European Journal of Sport Science
ISSN: 1746-1391 (Print) 1536-7290 (Online) Journal homepage: http://www.tandfonline.com/loi/tejs20
Tactical behaviour of winning athletes in major championship 1500-m and 5000-m track finals Sonia Aragón, Daniel Lapresa, Javier Arana, M. Teresa Anguera & Belén Garzón To cite this article: Sonia Aragón, Daniel Lapresa, Javier Arana, M. Teresa Anguera & Belén Garzón (2016) Tactical behaviour of winning athletes in major championship 1500-m and 5000-m track finals, European Journal of Sport Science, 16:3, 279-286, DOI: 10.1080/17461391.2015.1009494 To link to this article: http://dx.doi.org/10.1080/17461391.2015.1009494
Published online: 09 Feb 2015.
Submit your article to this journal
Article views: 525
View related articles
View Crossmark data
Citing articles: 2 View citing articles
Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=tejs20 Download by: [The University Of Melbourne Libraries]
Date: 06 May 2017, At: 05:36
European Journal of Sport Science, 2016 Vol. 16, No. 3, 279–286, http://dx.doi.org/10.1080/17461391.2015.1009494
ORIGINAL ARTICLE
Tactical behaviour of winning athletes in major championship 1500-m and 5000-m track finals
SONIA ARAGÓN1, DANIEL LAPRESA1, JAVIER ARANA2, M. TERESA ANGUERA3, & BELÉN GARZÓN1 1
Department of Educational Sciences, University of La Rioja, Logroño, Spain, 2Department of Education, International University of La Rioja (UNIR), Logroño, Spain, 3Department of Behavioural Sciences Methodology, Institute of Brain, Cognition and Behavior, University of Barcelona, Barcelona, Spain
Abstract This article analyses the tactics employed by middle-distance (1500-m) and long-distance (5000-m) runners from an observational methodology perspective. The subject of investigation has received little attention from specialists in the field of athletics, with most research focusing on physiological studies of athlete performance. Using an ad hoc observation tool and a database containing systematically recorded data we detected time patterns (T-patterns) within the data recorded using the Theme software program (version 5.0), and analysed the tactics employed by winners of the men’s 1500-m and 5000-m finals of the World Championships in Athletics [Edmonton 2001, Paris 2003, Helsinki 2005 (1500-m final only), Osaka 2007 (1500-m final only), Berlin 2009 and Daegu 2011], the European Athletics Championships (Munich 2002, Göteborg 2006, and Barcelona 2010) and the Olympic Games (Sydney 2000, Athens 2004, Beijing 2008 and London 2012). T-pattern detection and investigation of the relationship between category systems corresponding to the criteria comprising the observation tool revealed both similarities (starting lane and lane used during race, runner’s position during race and sprint zone and lane) and differences (variations in pace, zones in which changes of pace occur, sprint initiation zone and winner’s position at the start of the sprint) between the two disciplines. Keywords: Tactics, behaviour, performance, 1500-m, 5000-m, T-patterns
Introduction While tactics employed in team sports have been the focus of considerable research (Lapresa, Álvarez, Arana, Garzón, & Caballero, 2013; Remmert, 2003; Szczepański, 2008), those used in middle- and longdistance track events have received relatively little attention. Interest in qualitative research methods has grown in the field of athletics (Pitney & Parker, 2002; Young & Salmela, 2010), but few studies have focused on the tactical approaches of middle- and long-distance runners (Brown, 2005; Renfree & St. Clair, 2013; Tucker, Lambert, & Noakes, 2006). Furthermore, the studies that do touch on these tactics are generally interested in the physiological aspects of running and training (Duffield, Dawson, & Goodman, 2005; Hill, 1999) or have a largely biomedical focus, with an emphasis on athlete performance (Hanley, Bissas, & Drake, 2013; Hugh,
2009; Paavolainen, Häkkinen, Hämäläinen, Nummela, & Heikki, 1999). Tactics are an important component of longdistance track events, however, as they frequently play a decisive role in outcome (De Koning, Bobbert, & Foster, 1999). As stated by Thiel, Foster, Banzer, and De Koning (2012), the tactical behaviour of athletes varies considerably depending on whether their aim is to win the race or to beat a record. As time is less important than victory in championship track events, the choice of one tactic over another is determined, not only by the characteristics of the athlete, but also by those of his or her rivals (Jones & Whipp, 2002). Elite runners use different pacing strategies to break away from the group, although in international competitions, it is generally the sprint in the last lap that determines victory (Thiel et al., 2012).
Correspondence: D. Lapresa, Universidad de La Rioja Edificio Vives, C/Luis de Ulloa s/n, 26004 Logroño, La Rioja, Spain. E-mail: daniel. [email protected] © 2015 European College of Sport Science
280
S. Aragón et al.
With a view to adding to the limited body of knowledge on runner tactics in middle-distance (1500-m) and long-distance (5000-m) races, we decided to analyse the men’s finals of the World Championships in Athletics, the European Athletics Championships and the Olympic Games between 2000 and 2012 to investigate the tactics employed by winners of 1500-m and 5000-m races and identify similarities and differences in tactical behaviour in the two disciplines. Methods We employed an observational methodology design (Anguera, 1979). Observational methodology is widely used in both team sports (Camerino, Chaverri, Anguera, & Jonsson, 2012; Lapresa, Anguera, Alsasua, Arana, & Garzón, 2013) and individual sports (Gutiérrez-Santiago, Prieto, Camerino, & Anguera, 2011; Lapresa, Ibáñez, Arana, Garzón, & Amatria, 2011) as these fulfil the basic requirements proposed by Anguera and Hernández-Mendo (2013): habitual behaviour (tactical behaviour is a common component of track events), natural context (official competition races), absence of a standard instrument and perceptibility. These conditions are all guaranteed in the track events analysed in our study. The methodology chosen is also justified considering that a purpose-built observation tool was used to record, analyse and interpret the tactics employed by middle- and long-distance runners. The construct validity of the instrument was ensured by building it on state-of-the-art expertise in the field instrument. The observational design, as per Anguera, BlancoVillaseñor, Hernández-Mendo, and Losada (2011), was nomothetic/follow-up/multidimensional (N/F/ M): nomothetic because the individual athletes were not acting as a unit; follow-up at both the inter-sessional level (search for similarities and differences in the tactical approaches of race winners) and intra-sessional level (frame-by-frame study of tactical behaviour throughout the race, enabling the detection of T-patterns); and multidimensional (different observation dimensions/criteria) with proxemic response. Finally, the observation was non-participative and characterized by complete perceptivity (direct observation). Participants The research project was approved by a scientific committee at the University of La Rioja in accordance with the Ethical Principles of Psychologists and Code of Conduct of the American Psychological Association, and the rules of the Ethics Committee of the Spanish Association of Psychologists.
We used an intentional sample of all the participants in the men’s 1500-m and 5000-m finals at the World Championships in Athletics (Edmonton 2001, Paris 2003, Berlin 2009, and Daegu 2011), the European Athletics Championships (Munich, 2002, Göteborg 2006, and Barcelona 2010) and the Olympic Games (Sydney 2000, Athens 2004, Beijing 2008, and London 2012). We also analysed the 1500-m men’s finals of the World Championships in Helsinki (2005) and Osaka (2007) but not the 5000-m finals, as over 10% of the total race times were not observable. All three events were top-class competitions featuring the world’s best athletes. Observation tool The observation tool combined a field format (necessary as the design was multidimensional) and a category system (Anguera, Magnusson, & Jonsson, 2007), which is particularly appropriate as all the criteria met the following requirements: they were atemporal and fit into an available theoretical framework (Daniels, 1998; Glover & Glover, 2005; Martin & Coe, 1997; Thiel et al., 2012; Tucker et al., 2006; USA Track & Field, 2000). The categories built were exhaustive and mutually exclusive. The observation tool was composed of fixed and variable criteria. The fixed criteria were competition (World Championship, European Championship, Olympic Games), distance (1500m, 5000-m) and race (Sydney, Edmonton, Munich, Paris, Athens, Helsinki, Göteborg, Osaka, Beijing, Berlin, Barcelona, Daegu and London). The variable criteria (Table I and Figure 1) were designed to obtain detailed information on the tactics employed by race winners based on their interaction with their rivals. Recording tool Data were recorded systematically using a combination of alphabetical and numerical codes. According to Bakeman’s (1978) criteria, the data analysed were of a concurrent, time-based type (type IV). The Match Vision Studio software program (version 3.0) was used to record and code the data. This program facilitates the detection of T-patterns as it works with a frame rate of 25 frames per second. Data quality: agreement between observations and generalizability of results The study data were recorded and coded by two observers, who were trained in three stages: (a) an initial theoretical stage in which the basic concepts, criteria and categories of the observation tool were explained; (b) a theoretical/practical stage, in which
Tactical behaviour of winning athletes
281
Table I. Variable criteria used in observation tool No.
