Cardiff School of Sport DISSERTATION ASSESSMENT PROFORMA: Empirical 1
Student name: Daniel St Paul Student ID: ST10001361
Programme: SES
Dissertation title: The Performance Analysis of Race Distribution for Elite Male 400m Hurdlers. Supervisor: Darrell Cobner
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Introduction and literature review
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1 This form should be used for both quantitative and qualitative dissertations. The descriptors associated with both quantitative and qualitative dissertations should be referred to by both students and markers. 2 There is scope within qualitative dissertations for the RESULTS and DISCUSSION sections to be presented as a combined section followed by an appropriate CONCLUSION. The mark distribution and criteria across these two sections should be aggregated in those circumstances. CARDIFF METROPOLITAN UNIVERSITY Prifysgol Fetropolitan Caerdydd
CARDIFF SCHOOL OF SPORT
DEGREE OF BACHELOR OF SCIENCE (HONOURS)
SPORT AND EXERCISE SCIENCE
THE PERFORMANCE ANALYSIS OF RACE DISTRIBUTION FOR ELITE MALE 400M HURDLERS
Dissertation submitted under the discipline of PERFORMANCE ANALYSIS
DANIEL ST PAUL
ST 10001361
DANIEL ST PAUL
ST10001361
CARDIFF SCHOOL OF SPORT
CARDIFF METROPOLITAIN UNIVERSITY
THE PERFORMANCE ANALYSIS OF RACE DISTRIBUTION FOR ELITE MALE 400M HURDLERS
Cardiff Metropolitan University
Prifysgol Fetropolitan Caerdydd
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Word count: 10563
Date: 18/03/13
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Table of Contents
Page No:
Acknowledgments i
Abstract ii
Chapter 1. Introduction
1.1 Introduction to Study 1 1.2 Direction of Study 2 1.3 Limitations 2 1.4 Delimitations 2 1.5 Definition of Terms 3
Chapter 2. Literature review
2.1 Performance Analysis 4 2.2 The 400m Hurdles 5 2.3 The Coaching Process 9 2.4 Performance Indicators 10 2.5 Performance Analysis in the 400m Hurdles 12
Chapter 3. Method
3.1 Research Design 17 3.2 Data Collection 18 3.3 Coding Template 19 3.4 Procedure 21 3.5 Data Processing 22 3.6 Models 22 3.7 Reliability 22
Chapter 4. Results Page No:
4.1 Results 24 4.2 Touchdown Splits 24 4.3 200m Splits 25 4.3.1 PI1, PI2, PI3 27 4.4 Stride Pattern 28 4.5 Flight Time 30 4.6 Race Distribution 32 4.6.1 Models 32 4.6.2 Visualisation of Race Distribution 34
Chapter 5. Discussion
5.1 Discussion 37 5.2 Delhi 37 5.3 Touchdown Times 38 5.3.1 200m Splits 40 5.4 Stride Pattern 40 5.5 Flight Time 41 5.6 Race Distribution 42 5.6.1 Subject A 43 5.6.2 Subject B 43 5.6.3 Subject C 43 5.6.4 Subject D 44 5.6.5 Application 44 5.7 Limitations 45
Chapter 6. Conclusion Page No:
6.1 Conclusion of Study 46 6.2 Future Research 47
References 48
Appendices
Appendix A A-1 Appendix B B-1 Appendix C C-1 Appendix D D-1
List of Tables
Table Title Page No:
1. A matrix presenting performance indicators 11 identified by the relevant literature to date.
2. Performance indicators and operational definitions. 17
3. Mean absolute difference reliability test results on 23 Felix Sanchez’s performance at the 2012 London
Olympic Games.
4. Matrix indicating levels of significance between 24 hurdle segments.
5. PI values for this study, excluding the Delhi final. 27
6. PI values for this study, including the Delhi final. 37
7. Matrix indicating levels of significance between 39 hurdle segments (excluding Delhi).
One-Way ANOVA of touchdown splits from all 8. A-1 athletes (excluding Delhi).
9. Scheffe Post Hoc Test of touchdown splits from all A-2 athletes (excluding Delhi).
10. One-Way ANOVA of athletes’ 200m splits and PI1, B-1 PI2 and PI3.
11. Scheffe Post Hoc Test of athletes’ 200m splits and B-2 PI1, PI2 and PI3.
12. Stride pattern data of all the subjects. C-1
13. One-Way ANOVA of flight times (‘-1’ to ‘+1’) D-1
14. Pearson’s Correlation of flight times (‘-1’ to ‘+1’) D-1
Table Title Page No: 15. One-Way ANOVA of extended flight times (‘-2’ to D-2 ‘+2’).
16. Pearson’s Correlation of extended flight times (‘-2’ D-2 to ‘+2’).
List of Figures
Figure Title Page No:
1. Standard set up of the 400m Hurdles event. 8
2. A simple schematic diagram representing the 9 coaching process.
3. Some performance indicators that contribute to 10 success or improved performance in hurdle events, adapted from Hughes and Bartlett (2002).
4. Contact points extended to two strides either side 14 of the hurdle.
5. Coding window developed to quantify the 19 performance indicators chosen.
6. Diagram illustrating what each code button 20 represented in terms of instances in a 400m hurdle
race.
7. Breakdown of the coding template. 20
8. Boxplot graph representing the range of touchdown 25 splits obtained by elite athletes within each hurdle
section.
9. Boxplot graph representing the range of 1st 200m 25 times at each 400m hurdle final.
10. Boxplot graph representing the range of 2nd 200m 26 times at each 400m hurdle final.
11. Boxplot graph representing the range of 200m 26 differentials from athletes in each final.
Figure Title Page No:
12. Comparison between performance indicator values 28 obtained by Greene et al. (2011) and this study.
13. Frequency distribution of the number of strides for 29 each race segment.
14. Frequency distribution of the number of strides for 29 each race segment (Ditroilo and Marini, 2001).
15. Scatter plot showing a weak positive relationship 31 between flight time (‘-1’ to ‘+1’) and performance
time.
16. Scatter plot showing a slightly negative relationship 31 between extended flight time (‘-2’ to ‘+2’) and
performance time.
17. Differences at each hurdle segment between the 32 proposed model, and the two other models
obtained from elite athlete coaches.
18. Subject A’s three races plotted against Model 1. 33
19. Subject A’s three races plotted against Model 2. 33
20. Subject A’s race distribution 34
21. Subject B’s race distribution. 35
22. Subject C’s race distribution. 35
23. Subject D’s race distribution. 36
24. Boxplot graph representing the range of touchdown 39
splits obtained by elite athletes within each hurdle
segment, excluding race data from Delhi.
Acknowledgements
Firstly, I would like to thank Darrell Cobner for his continued support throughout the process of completing this dissertation. Secondly, I would like to thank my family and friends for their continued encouragement.
i Abstract
The finals of the male 400m hurdles events, at the 2012 London Olympics, 2012 London Diamond League, 2011 World Championships in Daegu and 2010 Commonwealth Games in Delhi, were observed for analysis. A computerised notation system was used to examine performance indicators such as touchdown times, 200m splits, stride patterns and flight times, in order to understand the race distribution of present day, elite male 400m hurdlers.
This study revealed that present day elite male 400m hurdlers tend to run a considerably quicker 1st 200m compared to their 2nd 200m. This resulted in greater 200m differentials, which disagreed with most previous literature that stated lower 200m differentials correlate to better overall performance. Furthermore, flight times were found to have a weak positive correlation to overall performance times, whereas, extended flight times were found to have a weak negative correlation. This study suggested that it was due to the importance of stride pattern and rhythm that these results were found.
