3,99±2,62 0,394 Assists 0,92±1,23 3,00±2,46 1,92±1,88 1,02±1,27 2,23±2,16 0,407 Personal fouls 1,78±1,33 2,32±1,28 2,17±1,32 1,79±1,29 2,18±1,31 0,159 Turnovers 0,81±0,97 1,74±1,42 1,24±1,18 0,90±1,01 1,38±1,23 0,280 Steals 0,42±0,69 1,03±1,07 0,78±0,93 0,49±0,77 0,84±0,98 0,245 Blocks 0,23±0,55 0,39±0,77 0,28±0,65 0,25±0,55 0,34±0,68 0,090 Points 4,34±3,64 12,80±6,03 8,46±4,79 5,40±4,10 10,32±5,28 0,628 Age 21,78±2,56 25,40±2,64 25,55±1,67 30,44±2,37 30,42±1,90 Match Break 1,87±0,85 1,94±0,86 1,92±0,86 1,88±0,91 1,92±0,94 Distance 198,62±409,28 259,21±486,04 231,76±451,36 257,14±496,80 245,51±446,07 Time played 12,23±4,28 28,89±3,44 19,69±2,64 13,26±3,71 23,63±3,31 Point Guard 14,1% 26,3% 23,6% 14,8% 21,3% Shooting Guard 18,7% 19,0% 26,5% 16,3% 19,5% Small Forward 21,3% 21,2% 25,9% 15,2% 16,3% Power Forward 19,7% 17,1% 18,9% 23,3% 21,0% Center 16,7% 9,6% 18,4% 31,3% 24,0%
8
of shooting guard (26.5%) and small forward (25.9%) with being the number of minutes played intermediate (19.69 ± 2.64).Clusters 4 and 5 are composed of older players (30.44 ± 2.37) and (30.42 ± 1.90) with the time played (13.26 ± 3.71) and (23.63 ± 3.31) respectively, however these two clusters group mainly the players with the specific position, centers (31.3%) and (24.0%).
Discussion
This study aimed to examine the variation of individual performance profiles in basketball, between national and international leagues. Moreover, the aim was to examine if the magnitude of this variation is affected by age and playing position.
The cluster analysis showed that the number of clusters is different between both competitions, since in the international league were obtained three clusters and in national leagues five clusters. This division helps to perceive the differentiated characterization between competitions. In the international league, the smaller number of clusters presented demonstrates the homogeneity of performance profiles. In the national competitions, the separation of the clusters reflected a greater heterogeneity of the competition.
The best teams deal with great periods of congestion (Dupont et al. 2010;Lago et al. 2011;Dellal et al. 2013;Djaoui et al. 2014;Carling et al. 2015;Folgado et al. 2015). The results of this study shown that in international league, the match break was very similar for all clusters (cluster 1 = 3.44 ± 1.41; cluster 2 = 3.44 ± 1.33 and cluster 3 = 3.45 ± 1.44). It suggests that recovery time not compromised the performance indicators of these clusters in the competition. It is expected that the recruitment process could also be affected by this, and therefore, general managers/coaches could also be influenced by the perception of players’ physical and physiological profile.
On the other hand, in national competitions, the results reveal differences in the match break variable from cluster to cluster. This fact indicate that match break influenced the performance of the players. Cluster 1 and 4 recorded the shortest recovery time, curiously, they obtained the worst performance indicators. According to Folgado, Duarte, Marques & Sampaio (2015), this can be an important factor of increasing the risk of injury without adequate recovery from fatigue. An example of this is the higher negative ratio obtained between the free-throw attempts and made, such as in the assists, by the players that compose these groupings, where
9
the differences were accentuated with the other clusters. These clusters have less recovery time, which speculates that their performances can be compromised due to a time of negligent recovery (Nedelec et al., 2014).