Variable
1
Starting position
2 3
Group Position
4
Sprint
5
Sprint lane
6
Pace
7
Lap number
8
Track zone
9
Group formation Lane
10
Category Starting lane and zone: Lane 1 inner (PL1IN)/Lane 1 outer (PL1OU)/Lane 2 inner (PL2IN)/Lane 2 outer (PL2OU)/Lane 3 inner (PL3IN)/Lane 3 outer (PL3OU)/Lane 4 inner (PL4IN)/Lane 4 outer (PL4OU)/Lane 5 inner (PL5IN)/Lane 5 outer (PL5OU)/Lane 6 inner (PL6IN)/Lane 6 outer (PL6OU)/Lane 7 inner (PL7IN)/Lane 7 outer (PL7OU)/Lane 8 inner (PL8IN). Arrangement of runners. Single file (SFG)/Horizontal: pack, with more than one runner per lane (HPG). Position of athlete within group of 15 runners: AP1/AP2/AP3/AP4/AP5/AP6/AP7/AP8/AP9/AP10/AP11/AP12/ AP13/AP14/AP15. Change of pace or acceleration made by athlete in final stretch (last 400 m). By runner being observed (SPR)/by other runners (SPO). A runner is considered to initiate a sprint when there is noticeable increase in speed directly related to the final sprint. Lane the sprint takes place in. Lane 1 inner (SP1IN)/Lane 1 outer (SP1OU)/Lane 2 inner (SP2IN)/Lane 2 outer (SP2OU)/Lane 3 inner (SP3IN)/Lane 3 outer (SP3OU)/Lane 1 centre (SP1)/Lane 2 centre (SP2)/Lane 3 centre (SP3)/Other lanes (SPOL). Variations in pace. Even pace: Fast even pace, with runners showing clear difficulties keeping up. Opening up of group: Group forming a file (FEP)/Slow even pace, with runners not showing difficulties keeping up. Opening up of group: Group forming a horizontal pack (SEP). Changes in pace: By runner being observed (CPR)/by a member of the same team or country as the runner being observed (CPT)/by a rival (CPC). According to the rules of the International Association of Athletics Federations, in the 1500 m, runners start from the 300-m mark and run three complete laps (four laps in total: L1, L2, L3, L4). In the 5000 m, runners start at the 200-m line and run 13 laps (L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11, L12, L13). The track is divided into eight equal parts. The point of reference is the finish line that marks the end of zone 8 (Z8) and the start of zone 1 (Z1) (Figure 1). Compact group (CG)/Group that loses at least one member in the last positions (difference of more than 1 stride) (BRG)/The first member of the group breaks away from the leading pack by more than one stride (BFG). The lane the runner is in: RL1/RL2/RL3/RL4/RL5/RL6/RL7/RL8.
the observers were trained to use the software programmed with the tool; and (c) a practical stage in which the observers recorded and coded the data from a race not included in the sample. Training was considered complete when a level of interobserver
agreement of over 0.80 (kappa statistic) was obtained. Once the data for all the races had been recorded, both intraobserver agreement (agreement between data packages 1a and 1b processed by observer 1)
Figure 1. Track divided into zones. Note how each stretch (straight and/or bend) is divided into two zones.
282
S. Aragón et al.
and interobserver agreement (agreement between data packages 1a and 1b and the corresponding packages recorded by observer 2) were analysed. Agreement was assessed by Cohen’s kappa statistic using the software program SDIS-GSEQ, version 5.1, taking into consideration the recommendations of Bakeman and Quera (2001). According to the criteria of Landis and Koch (1977), the level of agreement was “almost perfect” as the kappa was higher than 0.81 for all cases (races and data packages). Within the generalizability theory (Cronbach, Gleser, Nanda, & Rajaratnam, 1972), the Category/ Race (C/R) design was used for the two race disciplines. The high values obtained in both cases – (r2) = 0.951 for 1500-m and (r2) = 0.990 for 5000 m – indicate that the combination of facets used provides a reliable means of explaining the variability in the two disciplines. Generalized linear model analysis showed that most of the variance was explained by the category component (77%) and its interaction with the race component (23%) in the 1500-m event. The corresponding percentages for the 5000-m event were 81% and 19%, respectively. Finally, the relative generalizability coefficients for both events – e2 = 0.978 for 1500 m and e2 = 0.982 for 5000 m – also indicated a high degree of generalizability, demonstrating the interpretative reliability of the data recorded across the different races in each discipline. Data analysis Two types of analysis were performed: detection of T-patterns using Theme (version 5.0) and investigation of the relationships between categorical variables using SPSS (version 15.0). The Theme software program is based on a powerful algorithm developed by (Magnusson, 1996) to detect sequential and time patterns that exist but are not immediately visible within data records (Lapresa, Arana, Anguera, & Garzón, 2013). According to Magnusson (2000, p. 94), “if A is an earlier and B a later component of the same recurring T-pattern then after an occurrence of A at t, there is an interval [t + d1, t + d2] (d2 ≥ d1 ≥ d0) that tends to contain at least one occurrence of B more often than would be expected by chance”. The following search parameters were used (for further information see Reference Manual; PatternVision Ltd & Noldus Information Technology bv, 2004): (1) a frequency of occurrence of 2 or more; (2) a significance level of 0.05; (3) acceptance of a Tpattern if Theme detected (from among all the randomly generated relations) n relations – with (n/ 200) < 0.05 – that had a critical interval (time distance) equal to or smaller than the critical intervals of the relation being tested; (4) activation of the
redundancy reduction tool, which means that a new pattern is eliminated if over 90% of its occurrences start and finish at the same time as existing patterns; (5) deactivation of the fast requirement option at all levels. In fast patterns, the lower time limit of the critical interval is set at 0, meaning that the components within this interval tend to occur in relatively close succession. Information from patterns with components that are related despite being separated in time are therefore not lost, as ultimately all patterns detected in a race are relevant for the purpose of the study. The chi-square test was used to investigate associations between categorical variables. Each of the 1500-m and 5000-m trials was divided into two stages: the race (from the gunshot to the start of the sprint) and the sprint (from the start of the first sprint to the crossing of the finish line by the winner). For both stages, categories corresponding to the criteria in the observation tool were grouped to investigate the existence of significant intra- and interdiscipline differences.
Results T-patterns T-pattern analysis detected relevant behavioural patterns that describe, both sequentially and temporally, effective tactics in both the 1500-m and 5000-m events. Table II shows the T-patterns detected in both disciplines according to the search parameters used. It is noteworthy that patterns were detected in 9 of the 13 1500-m finals analysed and in 8 of the 5000-m finals. Associations between categorical variables Despite an exhaustive grouping of categories for the race stage, significant differences (χ2 = 4.033; P = .045) were only found on comparing the starting position – central/outer lane (lanes 4, 5, 6, 7) vs outer/inner lane (lanes 1, 2, 3, 8) – in both disciplines. Noteworthy intra-discipline differences (χ2 = 3.682; P = .055; Fisher, 1956) were also found for the position of the winner during the race (first five positions vs. subsequent positions). Interdiscipline differences (χ2 = 5.046; P = .025) were observed for zones in which a change of pace was initiated. In the 1500-m races, this occurred mainly on the straights, while in the 5000-m races, it occurred mainly on the bends. Differences (χ2 = 8.700; P = .034) were also detected for the area in which change of pace occurred when the start and end zones of the straights and the start and end zones of the bends were grouped together. In the 1500-m races, sprints were initiated mostly at the
Tactical behaviour of winning athletes
283
Table II. T-patterns detected in 1500-m and 5000-m races, showing order numbers, string-like patterns, characteristics and races in which the patterns occur and distance in frames between events comprising each pattern Order no.
