Bishop, D. C. and Wright, C. University of Lincoln. Abstract

Transcription

1 A time-motion analysis of professional basketball to determine the relationship between three activity profiles: high, medium and low intensity and the length of the time spent on court. Bishop, D. C. and Wright, C. University of Lincoln. Abstract The aim of this study was to determine an exercise to rest profile for basketball, identifying if a relationship existed between total time spent on court and the intensity levels of the players. Five BBL matches (n =6) were filmed using footage which was captured and observed using the Noldus Observer Pro system. The total time and duration of high, medium and low intensity activity was configured with average time bouts of 1.5 s (± 1 ) for high intensity, 3.4 s (± 0.3) for medium intensity and 4.4 s (± 0.4) for low intensity exercise. Results indicated an exercise to rest ratio of 1:4:5 between high, medium and low exercise. No significant relationship was evident between the activity profiles high, medium and low intensity exercise and the total time spent on court with correlation values < 0.17 (p<0.05). The results provide evidence which refutes previous findings highlighting the link between medium and low intensity activities and their role in energy provision through oxidative metabolism as opposed to anaerobic energy pathways. These findings have a number of implications for conditioning and tactical considerations of basketball. 1. Introduction The process of bridging the gap between research and practice, so that scientific knowledge about team sports can be discussed and put into practice has been an ongoing problem (Reilly 1994). Understanding the demands of competitive sport is essential when designing the conditioning elements of training programmes, estimating energy requirements and reducing injuries (O Donoghue and Parker, 2002). Thus, it is important to analyse performance during competition using appropriate performance indicators. Heart rate (Ali and Farrally, 1991., Cappranic 2001) and lactate accumulation (Bangsbo, 1994a) have proved to be too intrusive to provide an indicator of physiological strain incurred during live match play. Performance analysis has been integrated to provide objective assessment of live performance and its physiological requirements in a non-intrusive manner (Lyons, 2003). Movement and exercise-rate studies of sports such as tennis, soccer and rugby have revealed much about the time which athletes spend moving at various speeds (More, 2002). 130

2 Notational and match analysis, has attempted to create a valid and reliable record of performance of soccer, by comparing the activity profiles of different positional roles in terms of the distribution of match time among a variety of activities (Withers et al., 1982.; Bangsbo et al., 1991 and O Donoghue et al., 2001). Research considering the physiological demands of soccer has commonly grouped high intensity activities and categorised them as work, with all other activities being classified as rest. Despite the use of exercise to rest ratios amongst soccer, investigative research has failed to quantify basketball in such a manner. By following the example set in soccer, specific information of this sort should provide further insight to the energy requirements and demands of the sport, which need to be carefully considered when developing training or testing procedures (More, 2002). The aim of the investigation is to determine the exercise to rest ratio for basketball and to identify if there is a relationship between three activity profiles: high, medium and low intensity and the length of the time spent on court. If it is possible to identify whether the length of time spent on court influences the type of activity it might be possible to determine a maximum court length time. This information could then be used to maximise the amount of high intensity expenditure during the game by manipulating variables within the coach s control, such as tactical changes or substitutions. This investigation should provide further insight into how these activity profiles are influenced by time spent on court during tight games, and how adaptations in conditioning and training can be made to stimulate the demands that the players are exposed too. 2. Method Participants Ethical approval and informed consent was granted to film five competitive basketball games of a top flight professional British Basketball League (BBL) team. Only the starting six players would be observed throughout this study as these players are likely to play the majority of the time during the game, while the remaining players are unlikely to make a significant contribution to the game. The six players observed and analysed were not informed so their on court behaviour and activity would not be altered. Game Analysis Five consecutive home basketball games were video recorded using a Sony DRV900E Digital Camera from a vantage point within the sports arena. The Nordulus Observer pro software was then used to analyse the 5 recorded games for all six players using the following code. High intensity activity was given the key code 1. This key would be entered when an activity was observed which was perceived to be exhibiting maximal effort and movements at a high rate of speed including sprints, defensive slides, shooting, jumping, physical pushing and shoving in the post, under the basket. 131

