sports
Article
Cycling as the Best Sub-8-Hour Performance Predictor
in Full Distance Triathlon
Caio Victor Sousa 1,2,† , Lucas Pinheiro Barbosa 1,† , Marcelo Magalhães Sales 3 ,
Patrick Anderson Santos 1 , Eduard Tiozzo 2 , Herbert Gustavo Simões 1 ,
Pantelis Theodoros Nikolaidis 4 and Beat Knechtle 5, *
1
2
3
4
5
*
†
Graduate Program in Physical Education, Catholic University of Brasília, Brasília 71966-700, DF, Brazil;
cvsousa89@gmail.com (C.V.S.); lduarte.barbosa@gmail.com (L.P.B.); patricksantospas@gmail.com (P.A.S.);
hgsimoes@gmail.com (H.G.S.)
Miller School of Medicine, University of Miami, Miami, FL 33136, USA; etiozzo@med.miami.edu
Department of Physical Education, Goiás State University, Quirinopolis 75860-000, GO, Brazil;
marcelomagalhaessales@gmail.com
Exercise Physiology Laboratory, Nikaia 12244, Greece; pademil@hotmail.com
Institute of Primary Care, University of Zurich, CH-8006 Zurich, Switzerland
Correspondence: beat.knechtle@hispeed.ch; Tel.: +41-(0)-71-226-93-00 (ext. 9301)
These authors contributed equally to this work.
Received: 15 December 2018; Accepted: 16 January 2019; Published: 18 January 2019
Abstract: For any triathlon distance (short, Olympic, half-distance and full-distance), competitors
spend more time cycling than swimming or running, but running has emerged as the discipline
with the greatest influence on overall performance at the Olympic distance. However, there is
a lack of evidence on which discipline has the greatest influence on performance in the overall
full-distance triathlon (3.8 km swimming/180 km cycling/42.195 km running), especially for the
fastest performing athletes of all time. The total race times of 51 fastest triathletes (sub-8-hour) were
studied, while for the split times, a sample of 44 participants was considered. The discipline that
seemed to better predict total race time was cycling (coefficient = 0.828; p < 0.001), followed by running
(coefficient = 0.726; p < 0.001) and swimming (coefficient = 0.476; p < 0.001). Furthermore, cycling
was the discipline with the highest performance improvement over the years, whereas running had
a slightly decrease. In conclusion, cycling seems to be the discipline with greater influence in final
result for the full-distance triathlon.
Keywords: swimming; running; exercise training; athlete
1. Introduction
In the very beginning of triathlon in 1978, a competition was held in Hawaii in which 12 athletes
completed 2.4 miles (3.8 km) of swimming, 112 miles (180 km) of cycling and followed by 26.2 miles
(42.195 km) of running, with the best race time of 11 h, 46 min and 58 s [1]. This official full-distance
triathlon has been growing in popularity, with new race routes being added every year worldwide and
elite athletes reaching great performances in each race [1,2]. For instance, nowadays, the performance
of 11:56:58 is not even eligible to qualify an amateur athlete below 55 years old to the Ironman World
Championship [2].
The analysis of triathlon performance has been useful and necessary for both amateurs and
professionals [1,3,4]. Thus, there are studies investigating several aspects such as physiological,
nutritional and strategies of pace in every modality for a better performance in a full-distance
triathlon [5–8].
Sports 2019, 7, 24; doi:10.3390/sports7010024
www.mdpi.com/journal/sports
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In that regard, new scientific evidence about triathlon has come up every year in order to better
serve the athletes and coaches, including trending and performance analysis. For instance, although
most time of the race is spent on cycling in any triathlon distances (short, Olympic, half-distance and
full-distance) [2,8], there are studies reporting that running is the discipline that most influences the
overall performance (race time) in the Olympic distance (1.5 km swim; 40 km cycle; 10 km run) [9–11].
The Olympic distance triathlon has several differences in comparison to full-distance triathlon,
starting with the profile of elite athletes. The top 20 athletes competing at Olympic distances are,
in average, 10 years younger than the best 20 athletes competing in full-distance triathlon [11–13].
Physiological parameters also differ, since Olympic distance has a much greater glycolytic activity than
full distance [14]. Finally, drafting rules and formation of cycling packs, which reduce wind resistance
to conserve energy, are allowed only in the Olympic distance [3].