String-like pattern
Characteristics
Races and internal intervals
1500.1
[pl4in (sfg, ap4, cpc, l3z4, cg, rl1 sfg, ap3, cpc, l3z5, cg, rl1)]
Occurrences = 2; Length = 3 Duration = 6844
Osaka: 3301; 115 Berlin: 3303; 123
1500.2
[hpg, ap2, cpc, l3z6, cg, rl1 (sfg, ap2, spo, l4z2, rl1 ap1)]
Occurrences = 2; Length = 3 Duration = 4053
Sydney: 983; 1267 Barcelona: 771; 1030
1500.3
[pl4in (sfg, ap4, cpc, l3z4, cg, rl1)]
Occurrences = 2; Length = 2 Duration = 6390
Osaka: 3085 Berlin: 3303
1500.4
(sfg, ap1, spr, sp2, l4z8 ap1)
Occurrences = 3; Length = 2 Duration = 205
Sydney: 20 Athens: 95 Berlin: 87
1500.5
(sfg, ap1, cpr, l4z1, cg, rl1 ap1)
Occurrences = 2; Length = 2 Duration = 2514
Paris: 1319 London: 1193
1500.6
(sfg, ap1, spr, sp1in, l4z5 ap1)
Occurrences = 2; Length = 2 Duration = 1272
Göteborg: 539 Daegu: 731
1500.7
(sfg, ap1, spr, sp1in, l4z6 ap1)
Occurrences = 2; Length = 2 Duration = 852
Barcelona: 325 Daegu: 525
5000.1
(ap1, spr, sp1, l13z8 ap1)
Occurrences = 2; Length = 2 Duration = 154
Göteborg: 60 Beijing: 92
5000.2
(hpg, ap1, fepl12z1, cg, rl1 ap1)
Occurrences = 2; Length = 2 Duration = 4493
Berlin: 1621 Daegu: 2870
5000.3
[pl7ou (hpg, ap2, cpc, l10z5, cg, rl1)]
Occurrences = 2; Length = 2 Duration = 30,830
Sydney: 15,186 Göteborg: 15,649
5000.4
(ap2, spr, sp1ou, l13z7 ap1)
Occurrences = 3; Length = 2 Duration = 794
Paris: 180 Athens: 341 Göteborg: 270
5000.5
(sfg, ap1, cpr, l13z2, brg, rl1 ap1)
Occurrences = 2; Length = 2 Duration = 1397
Beijing: 665 Berlin: 730
5000.6
(sfg, ap1, cpr, l11z3, brg, rl1 ap1)
Occurrences = 2; Length = 2 Duration = 6207
Göteborg: 3947 Berlin: 2258
5000.7
(sfg, ap2, cpc, l13z1, brg, rl1 ap1)
Occurrences = 2; Length = 2 Duration = 2501
Edmonton: 1344 Göteborg: 1155
5000.8
(sfg, ap1, cpr, l9z4, bfg,rl1 ap1)
Occurrences = 2; Length = 2 Duration = 8240
Paris: 4207 Beijing: 4031
start and end of the straights (35.4% and 31.3%, respectively), while in the 5000-m races, they were initiated mostly at the end of straights (31.5%) or at the start of bends (32.4%). In the sprint stage (which occurred during the last lap in all cases), interdiscipline differences (χ2 = 4.531; P = .033) were observed for initiation of the sprint at the start or end of a stretch, regardless of whether this was a straight or a bend. In the 1500-m event, race winners started the sprint at the end of a stretch (zones 4 and 8 in the case of straights and zones 2 and 6 in the case of bends), while in the 5000-m event, the tendency was to start the sprint on the straight (start or end). Additionally, in both disciplines the final sprint was mostly initiated in lane 1 (χ2 = 2.901; P = .089). Finally, in the shorter event, all the race winners were among the first four runners when the sprint was initiated, while in the longer event, they were among the first three runners.
Table III summarizes the results of the T-pattern analysis and associations between categorical variables.
Discussion and conclusions In the 1500-m event, we observed general tactics that are consistent with strategies described in the existing theoretical framework, namely, a slow, even pace until the last lap (Thiel et al., 2012; Tucker & Noakes, 2009; Tucker et al., 2006), a final sprint in the last 100 m (Glover & Glover, 2005; Martin & Coe, 1997; USA Track & Field, 2000), powerful acceleration after the first 300 m (Martin & Coe, 1997) and an even pace with an increase in speed leading up to a final sprint in the last 400 m (Daniels, 1998; Martin & Coe, 1997). However, our analysis also revealed previously undescribed tactics employed by race winners in the 1500-m event. First, changes of pace tend to occur on
T-pattern
5000.3 5000.2 5000.3 5000.5 5000.6 5000.7 5000.8 5000.3 5000.5 5000.7 5000.1 5000.4 5000.1 5000.4 5000.1 5000.4 5000.1 4–7 Among first five runners Bend Straight Last lap Among first three runners Lane 1 centre lane 1 inner 1500.1 1500.3 1500.1 1500.2 1500.3 1500.5 1500.1 1500.3 1500.4 1500.6 1500.7 1500.7 1500.4 1500.6 1500.7 1500.6 1500.7 4–7 Among first five runners Straight Last lap Bend Among first four runners Lane 1 centre and lane 1 inner
Behaviour Effective tactical behaviour
Starting lane Position in race Change of pace zone Sprint lap Sprint initiation zone Position on initiation of sprint Sprint lane
T-pattern
5000 m 1500 m
straights (specifically in zone 4). The strategy underling this change in pace is to be able to launch an attack on a bend, forcing rivals to cover more metres and expend more energy. Top runners use pace variations as part of their winning tactics (Thiel et al., 2012). Second, sprints are mainly initiated in zone 2 (bend) during the last lap, enabling runners to build up to an end spurt in the last 100 m of the race (Tucker et al., 2006). Third, winning runners are among the first four runners when the sprint – a terminal acceleration which increases the anaerobic energy contribution (Foster et al., 2004) – is initiated. We also observed general tactical behaviours, already described in the theoretical framework, for 5000-m runners, namely, an even pace up to the third kilometre of the race, followed by a progressive increase in speed, building up to a sprint (Martin & Coe, 1997; Thiel et al., 2012) or a final sprint in the last 100 m (Daniels, 1998; Martin & Coe, 1997). Novel tactics employed by 5000-m race winners were also revealed by our analysis. First, winners tend to be among the leading five runners throughout the race. This supports the importance of maintaining homeostasis, i.e. by following this tactic, runners avoid having to expend unnecessary energy to take the lead or to mark the pace of the race (Tucker et al., 2006) and also reduce the risk of an unexpected end spurt by a rival. Second, changes in pace occur mainly on bends (zones 1, 2 and 5), similarly to in the 1500-m event. Third, sprints are mostly initiated in zones 3 or 4 during the last lap, enabling a progressive increase in speed culminating in an end spurt (Foster et al.,1993; Tucker et al., 2006). Fourth, winners are among the first three runners when the sprint is initiated, allowing them to achieve a good position to win during the final spurt. This suggests 5000-m runners also reserve some capacity for anaerobic energy turnover for the final sprint, as 1500-m runners do (Foster et al., 2004). Differences were also detected between the tactics employed by 1500-m and 5000-m runners. In the 1500-m, changes of pace occur mainly on straights (mostly in zone 4), while in the 5000-m, they tend to occur on bends (mainly in zones 1, 2 and 5). This difference would be explained by the fact that shortdistance runners, who have greater power and anaerobic capacity than long-distance runners (Tucker et al., 2006), need to be ready to launch an attack on the straight, looking for a more powerful change of pace. Winning runners in the 1500-m initiate their sprint in zones 2 (end of bend) or 4 (end of straight) in preparation for an end spurt (Tucker et al., 2006), while those in the 5000-m initiate their sprint in the initial straight (zones 3 and 4) following a progressive increase in speed (Foster et al., 1993; Tucker et al., 2006). Finally, winning
Behaviour
S. Aragón et al.
Table III. Effective tactics: summary of results
284
Tactical behaviour of winning athletes runners are among the first four runners when the sprint is initiated in the 1500-m event but among the first three runners in the 5000-m event. Our analyses also revealed several previously undescribed tactics shared by race winners in both disciplines. Being in the lead or in the leading pack is a determining factor for victory in both disciplines, with athletes seeking to minimise physiological perturbations through controlling an even pace early in the race and allowing the selection of a good position for the final sprint. Starting the race in lanes 4–7 is also an important determinant of success. (Lane positions are assigned based on performance in previous phases on the competition.) Finally, sprints are initiated in the last lap, generally in the inner or central part of lane 1 (to shorten the distance and occupy prime positions) and may or may not be initiated by the winner of the race. Our findings provide invaluable insights into successful behavioural tactics employed by winners of 1500-m and 5000-m races in top-class competitions. There are both similar and differing tactics employed by winners of 1500-m and 5000-m events at international championships. Funding We gratefully acknowledge the support of the Spanish Government project Observación de la interacción en deporte y actividad física: Avances técnicos y metodológicos en registros automatizados cualitativos-cuantitativos (Secretaría de Estado de Investigación, Desarrollo e Innovación del Ministerio de Economía y Competitividad) during the period 2012–2015 [grant number DEP2012-32124].