Race distribution was explored through a predictive model developed by this study. Dense, naturalistic and visual representations of athletes race distributions were conveyed in a form intuitive for coaches and athletes. It was found that athletes race distributions each had a certain characteristic, which were found to be very important for coaches to understand. It is suggested by this study that coaches continue to develop and use this form of analysis as a tool for coaching, enabling more detailed and thorough race strategies to be produced.
For future research, it would be more valuable to analyse a greater number of elite male subjects with higher quality footage in order to create a wider and more accurate range of results of which more solid results could be found and compared.
ii
CHAPTER 1 INTRODUCTION
1.1 Introduction to study
The 400m hurdle race is technically one of the most demanding track events and is a challenge for both the coach and athlete (Lindeman, 1995; Boyd, 2011). It has been identified by Boyd (2011) that athletes wanting to compete in the event, need to be strong 400m flat runners, as well as smooth and confident hurdlers that can hurdle off alternate legs. Lindeman (1995) also emphasised the need for a unique awareness of stride pattern, which requires special concentration and rhythm throughout the race. Literature on the 400m hurdles commonly refers to either technical or tactical aspects of the race.
In video based analysis, when feedback is provided in an appropriate manner, motor skill acquisition improves significantly (Liebermann et al. 2002, and Hughes and Franks, 2008). Recently, advances in information technology have made it possible to augment and improve the feedback athletes receive during training and competition (Liebermann et al. 2002). The advantages of this development entail higher levels of efficiency and specificity. Analysis packages can be tailored to highlight specific areas of performance and can also efficiently present feedback shortly after competition or training (Hughes and Franks, 1997).
This investigation will explore the tactical aspects of the 400m hurdles race and the performance indicators associated with race distribution. The majority of previous researchers have analysed athletes technically, through physiology and biomechanics. Literature on the tactical aspect of the 400m hurdles is not widely found in the area of performance analysis, especially when regarding race distribution. However, these studies frequently conclude that more research needs to take place. In addition to this, different ways of visual representation were explored to determine the best way of producing data that coaches and athletes could understand and utilize. Hughes and Franks (1997) identified the development of a database as crucial, since it is sometimes possible, if large enough, to formulate predictive models as an aid to the analysis of different sports, subsequently enhancing future training and performance.
1 1.2 Direction of Study
The purpose of this study was to analyse the race distribution of present day elite male 400m hurdle athletes. This study developed the methods Cobner (2012) and Greene et al. (2011) applied to their investigations and further explored the area of predictive models. The performance indicators analysed, acted as a comparison tool to measure and gain a better understanding of race distribution in the modern day male 400m hurdler.
1.3 Limitations
The actual footage obtained for this investigation was televised, and as many researchers have found out, reliable and accurate data sets are subject to the positioning and angles of the camera. In some cases an athlete would be cut from the picture altogether, in which case, the coding for that instance would be unusable.
Another limitation, although not certain, was raised from using the official clock presented by the television station for coding. Due to the footage not being official footage, some key frames may have been cut out during the recording process making the clock jump unevenly between frames. This may have affected the accuracy of the current investigation.
1.4 Delimitations
Due to availability of footage, this investigation only analysed male 400m hurdlers from the finals of the London Olympics (2012), London Diamond League (2012), World Championships, Daegu (2011) and the Commonwealth Games, Delhi (2010). As only elite athletes were investigated, the results of this study would not be applicable to junior or lower level athletes. The findings will also be of no relevance to female athletes.
2 1.5 Definition of Terms
Race Distribution Refers to an athlete’s distribution of effort or race strategy employed during a race.
Touchdown The first contact of the foot after hurdle clearance (‘+1’).
H1 H1 refers to hurdle one, H2 would mean hurdle two, and so on until H10.
Stride Pattern The number of strides taken between hurdles. Including the start to H1 and H10 to the finish. Stride patterns are normally planned before races.
Flight Time The time taken from take-off (‘-1’) to touchdown (‘+1’).
Predictive Model In regards to 400m hurdle performance, a predictive model is used to predict the touchdown times athletes should theoretically be obtaining to perform at a certain level or time.
3
CHAPTER 2 LITERATURE REVIEW
2.1 Performance Analysis
Performance analysis involves the investigation of actual sports performance or training, and can be undertaken as part of an academic investigation or as part of applied activities in coaching, media or judging contexts (Hughes and Franks, 1997). What distinguishes performance analysis from other disciplines is that it is principally concerned with actual sports performance rather than activities undertaken in laboratory settings or data gathered from questionnaires or interviews (Hughes and Franks, 2008).
The practical value of performance analysis is that well-chosen performance indicators help coaches highlight good and bad performances of an individual or a team and facilitate comparative analysis (Hughes and Franks, 2008). Delalija and Babic (2008) suggested that due to the improvement of sports science in recent years, incredible advances in training methods and technology have led to the equality of sprinters’ quality. Hence, the differences between competition results are becoming smaller and are even expressed in thousandths of a second in the 100m sprints (Delalija and Babic, 2008). This has led to the evident occurrence of performance analysis and identification of factors that cause such a small difference, yet, become the main issue in current research studies.
In any sporting situation it is difficult for coaches to notice or remember all the key events that occur within a performance or training session. Analysis based on accurate observation is a key tool for any coach or athlete for improving future performance. The introduction of information technology in the sport performance environment has led to the emergence of computerised notation (Liebermann et al. 2002). Computerised notation allows efficient and immediate feedback, indication of areas requiring improvement, evaluation of tactics and technique, and selective searching through a video recording of a performance (Franks et al. 1983).
4 2.2 The 400m Hurdles
The 400m hurdles event consists of 10 hurdles spaced 35 metres apart. There is a 45 metre run in to the first hurdle and a 40 metre run off the final hurdle to the finish line. For men the hurdles are 3’0 (91.4 cm) in height. In a competition such as the Olympics, athletes normally have to navigate their way through one qualifying round and a semi-final before reaching the final, in which eight athletes will compete for a medal.
In a technical report by Hillier (2012), it stated that a successful 400m hurdler is not always that fastest (on the flat) but the athlete that can best maintain rhythm, efficiency and momentum throughout the race. In a recent study by Zouhal et al. (2010), the 400m flat was compared with the 400m hurdles using the accumulation oxygen deficit method (AOD) to determine the relative contribution of aerobic and anaerobic energy systems. It was found that the 400m hurdles showed significantly higher aerobic contributions compared to the 400m flat events. Reports by Duffield et al. (2005) and Gastin (2001) suggested a predicted ‘‘crossover point’’ where the relative dominance of the energy systems change for the 400m flat. As a follow up, Zouhal et al. (2010) compared the crossover points of the 400m hurdles and 400m flat. For the 400m flat, the crossover occurred within 30–35 seconds of the start of exercise and the aerobic energy system was dominant thereafter. During the 400m hurdles, aerobic and anaerobic contribution curves showed a crossover point that occurred before the 15th second evidently supporting the notion that the 400m hurdles has a higher aerobic contribution.
Tactically, the findings of Zouhal et al. (2010) are very significant as they suggest that the 400m hurdles is a more controlled event in which pacing strategies could be used and have more effect on outcome. Zouhal et al. (2010) also mentioned that running velocity is significantly lower in 400m hurdle events than 400m flat events. This can be explained based on the fact that the start of the 400m flat is faster in the first 100m and that the following average speed is always higher (Dakin, 2002). Hillier (2012) and Lindeman
5 (1995) expressed the importance of the start in that the athletes setup their race distribution and rhythm well, in order to be strong over the last 3 hurdles.