The cluster 2 composed by players that act in the positions of the perimeter, presented a larger match break, despite the small differences but for the clusters with intermediate recovery time, however the match stats of this cluster were the best results obtained. In contrast, the fact of match break variable resulting from cluster 3 and 5 was identical and their respective performances differed, may indicate that this variable had no impact on their performance, speculating that other external factors, such us age, time played or distance traveled will play a differentiating role in performance of these clusters.
Contrary to the match break variable, the distance traveled in Euroleague showed larger differences between clusters (cluster 1 = 863,65 ± 1077,85; cluster 2 = 944,30 ± 1154,12 and cluster 3 = 889,18 ± 1111,51), suggesting a direct influence in both sides of the court (field goals made and missed, free-throw made and missed, assists and defensive rebounds).
These results reflect that the distance traveled variable have contributed in a relevant way to divide in an optimal number of clusters and consequently to the display of different performances between the clusters. Curiously, the younger players travel less distances, but show the worst performances (e.g., ratio between field goals made and missed, such us free-throw made and missed). The older players traveled more kilometers, which combined with their advanced age and time played, could be the main reasons for their poor performances in the defensive tasks (defensive rebounds and blocks). On the other hand, middle-aged players who traveled intermediate distances have achieved effective performances, both offensively and defensively throughout the games, with the cluster being more successful. This information may indicate that players can be better prepared to withstand the accumulated fatigue, as a consequence of the congested calendars, thus being able to maintain their performances. In the remaining clusters (3, 4 and 5) it was verified that despite having performed a greater distance traveled than cluster 1, obtained better match stats.
Regarding the age and playing time of the performance profiles displayed in the international league revealed the close relationship between the time played and the players' age, being these factors in large part, had a great discriminatory influence. Interestingly, the range of ages obtained in the different international clusters was between (23.92 ± 2.84) and (30.80 ± 2.09), which may reflect the degree of competitive requirement in this competition. On other hand,
10
the age range presented on national competitions is between (21.78 ± 2.56) and (30.44 ± 2.37), suggesting that the demand patterns competition. It should be noted that the heterogeneity presented is due in part to the greater number of performance profiles that constitute this competition.
The younger players (23.92 ± 2.84), played less time (14.38 ± 4.97) and had a small influence on the game, showed worst performances in the defensive and offensive performance indicators. These players are younger, their youthfulness can be the reason for the more mistakes made (Williams, A. M., & Ford, P. R., 2008; Swann, C. et al, 2012), demonstrating that the players’ experience is an important factor in internationals competitions. Because of that, coaches prefer the more experienced players taking into account the more demanding competitive requirements of the Euroleague, comparatively to the domestic competitions (Gonzalez et al, 2013). So, an interesting solution that can be found by the coaches, is giving progressively more minutes to them, allowing good conditions to their development, principally when a larger competitive discrepancy between teams exist, assigning certain objectives to fulfill, with the purpose of increasing their competitive experience at a highest level of play (Ericsson, K. A., 2003).
The older players (mainly centers) played an intermediate number of minutes. These players add, in addition to match stats, the competitive experience to their teams, one of the requirements that seems to be fundamental in this competition. According to Blomqvist, Luhtanen and Laakso, (2000), the experience influences game performance and is linear with age, performance, and time played. The middle-aged players, played a larger number of minutes, revealed better performances in offensive statistics (such as free-throw made = 2.07 ± 2.15 and free-throw missed = 1.81 ± 2.06) and showed great efficiency in certain defensive actions (defensive rebounds). This players tend to be more successful (Sampaio et al, 2010; Sampaio and Janeira, 2003), since they maintain their efficiency performances, both offensively and defensively throughout the game. This condition results in a larger number of minutes played comparatively with their teammates, perhaps because a greater influence in the game (Casals and Martinez, 2013; Mateus et al, 2015). This information, suggests that coaches should be aware of the individual characteristics of their players (e.g. players' age), when recruiting players for the building of their teams.