References Anguera, M. T. (1979). Observational typology. Quality & quantity. European-American Journal of Methodology, 13, 449–484. Anguera, M. T., Blanco-Villaseñor, A., Hernández-Mendo, A., & Losada, J. L. (2011). Diseños observacionales: Ajuste y aplicación en psicología del deporte [Observational designs: Their suitability and application in sports psychology]. Cuadernos de Psicología del Deporte, 11(2), 63–76. Anguera, M. T., & Hernández-Mendo, A. (2013). Observational methodology in sport sciences. e-balonmano.com: Journal of Sport Science, 9, 135–160. Anguera, M. T., Magnusson, M. S., & Jonsson, G. K. (2007). Instrumentos no estándar [Non-standard instruments]. Avances en medición, 5(1), 63–82. Bakeman, R. (1978). Untangling streams of behavior: Sequential analysis of observation data. In G. P. Sackett (Ed.), Observing behavior, Vol. II: Data collection and analysis methods (pp. 63– 78). Baltimore, MD: University Park Press. Bakeman, R., & Quera, V. (2001). Using GSEQ with SPSS. Metodología de las Ciencias del Comportamiento, 3, 195–214. Brown, E. (2005). Running strategy of female middle distance runners attempting the 800m and 1500m “Double” at a major championship: A performance analysis and qualitative investigation. International Journal of Performance Analysis in Sport, 5(3), 73–88.
285
Camerino, O., Chaverri, J., Anguera, M. T., & Jonsson, G. K. (2012). Dynamics of the game in soccer: Detection of tpatterns. European Journal of Sport Science, 12, 216–224. doi:10.1080/17461391.2011.566362 Cronbach, L. J., Gleser, G. C., Nanda, H., & Rajaratnam, N. (1972). The dependability of behavioral measurements: Theory of generalizability for scores and profiles. New York, NY: Wiley. Daniels, J. (1998). Daniels’ running formula. Champaign, IL: Human Kinetics. De Koning, J. J., Bobbert, M. F., & Foster, C. (1999). Determination of optimal pacing strategy in track cycling with an energy flow model. Journal of Science and Medicine in Sport, 2, 266–277. doi:10.1016/S1440-2440(99)80178-9 Duffield, R., Dawson, B., & Goodman, C. (2005). Energy system contribution to 1500- and 3000-metre track running. Journal of Sport Sciences, 23, 993–1002. doi:10.1080/02640410400021963 Fisher, R. A. (1956). Statistical methods and scientific inference. Edinburgh: Oliver & Boyd. Foster, C., de Koning, J. J., Hettinga, F., Lampen, J., Dodge, C., Bobbert, M., & Porcari, J. P. (2004). Effect of competitive distance on energy expenditure during simulated competition. International Journal of Sports Medicine, 25, 198–204. doi:10.1055/s-2003-45260 Foster, C., Snyder, A. C., Thompson, N. N., Green, M. A., Foley, M., & Schrager, M. (1993). Effect of pacing strategy on cycle time trial performance. Medicine and Science in Sports and Exercise, 25, 383–388. Glover, B., & Glover, S. F. (2005). Manual del corredor de competición [The competitive runner’s handbook]. Badalona: Paidotribo. Gutiérrez-Santiago, A., Prieto, I., Camerino, O., & Anguera, M. T. (2011). The temporal structure of judo bouts in visually impaired men and women. Journal of Sports Sciences, 29, 1443–1451. Hanley, B., Bissas, A., & Drake, A. (2013). Kinematic characteristics of elite men’s 50 km race walking. European Journal of Sport Science, 13, 272–279. doi:10.1080/17461391.2011. 630104 Hill, D. V. (1999). Energy system contributions in middledistance running events. Journal of Sport Sciences, 17, 477– 483. doi:10.1080/026404199365786 Hugh, R. (2009). A new modelling approach demonstrating the inability to make up for lost time in endurance running events. IMA Journal of Management Mathematics, 20, 109–120. Jones, A. M., & Whipp, B. J. (2002). Bioenergetic constraints on tactical decision making in middle distance running. British Journal of Sports Medicine, 36, 102–104. doi:10.1136/bjsm. 36.2.102 Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159– 174. doi:10.2307/2529310 Lapresa, D., Álvarez, L., Arana, J., Garzón, B., & Caballero, V. (2013). Observational analysis of the offensive sequences that ended in a shot by the winning team of the 2010 UEFA Futsal Championship. Journal of Sport Sciences, 31, 1731–1739. doi:10.1080/02640414.2013.803584 Lapresa, D., Anguera, M. T., Alsasua, R., Arana, J., & Garzón, B. (2013). Comparative analysis of T-patterns using real time data and simulated data by assignment of conventional durations: The construction of efficacy in children’s basketball. International Journal of Performance Analysis in Sport, 13, 321–339. Lapresa, D., Arana, J., Anguera, M. T., & Garzón, B. (2013). Comparative analysis of the sequentiality using SDIS-GSEQ and THEME: A concrete example in soccer. Journal of Sport Sciences, 31, 1687–1695. doi:10.1080/02640414.2013.796061 Lapresa, D., Ibáñez, R., Arana, J., Garzón, B., & Amatria, M. (2011). Spatial and temporal analysis of karate kumite moves: Comparative study of the senior and 12–13 year old groups.
286
S. Aragón et al.
International Journal of Performance Analysis in Sport, 11(1), 57–70. Magnusson, M. S. (1996). Hidden real-time patterns in intra- and inter-individual behavior. European Journal of Psychological Assessment, 12, 112–123. doi:10.1027/1015-5759.12.2.112 Magnusson, M. S. (2000). Discovering hidden time patterns in behavior: T-patterns and their detection. Behavior Research Methods, Instruments, & Computers, 32(1), 93–110. doi:10.3758/ BF03200792 Martin, D. E., & Coe, P. N. (1997). Better training for distance running. Champaign, IL: Human Kinetics. Paavolainen, L., Häkkinen, K., Hämäläinen, I., Nummela, A., & Heikki, R. (1999). Explosive-strength training improves 5-km running time by improving running economy and muscle power. Journal of Applied Physiology, 86, 1527–1533. PatternVision Ltd & Noldus Information Technology bv. (2004). Theme, powerful tool for detection and analysis of hidden patterns in behaviour. Reference manual, version 5.0. Wageningen: Author. Pitney, W. A., & Parker, J. (2002). Qualitative research applications in athletic training. Journal of Athletic Training, 37, 168–173. Remmert, H. (2003). Analysis of group-tactical offensive behavior in elite basketball on the basis of a process orientated model. European Journal of Sport Science, 3(3), 1–12. doi:10.1080/ 17461390300073311 Renfree, A., & St. Clair. (2013). Influence of different performance levels on pacing strategy during the women’s world
championship marathon race (2013). International Journal of Sport Physiology and Performance, 8, 279–285. Szczepański, Ł. (2008). Measuring the effectiveness of strategies and quantifying players’ performance in football. International Journal of Performance Analysis in Sport, 8(2), 55–66. Thiel, C., Foster, C., Banzer, W., & De Koning, J. (2012). Pacing in Olympic track races: Competitive tactics versus best performance strategy. Journal of Sports Sciences, 30, 1107–1115. doi:10.1080/02640414.2012.701759 Tucker, R., Lambert, M. I., & Noakes, T. D. (2006). An analysis of pacing strategies during men’s world-record performances in track athletics. International Journal of Sports Physiology and Performance, 1, 233–245. Tucker, R., & Noakes, T. D. (2009). The physiological regulation of pacing strategy during exercise: A critical review. British Journal of Sports Medicina, 43(6), e1–e9. doi:10.1136/ bjsm.2009.057562 USA Track & Field. (2000). USA track & field coaching manual. Champaign, IL: Human Kinetics. Young, B. W., & Salmela, J. H. (2010). Examination of practice activities related to the acquisition of elite performance in Canadian middle distance running. International Journal of Sport Psychology, 41, 73–90.