Lindeman (1995) suggested that the link between stride pattern and successful race distribution is of vital importance. In covering the first hurdle, most professional athletes should have a predetermined stride pattern resulting in smooth transitions to sprinting between hurdles (Lindeman, 1995). The ideal stride pattern would be a consistent pattern of an odd number of steps between hurdles, which allows the hurdler to take all hurdles on with the same lead leg (Lindeman, 1995). However, this is very rare and hurdlers are normally forced to make a transition to a greater number of strides due to fatigue. Lindeman (1995) indentified three forms of transition, the most common of which is a single alternate, where a left lead-legged hurdler would have to transition from 13 strides to 14 strides requiring him to then hurdle with a right lead leg for the rest of the race. The other option would be to use a dual alternate transition where the hurdler with a preferred left lead leg would take 14 strides and use a right lead leg, then 14 again to get back to the preferred left lead leg, and then finish the race with their left.
Race distribution fundamentally depicts the distribution of effort of an athlete throughout a race. This can effectively be measured through determining touchdown times (Lindeman, 1995). As most studies in the area have found, major discrepancies in the chart of a race can point to errors in judgement of transitions and late-race adjustments, as well as where fatigue sets in (Lindeman, 1995). Dirolio and Marini (2001) produced a study, which used video analysis to accurately determine touchdown times and stride patterns of elite athletes competing at the 2000 Olympic Games. They found that the best performances came from those athletes who had the lowest time differentials between the 1st and 2nd 200m.
When discussing and analysing the 400m hurdles, it is important to take into account tactical and technical factors that influence performance. However, Hillier (2012) suggested that in a coaching perspective, it is also important to understand the type of athlete you are dealing with and train them accordingly
6 so that you can take advantage of their natural strengths. Hillier (2012) proposed 3 main types of 400m hurdlers in which an athlete could either fit completely or be a hybrid of one or more of these:
1) Speed Hurdler – These hurdlers are vey quick on the flat but generally don’t have good natural rhythm. They don’t have very good understanding of race distribution and often go off fast and ‘hold on’ at the end of races, which they get away with due to their flat speed. Speed hurdlers, however, often underachieve at major events where they have to run consistently through several rounds. Kerron Clement is a stereotypical speed hurdler.
2) Strength Hurdlers – These hurdlers tend not to be very quick over short distances and may even train like middle distance runners. However, they are physically very strong and fit, and often run a very even paced race if they get their stride pattern right. Due to their strength, consistency is the key to this class of athlete and can pay off during major competitions where fitness plays a key role. Felix Sanchez is a stereotypical strength hurdler.
3) Rhythm Hurdlers – These hurdlers have superb natural rhythm and spatial awareness. In most cases, the difference between their 400m flat time and 400m hurdles time are only around 2.5 seconds. They are good at ‘sighting’ the hurdles and therefore maintain momentum into and off the hurdles well, which results in less energy being expended over hurdles and a strong finish. Rhythm hurdlers will be able to pace themselves accurately and normally do well in major or one-off competitions. Dai Greene is a stereotypical rhythm hurdle.
7
Fig 1. Standard set up of the 400m Hurdles event.
8 2.3 The Coaching Process
The coach’s role is about enhancing player or athlete performance, a principal means by which this is achieved through feedback (Hughes and Franks, 2008). The feedback a coach receives from performance assessments and results, influence what feedback is then given to the athlete (Lyle, 2002). The primary purpose of performance analysis in a coaching context is to provide information about sports performance that will assist coach and player decision-making (O’Donoghue, 2006). The coaching process can be thought of as an ongoing cycle of performance and practice (Figure 2).
Fig 2. A simple schematic diagram representing the coaching process (Lyle, 2002).
Historically, coaching intervention has been based on subjective observation. However, limitations of memory and observational difficulties prevent the collection of detailed and reliable information needed to secure desired behavioural changes (Hughes and Franks, 2008). Hence, the application of more objective measuring tools, which come in the form of video analysis and computerised notation systems. Thus, successful coaching hinges on the collection and analysis of unbiased, objective data (Hughes and Franks, 2008).
9 Although the coaching process is an ongoing cycle, Hughes and Bartlett (2002) stated the need for comparison, as an individual’s performance profile can become distorted if the correct comparisons are not made. Hughes and Bartlett (2002) suggested that performance data for an individual should either be presented in relation to the opponent’s data, individuals of the same standard, or to their own profiles of previous performances.
2.4 Performance Indicators
Similar to other studies in the area of race analysis, this study identifies performance indicators in which performance can be explored and judged. A performance indicator should be a selection or combination of action variables that aim to define some or all aspects of performance (Hughes and Bartlett, 2002). In addition, performance indicators should clearly relate to successful performance or outcome (Figure 3) (Hughes and Barelett, 2002).
Fig 3. Some performance indicators that contribute to success or improved performance in hurdle events, adapted from Hughes and Bartlett (2002).
10 Ditroilo and Marini (2001) suggested a lack of attention to race distribution and race strategy, due to the modern athletes stronger physiological qualities. They believe they can counterbalance the effects of rhythm, technique and strategy with more emphasis on muscular and physiological aspects. As Yasui et al. (1996) indentified however, understanding key areas within a race and knowing which parameters are most useful for improving actual race performance may be just as important as technically mastering the take-off phase when margins for winning are so small.
Pendergast (1991) analysed the split times, hurdle clearance times, average stride lengths and speed throughout the race of Andre Philips during the 1988 Olympic 400m hurdle final, and was one of the first to do so. Since then, research on the 400m hurdles in the area of performance analysis has mainly focused on analysing the significance of various performance indicators on race performance. Table 1 shows the most common performance indicators used to analyse the 400m hurdles.
Table 1. List of performance indicators identified by the relevant literature to date.
Performance Indicator Literature
Brown (2005), Bruggerman et al. (1999), Dirolio Touchdown/ Split Times and Marini (2001), Yasui et al. (1996). Stride Pattern Dirolio and Marini (2001), Letzelter (2004). Stride Frequency Letzelter (2004). 200m Splits Dirolio and Marini (2001), Greene et al. (2011). Flight Times Quinn (2010). Dakin (2002), Dirolio and Marini (2001), Hillier Speed (2012). Biomechanical Factors Iskra and Coh (2011).
11 2.5 Performance Analysis in the 400m Hurdles
According to O’Donoghue (2008), time-motion analysis of running events should have a clear purpose rather than simply determining and presenting split times. Bruggerman et al. (1999) investigated the 100m at the World Athletics Championships in 1997 in Athens. By investigating reaction times, split times, parameters derived from kinematic analysis and the mean velocity for every 10m section, they were able to provide information on how elite athletes ran the 100m. Brown (2005) compared the strategies of female athletes attempting the 800m and 1500m double with those entering a single event. Split-times were paired with nominal variables of the athlete’s positions during the race, which gave insight into the similarities and differences of the tactics employed by different types of athletes. Letzelter (2004) examined stride rhythm, stride frequency and stride length of female 400m hurdle athletes. It was found that differences in stride frequency were statistically significant when contributing to an athlete’s performance.
