Skip to main content
Download PDF

Swimming turn performance: the distinguishing factor in 1500 m world championship freestyle races?

BMC Research Notes volume 14, Article number: 248 (2021) Cite this article

Abstract

Objective

Turn sections represent the second largest part of total race time in 1500 m freestyle races and may substantially affect race results. Therefore, the aim of the study was to investigate individual race strategies and compare the effect of start, swim, and turn performances between short-course and long-course races. Video footages were collected from all 16 male finalists at the 2018 short and 2019 long-course World swimming championships (age 23.06 ± 2.3 years, FINA points 941 ± 42) for subsequently analysis of start, turn, and swim performance.

Results

The larger number of turns in short-course races resulted in significantly faster race times (p = 0.004), but slower mean turn times compared to long-course races (p < 0.001). Total race velocity closely correlated with swim and turn but not start section velocity in short- (r ≥ 0.80, p ≤ 0.017) and long-course races (r ≥ 0.83, p ≤ 0.011). Analysis of individual race strategies showed that turn performance affected race results in 6 (75%) and 3 (37.5%) of the 8 world-best 1500 m swimmers in short-course and long-course races, respectively. Medal standing was improved for 1st, 3rd, and 4th ranked short- as well as 1st and 2nd ranked long-course finalist. Coaches, athletes, and performance analysts may carefully consider the importance of turn performance additionally to free-swimming skills.

Introduction

Performance analysis has become routine procedure in high-performance sports, in order to evaluate the training process, discover potentials, and investigate key performance indicators [1, 2]. As pool swimming is affected by little environmental factors, swimming performance can be assessed based on real race scenarios with no equipment interfering with the swimmer´s movement pattern [3, 4]. Performance analysts commonly divide swim races into several sections. The start section includes the block phase, flight phase, underwater phase, and transition to full-stroke swimming [5, 6]. Contribution of start performance in 50 m events was 26.1% [7] but continuously decreased for 100 m and 200 m races [8, 9] and may be of minor importance for 1500 m freestyle [10]. Here, turns that are used for directional change and to reaccelerate the swimmer by pushing of the pool wall at the beginning of each lap may substantially affect the race result [11]. The turn sections are commonly analyzed from 5 m before wall contact until resurfacing after the underwater phase, which varies in its length depending on the race distance [12, 13]. Distance between the start and turns determine swim section [3, 8, 14].

While parameters related to free-swimming have been extensively investigated [1, 15,16,17], turns represent 19.69 ± 0.24% [8] and 36.87 ± 0.61% [18] of total race time in 100 m and 1500 m long-course freestyle races, respectively. Significant performance variations were attributed to turn parameters, i.e. 5 m in (time before wall contact), breakout time, breakout distance, 15 m out (time after wall contact) [8, 10, 18]. However, in these particular studies, turn sections were based on FINA rules that allow an underwater phase up to the 15 m mark [19]. As mean breakout distance was 5.48 ± 0.87 m in these 1500 m races [18], free-swimming skills may have large affected turn performance. Therefore, previous studies suggested to isolate the turn and swim sections [12, 13] with particular attention to the last 5 m before and initial 5 m after wall contact [20, 21].

In long-course races (50 m pool length), the effect of turn performance on race results increased with race distance, hence number of turns involved [8,9,10, 18]. As the number of turns is twice as high in 1500 m short-course races (25 m pool length), turn performance may show an even larger effect on race results. While analysis of world-class athletes provides unique insights into human’s highest possible performance, such analyses are naturally limited by a small number of subjects [22, 23]. Hence, performance at World championship level should be investigated based on individualized responses and case reports in addition to the assessment of mean values [24,25,26]. Therefore, the aim of the study was to investigate individual race strategies of World championship finalists and compare the effect of start, swim, and turn performances on results of 1500 m freestyle events between short-course and long-course races.

