Human Movement Science xxx (2013) xxx–xxx

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Human Movement Science journal homepage: www.elsevier.com/locate/humov

Kinematics of marathon running tactics Wlodzimierz S. Erdmann ⇑, Patrycja Lipinska J. Sniadecki University School of Physical Education and Sport, Gdansk, Poland

a r t i c l e

i n f o

a b s t r a c t

Article history: Available online xxxx

Ó 2013 Elsevier B.V. All rights reserved.

PsycINFO classification: 3720 Keywords: Marathon running Tactics Kinematics Pacing Top runners Gebrselassie

1. Introduction Many human activities requiring strenuous effort are often performed in an incorrect manner, with may lead to early exhaustion. Among other factors, this may be caused by an inappropriate distribution of effort over the course of an event, as is in long-distance running. Long-distance runners usually have the following general aims in mind in designing their running tactics: (1) to end first at the finish line (not necessarily with the best time ever), usually in regular championships, or (2) to run the best possible time so as to break a record. Studies of running tactics are very helpful for both runner and coach in determining the ideal tactics for a particular future run (Arska, 1972; Gabrys & Celeban, 1996; Sozanski, 2007). Noakes (2000, 2003), Lucia, Olivan, Bravo, Gonzales-Freire, and Foster (2008) and Joyner, Ruiz, and Lucia (2011) all investigated the efficiency of running. They concluded that in order to achieve a very good result in marathon running (42,195 m), running economy constitutes one of the most important

⇑ Corresponding author. Address: Department of Biomechanics and Sport Engineering, J. Sniadecki University School of Physical Education and Sport, 1 Gorskiego Str., Gdansk 80-336, Poland. Tel.: +48 60 5304939 (mobile), +48 58 5547105 (office), fax: +48 58 5547166. E-mail address: [email protected] (W.S. Erdmann). 0167-9457/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.humov.2013.07.006

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features defining the successful runner. According to Williams (2007), it is not easy to identify and universally apply patterns of efficient movement for runners. These patterns still not to be found. Erdmann and Lipinska (2003a, 2003b, 2006), Lipinska (2006), Lipinska and Erdmann (2007) investigated marathon running at the highest competitive level by examining the velocity distribution during marathon running. The following example illustrates the kind of data and insights obtained. In the Berlin 2002 marathon, the female runner Takahashi attempted to break the world record. She ran the first 5 km at a mean velocity higher than 5.00 m/s. This was too fast since the mean velocity for the female marathon world’s best run was at that time 5.07 m/s. Her velocity gradually decreased in the course of the race, resulting in a lower velocity during the second part of the run than during the first. Her mission to break the world record failed (Erdmann & Lipinska, 2003b). Within the theory of a particular sport discipline different models are employed, among which scientific models are crucial. A scientific model incorporates a theoretical approach, which takes into account all the necessary quantities and their values that scientists believe must be included in the perfect competitor (a master). Scientific models may be rooted in exercise physiology, biomechanics, psychology and other disciplines (Shephard & Astrand, 2000; Noakes, 2000; Sadowski, 2009; Sozanski, 2007). Practically meaningful scientific models for long-distance running speak to issues of strategy and tactics. Strategy, among other important problems, takes into account which players the team will comprise during a race, or which runners will participate in the competition as leading runners or as pacemakers. Different pacemakers are chosen for different races. Unfortunately, they are usually selected a short time before a run and sometimes there is not enough time to prepare properly for the run in case of an initiative to establish a new record time. In addition to biomechanical quantities (work and power), runners use an index called ‘‘pacing’’, which indicates how many minutes a runner should take to run one or more kilometers. During competition this index helps runners to adopt proper tactics by taking into account the load distribution during the event. The load is measured as the velocity at which runners run the distance. For long-distance running, especially for a marathon run, a group model is available at present, based on Ethiopians and Kenyans. For over 10 years, the world long-distance running model of a master, especially in marathon running, has been personified in Haile Gebre Selassie or Gebrselassie (Ethiopia). He is considered by many to be the greatest long-distance runner of all time.