Greene et al. (2011) attempted to determine whether or not the race leader influenced elite male 400m hurdle tactics. They proposed three performance indicators to evaluate performance, of which all three related to percentage increases in time between the first and second half of a race. It was concluded that the race leader didn’t influence tactics of the other competitors although it was suggested by the authors that future studies would find it useful to look at a greater number of subjects. Dirolio and Marini (2001) also faced limitations within methodology where all participants being observed were from the same competition, making the study very one-dimensional. They also decided to include heats and semi-final races in their analysis, therefore making the assumption that every athlete had produced their best effort through each round. Yasui et al. (1996) produced a study in which model interval time in the men’s 400m hurdle race was analysed. They calculated and evaluated the equations for the model interval time with a total sample size of 651 athletes. However, unlike more recent research, the large sample size meant the study was subject to external factors as not all of the athletes would have been top class performers or at their physical peak
12 judging by the performance times, which ranged from 48.79 seconds to 59.45 seconds. Hence, despite grouping the participants by performance time, the results would not have been applicable to elite level performers. The literature available proves that looking at race strategy and race distribution could potentially benefit performance although careful consideration of the methodology employed is needed.
Ditroilo and Marini (2001) produced a similar study, which aimed to contribute to understanding through normal race distribution models from the world’s top athletes. It analysed whether these distributions differed in comparison to the ideal model, and identified which parameters were most useful for the study of race performance. Overall race distribution was analysed as opposed to just interval times. They concluded that poor race distribution normally led to poor performance. They also emphasised the importance of the second half of the race, as the best performers had the lowest time differentials between the first and second half of the event. Ditroilo and Marini (2001) essentially questioned the strategies employed by all the 400m hurdle runners in the 2000 Sydney Olympics, where the peak of speed was repeatedly obtained early on in the race. This study aimed to use Ditroilo and Marini’s (2001) work and also act as a comparison in which recent changes in race distribution employed by 400m hurdlers at major events were identified.
A recent study by Quinn (2010) identified that hurdle clearance performance may be a contributing factor to the overall performance. Although the model predicted a small effect on the overall race time and Boyd (2011) identified that only 8% of a race is taken up by hurdle clearance time, for a world-class athlete, an improvement by a few hundredths of a second can be significant (Quinn, 2010). Cobner (2012) produced a blog in which hurdle clearance time was explored in the analysis of race distribution. The traditional split points of which most studies incorporated, were extended to incorporate contact points of the two strides either side of each hurdle (Figure 4). This was thought to not only generate an indication of flight time, but also multiple derivatives of timings around the hurdles to explore (e.g. -2 to +2, -1 to +2 etc.).
13
Fig 4. Contact points extended to two strides either side of the hurdle (Cobner, 2012).
The search for a ‘perfect’ race model of the 400m hurdles has been undertaken by many technical and sports science experts. Very often, the results of these studies using race models, show the athlete not having reached anywhere near the proposed model of performance (Ditroilo and Marini, 2001). Lots of research has questioned this through biomechanical factors (Iskra and Coh, 2011) and even external factors such as wind and altitude (Quinn, 2010). This may also just be down to coaches and athletes neglecting the importance of effective race distribution.
In other cyclical disciplines, race distribution is on the verge of being established as an important factor for successful performance and has been backed through recent literature. Collard (2007) analysed swimming speeds for 6 sections of the 50m-backstroke race in which performance was compared in zones swum underwater and at the surface. By breaking down the races using video analysis, it was revealed that swimmers went faster underwater. Siders (2010) recently produced a study in which relay exchange times were investigated from the 2007 and 2008 USA NCAA Divisions 1 and 2 Swimming and Diving Championships. The findings of this study were limited, however, due to its use of one convenient sample of competitors. The study also assumed that results of competition reflected pre-competition
14 training and racing strategies. Chen et al. (2007) used cluster analysis to identify elite swimmers’ race patterns. Various race components and race results were used to arrange elite swimmers according to similarity in their race patterns. The outputs included a number of key race components in connection with Ian Thorpe’s race results at the 2000 Sydney Olympics. These outputs conveyed race patterns in a form intuitive for coaches.
Corbett (2009) investigated the pacing strategies adopted by elite athletes during the 1-km time trial and the 3- and 4-km individual pursuit at the Cycling World Championships (2006-08). The events examined showed similar pacing profiles, characterised by an initial acceleration to a high speed, followed by a progressive decay in lap times. In both events it was found that a quick first 250m was paramount for good performance, however in the 3- and 4-km individual pursuit, small alterations in terms of an overly quick start appeared to be important, at elite level. The significance of understanding distribution of effort in a race is highlighted through recent literature on arguably the newest sport in the Olympic cycle, BMX racing. Cowell et al. (2011) conducted one of the first studies in race distribution for BMX racing and successfully improved understanding of the sport, revealing total time spent either pedalling, pumping or jumping in a race.
As the literature highlights, the need for more research on race distribution of the 400m hurdles is evident. The lack of relevant research on the 400m hurdles may be down to the fact that race distribution is still trying to find its place within coaching processes. As race distribution is a fairly new idea, normalising the way in which it is analysed may also be hindering its progress. Hughes and Bartlett (2002) also argued for the need for greater attention to the principles of feedback-technique and normalisation of performance indicators to aid coaches. It is evident through the current research that there is no standardised way of analysing performance indicators such as hurdle clearance time and touchdown times.
15 It is also evident, however, that race distribution has made an impact on recent literature especially in other cyclical disciplines. Chun et al. (2007) suggested that the next step in research is to find dense, naturalistic, visual representations that convey quantitative information efficiently and effectively. This relates to displaying feedback, which is more insightful to coaches, in order for them to find more advantageous race strategies for their athletes. The purpose of this study was to update past research on the analysis of 400m hurdle race distribution, incorporate hurdle clearance time as a performance indicator and to examine which performance factors are most useful for the study of race distribution. This study also aimed to benefit coaches and athletes through further insight into race strategy, and provide them with a simple method for analysing race distribution in the 400m hurdles.
16
CHAPTER 3 METHOD
3.1 Research Design
A computerised notation system was developed for the collection of the relevant quantitative data. The use of a computerised notation system allowed for flexible and highly efficient data processing as a result of recent advances in data entry technology (O’Donoghue, 2010). The system used was developed with the upmost consideration for efficiency, where coded data could easily be exported and organised for analysis. The system was specifically designed to analyse one athlete at a time and heavily built around the four contact points identified by Cobner (2012) (Figure 4). The performance indicators and accompanying operational definitions chosen for the data collection and data analysis process are shown in Table 2.
Table 2. Performance indicators and operational definitions.
Performance Operational Definitions Indicators
Touchdown Splits/Split Times from the start to landing of the first hurdle. Landing to Times landing between each hurdle unit, and landing off the last hurdle to the finish (Hucklekemkes, 1991).
Flight Times Time taken from take-off (toe-off) to landing over each hurdle (Quinn, 2010).
Stride Pattern The number of strides taken to the first hurdle, between hurdles and to the finish line (Boyd, 2011).
PI1 The percentage increase in time from the first half of the race, based on the assumption that the athlete ran even paced between hurdles 5 and 6 with 200m being 15m after hurdle 5 (Greene et al. 2011).
PI2 The percentage increase in time from touchdown times between hurdles 1-4 to touchdown times between hurdles 7-10 (Greene et al. 2011).
PI3 The percentage increase in time from touchdown times between hurdles 1-2 to touchdown times between hurdles 9-10 (Green et al. 2011).