ISSN: 1746-1391 (Print) 1536-7290 (Online) Journal homepage: http://www.tandfonline.com/loi/tejs20
Tactical behaviour of winning athletes in major championship 1500-m and 5000-m track finals Sonia Aragón, Daniel Lapresa, Javier Arana, M. Teresa Anguera & Belén Garzón To cite this article: Sonia Aragón, Daniel Lapresa, Javier Arana, M. Teresa Anguera & Belén Garzón (2016) Tactical behaviour of winning athletes in major championship 1500-m and 5000-m track finals, European Journal of Sport Science, 16:3, 279-286, DOI: 10.1080/17461391.2015.1009494 To link to this article: http://dx.doi.org/10.1080/17461391.2015.1009494
Published online: 09 Feb 2015.
Submit your article to this journal
Article views: 525
View related articles
View Crossmark data
Citing articles: 2 View citing articles
Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=tejs20 Download by: [The University Of Melbourne Libraries]
Date: 06 May 2017, At: 05:36
European Journal of Sport Science, 2016 Vol. 16, No. 3, 279–286, http://dx.doi.org/10.1080/17461391.2015.1009494
ORIGINAL ARTICLE
Tactical behaviour of winning athletes in major championship 1500-m and 5000-m track finals
SONIA ARAGÓN1, DANIEL LAPRESA1, JAVIER ARANA2, M. TERESA ANGUERA3, & BELÉN GARZÓN1 1
Department of Educational Sciences, University of La Rioja, Logroño, Spain, 2Department of Education, International University of La Rioja (UNIR), Logroño, Spain, 3Department of Behavioural Sciences Methodology, Institute of Brain, Cognition and Behavior, University of Barcelona, Barcelona, Spain
Abstract This article analyses the tactics employed by middle-distance (1500-m) and long-distance (5000-m) runners from an observational methodology perspective. The subject of investigation has received little attention from specialists in the field of athletics, with most research focusing on physiological studies of athlete performance. Using an ad hoc observation tool and a database containing systematically recorded data we detected time patterns (T-patterns) within the data recorded using the Theme software program (version 5.0), and analysed the tactics employed by winners of the men’s 1500-m and 5000-m finals of the World Championships in Athletics [Edmonton 2001, Paris 2003, Helsinki 2005 (1500-m final only), Osaka 2007 (1500-m final only), Berlin 2009 and Daegu 2011], the European Athletics Championships (Munich 2002, Göteborg 2006, and Barcelona 2010) and the Olympic Games (Sydney 2000, Athens 2004, Beijing 2008 and London 2012). T-pattern detection and investigation of the relationship between category systems corresponding to the criteria comprising the observation tool revealed both similarities (starting lane and lane used during race, runner’s position during race and sprint zone and lane) and differences (variations in pace, zones in which changes of pace occur, sprint initiation zone and winner’s position at the start of the sprint) between the two disciplines. Keywords: Tactics, behaviour, performance, 1500-m, 5000-m, T-patterns
Introduction While tactics employed in team sports have been the focus of considerable research (Lapresa, Álvarez, Arana, Garzón, & Caballero, 2013; Remmert, 2003; Szczepański, 2008), those used in middle- and longdistance track events have received relatively little attention. Interest in qualitative research methods has grown in the field of athletics (Pitney & Parker, 2002; Young & Salmela, 2010), but few studies have focused on the tactical approaches of middle- and long-distance runners (Brown, 2005; Renfree & St. Clair, 2013; Tucker, Lambert, & Noakes, 2006). Furthermore, the studies that do touch on these tactics are generally interested in the physiological aspects of running and training (Duffield, Dawson, & Goodman, 2005; Hill, 1999) or have a largely biomedical focus, with an emphasis on athlete performance (Hanley, Bissas, & Drake, 2013; Hugh,
2009; Paavolainen, Häkkinen, Hämäläinen, Nummela, & Heikki, 1999). Tactics are an important component of longdistance track events, however, as they frequently play a decisive role in outcome (De Koning, Bobbert, & Foster, 1999). As stated by Thiel, Foster, Banzer, and De Koning (2012), the tactical behaviour of athletes varies considerably depending on whether their aim is to win the race or to beat a record. As time is less important than victory in championship track events, the choice of one tactic over another is determined, not only by the characteristics of the athlete, but also by those of his or her rivals (Jones & Whipp, 2002). Elite runners use different pacing strategies to break away from the group, although in international competitions, it is generally the sprint in the last lap that determines victory (Thiel et al., 2012).
Correspondence: D. Lapresa, Universidad de La Rioja Edificio Vives, C/Luis de Ulloa s/n, 26004 Logroño, La Rioja, Spain. E-mail: daniel. [email protected] © 2015 European College of Sport Science
280
S. Aragón et al.
With a view to adding to the limited body of knowledge on runner tactics in middle-distance (1500-m) and long-distance (5000-m) races, we decided to analyse the men’s finals of the World Championships in Athletics, the European Athletics Championships and the Olympic Games between 2000 and 2012 to investigate the tactics employed by winners of 1500-m and 5000-m races and identify similarities and differences in tactical behaviour in the two disciplines. Methods We employed an observational methodology design (Anguera, 1979). Observational methodology is widely used in both team sports (Camerino, Chaverri, Anguera, & Jonsson, 2012; Lapresa, Anguera, Alsasua, Arana, & Garzón, 2013) and individual sports (Gutiérrez-Santiago, Prieto, Camerino, & Anguera, 2011; Lapresa, Ibáñez, Arana, Garzón, & Amatria, 2011) as these fulfil the basic requirements proposed by Anguera and Hernández-Mendo (2013): habitual behaviour (tactical behaviour is a common component of track events), natural context (official competition races), absence of a standard instrument and perceptibility. These conditions are all guaranteed in the track events analysed in our study. The methodology chosen is also justified considering that a purpose-built observation tool was used to record, analyse and interpret the tactics employed by middle- and long-distance runners. The construct validity of the instrument was ensured by building it on state-of-the-art expertise in the field instrument. The observational design, as per Anguera, BlancoVillaseñor, Hernández-Mendo, and Losada (2011), was nomothetic/follow-up/multidimensional (N/F/ M): nomothetic because the individual athletes were not acting as a unit; follow-up at both the inter-sessional level (search for similarities and differences in the tactical approaches of race winners) and intra-sessional level (frame-by-frame study of tactical behaviour throughout the race, enabling the detection of T-patterns); and multidimensional (different observation dimensions/criteria) with proxemic response. Finally, the observation was non-participative and characterized by complete perceptivity (direct observation). Participants The research project was approved by a scientific committee at the University of La Rioja in accordance with the Ethical Principles of Psychologists and Code of Conduct of the American Psychological Association, and the rules of the Ethics Committee of the Spanish Association of Psychologists.
We used an intentional sample of all the participants in the men’s 1500-m and 5000-m finals at the World Championships in Athletics (Edmonton 2001, Paris 2003, Berlin 2009, and Daegu 2011), the European Athletics Championships (Munich, 2002, Göteborg 2006, and Barcelona 2010) and the Olympic Games (Sydney 2000, Athens 2004, Beijing 2008, and London 2012). We also analysed the 1500-m men’s finals of the World Championships in Helsinki (2005) and Osaka (2007) but not the 5000-m finals, as over 10% of the total race times were not observable. All three events were top-class competitions featuring the world’s best athletes. Observation tool The observation tool combined a field format (necessary as the design was multidimensional) and a category system (Anguera, Magnusson, & Jonsson, 2007), which is particularly appropriate as all the criteria met the following requirements: they were atemporal and fit into an available theoretical framework (Daniels, 1998; Glover & Glover, 2005; Martin & Coe, 1997; Thiel et al., 2012; Tucker et al., 2006; USA Track & Field, 2000). The categories built were exhaustive and mutually exclusive. The observation tool was composed of fixed and variable criteria. The fixed criteria were competition (World Championship, European Championship, Olympic Games), distance (1500m, 5000-m) and race (Sydney, Edmonton, Munich, Paris, Athens, Helsinki, Göteborg, Osaka, Beijing, Berlin, Barcelona, Daegu and London). The variable criteria (Table I and Figure 1) were designed to obtain detailed information on the tactics employed by race winners based on their interaction with their rivals. Recording tool Data were recorded systematically using a combination of alphabetical and numerical codes. According to Bakeman’s (1978) criteria, the data analysed were of a concurrent, time-based type (type IV). The Match Vision Studio software program (version 3.0) was used to record and code the data. This program facilitates the detection of T-patterns as it works with a frame rate of 25 frames per second. Data quality: agreement between observations and generalizability of results The study data were recorded and coded by two observers, who were trained in three stages: (a) an initial theoretical stage in which the basic concepts, criteria and categories of the observation tool were explained; (b) a theoretical/practical stage, in which
Tactical behaviour of winning athletes
281
Table I. Variable criteria used in observation tool No.