Main text

Materials and methods

Participants

Video footages were collected from 8 male finalists of the 1500 m freestyle events at the Hangzhou 2018 (age 22.8 ± 2.4 years, FINA points 953 ± 27) (short-course) and Gwangju 2019 (age 23.3 ± 2.2 years, FINA points 951 ± 23) (long-course) World championships for subsequent analyses of start, turn, and swim sections. All participants of the FINA World swimming championships provided written informed consent to the organizer that all video material collected during competition can be used for television broadcasting and race analyses by the participating nations. All data were anonymized before the analysis. The study was approved by the Ethical committee of the Palacký University of Olomouc (Registration-Number: 77/2020) and in accordance with the Declaration of Helsinki.

Data collection

For the short-course World championships, a camera (Canon XA35, Canon Inc., Tokio, Japan) was positioned on top of the stands 30 m above water level and about 100 m from the side of the pool. The camera was placed perpendicular to the direction of swimming and exactly in the middle of the pool (12.5 m apart from both pool ends). The camera zoom was set before the races to assure clear vision across the entire pool. To ensure same conditions for the long-course World championships (50 m pool length), two cameras of the same type used before, were positioned 12.5 m apart from both ends of the pool. Cameras were synchronized via wireless LAN connection and recorded half to the pool each. Video footages were recorded as mp4 format with 50 frames-per-seconds and an image qualify of 1920 × 1080 full HD.

Data analysis

Split times for start, swim, and turn sections were analysed using Dartfish (Team pro Data 9, Dartfish, Fribourg, Switzerland). The light flash of the timing system that was synchronized to the starting signal was used to synchronize video footages for the race analysis. Race results were obtained from the official electronical timing system (Omega Timing, Biel/Bienne, Switzerland).

Based on mean breakout distances of 10.69 ± 1.18 m and 10.11 ± 1.25 m for short- and long-course races, respectively, start section was determined by the time from the starting signal until top of the swimmer’s head passed the 10 m mark visible on the lane ropes. Based on previously reported breakout distances between 4.64 ± 0.23 m and 5.48 ± 0.87 m in 1500 m freestyle races [18, 27], turn sections were analysed from 5 m before until 5 m after wall contact. Swimmer’s head passing the 5 m mark (short- and long-course), 20 m mark (short-course), and 45 m mark (long-course) visible on the lane ropes were used as reference points [28]. Race sections beyond start and turn section determined the swim section. Accuracy of all markers were checked using a measuring tape before the race. To compare a potential fatigue effect between short- and long-course races, mean turn times were compared across the 1st, 2nd and 3rd 500 m section of each event.

Statistical analysis

The data are presented in mean ± standard deviation (SD). Normality was verified with Shapiro-Wilks test and alpha-level of 0.05 indicated statistical significance. Spearman’s correlation coefficient was applied, to assess relationship between mean velocities and ranking in all race sections. For Spearman’s correlation, a critical value of 0.643 with an alpha level of 0.05 was reported to a data sample of eight participants [29]. Analysis of variance (ANOVA) was used with section times as the dependent variable and pool length (short-course vs. long-course) and race section (start vs. swim vs. turn) as categorical factors with Tukey´s post-hoc test. A one-way ANOVA was used to compare total race time between short- and long-course races. To investigate accuracy, 50% of all races were analyzed by another experienced race analysts. Inter-rater reliability was assess using Intraclass-correlation coefficient (ICC) between the repeated measures and showed an ICC of 0.988–0.989 and 0.991–0.992 for short-course and long-course World championships, respectively. Statistical analysis was performed using the STATISTICA software version 13.4.0.14. (StatSoft Inc., Tulsa, USA).

Results

The times for start, swim, and turn sections are presented in Table 1 (mean ± standard deviation) for short-course and long-course 1500 m races. Total race time was significantly faster for short-course (865.09 ± 12.90) compared to long-course (889.57 ± 13.98; p = 0.004). Additionally, in short-course, swimmers spent significantly less time in the swim (64.52% vs. 83.04%; p < 0.001) and more time in the turn sections (35.04% vs. 16.53%; p < 0.001) compared to long-course, respectively. However, mean turn times were significantly faster for long-course compared to short-course (5.07 ± 0.18 vs. 5.14 ± 0.10 s; p < 0.001).