There are some analyses of the full-distance triathlon involving physiological and morphological
parameters to predict overall performance [12]. However, there is a lack of evidence regarding which
modality most influences the overall performance in the full-distance triathlon, especially among
the fastest elite triathletes. Moreover, all performance trends published so far were focused on age
groups (amateurs), which may have a completely different strategy than professionals. The analysis of
sub-8-hour performance enables the selection of the top-level athletes. Thus, the aim of present study
was to identify which modality can better predict the total race time and explain performance trends
in each discipline in a sub-8-hour full-distance triathlon from 1997 to 2018.
2. Materials and Methods
All procedures used in the study were approved by the Institutional Review Board of Kanton St.
Gallen, Switzerland, with a waiver of the requirement for informed consent of the participants given
the fact that the study involved the analysis of publicly available data.
We obtained data from publicly available databases (www.ironman.com, www.challenge-family.
com and www.challenge-roth.com). All official total race times among Ironman and Challenge
races, including World and Continental Championships, performed under 08:00:00 from 1997 up to
November 2018 were recorded. Forty-nine records were initially eligible to be included, but three were
excluded because split times were not available, and six more were excluded because transition times
were not separated from swim/cycle/run times. Therefore, the total sample included in the analysis
was composed by 51 races, but split analyses included 44 only.
Initially, an exploratory analysis of the data was performed, with median, 25 and 75 percentiles, as
well as the fastest and slowest sub-8-hour performances displayed in Table 1. Further, all data
were transformed in seconds and then a stepwise multiple linear regression was performed
using total race time as the dependent variable and splits and transitions times as independent
variables. A bivariate association analysis was also performed between total race time and each
split time, the Spearman correlation coefficient was applied. A linear regression from each discipline
(swimming/cycling/running) with the year of the race was also performed. The significance level
was set as 5% (p < 0.05), and all procedures were performed using SPSS v21.0 (IBM SPSS Statistics for
Windows, Version 21.0. 2012. Armonk, NY, USA: IBM Corp).
Table 1. Total race, splits and transition times of sub-8-hour full distance triathlon (h:min:s).
Splits
Median (25–75 Percentile)
Min
Max
Overall
Swim
Cycle
Run
T1
T2
07:55:12 (07:51:30–07:58:29)
00:48:01 (00:46:43–00:49:47)
04:15:47 (04:11:51–04:21:41)
02:44:25 (02:41:17–02:48:03)
00:02:22 (00:01:58–00:02:35)
00:01:39 (00:01:15–00:02:06)
07:35:41
00:41:33
04:02:17
02:35:21
00:01:19
00:00:54
07:59:59
00:55:23
04:29:34
02:58:18
00:03:19
00:02:47
T1—transition 1 (swim to cycle); T2—transition.
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3. Results
The records included data from 14 different races, the Challenge Roth (Germany) being the
race with more sub-8-hour performances, including the World Record in performance (07:35:41)
by the Olympic Gold Medalist (2008) and two times Ironman World Champion (2015 and 2016),
Jan Frodeno from Germany. The discipline with the greatest proportion of the total race time was
cycling (54.13% ± 1.06), followed by running (34.83% ± 1.02), swimming (10.20% ± 0.64) and the sum
of transition times (0.84% ± 0.18).
The best model from the stepwise multiple regression included Swimming, Cycling and Running
split times with adjusted (aj ) coefficient of determination of 67.7% (R2 = 0.835; R2 aj = 0.677; p < 0.001).
The discipline that best predicts total race time was cycling (coefficient = 0.868; p < 0.001), followed
by running (coefficient = 0.726; p < 0.001) and swimming (coefficient = 0.476; p < 0.001). Further,
the correlation analyses indicate that the total race time was best associated with Cycling performance
(r = 0.520), followed by Swimming (r = 0.327) and Running (r = 0.151). See Figure 1 for details with the
standardized beta from multiple regression and Spearman coefficient.
Figure 1. Standardized coefficient from stepwise multiple regression using total race time as dependent
variable of sub-8-hour performance in full distance triathlon (3.8 km swimming/180 km cycling/42.195 km
running) and Spearman correlation coefficient between each variable and overall race time.
The linear and non-linear regressions of total performances and race year did not reach a
significant result and the generation of the equation was not reliable (R2 < 0.5) to predict a sub
7h30min performance (Figure 2).
Figure 2. Dispersion of sub-8-hour performances in full distance triathlon from 1997 to 2018. Red stars
indicate the best of each year.
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The linear regression shows a negative and significant slope for cycling from 1999 to 2018, whereas
it shows a significant and positive slope for running. Swimming did not show a significant slope
(Figure 3). The results suggest that performance has improved in cycling, remained the same in
swimming and decreased in the running throughout the years.