Variable
1
Starting position
2 3
Group Position
4
Sprint
5
Sprint lane
6
Pace
7
Lap number
8
Track zone
9
Group formation Lane
10
Category Starting lane and zone: Lane 1 inner (PL1IN)/Lane 1 outer (PL1OU)/Lane 2 inner (PL2IN)/Lane 2 outer (PL2OU)/Lane 3 inner (PL3IN)/Lane 3 outer (PL3OU)/Lane 4 inner (PL4IN)/Lane 4 outer (PL4OU)/Lane 5 inner (PL5IN)/Lane 5 outer (PL5OU)/Lane 6 inner (PL6IN)/Lane 6 outer (PL6OU)/Lane 7 inner (PL7IN)/Lane 7 outer (PL7OU)/Lane 8 inner (PL8IN). Arrangement of runners. Single file (SFG)/Horizontal: pack, with more than one runner per lane (HPG). Position of athlete within group of 15 runners: AP1/AP2/AP3/AP4/AP5/AP6/AP7/AP8/AP9/AP10/AP11/AP12/ AP13/AP14/AP15. Change of pace or acceleration made by athlete in final stretch (last 400 m). By runner being observed (SPR)/by other runners (SPO). A runner is considered to initiate a sprint when there is noticeable increase in speed directly related to the final sprint. Lane the sprint takes place in. Lane 1 inner (SP1IN)/Lane 1 outer (SP1OU)/Lane 2 inner (SP2IN)/Lane 2 outer (SP2OU)/Lane 3 inner (SP3IN)/Lane 3 outer (SP3OU)/Lane 1 centre (SP1)/Lane 2 centre (SP2)/Lane 3 centre (SP3)/Other lanes (SPOL). Variations in pace. Even pace: Fast even pace, with runners showing clear difficulties keeping up. Opening up of group: Group forming a file (FEP)/Slow even pace, with runners not showing difficulties keeping up. Opening up of group: Group forming a horizontal pack (SEP). Changes in pace: By runner being observed (CPR)/by a member of the same team or country as the runner being observed (CPT)/by a rival (CPC). According to the rules of the International Association of Athletics Federations, in the 1500 m, runners start from the 300-m mark and run three complete laps (four laps in total: L1, L2, L3, L4). In the 5000 m, runners start at the 200-m line and run 13 laps (L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11, L12, L13). The track is divided into eight equal parts. The point of reference is the finish line that marks the end of zone 8 (Z8) and the start of zone 1 (Z1) (Figure 1). Compact group (CG)/Group that loses at least one member in the last positions (difference of more than 1 stride) (BRG)/The first member of the group breaks away from the leading pack by more than one stride (BFG). The lane the runner is in: RL1/RL2/RL3/RL4/RL5/RL6/RL7/RL8.
the observers were trained to use the software programmed with the tool; and (c) a practical stage in which the observers recorded and coded the data from a race not included in the sample. Training was considered complete when a level of interobserver
agreement of over 0.80 (kappa statistic) was obtained. Once the data for all the races had been recorded, both intraobserver agreement (agreement between data packages 1a and 1b processed by observer 1)
Figure 1. Track divided into zones. Note how each stretch (straight and/or bend) is divided into two zones.
282
S. Aragón et al.
and interobserver agreement (agreement between data packages 1a and 1b and the corresponding packages recorded by observer 2) were analysed. Agreement was assessed by Cohen’s kappa statistic using the software program SDIS-GSEQ, version 5.1, taking into consideration the recommendations of Bakeman and Quera (2001). According to the criteria of Landis and Koch (1977), the level of agreement was “almost perfect” as the kappa was higher than 0.81 for all cases (races and data packages). Within the generalizability theory (Cronbach, Gleser, Nanda, & Rajaratnam, 1972), the Category/ Race (C/R) design was used for the two race disciplines. The high values obtained in both cases – (r2) = 0.951 for 1500-m and (r2) = 0.990 for 5000 m – indicate that the combination of facets used provides a reliable means of explaining the variability in the two disciplines. Generalized linear model analysis showed that most of the variance was explained by the category component (77%) and its interaction with the race component (23%) in the 1500-m event. The corresponding percentages for the 5000-m event were 81% and 19%, respectively. Finally, the relative generalizability coefficients for both events – e2 = 0.978 for 1500 m and e2 = 0.982 for 5000 m – also indicated a high degree of generalizability, demonstrating the interpretative reliability of the data recorded across the different races in each discipline. Data analysis Two types of analysis were performed: detection of T-patterns using Theme (version 5.0) and investigation of the relationships between categorical variables using SPSS (version 15.0). The Theme software program is based on a powerful algorithm developed by (Magnusson, 1996) to detect sequential and time patterns that exist but are not immediately visible within data records (Lapresa, Arana, Anguera, & Garzón, 2013). According to Magnusson (2000, p. 94), “if A is an earlier and B a later component of the same recurring T-pattern then after an occurrence of A at t, there is an interval [t + d1, t + d2] (d2 ≥ d1 ≥ d0) that tends to contain at least one occurrence of B more often than would be expected by chance”. The following search parameters were used (for further information see Reference Manual; PatternVision Ltd & Noldus Information Technology bv, 2004): (1) a frequency of occurrence of 2 or more; (2) a significance level of 0.05; (3) acceptance of a Tpattern if Theme detected (from among all the randomly generated relations) n relations – with (n/ 200) < 0.05 – that had a critical interval (time distance) equal to or smaller than the critical intervals of the relation being tested; (4) activation of the
redundancy reduction tool, which means that a new pattern is eliminated if over 90% of its occurrences start and finish at the same time as existing patterns; (5) deactivation of the fast requirement option at all levels. In fast patterns, the lower time limit of the critical interval is set at 0, meaning that the components within this interval tend to occur in relatively close succession. Information from patterns with components that are related despite being separated in time are therefore not lost, as ultimately all patterns detected in a race are relevant for the purpose of the study. The chi-square test was used to investigate associations between categorical variables. Each of the 1500-m and 5000-m trials was divided into two stages: the race (from the gunshot to the start of the sprint) and the sprint (from the start of the first sprint to the crossing of the finish line by the winner). For both stages, categories corresponding to the criteria in the observation tool were grouped to investigate the existence of significant intra- and interdiscipline differences.
Results T-patterns T-pattern analysis detected relevant behavioural patterns that describe, both sequentially and temporally, effective tactics in both the 1500-m and 5000-m events. Table II shows the T-patterns detected in both disciplines according to the search parameters used. It is noteworthy that patterns were detected in 9 of the 13 1500-m finals analysed and in 8 of the 5000-m finals. Associations between categorical variables Despite an exhaustive grouping of categories for the race stage, significant differences (χ2 = 4.033; P = .045) were only found on comparing the starting position – central/outer lane (lanes 4, 5, 6, 7) vs outer/inner lane (lanes 1, 2, 3, 8) – in both disciplines. Noteworthy intra-discipline differences (χ2 = 3.682; P = .055; Fisher, 1956) were also found for the position of the winner during the race (first five positions vs. subsequent positions). Interdiscipline differences (χ2 = 5.046; P = .025) were observed for zones in which a change of pace was initiated. In the 1500-m races, this occurred mainly on the straights, while in the 5000-m races, it occurred mainly on the bends. Differences (χ2 = 8.700; P = .034) were also detected for the area in which change of pace occurred when the start and end zones of the straights and the start and end zones of the bends were grouped together. In the 1500-m races, sprints were initiated mostly at the
Tactical behaviour of winning athletes
283
Table II. T-patterns detected in 1500-m and 5000-m races, showing order numbers, string-like patterns, characteristics and races in which the patterns occur and distance in frames between events comprising each pattern Order no.