Table 1 Descriptive data with mean ± standard deviation (SD). Section times were compared between pool lengths and race section using a mixed-design analysis of variance (ANOVA)
Full size table

Analysis of variance (ANOVA) revealed that mean turn times of the first 500 m section were not different between short-course and long-course (p = 0.381). Thereafter, turn times became significantly slower for the short-course (p < 0.001) but remained stable for the long-course races (p = 0.732).

Correlation analysis revealed close correlations between the final ranking with swim and turn section ranking in both, short- (r = 0.074, p = 0.037; r = 0.81, p = 0.015) and long-course (r = 0.93, p < 0.001; r = 0.93, p < 0.001). Additionally, total race velocity correlated with swim and turn section velocities in short- (r = 0.80, p = 0.017; r = 0.81, p = 0.015) and long-course (r = 0.98, p < 0.001; r = 0.83, p = 0.011). Start performance was not related to final ranking or total race velocity (Table 2).

Table 2 Spearman’s correlation coefficient between final ranking and section rankings as well as total race velocity and section velocities
Full size table

Figure 1 illustrates the individual response of the swimmer’s swim and turn performance on final ranking. Turn performance affected final ranking in 6 (75%) and in 3 (37.5%) out of 8 short- and long-course World championship finalists, respectively. In particular, for the highest ranked swimmers, turn performance improved medal standing, i.e. 1st, 3rd, and 4th ranked short-course finalist as well as 1st and 2nd ranked long-course finalist. For instance, in short-course races, the swimmer ranked 2nd (545.35 s) swam 4.71 s (0.86%) faster than the winner of the race (550.06 s) but lost 5.18 s (1.75%) in the turn sections. Moreover, swimmer ranked 3rd only showed the 6th fastest swimming time. However, this swimmer gained up to 14.9 s (5.24%) due to the turns and outperformed the 4th, 5th, and 6th ranked swimmers with second fastest total turn time. In long-course races, swimmer ranked 3rd swam 1.69 s (0.23%) and 1.86 s (0.26%) faster than the 1st and 2nd ranked swimmer. However, final ranking was determined by faster turn times for 1st (3.56 s; 2.51%) and 2nd (2.72 s; 1.92%) compared to 3rd ranked swimmers.

Fig. 1
figure 1

Individual differences to the fastest swimmer for total race time, swim, and turn section at the short-course and long-course world championships

Full size image

Discussion

The present study showed importance of turn performance in 1500 m freestyle short- and long-course races. The push-off from the pool wall and subsequent underwater phase with application of undulating kicking accelerates swimmer above free-swimming speed [30]. Swimmers aim to lengthen the underwater phase, as drag forces are lower under water than at its surface [31]. However, excess breath holding increases anaerobic demand and may interfere with swimmers free-swimming abilities [32]. Therefore, with increasing race distance, swimmers successively reduce length of underwater phase down to 4.64 ± 0.23 m in the 1500 m freestyle [27]. While long-distance swimmers apply rather slow and energy-saving leg kicking [33], push-off from the wall and undulating kicking during the underwater phase place a high demand on leg muscles [11]. Future research needs to investigate whether length of the underwater phase and conditioning of leg muscles may provide potential for future performance developments in long-distance swimmers.

The present and previous studies showed significantly faster race times in short- compared to long-course races [34] probably due to twice the number of turns involved, hence repeated velocity gains from wall push-off [30, 35]. However, mean turn performance was slower in short-course races. A detailed analysis revealed equal turn times in the first 500 m sections of short- and long-course races. However, with the remaining two 500 m sections, turn times became slower in short- but not long-course races. The high demand for leg muscles during push off from the wall [11, 33] and repeated breath holding during the underwater phase [32] may have resulted in a fatigue effect during short-course races. However, swimmers accelerate beyond free-swimming speed with the push-off after the turn and swimming performance should basically benefit from the larger resistance provided by the pool wall assuming an adequate conditioning for leg muscles [36]. Therefore, swimmers may have applied a more careful pacing strategy and more energy conserving turns due to higher number of turns available in short-course races. Although metabolic energy supply cannot be decoupled between the turn and swim section, based on faster turn times on long-course races, short-course races may have not yet found its limit [37]. The question arises whether the in average 0.07 s faster turn times seen in long-course races could be applied in 25 m pool competition, hence short-course races. With 30 additional turns this would add up to a performance gain of 2.03 s and beat the current world record [38] by 0.95 and 0.22 s for the 1st and 2nd ranked swimmer of the recent 2018 World short-course swimming championships. However, multiple variables interact in a 1500 m freestyle race and the present hypothesis is yet to be evaluated by future studies.