3 Medium intensity was given the key code 2. These activities were perceived movements which demonstrated sub-maximal effort or movements at rates below maximal sprinting speed, movements completed without exhaustion with no particular urgency, including jogging to maintain and recover position. Low intensity movements were given the key code 3. These activities included little if any body movement including walking and standing. A fourth code was also implemented. The key code 4 was given to any stoppages in live play including substitutions, time outs and moments when the ball went out of the playing area. The Noldus intra-observer reliability and typical error of measurement was used to assess the reliability of the data collated. The Noldus intra-observer reliability programme was run to calculate the reliability of a randomly selected piece of video footage coded twice to assess intra-reliability. The tolerance level within which events are scored as matches was two seconds. The results would be provided as a percentage. A second reliability study was also performed but using the method of typical error of measurement (TEM) on both sets of reliability data, using the formula (Gore 2000): TEM = ( (d 2 / n) % TEM = (TEM / [M1 + M2] / 2 ) * 100 The data will be presented as a percentage of the total time spent on court, as well as the mean duration of each discrete activity during the game. By representing the data in a number of varied forms some comparisons can be drawn between existing research in this area. The limited time-motion analysis research has failed to reach a consensus on the most appropriate way to formulate results. Data analysis will initially be investigative to identify the physiological demands of basketball in the form of an exercise to rest ratio and time bouts of the exercise components. A Pearson s Correlation will be used to determine if there is any correlation evident between this investigation s independent variables or predictor variables (high, medium and low intensity activity) and the dependent variable total time spent on court. 3. Results The Noldus Observe Pro intra-observer reliability programme indicated that the reliability study was good, highlighted by a 90.5% agreement between the sequence and durations of the observed activity profile s high, medium and low intensity. A second reliability study was performed implementing TEM as outlined by Gore (2000). All three activity profiles exhibited good individual reliability as TEM was rated ( 5% TEM (Duthie et al. 2003), refer to Table

4 Table 1. The reliability of activity profiles using TEM and intra-observer reliability. Activity Profile TEM High Intensity 4.5%* Medium Intensity 0.3%* Low Intensity 0.6%* Intra-observer 90.5% TEM: Typical Error Measurement, Intra-observer reliability using Noldus Observe Pro. * Considered good reliability for time-motion analysis studies (Duthie et al. 2003), Figure 1 highlights that the mean duration of each discrete bout of activity is fairly consistent for all the activity profiles over the five competitive games played. The mean duration of high intensity activity was 1.5 (± 1) seconds. Over the five games, this ranged from seconds. The mean of medium intensity was 3.4 (± 0.3) seconds with a range of seconds and a mean for low intensity of 4.4 (± 0.4) seconds ranging from seconds. 6 5 High Medium Low Duration (sec) Game Figure 1. The teams average duration of each individual bout of high, medium and low exercise activity recorded in seconds. 133

5 Table 2. Percentage of total time spent performing each activity profile for all six players over the five games. Percentage of Time High Intensity Medium Intensity Low Intensity P P P P P P Av, SD 6.1± ± ±3.7 The data for Table 2 can also be represented as a ratio of the time spent between each activity profile, an activity ratio for high: medium: low activity per quarter would be 1:4:5. This suggests that to every 1 second of high intensity activity performed during the game 4 seconds of medium and 5 seconds of low intensity activity will be completed. Table 3. The correlation between total time spent performing each activity profile and the total time spent on court for all six players over the five games. High Medium Low Game Score Intensity Intensity Intensity Game W 102 v 74 Game W 82 v 70 Game W 105 v 83 Game W 79 v 77 Game W 103 v 83 Combined correlation Using Pearson s Correlation no real correlation was evident between the variable total time spent on court and the intensity profiles high, medium and low intensity. However there was evidence that a correlation could exist between the variable total time spent on court and the intensity profiles high, medium and low intensity in the individual games 1, 4 and