String-like pattern
Characteristics
Races and internal intervals
1500.1
[pl4in (sfg, ap4, cpc, l3z4, cg, rl1 sfg, ap3, cpc, l3z5, cg, rl1)]
Occurrences = 2; Length = 3 Duration = 6844
Osaka: 3301; 115 Berlin: 3303; 123
1500.2
[hpg, ap2, cpc, l3z6, cg, rl1 (sfg, ap2, spo, l4z2, rl1 ap1)]
Occurrences = 2; Length = 3 Duration = 4053
Sydney: 983; 1267 Barcelona: 771; 1030
1500.3
[pl4in (sfg, ap4, cpc, l3z4, cg, rl1)]
Occurrences = 2; Length = 2 Duration = 6390
Osaka: 3085 Berlin: 3303
1500.4
(sfg, ap1, spr, sp2, l4z8 ap1)
Occurrences = 3; Length = 2 Duration = 205
Sydney: 20 Athens: 95 Berlin: 87
1500.5
(sfg, ap1, cpr, l4z1, cg, rl1 ap1)
Occurrences = 2; Length = 2 Duration = 2514
Paris: 1319 London: 1193
1500.6
(sfg, ap1, spr, sp1in, l4z5 ap1)
Occurrences = 2; Length = 2 Duration = 1272
Göteborg: 539 Daegu: 731
1500.7
(sfg, ap1, spr, sp1in, l4z6 ap1)
Occurrences = 2; Length = 2 Duration = 852
Barcelona: 325 Daegu: 525
5000.1
(ap1, spr, sp1, l13z8 ap1)
Occurrences = 2; Length = 2 Duration = 154
Göteborg: 60 Beijing: 92
5000.2
(hpg, ap1, fepl12z1, cg, rl1 ap1)
Occurrences = 2; Length = 2 Duration = 4493
Berlin: 1621 Daegu: 2870
5000.3
[pl7ou (hpg, ap2, cpc, l10z5, cg, rl1)]
Occurrences = 2; Length = 2 Duration = 30,830
Sydney: 15,186 Göteborg: 15,649
5000.4
(ap2, spr, sp1ou, l13z7 ap1)
Occurrences = 3; Length = 2 Duration = 794
Paris: 180 Athens: 341 Göteborg: 270
5000.5
(sfg, ap1, cpr, l13z2, brg, rl1 ap1)
Occurrences = 2; Length = 2 Duration = 1397
Beijing: 665 Berlin: 730
5000.6
(sfg, ap1, cpr, l11z3, brg, rl1 ap1)
Occurrences = 2; Length = 2 Duration = 6207
Göteborg: 3947 Berlin: 2258
5000.7
(sfg, ap2, cpc, l13z1, brg, rl1 ap1)
Occurrences = 2; Length = 2 Duration = 2501
Edmonton: 1344 Göteborg: 1155
5000.8
(sfg, ap1, cpr, l9z4, bfg,rl1 ap1)
Occurrences = 2; Length = 2 Duration = 8240
Paris: 4207 Beijing: 4031
start and end of the straights (35.4% and 31.3%, respectively), while in the 5000-m races, they were initiated mostly at the end of straights (31.5%) or at the start of bends (32.4%). In the sprint stage (which occurred during the last lap in all cases), interdiscipline differences (χ2 = 4.531; P = .033) were observed for initiation of the sprint at the start or end of a stretch, regardless of whether this was a straight or a bend. In the 1500-m event, race winners started the sprint at the end of a stretch (zones 4 and 8 in the case of straights and zones 2 and 6 in the case of bends), while in the 5000-m event, the tendency was to start the sprint on the straight (start or end). Additionally, in both disciplines the final sprint was mostly initiated in lane 1 (χ2 = 2.901; P = .089). Finally, in the shorter event, all the race winners were among the first four runners when the sprint was initiated, while in the longer event, they were among the first three runners.
Table III summarizes the results of the T-pattern analysis and associations between categorical variables.
Discussion and conclusions In the 1500-m event, we observed general tactics that are consistent with strategies described in the existing theoretical framework, namely, a slow, even pace until the last lap (Thiel et al., 2012; Tucker & Noakes, 2009; Tucker et al., 2006), a final sprint in the last 100 m (Glover & Glover, 2005; Martin & Coe, 1997; USA Track & Field, 2000), powerful acceleration after the first 300 m (Martin & Coe, 1997) and an even pace with an increase in speed leading up to a final sprint in the last 400 m (Daniels, 1998; Martin & Coe, 1997). However, our analysis also revealed previously undescribed tactics employed by race winners in the 1500-m event. First, changes of pace tend to occur on
T-pattern
5000.3 5000.2 5000.3 5000.5 5000.6 5000.7 5000.8 5000.3 5000.5 5000.7 5000.1 5000.4 5000.1 5000.4 5000.1 5000.4 5000.1 4–7 Among first five runners Bend Straight Last lap Among first three runners Lane 1 centre lane 1 inner 1500.1 1500.3 1500.1 1500.2 1500.3 1500.5 1500.1 1500.3 1500.4 1500.6 1500.7 1500.7 1500.4 1500.6 1500.7 1500.6 1500.7 4–7 Among first five runners Straight Last lap Bend Among first four runners Lane 1 centre and lane 1 inner
Behaviour Effective tactical behaviour
Starting lane Position in race Change of pace zone Sprint lap Sprint initiation zone Position on initiation of sprint Sprint lane
T-pattern
5000 m 1500 m
straights (specifically in zone 4). The strategy underling this change in pace is to be able to launch an attack on a bend, forcing rivals to cover more metres and expend more energy. Top runners use pace variations as part of their winning tactics (Thiel et al., 2012). Second, sprints are mainly initiated in zone 2 (bend) during the last lap, enabling runners to build up to an end spurt in the last 100 m of the race (Tucker et al., 2006). Third, winning runners are among the first four runners when the sprint – a terminal acceleration which increases the anaerobic energy contribution (Foster et al., 2004) – is initiated. We also observed general tactical behaviours, already described in the theoretical framework, for 5000-m runners, namely, an even pace up to the third kilometre of the race, followed by a progressive increase in speed, building up to a sprint (Martin & Coe, 1997; Thiel et al., 2012) or a final sprint in the last 100 m (Daniels, 1998; Martin & Coe, 1997). Novel tactics employed by 5000-m race winners were also revealed by our analysis. First, winners tend to be among the leading five runners throughout the race. This supports the importance of maintaining homeostasis, i.e. by following this tactic, runners avoid having to expend unnecessary energy to take the lead or to mark the pace of the race (Tucker et al., 2006) and also reduce the risk of an unexpected end spurt by a rival. Second, changes in pace occur mainly on bends (zones 1, 2 and 5), similarly to in the 1500-m event. Third, sprints are mostly initiated in zones 3 or 4 during the last lap, enabling a progressive increase in speed culminating in an end spurt (Foster et al.,1993; Tucker et al., 2006). Fourth, winners are among the first three runners when the sprint is initiated, allowing them to achieve a good position to win during the final spurt. This suggests 5000-m runners also reserve some capacity for anaerobic energy turnover for the final sprint, as 1500-m runners do (Foster et al., 2004). Differences were also detected between the tactics employed by 1500-m and 5000-m runners. In the 1500-m, changes of pace occur mainly on straights (mostly in zone 4), while in the 5000-m, they tend to occur on bends (mainly in zones 1, 2 and 5). This difference would be explained by the fact that shortdistance runners, who have greater power and anaerobic capacity than long-distance runners (Tucker et al., 2006), need to be ready to launch an attack on the straight, looking for a more powerful change of pace. Winning runners in the 1500-m initiate their sprint in zones 2 (end of bend) or 4 (end of straight) in preparation for an end spurt (Tucker et al., 2006), while those in the 5000-m initiate their sprint in the initial straight (zones 3 and 4) following a progressive increase in speed (Foster et al., 1993; Tucker et al., 2006). Finally, winning
Behaviour
S. Aragón et al.
Table III. Effective tactics: summary of results
284
Tactical behaviour of winning athletes runners are among the first four runners when the sprint is initiated in the 1500-m event but among the first three runners in the 5000-m event. Our analyses also revealed several previously undescribed tactics shared by race winners in both disciplines. Being in the lead or in the leading pack is a determining factor for victory in both disciplines, with athletes seeking to minimise physiological perturbations through controlling an even pace early in the race and allowing the selection of a good position for the final sprint. Starting the race in lanes 4–7 is also an important determinant of success. (Lane positions are assigned based on performance in previous phases on the competition.) Finally, sprints are initiated in the last lap, generally in the inner or central part of lane 1 (to shorten the distance and occupy prime positions) and may or may not be initiated by the winner of the race. Our findings provide invaluable insights into successful behavioural tactics employed by winners of 1500-m and 5000-m races in top-class competitions. There are both similar and differing tactics employed by winners of 1500-m and 5000-m events at international championships. Funding We gratefully acknowledge the support of the Spanish Government project Observación de la interacción en deporte y actividad física: Avances técnicos y metodológicos en registros automatizados cualitativos-cuantitativos (Secretaría de Estado de Investigación, Desarrollo e Innovación del Ministerio de Economía y Competitividad) during the period 2012–2015 [grant number DEP2012-32124].