Conclusion

The present study showed that turn performance could be the distinguishing factor in World-championship 1500 m freestyle races. Turn performance affected final ranking in 6 (short-course) and in 3 (long-course) out of 8 World championships finalists. Coaches, athletes, and performance analysts should carefully consider the importance of turn performance in addition to free-swimming skills.

Limitations

Analyses in top-elite athletes, i.e. the eight world best 1500 m swimmers in the short- and long-course championships, naturally come with a low number of participants. Although the current results of such a low number of world-class swimmers cannot be translated to a general population of competitive swimmers, such analyses provide unique insights into individual race strategies. Marginal differences should not be neglected as they may provide the distinguishing factor for final ranking. Therefore, further studies need to verify these findings based on data collections across finalists of multiple World championships. Additionally, individualized distances measurements of underwater phase and breakout distances after the turn would allow further insights and a detailed analysis of turn strategies [12].

Availability of data and materials

All data are available on request from the corresponding author Marek Polach.

References

  1. Hellard P, Dekerle J, Avalos M, Caudal N, Knopp M, Hausswirth C. Kinematic measures and stroke rate variability in elite female 200-m swimmers in the four swimming techniques : Athens 2004 Olympic semi- finalists and French National 2004 Championship semi-finalists. J Sports Sci. 2008;26(1):35–46.

    Article  CAS  Google Scholar 

  2. Lyons K. Performance analysis in applied contexts. Int J Perform Anal Sport. 2005;5(3):155–62.

    Article  Google Scholar 

  3. Smith DJ, Norris SR, Hogg JM. Performance evaluation of swimmers scientific tools. Sports Med. 2002;32(9):539–54.

    Article  Google Scholar 

  4. O’Donoghue P. The use of feedback videos in sport. Int J Perform Anal Sport. 2006;6(2):1–14.

    Article  Google Scholar 

  5. Vantorre J, Seifert L, Fernandes R, Vilas-boas JP, Chollet D. Kinematical profiling of the front crawl start. Int J Sports Med. 2010;31(1):16–21.

    Article  CAS  Google Scholar 

  6. Tor E, Pease D, Ball KA. Characteristics of an elite swimming start. Biomech Med Swim Conf. 2014;1:257–63.

    Google Scholar 

  7. Cossor JM, Mason BR. Swim start performances at the Sydney 2000 Olympic Games. In: International society of biomechanics in Sports Volume: XIX International Symposium. Univ San Fr; 2001. vol. 19, p. 70–74

  8. Morais JE, Marinho DA, Arellano R, Barbosa TM. Start and turn performances of elite sprinters at the 2016 European Championships in swimming. Sport Biomech. 2019;18(1):100–14.

    Article  Google Scholar 

  9. Marinho DA, Barbosa TM, Neiva HP, Silva AJ, Morais JE. Comparison of the start, turn and finish performance of elite swimmers in 100 m and 200 m races. J Sport Sci Med. 2020;19(2):397–407.

    Google Scholar 

  10. Morais JE, Barbosa TM, Neiva HP, Marinho DA. Stability of pace and turn parameters of elite long-distance swimmers. Hum Mov Sci. 2019;63:108–19.

    Article  Google Scholar 

  11. Nicol E, Ball K, Tor E. The biomechanics of freestyle and butterfly turn technique in elite swimmers. Sport Biomech. 2019. https://0-doi-org.brum.beds.ac.uk/10.1080/14763141.2018.1561930.

    Article  Google Scholar 

  12. Veiga S, Cala A, Mallo J, Navarro E. A new procedure for race analysis in swimming based on individual distance measurements. J Sports Sci. 2013;31(2):159–65.