6 Under further analysis accumulating each players total time for the five games and correlating this to the total time spent working in each intensity profile high correlation values are evident. Medium and low intensity activity showed a high correlation R= 0.94, P<0.05, and the high intensity activity displayed a correlation of R= 0.75, P<0.05. The data must be interpreted with caution as the statistics lacked power with only 6 subjects inputted into the correlation test, due to the total times been accumulated over the five games for analysis. 4. Discussion The data of primary interest to this investigation s research question was to firstly identify a physiological intensity profile that can be used to help categorise performance in view of the percentage time the average player will spend performing high, medium and low intensity exercise. Secondly to investigate whether any correlations exist between the length of time a player spends on court and the amount of high, medium or low intensity exercise they perform, such information would be invaluable to the coach. The lack of comparative research considering the demands of elite basketball play (MacLaren 1996) makes it difficult to assess the value of this investigation s findings. Presented in terms of an exercise to rest ratio some comparisons can be drawn with football time motion analysis studies. The results highlighted an activity ratio between high: medium: low of 1: 4: 5. Exercise to rest ratios amongst football of 1:8 and 1:12 are typical in football research (Bangsbo, 1994; O Donoghue and Parker, 2002). If the ratios from this investigation are recalculated in a similar manner to previous football research (Bangsbo, 1994a; O Donoghue and Parker, 2002), all high intensity activity is considered as exercise and all other activities are defined as rest, then this investigation would exhibit a ratio of 1:9 similar to football activity ratios. The ratio suggested by this investigation shows that similarities exist between football and basketball, the inclusion of a medium intensity category allows the performance to be categorised in more depth and ensure a higher level of specificity when transferring from research to practice. McInnes et al. (1995) indicated that only 15% of live match play was spent performing high intensity activity and 65% of the time was engaged in activities of an intensity greater than walking (as defined in this investigation as medium intensity activity). These values reported by McInnes et al. (1995) were much higher than the ones noted in this investigation, as high intensity activity only accounted for 6.1% of the time, while medium intensity contributed 41.3%. There are a number of reasons that might have contributed to the large difference in the results gathered between these investigations. It should be considered that there are distinct weaknesses of McInnes et al. (1995) methodology. Unfortunately the study only monitored five players during one live competitive match while the remaining players were monitored during practice games. This makes McInnes et al. (1995) results very much representative of the one game observed, thus any assumptions made are very much limited as a result of few observations being utilised. Another valid reason for the difference in studies might be as a result of the 135

7 variation in the demands of elite Australian National Basketball play and the British Basketball League. The statistical analysis used to try and establish a relationship between the activity profiles (independent variables) and the total time spent on court (dependent variable), indicated no real relationships were evident between high, medium or low intensity exercise and total time spent on court over the five games. The findings from this investigation might provide evidence to refute previous findings that aerobic capacity is not a significant contributor to basketball performance (Fox, 1984; Hoffman et al., 1996). Previous literature has classified basketball as deriving 85% of its energy expenditure from the phosphagen stores (ATP and PC) and 15% of its energy from anaerobic glycogenolysis (Fox, 1984). Hoffman et al s. (1996) investigation indicated that anaerobic components have been shown to be high positive predictors of playing time, whereas aerobic capacity is suggested to have a negative relationship with playing time. Supra-maximal exercise represents work intensity higher than that required to elicit VO 2max (Crisafulli et al., 2002). This exercise intensity is frequently achieved during basketball (Bangsbo, 1996). In this kind of exercise anaerobic glycolytic and non-glycolytic energy release is very important in restoring the ATP used during muscular contraction (Bangsbo, 1996). During supra maximal exercise anaerobic alactacid energy sources are depleted after about 5-7 seconds (Hirvonen et al., 1987). If work continues after this period, the role of glycolysis becomes increasingly more important in energy production, as it can provide energy for up to 60 seconds before high levels of lactic acid in the muscles signal fatigue (Bergstrom et al., 1971). These systems would only liberate enough energy to fill the total duration of high intensity activity of one player per quarter, as high intensity activity, noted in this investigation, peaked at 40 seconds per quarter, but is interspersed by a high proportion of less vigorous work, even rest, which should allow phosphagen replenishment and oxidation of lactate. Degradation of creatine phosphate (CP) and to a lesser extent stored ATP, should provide a considerable amount of the energy for the short individual bouts of high intensity exercise (Williams, 1996) as their duration ranges from seconds, in this study. The CP will be rapidly re-synthesized during the long periods of low activity 52.5% and stoppage in play. During prolonged periods, if medium intensity is performed without frequent interruptions, there is likely to be an increased dependency on glycolysis, resulting in high levels of lactate if the intensity is above that of steady state. Such periods might also result in high levels of oxygen consumption and would be associated with high heart rate response (Williams, 1996). This provides further support to the complexity of basketball and particularly how players and coaches implement conditioning and training. The duration of individual bouts of exercise are similar to previous research McLean (1990) found that sprints lasted between 1 and 4 seconds, McInnes (1995) found that 51% of sprints lasted longer that 1.5 seconds, 27% lasted longer that 2 seconds, 12% lasted longer that 3 seconds, 5% lasted longer than 4 seconds. (McLean, 1990; McInnes, 1995). This investigation highlighted the average high intensity bout of exercise was 1.5 seconds, the 136