Figure 3. Dispersion and linear regression of split-times (seconds) of sub-8-hour performances in full
distance triathlon from 1999 to 2018; *: p < 0.05.
4. Discussion
The main finding of the present study was that sub-8-hour performances in full-distance triathlon
are better predicted by cycling performance, as compared to swimming or running. Furthermore,
the splits for cycling have improved over the years whereas the opposite was observed for running.
It is not novel that cycling has a great time contribution in the overall time in all triathlon
distances [2], including the top full triathlon performances. However, the impact of each discipline
for the overall race time seems to differ among various triathlon distances. Peeling and Landers [15]
previously reported that the swimming in shorter triathlon races (i.e., short and Olympic) will
determine if the athlete will cycle in the leading or chasing pack. Therefore, riding within a pack
would turn the cycling discipline less competitive and more about ‘saving energy’ for the running leg,
making cycling data very homogeneous in elite athletes and reducing the general influence of it in the
wouldrace
turntime.
the cycling discipline less competitive and more about ‘saving energy’ for the running
overall
In that regard, there is evidence showing that running has the greatest association with overall
Olympic distance performance [9,10]. Furthermore, recently Ofoghi, Zeleznikow, Macmahon, Rehula
and Dwyer [11] performed a more sophisticated analysis and concluded that swimming and running
are more important for the general success than cycling in an Olympic triathlon, even stating that
cycling has little or no influence in the outcome.
However, in a full distance triathlon it is not allowed to draft during cycling, thus there are
no packs. Moreover, since swimming has the smallest time contribution in a full-distance it is
absolutely possible that a poor swimmer make a great overall race time with good cycling and running
performances, reducing the influence of swimming, as our results indicate. Therefore, an explanation
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for which full-distance triathlons have opposite results in comparison to previous studies with Olympic
distance triathlon is due to the regulations of each race. Short and Olympic triathlons allow drafting in
cycling, whereas full-distance does not, therefore, since cycling is the discipline that comprehends the
largest portion of time in all distances (short, Olympic and full-distance) it is reasonable to infer that it
would be more important if the athletes had to do it solo.
The linear regression of split times showed that cycling times have improved since the first
sub-8-hour performance. There are several aspects regarding cycling performance that have improved
in the last two decades, including the methods of training and race strategies, but we believe that
aerodynamics is the most responsible for performance improvement on cycling. Ever since the
advanced methods to test a bike ride aerodynamics [16], such as wind tunnels, a lot has changed in the
bike frame to adapt the athlete to a better aero position and less energy expenditure [17,18], including
the geometry of the frame itself, aero wheels, aero helmets, aero outfit, all to reduce the drag and make
the athlete faster and more comfortable [17,18]. Unsurprisingly, cycling records are beaten every year
in full distance triathlon races. On the other hand, as a result of a faster pace on cycling, the subsequent
running performances have decreased from 1999 to 2018, which could indicate a change in strategy for
a better final performance in a full-distance event.
The implications of such results are in accordance with current literature regarding the
physiological and biomechanical implications in triathlon. There are evidences that cycling has
a stronger influence of peripheral fatigue of active muscles than running [19], whereas for running
the mechanical efficiency in regard to the cardiometabolic variables is more important [20]. Since,
throughout the years, athletes racing sub-8-hour full distance triathlon are becoming faster in cycling
and slightly slower in running, it is reasonable to infer that a greater neuromuscular fatigue is elicited
as a result of pushing harder in cycling, which is closely related with a decreased efficiency and
impaired performance in a subsequent running [20,21].
The results are applicable especially for elite athletes racing full distance triathlon events to
determine training focus and race strategy. For non-elite athletes these results should be interpreted
with caution, since the data only included the fastest performances ever. A possible limitation of the
present analysis is that the inclusion of only sub-8-hour performances excluded races locations with
tough environmental conditions (temperature and course), and that the race Challenge Roth is the one
with most sub-8-hour performances. However, we highlight that this is the first performance analysis
of the top-level athletes in full distance triathlon, and it could be beneficial for athletes and coaches to
better define their training and racing strategies.
5. Conclusions
In conclusion, cycling seems to be the discipline with greater influence in the overall result for
the full-distance triathlon. Cycling performance has improved among the sub-8-hour full Ironman
finishers, possibly due to improvement in aerodynamics and/or racing strategy. Future research
should include a bigger sample of athletes, including those with performances exceeding 8 h and
amateur athletes.