References Anguera, M. T. (1979). Observational typology. Quality & quantity. European-American Journal of Methodology, 13, 449–484. Anguera, M. T., Blanco-Villaseñor, A., Hernández-Mendo, A., & Losada, J. L. (2011). Diseños observacionales: Ajuste y aplicación en psicología del deporte [Observational designs: Their suitability and application in sports psychology]. Cuadernos de Psicología del Deporte, 11(2), 63–76. Anguera, M. T., & Hernández-Mendo, A. (2013). Observational methodology in sport sciences. e-balonmano.com: Journal of Sport Science, 9, 135–160. Anguera, M. T., Magnusson, M. S., & Jonsson, G. K. (2007). Instrumentos no estándar [Non-standard instruments]. Avances en medición, 5(1), 63–82. Bakeman, R. (1978). Untangling streams of behavior: Sequential analysis of observation data. In G. P. Sackett (Ed.), Observing behavior, Vol. II: Data collection and analysis methods (pp. 63– 78). Baltimore, MD: University Park Press. Bakeman, R., & Quera, V. (2001). Using GSEQ with SPSS. Metodología de las Ciencias del Comportamiento, 3, 195–214. Brown, E. (2005). Running strategy of female middle distance runners attempting the 800m and 1500m “Double” at a major championship: A performance analysis and qualitative investigation. International Journal of Performance Analysis in Sport, 5(3), 73–88.
285
Camerino, O., Chaverri, J., Anguera, M. T., & Jonsson, G. K. (2012). Dynamics of the game in soccer: Detection of tpatterns. European Journal of Sport Science, 12, 216–224. doi:10.1080/17461391.2011.566362 Cronbach, L. J., Gleser, G. C., Nanda, H., & Rajaratnam, N. (1972). The dependability of behavioral measurements: Theory of generalizability for scores and profiles. New York, NY: Wiley. Daniels, J. (1998). Daniels’ running formula. Champaign, IL: Human Kinetics. De Koning, J. J., Bobbert, M. F., & Foster, C. (1999). Determination of optimal pacing strategy in track cycling with an energy flow model. Journal of Science and Medicine in Sport, 2, 266–277. doi:10.1016/S1440-2440(99)80178-9 Duffield, R., Dawson, B., & Goodman, C. (2005). Energy system contribution to 1500- and 3000-metre track running. Journal of Sport Sciences, 23, 993–1002. doi:10.1080/02640410400021963 Fisher, R. A. (1956). Statistical methods and scientific inference. Edinburgh: Oliver & Boyd. Foster, C., de Koning, J. J., Hettinga, F., Lampen, J., Dodge, C., Bobbert, M., & Porcari, J. P. (2004). Effect of competitive distance on energy expenditure during simulated competition. International Journal of Sports Medicine, 25, 198–204. doi:10.1055/s-2003-45260 Foster, C., Snyder, A. C., Thompson, N. N., Green, M. A., Foley, M., & Schrager, M. (1993). Effect of pacing strategy on cycle time trial performance. Medicine and Science in Sports and Exercise, 25, 383–388. Glover, B., & Glover, S. F. (2005). Manual del corredor de competición [The competitive runner’s handbook]. Badalona: Paidotribo. Gutiérrez-Santiago, A., Prieto, I., Camerino, O., & Anguera, M. T. (2011). The temporal structure of judo bouts in visually impaired men and women. Journal of Sports Sciences, 29, 1443–1451. Hanley, B., Bissas, A., & Drake, A. (2013). Kinematic characteristics of elite men’s 50 km race walking. European Journal of Sport Science, 13, 272–279. doi:10.1080/17461391.2011. 630104 Hill, D. V. (1999). Energy system contributions in middledistance running events. Journal of Sport Sciences, 17, 477– 483. doi:10.1080/026404199365786 Hugh, R. (2009). A new modelling approach demonstrating the inability to make up for lost time in endurance running events. IMA Journal of Management Mathematics, 20, 109–120. Jones, A. M., & Whipp, B. J. (2002). Bioenergetic constraints on tactical decision making in middle distance running. British Journal of Sports Medicine, 36, 102–104. doi:10.1136/bjsm. 36.2.102 Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159– 174. doi:10.2307/2529310 Lapresa, D., Álvarez, L., Arana, J., Garzón, B., & Caballero, V. (2013). Observational analysis of the offensive sequences that ended in a shot by the winning team of the 2010 UEFA Futsal Championship. Journal of Sport Sciences, 31, 1731–1739. doi:10.1080/02640414.2013.803584 Lapresa, D., Anguera, M. T., Alsasua, R., Arana, J., & Garzón, B. (2013). Comparative analysis of T-patterns using real time data and simulated data by assignment of conventional durations: The construction of efficacy in children’s basketball. International Journal of Performance Analysis in Sport, 13, 321–339. Lapresa, D., Arana, J., Anguera, M. T., & Garzón, B. (2013). Comparative analysis of the sequentiality using SDIS-GSEQ and THEME: A concrete example in soccer. Journal of Sport Sciences, 31, 1687–1695. doi:10.1080/02640414.2013.796061 Lapresa, D., Ibáñez, R., Arana, J., Garzón, B., & Amatria, M. (2011). Spatial and temporal analysis of karate kumite moves: Comparative study of the senior and 12–13 year old groups.
286
S. Aragón et al.
International Journal of Performance Analysis in Sport, 11(1), 57–70. Magnusson, M. S. (1996). Hidden real-time patterns in intra- and inter-individual behavior. European Journal of Psychological Assessment, 12, 112–123. doi:10.1027/1015-5759.12.2.112 Magnusson, M. S. (2000). Discovering hidden time patterns in behavior: T-patterns and their detection. Behavior Research Methods, Instruments, & Computers, 32(1), 93–110. doi:10.3758/ BF03200792 Martin, D. E., & Coe, P. N. (1997). Better training for distance running. Champaign, IL: Human Kinetics. Paavolainen, L., Häkkinen, K., Hämäläinen, I., Nummela, A., & Heikki, R. (1999). Explosive-strength training improves 5-km running time by improving running economy and muscle power. Journal of Applied Physiology, 86, 1527–1533. PatternVision Ltd & Noldus Information Technology bv. (2004). Theme, powerful tool for detection and analysis of hidden patterns in behaviour. Reference manual, version 5.0. Wageningen: Author. Pitney, W. A., & Parker, J. (2002). Qualitative research applications in athletic training. Journal of Athletic Training, 37, 168–173. Remmert, H. (2003). Analysis of group-tactical offensive behavior in elite basketball on the basis of a process orientated model. European Journal of Sport Science, 3(3), 1–12. doi:10.1080/ 17461390300073311 Renfree, A., & St. Clair. (2013). Influence of different performance levels on pacing strategy during the women’s world
championship marathon race (2013). International Journal of Sport Physiology and Performance, 8, 279–285. Szczepański, Ł. (2008). Measuring the effectiveness of strategies and quantifying players’ performance in football. International Journal of Performance Analysis in Sport, 8(2), 55–66. Thiel, C., Foster, C., Banzer, W., & De Koning, J. (2012). Pacing in Olympic track races: Competitive tactics versus best performance strategy. Journal of Sports Sciences, 30, 1107–1115. doi:10.1080/02640414.2012.701759 Tucker, R., Lambert, M. I., & Noakes, T. D. (2006). An analysis of pacing strategies during men’s world-record performances in track athletics. International Journal of Sports Physiology and Performance, 1, 233–245. Tucker, R., & Noakes, T. D. (2009). The physiological regulation of pacing strategy during exercise: A critical review. British Journal of Sports Medicina, 43(6), e1–e9. doi:10.1136/ bjsm.2009.057562 USA Track & Field. (2000). USA track & field coaching manual. Champaign, IL: Human Kinetics. Young, B. W., & Salmela, J. H. (2010). Examination of practice activities related to the acquisition of elite performance in Canadian middle distance running. International Journal of Sport Psychology, 41, 73–90.