17 3.2 Data Collection
All the video footage used for observation was pre-recorded and saved into a Quicktime format, suitable for Apple Mac software. The footage was then analysed post-event using Sportscode version 9. The races chosen for analysis were:
Men’s 400m Hurdles Final – Olympic Games, London (2012) Men’s 400m Hurdles Final – IAAF Diamond League, London (2011) Men’s 400m Hurdles Final – World Championships, Daegu (2011) Men’s 400m Hurdles Final – Commonwealth Games, Delhi (2010)
The race footage chosen had to comply with the following criteria:
The footage had to be of current elite male 400m hurdle athletes. Athletes were considered elite if they were present in any major championship races such as the Olympic Games, Commonwealth or any IAAF Diamond League races, since the year 2010. The footage had to be of a high enough quality so that touchdown points could be clearly defined. Changes in camera angles were allowed. However, if more than three athletes were blocked or cut out during any of their touchdown points, the race footage was not considered. Athletes were only eligible for analysis if all four of their touchdown points could clearly be defined at each hurdle.
In total, 47 elite male 400m hurdle athletes were eligible for analysis over four major championship finals. However, during data analysis, it was found that the race in Delhi (2010) was not of a high enough standard compared to the other three races in terms of performance times and race distribution. Hence, although used for statistical significance testing, data from the Delhi (2010) race was not used for the development of a race model or any race distribution analysis.
18
3.3 Coding Template
Every code button had a red diamond symbol meaning it had a toggle action. This meant that an instance would only stop coding if done so manually. Instances were created to extract the timings of the relevant sections of the footage. Text label buttons are identified by a blue circle (Figure 7) and were used to categorise instances for the stats window.
Fig 5. Coding window developed to quantify the performance indicators chosen.
The four main code buttons used in the coding template were filled in green, as shown in Figure 5. Based on the four contact points identified by Cobner (2012), Figure 6 illustrates which section of a race each button created an instance for. ‘FLAT’ coded for the time taken to reach the contact point two strides before a hurdle (‘-2’), either from the start or from the contact point two strides after a hurdle (‘2’). ‘2 before’ coded for the time between contact points ‘-2’ and ‘-1’. ‘FLIGHT’ coded for the time between take-off (‘-1’) and touchdown (‘1’). ‘1 after to 2 after’ coded for the time between contact points ‘1’ and ‘2’.
19
Fig 6. Diagram illustrating what each code button represented in terms of instances in a 400m hurdle race. (S = Start and H1 = Hurdle One)
Activation links and exclusive links were also used in developing a fully functional coding template (Figure 7). Activation links (Green) allowed extra details of information to be collected without the need of manually executing the button. Exclusive links (Blue) were made between the four main code buttons meaning only one of the buttons could be activated at a time. This guaranteed accurate changeovers and no overlaps in regards to the instances. Activation links and exclusive links were also made from the four main code buttons, to the ‘T flat’ and ‘T flight’ buttons. This ensured that the coded instances were being categorised correctly.
Fig 7. Breakdown of the coding template.
20 3.4 Procedure
Prior to any notation, the footage of each race chosen for analysis was watched through thoroughly in order to pick out the athletes not eligible for observation. All the eligible athletes then had a timeline saved to their name and linked to the relevant race footage on Sportscode (Version 9).
To begin with, the start of a race would be estimated to initiate the coding process. From then on the procedure shown in Figure 6 would be followed for each of the ten hurdle segments. Once an athlete’s foot had made any clear contact with the track, it was accepted as a contact point. The athlete was only deemed to have finished the race once his torso had crossed the line. The ‘tab’ button was used to code an athletes finish point and to stop all other instances that were still coding. To work out accurate start times, the first instance on the ‘T Flat’ row of the timelines had to be adjusted until the final performance time on the stats window matched that of the athlete’s official race time.
A statistical window was constructed to extract the required data. The stats window grouped the instances from 1-11, which represented the following segments of a race:
S-H1, H2, H3, H4, H5, H6, H7, H8, H9, H10 and H10-F
The times of the instances were then organised into the relevant sections of the stats window for the actual splits (‘T.Flat’) and accumulative splits to be calculated. Other variables, such as athletes 200m splits and stride patterns, were entered manually. 200m split times were calculated as detailed in Table 2 and stride patterns were simply counted.
21 3.5 Data Processing After being compiled into tabulated and graphical format, SPSS 20.0.0.1 was used to perform statistical analysis on the data collected. For a difference to be considered significant, the P value calculated had to equal 0.05 or lower (p<0.05). The 35m split times (H2-H10) were compared using a One-Way ANOVA test. A post-hoc Scheffe test was executed for additional exploration of the differences among the split times and to provide specific information on which splits were significantly different from each other. The 200m splits and differences, and the three performance indicators (Table 2) identified by Greene et al. (2011) were then compared between race groups using the same procedure as before.
3.6 Race Model Two different race models obtained from two expert 400m hurdle coaches were analysed to gauge an idea of how athletes should potentially distribute their races. Exploration into a model based on the results obtained from the athletes who performed in the three most recent races (Olympics, Diamond League and Daegu) was undertaken in an attempt to better present race strategy visually, and to note any differences to the two more dated models mentioned. The athletes who performed in the three races, on average, ran 48.56 1.23 seconds. Hence, a proposed model was produced based on the performance of a 48.56 second run. Mean touchdown splits were converted into percentage of race completion values and explored.
3.7 Reliability An athlete’s performance at the 2012 London Olympic Games was observed and analysed on two separate occasions as an intra-operator reliability study. The testing of reliability can be used to validate the use of a notation system. The mean absolute difference was used as the reliability measure, where the degree to which the same observer was consistent in obtaining the same touchdown splits for the same athlete performing in the same race was used to assess the accuracy of the notation system used for data collection.
22 Table 3. Mean absolute difference reliability test results on an athlete’s performance at the 2012 London Olympic Games.
Mean Absolute Observation 1 Observation 2 Difference
5.633 5.620 0.013
3.491 3.492 0.001
3.641 3.641 0.000
3.845 3.844 0.001
3.912 3.912 0.000
4.187 4.187 0.000
4.191 4.192 0.001
4.260 4.260 0.000
4.393 4.360 0.033
4.578 4.612 0.034
5.309 5.320 0.011
0.009
23
CHAPTER 4 RESULTS
4.1 Results To explore the data collected, results are presented in table and graphical format and statistically analysed in SSPS 20.0.0.1. When presenting significance values from the one-way ANOVA and Scheffe post hoc tests, any value below 0.05 (p<0.05) was considered significant.
4.2 Touchdown Splits Table 4 presents the significant differences between touchdown splits in different hurdle groups. The details of the touch down splits and standard deviation within hurdle groups can be found in Figure 8.
Table 4. Matrix indicating levels of significance between hurdle segments.
Most hurdle groups are significantly different to each other in terms of touchdown splits. However, they show no significant differences to the groups either side of them, apart from H8-9 and H9-10. Figure 8 emphases the fact that the H8-9 and H9-10 segments of the race are the most significantly different to the rest of the groups. Figure 8 also identifies a larger standard deviation with H2-3, H3-4 and the last two hurdle groups, suggesting that these may be the segments of the race where race strategy or race distribution could make a difference.
24
Fig 8. Boxplot graph representing the range of touchdown splits obtained by elite athletes within each hurdle segment.
4.3 200m Splits
Figure 9 brought to attention the significant difference in 1st 200m splits produced in Delhi compared to London (p=0.000), Diamond (p=0.001) and Daegu (p=0.000). Figure 10 suggests no significant differences in 2nd 200m splits between the finals. Hence, as expected, Figure 11 featured Delhi performers displaying a generally smaller 200m differential than the other three finals. Between the other three finals excluding Delhi, no significant differences were found (p > 0.05). The breakdown of results for this section are displayed in Appendix B and were used, in addition, as part of the rationale for excluding Delhi’s data from performance indicator, stride pattern, flight time and race distribution analysis.