In national competitions, playing time may have created these clusters, having direct influence on the grouping of performances. Due to the significant differences exhibited among the 5
11
clusters. It is expected that the shorter the playing time, the worse the statistical performance indicators will be. The players with less time played, is less likely to achieve a reasonable performance (Sampaio et al, 2006), this fact was supported the results. Moreover, some performance statistics revealed trivial effects in the characterization of specific groups of players, which implies caution in their interpretation.
The players’ age served to discriminate completely the cluster 1, however this variable allowed to identify differences in the remaining clusters, grouping them into 3 distinct groups with a time interval of approximately 5 years. In cluster 1, age contributed much to a poor performance of these players with a direct influence on the time played of these players. On the other hand, what allows to differentiate the clusters 2 and 3 and the clusters 4 and 5 are the distances covered and the time of game. However, it is curious to note that within these clusters, those with the longest distances traveled get better performances, which ends up having repercussions on the time played. This information shown that travel big distances may involve crossing different time zones, being a detrimental situation to the players’ performance (Nutting et al., 2009; Steenland and Deddens, 1997).
The main differences between the two competitions are present in the variability between the different clusters obtained, with a greater heterogeneity to the national competitions, explained by the higher number of players and the larger diversity of leagues, resulting in different game and competition patterns. In fact, game standards vary between different countries (Sampaio et al., 2006), with clear differences in game quality and pace, in defensive and offensive requirements and in the players anthropometric characteristics. Moreover, in the internal competitions, coaches tend to pursue a player rotation strategy due the higher level discrepancy between teams (Rampinini et al., 2007), resulting in more several players performance patterns.
Moreover, in the international competition considerable differences between the distance traveled exist (table 1), resulting in large performance discrepancies between the different clusters. Since the rest time between the games is identical (3.44 ± 1.39) for all performance profiles, it seems that different players need different recovery times (Calleja-González et al., 2016). In contrast, in the internal competitions distance traveled and the rest time are smaller (table 2), which justify the tendency to give more minutes to players with lower propensity in the team. The scarce rest time in the domestic competitions, suggest that players and teams performances may decline after several consecutive games, revealing decreases in their physical abilities (Montgomery et al., 2008). Contrary to the distance traveled, it can have a
12
straight influence on the Euroleague performance profiles, which can induce different consequences in the teams’ behavior during the basketball games (Sergio J. Ibáñez et al., 2009).
Conclusions
The findings in the present study indicate that international competition has a direct impact on the performance of national competitions, due to the vast number of quality players that constitute the rosters of the teams participating in the international competitions have, from that arise different performance profiles. The results show that in the international league, external factors such as distance traveled, time played and players' age caused quite possibly the division by the presented clusters, since the match break variable was not shown to be a conditioning factor in this competition. In domestic competitions, distance traveled, match break and time played were the variables with a great discriminatory influence. This information can help in the development and optimization of performance profiles considering the variability of the sample, in order to facilitate the detailed planning for specific groups of players, contemplating the performance on the training session and on the game. It is expected that, these results can help the general managers and coaches understand the importance of match-break, distances traveled, players' age and time played, as for a better understanding the game patterns in different competitions. In addition, improving the effectiveness of recruiting players in relation to age and playing position, meeting the needs of the teams. It can also help development and optimization of performance profiles to facilitate detailed planning for specific groups of players, including performance in the training session and in the game.
13
Bibliography
Blomqvist, M., Luhtanen, P., & Laakso, L. (2000). Expert‐Novice Differences in Game Performance and Game Understanding of Youth Badminton Players. European Journal of
Physical Education, 5(2), 208-219.
Borresen, J., & Lambert, M. I. (2008). Quantifying training load: a comparison of subjective and objective methods. International journal of sports physiology and performance, 3(1), 16-30.
Calleja-González, J., Terrados, N., Mielgo-Ayuso, J., Delextrat, A., Jukic, I., Vaquera, A., ... & Ostojic, S. M. (2016). Evidence-based post-exercise recovery strategies in basketball. The Physician and sportsmedicine, 44(1), 74-78.