2. Concept of the work The aims of the present research were: (1) to examine pacing protocols, i.e., manners of load (velocity) distribution along the course by elite marathon runners, (2) to formulate recommendations for proper running tactics through proper load distribution, (3) to investigate the pacing of running of the long-time world’s best long-distance runner. We had the following hypotheses: (1) distribution of a load (velocity) during a marathon run should resemble an ascending line through the entire course; (2) fewer and smaller deviations from the line of running velocity are characterized by better results; (3) the world’s top long-distance runner runs with similar tactics in different runs when attempting to break the world record. We were further interested in finding answers to the following questions: (1) What are the characteristics of the pacing for the first runners at the finish line and for those further back? (2) What is a runner’s tactical approach to the first and last kilometers of the run? (3) What is the difference between velocities for the first and second parts of the run? (4) What are the velocity deviations, i.e., what accelerations and decelerations are made during the entire run? (5) Can the end results be predicted from the early stages of the run? (6) What are the differences between male and female marathon running? (7) How does the world’s top runner run consecutive stages of a marathon run when breaking or seeking to break the world record? (8) Does the world’s top runner run different races in the same manner?

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(9) What are the best tactics for breaking a world record in a marathon run? (10) What is the role of pacemakers in a run aimed at breaking a world record? 3. Methods There are millions of long-distance runners worldwide. In order to learn more about how the best of them run, 50 male and 50 female runners who finished first in four marathons were studied (EBBA runs). Furthermore, the marathon runs of the world’s best long-distance runner, Haile Gebrselassie, along with his pacemakers (different for each run), were investigated (BDBD runs). Runners of EBBA marathons were divided into groups (male and female): (A) first three at the finish line, (B) places 4 to 10, (C) places 11 to 50, (D) first 10 at finish line, (E) first 50 at finish line. For the BDBD runs, for the investigated stages of the distance, the time of the leading runner was taken into account. Gebrselassie was always among the leading group and finished first. The following EBBA marathon runs were investigated: (1) IAAF World Championships, Edmonton, 2001, (2) Boston Marathon, 2002, (3) Berlin Marathon, 2002, (4) Olympic Games, Athens, 2004. In Edmonton, during the male run (on August 3, 2001) it was cloudy, with a temperature of 19 °C. The start was at 6.45 pm. During the female run (on August 10, 2001) it was also cloudy, with a temperature of 16 °C. The start was at 8.00 am. During the Boston run (15 April 2002) the temperature was 21 °C, and it was partly cloudy, with a light headwind facing the runners. The start was at noon. The Berlin marathon took place on September 29, 2002. The temperature was 14 °C, and it was cloudy and windless. The start was at 10.00 am. In Berlin few world records were established. In 2004, at the time of the research, the male world record was 2:04:55 (2003, Paul Tergat, Kenya) and the female world record was 2:19:46 (2000, Tegla Loroupe, Kenya). During 2007–2009 the male world marathon record was established by Gebrselassie in Berlin 2007 and 2008 with runs of 2:04:23 and 2:03:59 respectively. The Olympic marathon in Athens 2004 took place along a historical course from the city of Marathon to the Olympic Stadium in Athens. The female marathon took place on 22 August 2004. The start was at 6.00 pm. There was a high temperature of 35 °C, and it was sunny, with no wind. The male marathon took place on August 29, 2004. The start was at 6.00 pm. The temperature reached 30 °C, and it was partly cloudy with a gentle wind. The following BDBD marathon runs were investigated: (1) Berlin 2007, (2) Dubai 2008, (3) Berlin 2008, (4) Dubai 2009. On the last Sunday of September about 40,000 runners (among them 30,000 foot runners) participate each year in the Berlin marathon. They start at 9.00 am. In 2007 and 2008 the temperature was 17 °C and 19 °C, respectively. Since 2000, every January, about 10,000 runners have participated in the Dubai marathon. They used to start at 7.00 am – now they start at 6.00 am. In 2008, at the start it was 15 °C and at the finish it was 20 °C. In 2009, there was light rain and the temperature was 16 °C. This is the marathon in the world with the highest price money. The winning runner is awarded a sum of 250,000 US dollars. Both Berlin and Dubai marathons meet the IAAF standards required for a run to qualify as a world record. New world records in Dubai would be rewarded with a prize of 1 million US dollars. This is an indicator of a high volitional involvement by the runner. Time data for every 5000 m, for the last 2195 m, for halves and for the entire distance were obtained from the websites of IAAF and organizing committees (www.iaaf (2001); www.bostonmarathon (2002); www.berlinmarathon (2002), 2007, 2008; www.athens, (2004), www.dubaimarathon, 2008, 2009). In addition, time data for every kilometer of the BDBD runs were gathered by Sean Hartnett of the University of Wisconsin – Eau Claire, who worked on behalf of the organizing committees (he was sitting in the car preceding the front runners). The data were given to the authors as openaccess data. Also, pace (minutes and seconds per 1 km) was obtained for different stages of the run. Distance and time data, velocities and accelerations were calculated: /1, 2/. Mean data for all groups and standard deviations and variations for groups of 50 runners were obtained.