25
Fig 9. Boxplot graph representing the range of 1st 200m times at each 400m hurdle final.
Fig 10. Boxplot graph representing the range of 2nd 200m times at each 400m hurdle final.
26
Fig 11. Boxplot graph representing the range of 200m differentials from athletes in each final.
4.3.1 PI1, PI2, PI3
The values (%) for each of the performance indicators identified by Greene et al. (2011) (Table 2) were 14.20 4.06, 22.69 6.09 and 27.21 6.47 respectively, excluding Delhi. The quartile ranges (LQ, MED and UQ) represent the range of values athletes were obtaining in terms of percentage increase in time between important segments of a race. Significant difference breakdowns of the three performance indicators are also detailed in Appendix B, although the results displayed similar conclusions where the Delhi race differed considerably from the other three races.
Table 5. PI values for this study, excluding the Delhi final. Performance Indicator Value LQ MED UQ PI1: % increase in time from 1st to 2nd 200m 14.20 12.15 13.19 16.55 PI2: % increase in time from hurdles 1-4 to 7-10 22.69 19.58 21.58 25.41 PI3: % increase in time from hurdles 1-2 to 9-10 27.21 23.77 25.79 30.51
27
Fig 12. Comparison between performance indicator values obtained by Greene et al. (2011) and this study.
Figure 12 clearly illustrates a difference between the data set used by Greene et al. (2011) and the data set used by the present study. The green sections represent the upper quartile ranges and the red sections represent the lower quartile ranges. The median is presented as the line separating the two ranges. It is apparent that the athletes used in the present study display larger percentage differences between the respective segments of a race. Figure 8 also shows a wider range of upper quartile percentages and narrower range of lower quartile percentages compared to the athletes used in the study by Greene et al. (2011).
4.4 Stride Pattern
Stride patterns of 19 athletes were collected including lead leg data, both of which are detailed in Appendix C. A larger data set would have allowed more insight into the patterns used by 400m hurdle athletes. However, from the data available, the model lead leg pattern observed was:
L, L, L, L, L, L, L, L, L, R
28 H7 and H8 were found to be the most frequent points of a change in stride pattern with 7 out of 19 athletes choosing to change at those hurdle segments. In addition, amongst all the athletes (n=19), changes in stride pattern only happened once (n=7) or twice (n=12) during a race.
Fig 13. Frequency distribution of the number of strides for each race segment.
Fig 14. Frequency distribution of the number of strides for each race segment (Ditroilo and Marini, 2001).
29 According to Figure 13, the model stride pattern, respective to each race segment, was:
22, 13, 13, 13, 13/14, 14, 15, 15, 15, 15
Ditrolio and Marini (2001) produced a similar frequency distribution graph (Figure 14), in which the model stride pattern read as:
21, 13, 13, 13, 13, 14, 14, 15, 15, 15
Although the stride count for each race segment had a larger range in Figure 14, it can be said that both frequency distribution graphs displayed almost identical stride pattern results.
4.5 Flight Time
Flight times were analysed traditionally between contact points ‘-1’ and ‘+1’. Athlete flight times ranged from 0.37 to 0.57 seconds and produced a mean of 0.50 0.02 seconds for all hurdle units. No significant differences (p= 0.146) were found between athletes. Extended flight times (Figure 4) were also analysed, between contact points ‘-2’ and ‘+2’. Athletes extended flight times ranged from 0.80 to 1.12 seconds and produced a mean of 0.92 0.02 seconds for all hurdle units. No significant differences (p= 0.855) were found between athletes either.
Despite this, Pearson’s r correlation coefficient was performed on both flight times (‘-1’ to ‘+1’ and ‘-2’ to ‘+2’) and performance times to test for any correlation between the two variables. A very weak (p= 0.622) positive correlation (r= 0.121) was found between flight times and performance times whereas a fairly weak (p= 0.205) negative correlation (r= -0.304) was found between extended flight times and performance times. Scatter plot graphs were also produce to visually illustrate any positive or negative relationships.
30
Fig 15. Scatter plot showing a weak positive relationship between flight time (‘-1’ to ‘+1’) and performance time.
Fig 16. Scatter plot showing a slightly negative relationship between extended flight time (‘-2’ to ‘+2’) and performance time.
31 4.6 Race Distribution
4.3.1 Models As mentioned, an aim of this study was to find dense, naturalistic, visual ways of representation that convey quantitative information efficiently and effectively. Straying from the norm of most research in the area that use velocity graphs to represent race distribution, it was decided to map an athlete’s race against a proposed model of performance instead.
Figure 17 displays the two more dated models obtained from elite coaches, plotted against the proposed 48.56-second model developed for this study. It illustrates how the proposed model of performance is constantly ahead of the pace set by model 1 and model 2. The proposed model differs at a mean of 0.66 0.16 seconds with model 1 and 0.70 0.13 seconds for model 2. Figure 17 also demonstrates how similar model 1 and model 2 are, with a maximum of only 0.10 seconds difference at H9.
Fig 17. Differences at each hurdle segment between the proposed model, and the two other models obtained from elite athlete coaches.
32
Fig 18. Subject A’s three races plotted against Model 1.
Fig 19. Subject A’s three races plotted against Model 2.
33 Figures 18 and 19 further back up the point made by Figure 17 that both model 1 and 2 are similar in nature. Although the models (1 and 2) allow for race distribution to be visually represented, the slow predicted touchdown times don’t fully present the data in enough detail to produce an understandable visual representation of subject A’s three races. Hence, the proposed model was preferred to produce more stimulating visual representations of race distribution.
4.3.2 Visuals of race distribution Only the athletes that appeared in all three of the finals, excluding Delhi (2010), were used to develop more detailed visual representations of race distribution as they presumably would have been more concerned with race distribution and strategy (Ditroilo and Marini, 2001). Each athlete’s three races were plotted against the proposed model for 48.56-second performances.
Fig 20. Subject A’s race distribution
34
Fig 21. Subject B’s race distribution.
Fig 22. Subject C’s race distribution.
35
Fig 23. Subject D’s race distribution.
36
CHAPTER 5 DISCUSSION
5.1 Discussion
This study analysed five variables, all of which were thought to affect race distribution. Statistical analysis and visual representations of raw data were explored to convey the results in a form intuitive for coaches and athletes. Although some results may not have formed any significant findings, all the variables analysed have proven to be essential aspects of a 400m hurdles. Therefore, effective representation and presentation of the relevant performance indicators are valid for discussion.
5.2. Delhi
The 400m hurdles final at the Commonwealth Games in Delhi (2010) was one of the races used to analyse elite male athletes. Among the finalists were athletes who had also ran in one or more of the other three finals, hence there was no debate that the race was considered elite. However, as mentioned, with the data presented and the full breakdown of results shown in Appendix B, it was decided to exclude Delhi data in all race distribution analysis.
Athletes who performed at Delhi were seen to have a significantly smaller 200m differential times, which had a mean of just 1.56 seconds. Figure 9 revealed that it was their 1st 200m that was allowing for such a small differential as their 2nd 200m times were deemed to have no significant differences between finals (Appendix B). It is also important to note that in Figure 8, all the outliers (21,23,27,129 and 182) were from athletes participating in the Delhi final. Table 6 shows the effect Delhi performers had on the values of the performance indicators identified by Greene et al. (2011).