Carling, C., Gregson, W., McCall, A., Moreira, A., Wong, D. P., & Bradley, P. S. (2015). Match running performance during fixture congestion in elite soccer: research issues and future directions. Sports Medicine, 45(5), 605-613.
Casals, M., & Martinez, A. J. (2013). Modelling player performance in basketball through mixed models. International Journal of Performance Analysis in Sport, 13(1), 64-82. Dellal, A., Lago-Peñas, C., Rey, E., Chamari, K., & Orhant, E. (2013). The effects of a congested fixture period on physical performance, technical activity and injury rate during matches in a professional soccer team. Br J Sports Med, bjsports-2012.
Djaoui, L., Wong, D. P., Pialoux, V., Hautier, C., Da Silva, C. D., Chamari, K., & Dellal, A. (2014). Physical activity during a prolonged congested period in a top-class European football team. Asian journal of sports medicine, 5(1), 47.
Drinkwater, E. J., Pyne, D. B., & McKenna, M. J. (2008). Design and interpretation of anthropometric and fitness testing of basketball players. Sports medicine, 38(7), 565-578. Duarte A, Davids K, Chow J, Passos P, Raab M. The development of decision making skill in sport: An ecological dynamics perspective. In: Duarte A, Hubert R, editors. Perspectives on Cognition and Action in Sport. United States of America: Nova Science Publishers, Inc., Suffolk; 2009. p. 157–69.
Dupont, G., Nedelec, M., McCall, A., McCormack, D., Berthoin, S., & Wisløff, U. (2010). Effect of 2 soccer matches in a week on physical performance and injury rate. The American
journal of sports medicine, 38(9), 1752-1758.
Ericsson, K. A. (2006). The influence of experience and deliberate practice on the development of superior expert performance. The Cambridge handbook of expertise and
expert performance, 38, 685-705.
Folgado, H., Duarte, R., Marques, P., & Sampaio, J. (2015). The effects of congested fixtures period on tactical and physical performance in elite football. Journal of sports
sciences, 33(12), 1238-1247.
Gamble, P. (2006). Periodization of Training for Team Sports Athletes. Strength & Conditioning Journal, 28(5), 56-66.
Gonzalez, A. M., Hoffman, J. R., Rogowski, J. P., Burgos, W., Manalo, E., Weise, K., ... & Stout, J. R. (2013). Performance changes in NBA basketball players vary in starters vs.
14
nonstarters over a competitive season. The Journal of Strength & Conditioning Research, 27(3), 611-615.
Gómez, M. A., Ibáñez, S. J., Parejo, I., & Furley, P. (2017). THE USE OF CLASSIFICATION AND REGRESSION TREE WHEN CLASSIFYING WINNING AND LOSING BASKETBALL TEAMS. Kinesiology, 49(1).
Gómez, M. A., Lorenzo, A., Ibañez, S. J., & Sampaio, J. (2013). Ball possession effectiveness in men's and women's elite basketball according to situational variables in different game periods. Journal of sports sciences, 31(14), 1578-1587.
Ibáñez, S. J., García, J., Feu, S., Lorenzo, A., & Sampaio, J. (2009). Effects of consecutive basketball games on the game-related statistics that discriminate winner and losing teams. Journal of sports science & medicine, 8(3), 458.
Kerlinger, F. N. F. N., & Pedhazur, E. J. (1973). Multiple regression in behavioral
research (No. 04; HA31. 3, K4.).
Lago-Peñas, C., Rey, E., Lago-Ballesteros, J., Casáis, L., & Domínguez, E. (2011). The influence of a congested calendar on physical performance in elite soccer. The Journal of
Strength & Conditioning Research, 25(8), 2111-2117.
Mateus, N., Gonçalves, B., Abade, E., Liu, H., Torres-Ronda, L., Leite, N., & Sampaio, J. (2015). Game-to-game variability of technical and physical performance in NBA players. International Journal of Performance Analysis in Sport, 15(3), 764-776.