Please cite this article in press as: Erdmann, W. S., & Lipinska, P. Kinematics of marathon running tactics. Human Movement Science (2013), http://dx.doi.org/10.1016/j.humov.2013.07.006

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v ¼ Ddi =Dti

ð1Þ

a ¼ Dvi =Dti

ð2Þ

where D is difference, d is distance (m), t is time (s), v is velocity (m/s), a is acceleration (m/ks2), i is (a) from 1 to 9 for 5000 m and 2195 m sections and (b) from 1 to 42 for 1000 and 1195 m sections (note: since acceleration data were very small a unit of 1/ks2 = 1/1000 s2 was introduced, e.g., Dvi = 5.76 m/s5.67 m/ s = 0.09 m/s (velocity difference between two consecutive 5 km sections); Dti = 900 s (sum of half times of two consecutive 5 km sections); a = Dvi/Dti = 0.09 m/s/900 s = 0.0001 m/s2 = 100.0 m/ks2). In order to predict the end results of a run from the data of previous sections of the same run, correlation coefficients were calculated between the time of consecutive n  5 km (where n = 1. . .8) sections of the distance and the end time of the entire marathon distance obtained by the first ten runners at the finish. In terms of mechanical and steering approaches, as well as from the physiological point of view of optimal running, a long-distance run should be performed at a steady velocity (Foster, Schrager, Sny_ der, & Thompson, 1994; Maronski, 1996; Rapoport, 2010). Wazny and Sadowski (1974) stated that in endurance disciplines all deviations, if any, from that steady velocity should be within ±2%. In order to assess this assumption, an additional quantity, i.e., quotient of deviations of velocity, was established. This quotation was defined as the absolute value of the acceleration divided by the mean velocity of the distance: /3/. The smaller the quotient, the better.

Qvd ¼ jamean j=vmean

ð3Þ

where Qvd is quotient of velocity deviations, jamean is mean absolute acceleration of 9 consecutive 5000 m and 2195 m sections (m/ks2), and vmean is mean velocity of the entire distance (m/s). Mean and standard deviations were calculated for all runs. Pearson’s coefficients were calculated between: (a) time of consecutive stages of the course and end time of the first ten runners; (b) quotient of velocity deviations and place at the finish. Welch’s t-test was used to support inequality of means of the end times of the EBBA runs for groups: A (1. . .3 runners at the finish line) and B (4. . .10), A (1. . .3) and C (11. . .50), B (4. . .10) and C (11. . .50), A+B (1. . .10) and C (11. . .50). A significance level of a = .01 was adopted. 4. Results 4.1. Times of runs 4.1.1. Times of EBBA marathons For male runners the best times, when comparing all four runs, for the first, third and tenth at the finish line were obtained at the Berlin course where the profile is the easiest among all EBBA courses. Also, data for the groups of the first 3, 10 and 50 at the finish line were the best in Berlin. Among women the best times for the first, third and tenth at the finish were obtained in Boston where the first fragment of the course runs downhill. Data for the first 3 at the finish were the best in Boston, but for the first 10 and the first 50 were the best in Edmonton. Table 1 Male time data (upper line, h:min:s) and mean pace data (lower line, min:s/km) of the 1st, 3rd, 10th and 50th runners at the finish and of the groups of the first 3, 10 and 50 runners at the finish of the marathon run. Runners Courses

1st

Edmonton

2:12:42 3:08.7 2:09:02 3:03.5 2:06:47 3:00.3 2:10:56 3:06.2

Boston Berlin Athens

3rd 2:13:18 3:09.5 2:09:45 3:04.5 2:06:52 3:00.4 2:12:11 3:08.0

10th 2:17:35 3:15.6 2:12:28 3:08.4 2:10:56 3:06.2 2:14:45 3:11.6

50th 2:31:42 3:35.7 2:29:47 3:33.0 2:24:55 3:26.1 2:22:37 3:22.8

3 mean

10 mean

mean

2:12:54 3:09.0 2:09:17 3:03.8 2:06:49 3:00.3 2:11:32 3:07.0

2:15:08 3:12.2 2:10:38 3:05.8 2:09:01 3:03.5 2:13:04 3:09.2

2:23:28 3:24.0 2:20:17 3:19.5 2:16:29 3:24.1 2:17:39 3:15.7

50 SD

var.