    Article  Google Scholar 

  13. Silveira GA, Araujo LG, Freitas EDS, Schütz GR, de Souza TG, Pereira SM, et al. Proposal for standardization of the distance for analysis of freestyle flip-turn performance. Braz J Kinantrop Hum Perform. 2011;13(3):177–82.

    Google Scholar 

  14. Kjendlie P, Haljand R, Fjortoft O, Stallman RK. The temporal distribution of race elements in elite swimmers. Port J Sport Sci. 2014;6(2):54–6.

    Google Scholar 

  15. Arellano R, Brown P, Cappaert J, Nelson RC. Analysis of 50-, 100-, and 200-m freestyle swimmers at the 1992 Olympic Games. J Appl Biomech. 1994;10(2):189–99.

    Article  Google Scholar 

  16. Craig AB, Pendergast DR. Relationships of stroke rate, distance per stroke, and velocity in competitive swimming. Med Sci Sport. 1979;11(3):278–83.

    Google Scholar 

  17. Pai YC, Hay JG, Wilson BD. Stroking techniques of elite swimmers. J Sports Sci. 1984;2(3):225–39.

    Article  Google Scholar 

  18. Morais JE, Barbosa TM, Forte P, Bragada JA, Flávio A, Castro DS, et al. Stability analysis and prediction of pacing in elite 1500 m freestyle male swimmers. Sport Biomech. 2020. https://0-doi-org.brum.beds.ac.uk/10.1080/14763141.2020.1810749.

    Article  Google Scholar 

  19. FINA. Swimming and facilities rules 2017–2021. Lausanne: FINA; 2020. https://resources.fina.org/fina/document/2021/01/12/b3885f9b-630a-4390-861d-4e7f6031f4a4/2017_2021_swimming_16032018.pdf. Accessed 1 May 2021.

  20. Blanksby B, Skender S, Elliott B, Mcelroy K, Landers G, Mcelroy K, et al. Swimming: an analysis of the rollover backstroke turn by age-group swimmers. Sport Biomech. 2004;3(1):1–14.

    Article  Google Scholar 

  21. Cossor JM, Blanksby BA, Elliott BC. The influence of plyometric training on the freestyle tumble turn. J Sci Med Sport. 1999;2(2):106–16.

    Article  CAS  Google Scholar 

  22. Barbosa AC, Valadão PF, Wilke CF, de Martins F, Silva DCP, Volkers SA, et al. The road to 21 seconds: a case report of a 2016 Olympic swimming sprinter. Int J Sport Sci Coach. 2019;14(3):393–405.

    Article  Google Scholar 

  23. Trappe S, Luden N, Minchev K, Raue U, Jemiolo B, Trappe TA. Skeletal muscle signature of a champion sprint runner. J Appl Physiol. 2015;118(12):1460–6.

    Article  Google Scholar 

  24. Rakovic E, Paulsen G, Helland C, Eriksrud O, Haugen T. The effect of individualised sprint training in elite female team sport athletes: A pilot study. J Sports Sci. 2018;36(24):2802–8.

    Article  Google Scholar 

  25. Friedmann B, Frese F, Menold E, Kauper F, Jost J, Bärtsch P. Individual variation in the erythropoietic response to altitude training in elite junior swimmers. Br J Sports Med. 2005;39(3):148–53.

    Article  CAS  Google Scholar 

  26. Haugen T, Seiler S, Sandbakk Ø, Tønnessen E. The training and development of elite sprint performance: an integration of scientific and best practice literature. Sport Med Open. 2019;5(1):44.

    Article  Google Scholar 

  27. Chow C, Hay JG, Wilson BD, Imel C. Turning techniques of elite swimmers. J Sports Sci. 1984;2(3):37–41.

    Article  Google Scholar 

  28. Veiga S, Roig A. Effect of the starting and turning performances on the subsequent swimming parameters of elite swimmers. Sport Biomech. 2017;16(1):34–44.