McLean, B. D., Coutts, A. J., Kelly, V., McGuigan, M. R., & Cormack, S. J. (2010). Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players. International journal of
sports physiology and performance, 5(3), 367-383.
Montgomery, P. G., Pyne, D. B., Hopkins, W. G., Dorman, J. C., Cook, K., & Minahan, C. L. (2008). The effect of recovery strategies on physical performance and cumulative fatigue in competitive basketball. Journal of sports sciences, 26(11), 1135-1145.
Nedelec M, McCall A, Carling C, Legall F, Berthoin S, Dupont G. The influence of soccer playing actions on the recovery kinetics after a soccer match. J Strength Cond Res 2014; 28(6): 1517-23.
Norusis, M. J. (1994). SPSS. SPSS Incorporated.
Nutting, A. W. (2009). Travel costs in the NBA production function. Journal of Sports Economics.
Nutting, A. W., & Price, J. (2015). Time Zones, Game Start Times, and Team Performance Evidence From the NBA. Journal of Sports Economics, 1527002515588136.
Pinder RA, Davids K, Renshaw I, Araujo D. Representative Learning Design and Functionality of Research and Practice in Sport. J Sport Exercise Psy. 2011;33(1):146–55. Rampinini, E., Coutts, A. J., Castagna, C., Sassi, R., & Impellizzeri, F. M. (2007). Variation in top level soccer match performance. International journal of sports medicine, 28(12), 1018-1024.
15
Sampaio, J., Drinkwater, E. J., & Leite, N. M. (2010). Effects of season period, team quality, and playing time on basketball players' game-related statistics. European Journal of Sport Science, 10(2), 141-149.
Sampaio, J., Ibañez Godoy, S. J., Gómez Ruano, M. Á., Lorenzo Calvo, A., & Ortega Toro, E. (2008). Game location influences basketball players performance across playing positions. International Journal of Sport Psychology, 39(3), 43-50.
Sampaio, J., & Janeira, M. (2005). A vantagem em casa nos jogos desportivos colectivos: revisão da literatura centrada no Basquetebol e no modelo de Courneya e Carron. revista portuguesa de ciências do desporto, 5(2), 235-246.
Sampaio, J., Janeira, M., Ibáñez, S., & Lorenzo, A. (2006). Discriminant analysis of game-related statistics between basketball guards, forwards and centres in three professional leagues. European journal of sport science, 6(3), 173-178.
Sampaio, J., McGarry, T., Calleja-González, J., Sáiz, S. J., i del Alcázar, X. S., & Balciunas, M. (2015). Exploring game performance in the national basketball association using player tracking data. PloS one, 10(7), e0132894.
.
Steenland, K., & Deddens, J. A. (1997). Effect of travel and rest on performance of professional basketball players. Sleep, 20(5), 366-369.
Swann, C., Keegan, R. J., Piggott, D., & Crust, L. (2012). A systematic review of the experience, occurrence, and controllability of flow states in elite sport. Psychology of Sport
and Exercise, 13(6), 807-819.
Tabachnick, B. G., & Fidell, L. S. (2007). Experimental designs using ANOVA. Thomson/Brooks/Cole.
Tanaka, H., & Seals, D. R. (2008). Endurance exercise performance in Masters athletes: age‐associated changes and underlying physiological mechanisms. The Journal of
physiology, 586(1), 55-63.
Thomson, K., Watt, A. P., & Liukkonen, J. (2009). Differences in ball sports athletes speed discrimination skills before and after exercise induced fatigue. Journal of sports science &
medicine, 8(2), 259.
Wallace, L. K., Slattery, K. M., & Coutts, A. J. (2009). The ecological validity and application of the session-RPE method for quantifying training loads in swimming. The
Journal of Strength & Conditioning Research, 23(1), 33-38.
Williams, A. M., & Ford, P. R. (2008). Expertise and expert performance in sport. International Review of Sport and Exercise Psychology, 1(1), 4-18.