8 small playing areas significantly contribute to the reduced duration of maximum sprints, placing a large emphasis on constant acceleration, deceleration and changing of direction. 5. Conclusions Despite previous indicators of the importance of high intensity activity (Fox, 1984; Hoffman, et al., 1996) the findings suggested by this investigation provide support for conditioning and training to replicate the exercise to rest ratios as prescribed. The notion that the more time spent on court would have a negative impact upon the level of high intensity exercise performed was unfounded; however caution must be applied when prescribing player rotations. It is important to consider that the style of play might influence the activity profiles, and if the team sticks to a certain style of play which is suggested to be conducive with winning or scoring this might influence the intensity levels. With some reservation it is suggested that this investigation s findings might provide evidence which refutes previous findings that aerobic capacity is not a significant contributor to basketball performance. The large contribution made by medium and low intensity and their possible role in providing energy for exercise through oxidative metabolism rather than anaerobic pathways. These proposed findings have a number of implications for conditioning and tactical considerations of basketball. It appears that basketball players require aerobic conditioning to facilitate the recovery between high intensity bouts where energy is derived from predominantly aerobic sources. It is important that basketball specific skills and techniques including shooting, jumping, free throws, dribbling, defensive and offensive patterns are replicated during the prolonged periods of medium intensity. Activities involving frequent acceleration and deceleration, structured in an interval format mimicking the work and rest durations presented in this investigation, are directly applicable to Basketball and should form the basis of training drills. 6. References Ali, A. and Rarrally, M. (1991). A computer-video aided time-motion analysis technique for match analysis. Journal of Sports Medicine and Physical Fitness, 31, Bangsbo, J. (1994). Energy demands in competitive soccer. Journal of Sport Science, 12, Bangsbo, J. (1996). Physiological factors associated with efficiency in high intensity exercise. Sports Medicine. 22,

9 Bangsbo, J., Norregaard, L. and Thorsoe, F. (1991). Activity profiles of competition soccer. Canadian Journal of Sports Science, 16, Bergstrom, J. Harries, R.C, Hultman, E. and Nordesjo, L. (1971). Energy rich phosphagens in dynamic and static work. In Muscle Metabolism during Exercise (edited by B. Pernow & B. Saltin), London: Plenum Press. Cappranic, L. Tessitore, A. and Guidetti, L. Heart rate and match analysis in pre-pubescent soccer players. Journal of Sports Science, 19, Crisafulli, A. Melis, F. and Tocco, F. (2002). External mechanical work versus oxidation energy consumption ratio during basketball field test. Journal of Sports Medicine and Physical Fitness. 42, Duthie, G., Pyne, D. and Hooper, S. (2003). The reliability of video based time-motion analysis. Journal of Human Movement Studies, 44, Fox, E. (1984). Sports Physiology 2 nd edition. Philadelphia: W.B. Saunders. Gore, C.H. (2000). Physiological tests for elite athletes, Champaign, Illinois: Human Kinetics. Hirvonen, J., Rehunen, S. and Rusko, H. (1987). Breakingdown of high energy phosphate compounds and lactate accumulation during short supramaximal exercise. European Journal of Applied Physiology, 56, Hoffman, J.R., Marsh, C.M., and Kramer, W. J. (1996). Relationship between athletic performance test playing time in elite college basketball players, Journal of Strength and Conditioning Research, 10, Lyons, K. (2003). Performance analysis for coaches & Game analysis. Sports Coach, July, MacLaren, D. (1990). Court games: Volleyball and basketball. In Physiology of Sports (edited by T. Reilly, P. Secher, P. Snell, and C. Williams). London: E. and F.N. Spon. McInnes, S.E. Carlson, J.S. and Mckenna, M. (1995). The physiological load imposed on basketball players during competition, Journal of sports Sciences, 13, More. K. (2002). Observation and analysis. Sports Coach UK, July, O Donoghue, P.G., Boyd, M., Lawlor, J. and Bleakely, E.W. (2001). Time-Motion analysis of elite semi-professional and amateur soccer competition. Journal of Human Movement Studies, 41,

10 O Donoghue, P. and Parker, D. (2002). Time-motion analysis of FA Premier League soccer competition. In (eds. M. Hughes and F. Tavares) World Congress of Notational Analysis of Sport IV, Porto, Portugal. Reilly, T. (1996). Motion analysis and physiological demands of soccer. In Science and Soccer (edited by T. Reilly), pp , London: E. & F.N. Spon. Withers, T.R., Roberts, G.D. and Davies, G.J. (1977). The maximum aerobic power, anaerobic power and body composition of south Australian male representatives in athletics, basketball, field hockey and soccer player, Journal of sports medicine, 17, Williams, C. (1996). Metabolic aspects of exercise of exercise. In Physiology of Sports 3 rd edition. (Edited by T. Reilly, N. Secher, P. Snell, and C. Williams), pp 3-11 London: E. and F.N. Spon. 139