    Article  Google Scholar 

  29. Gauthier TD. Detecting trends using Spearman’s rank correlation coefficient. Environ Forensics. 2001;2(4):359–62.

    Article  CAS  Google Scholar 

  30. Veiga S, Roig A. Underwater and surface strategies of 200 m world level swimmers. J Sports Sci. 2016;34(8):766–71.

    Article  Google Scholar 

  31. Tor E, Pease DL, Ball KA. How does drag affect the underwater phase of a swimming start ? J Appl Biomech. 2015;31(1):8–12.

    Article  Google Scholar 

  32. Rodríguez-Zamora L, Engan HK, Lodin-Sundström A, Schagatay F, Iglesias X, Rodríguez FA, et al. Blood lactate accumulation during competitive freediving and synchronized swimming. Undersea Hyperb Med. 2018;45(1):55–63.

    Article  Google Scholar 

  33. Kobayashi Yamakawa K, Shimojo H, Takagi H, Tsubakimoto S, Sengoku Y. Effect of increased kick frequency on propelling efficiency and muscular co-activation during underwater dolphin kick. Hum Mov Sci. 2017;54:276–86.

    Article  Google Scholar 

  34. Wolfrum M, Knechtle B, Rüst CA, Rosemann T, Lepers R. The effects of course length on freestyle swimming speed in elite female and male swimmers–a comparison of swimmers at national and international level. Springerplus. 2013;2(1):1–12.

    Article  Google Scholar 

  35. Keskinen OP, Keskinen KL, Mero AA. Effect of pool length on blood lactate, heart rate, and velocity in swimming. Int J Sports Med. 2007;28(05):407–13.

    Article  CAS  Google Scholar 

  36. Schumann M, Notbohm H, Bäcker S, Klocke J, Fuhrmann S, Clephas C. Strength-training periodization: no effect on swimming performance in well-trained adolescent swimmers. Int J Sports Physiol Perform. 2020;15(09):1272–80.

    Article  Google Scholar 

  37. Sandbakk Ø, Solli GS, Christer HH. Sex differences in world record performance: the influence of sport discipline and competition duration. Int J Sports Physiol Perform. 2018;13(1):2–8.

    Article  Google Scholar 

  38. FINA. Lausanne: FINA. fina-rankings/records;2018–2020. (2020) http://www.fina.org/fina-rankings/filter/records. Accessed 1 May 2021.

Download references

Acknowledgements

The authors thank the Czech and Swiss swimming federations for the opportunity to take part in both competitions. Moreover, we want to thank Dominik Vavrečka and Jakub Březina for their help with the data analysis and Miroslav Hanáček for his valuable consultation and advice.

Funding

There were no specific grants or funding for the present study.

Author information

Authors and Affiliations

  1. Faculty of Physical Culture, Department of Social Sciences in Kinanthropology, Palacký University Olomouc, Olomouc, Czech Republic

    Marek Polach

  2. Faculty of Physical Education and Sport, Department of Physiology and Biochemistry, Charles University Prague, Prague, Czech Republic

    Dan Thiel

  3. Department for Competitive Swimming, Czech Swimming Federation, Praha, Czech Republic

    Marek Polach, Dan Thiel & Jan Kreník

  4. Department for Elite Sport, Swiss Federal Institute of Sport Magglingen, Magglingen, Switzerland

    Dennis-Peter Born

  5. Department for High-Performance Sports, Swiss Swimming Federation, Berne, Switzerland

    Dennis-Peter Born

Authors
  1. Marek Polach
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dan Thiel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan Kreník
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dennis-Peter Born
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study design. MP, DT and JK collected the data. All authors analyzed the data, worked on the manuscript, and each of them contributed to the interpretation of the results. DPB supervised the project. All authors read and approved the final version of the manuscript before submitting it. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Marek Polach.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Ethical committee of the Palacký University of Olomouc (Registration-Number: 77/2020) and in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

There are no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Polach, M., Thiel, D., Kreník, J. et al. Swimming turn performance: the distinguishing factor in 1500 m world championship freestyle races?. BMC Res Notes 14, 248 (2021). https://0-doi-org.brum.beds.ac.uk/10.1186/s13104-021-05665-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s13104-021-05665-x

Keywords

BMC Research Notes

ISSN: 1756-0500

Contact us