Table 6. PI values for this study, including the Delhi final. Performance Indicator Value LQ MED UQ PI1: % increase in time from 1st to 2nd 200m 11.93 10.45 12.56 15.33 PI2: % increase in time from hurdles 1-4 to 7-10 20.47 17.72 21.58 24.36 PI3: % increase in time from hurdles 1-2 to 9-10 24.71 21.58 25.67 29.51
37 Comparisons between Table 5 and Table 6 reiterate the results seen in figures 9, 10 and 11 where percentage increases in time between halves of a race decrease when results from Delhi are concerned. It is also interesting to note that the results obtained including the Delhi final (Table 6) were more supportive of the results produced in the study by Green et al. (2011) than the results excluding Delhi (Table 5).
Although unrealistic, the differences found in the Delhi race may suggest a change in race strategy after 2010 as the other three races were during the 2011-2012 season. However, Green et al. (2011) identify that the tactics of athletes are influenced by the activity of early race leaders and that world- class athletes are better judges of race pace. Due to the lack of world-class athletes within the final, the race pace may have been misjudged, leading to slower 1st 200m times.
5.3 Touchdown Times
The touchdown data presented in the results section of this study (Table 4 and Figure 8) included Delhi results. Table 7 and Figure 24 represent the same results, however, with data from Delhi excluded. Although both versions show similar results, valid comparisons can be made to other studies in the area when Delhi is excluded.
Yasui et al.’s (1996) study concluded that the time between H6-7 was the most important touchdown time when concerning overall performance. Greene et al. (2011) also reported that times to hurdle 7 could be used as a predictor of overall race time in men’s races. However, results of the present study suggest that touchdown times between H8-9 and H9-10 were more salient due to the larger range of times obtained during those two hurdle segments compared to the rest (Figure 24). Hence, it can be said that the race is won depending on the performance of athletes over the last two hurdles. A case could also be made for H2-3 as this hurdle segment also reported a larger range and deviation between athletes (Figure 24).
38 Table 7. Matrix indicating levels of significance between hurdle segments (excluding Delhi).
Fig 24. Boxplot graph representing the range of touchdown splits obtained by elite athletes within each hurdle segment, excluding race data from Delhi.
39 5.3.1 200m Splits
On average, touchdown times increased throughout the course of a race, therefore, speed presumably decreased. These results agree with those of Ditrolio and Marini (2001), Green et al. (2011), Linderman (1995) and Pendergast (1991). However, all the studies mentioned suggest the best performances came from those athletes who had the lowest differential between the first and second half of races. The present study does not support these suggestions. On average, athletes in the three more recent races (London, Diamond League, Daegu) produced better performance times with larger differentials. Athlete’s performances in Delhi produced significantly lower 200m differentials, yet performance times were averagely slower. According to Figure 9, this was mainly down to a slower 1st 200m split.
Based on results, the present study suggests that faster 1st 200m splits produce better overall performance times. An explanation for this could be advancements in training or nutrition. Race strategy could also change due to these advancements. If an athlete displays greater lactic tolerance or greater levels of fitness, this would allow for a faster 1st 200m split. Quinn (2010) also identifies venue as a possible influence on performance. As the three races concerned are possibly the three most elite races on the athletic calendar, the event may have forced quicker starts. The London Olympics displayed some of the quickest 1st 200m splits, possibly due to the grandeur of the event. If this were not the case, more research with much larger elite sample sizes should be undertaken before assumptions about recent changes in race strategy could be made.
5.4 Stride Pattern
As seen in Figure 13, most athletes opt to start with 21-22 strides to the first hurdle followed by 13-14 strides between hurdles. Most athletes would then perform two change downs to eventually run 15 strides from H7-10, presumably finishing on their strongest lead leg. It is also evident that most athletes prefer hurdling off their left leg until the last hurdle.
40 It is no surprise that athletes prefer using a lead left leg, where they can run the entire curve on the inside of their lane. Lindeman (1995) found that a hurdler with a lead left leg could potentially run 24” closer to the inside of their lane and gain an entire meter (or .12 - .13 seconds) on athletes running the curves with a right lead leg. It is interesting to note however, that none of the athletes actually ran according to the model stride pattern and lead leg pattern stated in the results section of this study. Boyd (2011) indentifies that elite 400m hurdlers are required to learn to hurdle off alternate legs and to be able to adjust their stride patterns according to their fitness condition, track surface, lane draw, weather conditions and wind direction. Hence, it is quite possible that the race plan developed at the end of a season, will be different to that adopted at the start (Boyd, 2011).
Although stride pattern analysis can reveal important information on the standard required for 400m hurdlers to be successful, it would be of no use to elite coaches and athletes that already have specialised stride pattern plans. Lindeman (1995) correctly stresses the importance of being able to make adjustments during a race. Visually displaying errors in judgement would possibly be a better way of informing coaches and athletes about their stride patterns. Change down analysis could also be a better indicator of effective hurdling and is advised for future research.
5.5 Flight Time
It has been mentioned in previous research that hurdle clearance times only have a small effect on overall race time (Quinn, 2010). This was supported by the results of this study as only a mean of 2.43% of overall race time was taken up by flight time. No significant differences (p > 0.005) were found between athletes’ flight times either. A possibility of this could be down to the amount of key frames present in the footage used, making it hard to get accurate enough times for comparisons between athletes to be made. However, quick flight times may be a necessity for all elite 400m hurdlers, where flight time has already been mastered.
41 For this study, traditional split points (‘-1’ to ‘+1’) were extended (‘-2’ to ‘+2’) (Figure 4) to not only generate an indication of flight time, but also to explore multiple derivatives of timings around the hurdles. Again, no significant differences (p > 0.005) were found between athletes extended flight times. Figures 15 and 16 displayed the correlation results between the two split points and overall race time. Although weak correlation values were calculated, it was found that traditional split points had a positive correlation with performance times, whereas extended split points had a negative correlation with performance times.
It is understandable why quicker flight times positively correlate to faster overall times, however a negative correlation between extended flight times and performance times provides an interesting topic for discussion. A possible answer for this finding may stem from stride pattern. Lindeman (1995) and Boyd (2011) both mention the importance of making stride adjustments well in advance of a hurdle, instead of trying to rush an adjustment just before the hurdle. Being able to adjust stride length or frequency far in front of an approaching hurdle could aid the loss of velocity caused by ‘chopping’, ‘shuffling’ or ‘reaching’ (Lindeman, 1995).
5.6 Race Distribution
Dirolio and Marini (2001) and Yasui et al. (1996) have both identified the importance of exploring the times of fixed intervals in a 400m hurdle race. Most research in the area has presented race distribution in the form of speed (in meters per second) at every hurdle segment. However, this way of representing race distribution rarely reveals anything else other than an increase of speed after H1, followed by a gradual decrease in speed for the remainder of the race. This study aimed to provide a way in which quantitative touchdown data could be visually conveyed as feedback. This would thus be more insightful for coaches and athletes. Figures 20, 21, 22 and 23 display athletes three races against a model developed to represent the percent of race completion, at each hurdle segment, of an athlete running at a 48.56 second pace.
42 5.6.1 Subject A
Figure 20 depicts the race distribution of a very consistent hurdler. He doesn’t go out too fast at the start and maintains his rhythm very well. At H7, the athlete is able to dig deep and set himself up for a very strong finish. Compared to the other three subjects, it is apparent that Subject A is not as fast in terms of flat speed. However, his ability to run consistently and maintain rhythm over hurdles results in less energy being expended, allowing for a strong finish.