05:32

3.86

07:02

5.01

05:30

4.03

03:27

2.51

Please cite this article in press as: Erdmann, W. S., & Lipinska, P. Kinematics of marathon running tactics. Human Movement Science (2013), http://dx.doi.org/10.1016/j.humov.2013.07.006

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Table 2 Female time data (upper line, h:min:s) and mean pace data (lower line, min:s/km) of the 1st, 3rd, 10th and 50th runners at the finish and of the groups of the first 3, 10 and 50 runners at the finish of the marathon run. Runners Courses

1st

Edmonton

2:26:01 3:27.6 2:20:43 3:20.1 2:21:49 3:21.7 2:26:20 3:28.1

Boston Berlin Athens

3rd 2:26:18 3:28.0 2:26:01 3:27.6 2:26:10 3:27.8 2:27:20 3:29.5

10th 2:30:38 3:34.2 2:35:34 3:41.2 2:39:37 3:47.0 2:32:50 3:37.3

50th 3:14:29 4:36.5 3:00:56 4:17.3 2:58:18 4:13.5 2:50:01 4:01.8

3 mean

10 mean

mean

2:26:08 3:27.8 2:22:38 3:22.8 2:24:03 3:24.8 2:26:44 3:28.27

2:28:00 3:30.5 2:28:32 3:31.2 2:31:27 3:35.4 2:29:42 3:32.9

2:39:37 3:47.0 2:47:10 3:57.7 2:53:23 4:06.5 2:44:33 3:54.0

1

0..5 0..10 0..15 0..20 0..25 0..30 0..35 0..42

1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1

Correlation coefficient

Distance, km

6.85

21:36

12.46

20:24

12.40

Distance, km

Athens 2004 marathon Correlation coefficient

0..5 0..10 0..15 0..20 0..25 0..30 0..35 0..42

11:27

0..5 0..10 0..15 0..20 0..25 0..30 0..35 0..42

Berlin 2002 marathon

1

7.11

1

Distance, km

1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1

var.

11:20

Boston 2002 marathon Correlation coefficient

Correlation coefficient

Edmonton 2001 marathon 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1

50 SD

1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1

1

0..5 0..10 0..15 0..20 0..25 0..30 0..35 0..42

Distance, km

Fig. 1. Correlation coefficients between time of consecutive n  5 km (where: n = 1..8) fragments of the distance and end time of the whole marathon distance obtained by the first ten runners at the finish line; dark gray – female, light gray – male data.

Since the Edmonton marathon was run for a championship and not for a record time, the results obtained among male runners were the worst for mean values of the first 3, 10 and 50 at the finish comparing all four investigated marathon runs. Among women the slowest marathon run, taking into account the first 10 and 50 competitors at the finish, was the Berlin run. Detailed data of times and pace of runners obtained at the analyzed courses are presented in Table 1 (males) and Table 2 (females). Analysis of correlation coefficients between times at consecutive sections (for every 5 km) from the start and the finish time revealed different distributions of overcoming a load. In males during the first half of the distance it is hard to predict who will be in the first ten places at the finish. Correlation coefficients are low. In Edmonton the situation became clearer only after the 25th km when runners started to run the ascending fragment of terrain. A similar situation occurred in Boston where the ascending part of the distance came between the 26th and 33rd km. In Berlin the situation started to be more predictable after half the distance. In Athens the situation became more predictable after overcoming the huge hill around the 32nd km. Please cite this article in press as: Erdmann, W. S., & Lipinska, P. Kinematics of marathon running tactics. Human Movement Science (2013), http://dx.doi.org/10.1016/j.humov.2013.07.006

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In female runs, those in Edmonton, Boston and Berlin were predictable from the first few kilometers onwards, especially in Boston and Berlin where correlation coefficients were already high (about .8) after the first 5 km. In Athens the situation was clearer after the first half of the distance, and especially during the long ascending fragment of the run during the second half of the distance. See Fig. 1 for details. The mean end time data of the runs of the investigated groups A, B and C differed significantly. For female groups A (1. . .3 runners at the finish line) and B (4. . .10 runners) in the Boston marathon 2002 and for male groups A and B in the Athens marathon 2004, the p-value was

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