5.6.2 Subject B
Figure 21 depicts the race distributions of a very inconsistent hurdler. It is apparent that he has the ability to run a very fast 400m hurdle time, however, something has obviously let him down during the Diamond League and World Championship finals. This may possibly be due to injury or fitness levels at the time or this may be due to stride pattern or rhythm problems. However, when Subject B does get it right, he can produce performances as illustrated by his race distribution curve from the London Olympics. Subject B displays a similar race strategy to that of Subject A, however, due to his superior strength and fitness, Subject B is able to go out faster and maintain the pace until H7 where he can produce an even stronger and smoother finish.
5.6.3 Subject C
Figure 22 depicts the race distribution of a stereotypical speed hurdler. For all of Subject C’s races, the first 200m are run as if the athlete is looking to produce a sub 48 second performance. However, the athlete always ends up trying to ‘hold on’ at the end of his first 200m. Most would wonder why the athlete decides to start so fast, but this strategy could have been taken up due to his lack of race distribution understanding, or to cover up for his lack of technical ability. Despite this, Subject C still manages to record very fast times especially when he manages to maintain his fast start until just after H6.
43 5.6.4 Subject D
Figure 23 depicts the race distribution of another very good technical rhythm hurdler. As apparent through his very straight race distribution curves, Subject D is a very good judge of pace and is technically able to maintain rhythm and momentum throughout a race. His best performance came from the Diamond League final where a finishing ‘kick’ at H9 allowed him to finish quicker than he normally would. It is clear the Subject D may have gone out too fast for his own pace at the Olympic Games. However, it is unsure as to whether he may have been able to better his time from the Diamond League meet if it had not been for a possible misjudgement of rhythm or stride pattern at H8.
5.6.5 Application
The distribution graphs produced by this study make claim to being dense, naturalistic, visual representations that convey qualitative information efficiently and effectively. According to Chun et al. (2007) this is the next step in research, where coaches could use this method in order for them to find more advantageous race strategies for athletes. From a coaching perspective, it is important to understand the type of athlete you are dealing with and train them accordingly, to take advantage of any possible strength. This method of displaying race distribution will also help in this aspect as each subject (A, B, C and D) displayed the characteristics of a speed hurdler, strength hurdler, rhythm hurdler or a hybrid of one or more of these characteristics.
The race distribution graphs were standardised at the Y-axis in order for comparisons between subjects to be made. In an ideal situation however, a coach’s main focus would be the athlete’s touchdown times and how they faired against a proposed model of performance. A coach would essentially be able to look at one of the distribution graphs and point out where the athlete had gone wrong. Obviously, it is very rare that an athlete is going to run exactly as the model proposes, hence, the margin between correct and incorrect race distribution would be for the coach to decide.
44 5.7 Limitations
The model used in this study was proposed based on the results of 27 elite male athletes. Their times ranged from 47-49 seconds, hence why a 48.56 second race was modelled. The limitation to this is that race distribution curves, for athletes lying at either end of the range, may become distorted or over exaggerated due to the proposed performance time being too quick or slow. Therefore, it would be better if race models, such as the one used in this study, were developed for athletes aiming to run faster or slower than 48.56 seconds.
Flight times were only accurate to 2 decimal places. As it was found that only 2.43% of overall performance times were take up by flight time, ideally more accurate values would have increased the validity of the findings detailed in the results section of this study. The data collection procedure was not sensitive enough due to the amount of frames the footage produced in a second. In an ideal situation, data should have been accurate to 3 decimal places, especially when it came to analysing flight times.
As mentioned, actual footage used for analysis proved to be a major limitation. In addition to quality issues that effected accuracy of the data collected, camera angles ruled out the use of athletes that were cut out during important actions of a race. Because of this, the sample size used in this study was substantially smaller than expected.
As a result of this, the greatest limitation to this study was the number of athletes available for analysis. Only 27 elite male 400m hurdle athletes were analysed and 8 of them were excluded in certain aspects of analysis due to significant differences found in their race approach. There was also a limited amount of research where the results found could be compared with this study, making it difficult to produce any conclusions on the progress in elite 400m hurdle race distribution.
45
CHAPTER 6 CONCLUSION
6.1 Conclusion of Study
The aim of this study was to gain a better understanding of race distribution in the present day elite male 400m hurdler. By developing the methods of Cobner (2012) and Green et al. (2011), this has been achieved and revealed many findings. In addition to this, the study took on the task of exploring the area of predictive models and finding efficient and effective ways of visually representing race distribution.
The results presented show clear indication that race distribution and race strategies are present in modern day elite male 400m hurdlers. It was found that athletes displayed certain characteristics that fell into the categories detailed by Hillier (2012). The use of a race model also proved to be an effective method to visually detail the race distributions of individual athletes races.
In terms of race distribution, it was found that present day elite 400m hurdlers tend to run the first 200m a lot quicker than pervious research stated. As a result, 200m differentials were calculated to be a lot larger than expected which disagreed with the idea that lower 200m differential correlated to better overall performance.
A positive correlation between flight times (‘-1’ to ‘+1’) and performance times were calculated whereas a negative correlation between extended flight times (‘-2’ to ‘+2’) and performance times was found. Only weak correlation values were found. However, the findings make apparent the need for further exploration into the area.
Although exploration into stride pattern was deemed insignificant, it is no question that stride pattern may be very influential when it comes to flight times and race distribution. In hindsight, it is important that future research does not neglect the importance of stride pattern to overall performance.
46 In conclusion, the methods used by this study efficiently and effectively detailed the most important aspects of the 400m hurdles and race distribution. The visual representations used were insightful and easy to understand and will hopefully become an invaluable tool for coaches and athletes alike. Race distribution analysis will inevitably be the best way to explore elite male 400m hurdle performance, therefore it is apparent that continued research in the area is needed.
6.2 Future Research
The points below present recommendations for future research based on the limitations of this study. These include:
Repeating the study with a larger number of elite athletes. And possibly separating the athletes into groups according to their performance times, if the sample size allows.
Obtaining better quality footage, where key frames would not be missing and contact points can easily be defined.
Performing a more substantial reliability test to explore the validity of results obtained by different operators.
Producing more race models, which can be used for athletes aiming for times above, or below, 48.56 seconds.
Further exploring the idea of extended flight times with more accurate data.
Performing more studies such as the present one to develop a better understanding of the development of race strategy.
47
REFERENCES
Boyd, R. (2011). 400m Hurdles. Modern Athlete and Coach, 49 (3), 26-32.
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APPENDICES
APPENDIX A
Touchdown Splits
Table 8. One-Way ANOVA of touchdown splits from all athletes (excluding Delhi).
A-1 Table 9. Scheffe Post Hoc Test of touchdown splits from all athletes (excluding Delhi).
A-2
APPENDIX B
200m Splits and PI1, PI2 and PI3
Table 10. One-Way ANOVA of athletes’ 200m splits and PI1, PI2 and PI3.
B-1 Table 11. Scheffe Post Hoc Test of athletes’ 200m splits and PI1, PI2 and PI3.
B-2
APPENDIX C
Stride Pattern
Table 12. Stride pattern data of all the subjects.
C-1
APPENDIX D
Flight Times (‘-1’ to ‘+1’)
Table 13. One-Way ANOVA of flight times (‘-1’ to ‘+1’)
Table 14. Pearson’s Correlation of flight times (‘-1’ to ‘+1’)
D-1 Extended Flight Times (‘-2’ to ‘+2’)
Table 15. One-Way ANOVA of extended flight times (‘-2’ to ‘+2’).
Table 16. Pearson’s Correlation of extended flight times (‘-2’ to ‘+2’).
D-2