Author Contributions: Conceptualization, C.V.S., L.P.B., P.T.N. and B.K.; methodology, C.V.S., L.P.B., M.M.S.,
P.A.S., P.T.N. and B.K.; formal analysis, C.V.S., L.P.B. and M.M.S.; writing—original draft preparation, C.V.S. and
L.P.B.; writing—review and editing; C.V.S., L.P.B., M.M.S., P.A.S., E.T., H.G.S., P.T.N. and B.K.; visualization, C.V.S.,
L.P.B., M.M.S., P.A.S., E.T., H.G.S., P.T.N. and B.K.; supervision, P.T.N., and B.K.; project administration, P.T.N.,
and B.K.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Lepers, R. Analysis of Hawaii ironman performances in elite triathletes from 1981 to 2007. Med. Sci.
Sports Exerc. 2008, 40, 1828–1834. [CrossRef] [PubMed]
Sports 2019, 7, 24
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
6 of 6
Knechtle, B.; Zingg, M.A.; Rosemann, T.; Rust, C.A. Sex difference in toperformers from Ironman to double
deca iron ultra-triathlon. Open Access J. Sports Med. 2014, 5, 159–172. [CrossRef] [PubMed]
Knechtle, B.; Nikolaidis, P.T.; Rosemann, T.; Rust, C.A. Ironman Triathlon. Praxis 2016, 105, 761–773.
[CrossRef] [PubMed]
Knechtle, B.; Knechtle, R.; Stiefel, M.; Zingg, M.A.; Rosemann, T.; Rust, C.A. Variables that influence Ironman
triathlon performance—What changed in the last 35 years? Open Access J. Sports Med. 2015, 6, 277–290.
[CrossRef] [PubMed]
Marcos-Serrano, M.; Olcina, G.; Crespo, C.; Brooks, D.; Timon, R. Urinary Steroid Profile in Ironman
Triathletes. J. Hum. Kinet. 2018, 61, 109–117. [CrossRef]
Maunder, E.; Kilding, A.E.; Plews, D.J. Substrate Metabolism During Ironman Triathlon: Different Horses on
the Same Courses. Sports Med. 2018, 48, 2219–2226. [CrossRef]
Sousa, C.; Aguiar, S.; Olher, R.; Sales, M.; Moraes, M.; Nikolaidis, P.; Rosemann, T.; Knechtle, B.; Simões, H.
Hydration status after an Ironman triathlon: A meta-analysis. J. Hum. Kinet. 2019. [CrossRef]
Pryor, J.L.; Adams, W.M.; Huggins, R.A.; Belval, L.N.; Pryor, R.R.; Casa, D.J. Pacing Strategy of a Full
Ironman Overall Female Winner on a Course With Major Elevation Changes. J. Strength cond. Res. 2018.
[CrossRef]
Vleck, V.E.; Burgi, A.; Bentley, D.J. The consequences of swim, cycle, and run performance on overall result
in elite olympic distance triathlon. Int. J. Sports Med. 2006, 27, 43–48. [CrossRef]
Fernandez-Revelles, A.B. Infographic. Correlation between phases and final result in Men’s triathlon
competition at the Olympic Games in Sydney 2000. Br. J. Sports Med. 2018. [CrossRef]
Ofoghi, B.; Zeleznikow, J.; Macmahon, C.; Rehula, J.; Dwyer, D.B. Performance analysis and prediction in
triathlon. J. Sports Sci. 2016, 34, 607–612. [CrossRef] [PubMed]
Knechtle, B.; Wirth, A.; Rosemann, T. Predictors of race time in male Ironman triathletes: Physical
characteristics, training, or prerace experience? Percept. Motor Skills 2010, 111, 437–446. [CrossRef] [PubMed]
Rust, C.A.; Knechtle, B.; Rosemann, T.; Lepers, R. Sex difference in race performance and age of peak
performance in the Ironman Triathlon World Championshifrom 1983 to 2012. Extrem. Physiol. Med. 2012,
1, 15. [CrossRef] [PubMed]
Sleivert, G.G.; Rowlands, D.S. Physical and physiological factors associated with success in the triathlon.
Sports Med. 1996, 22, 8–18. [CrossRef]
Peeling, P.; Landers, G. Swimming intensity during triathlon: A review of current research and strategies to
enhance race performance. J. Sports Sci. 2009, 27, 1079–1085. [CrossRef]
Debraux, P.; Grappe, F.; Manolova, A.V.; Bertucci, W. Aerodynamic drag in cycling: Methods of assessment.
Sports Biomech. Int. Soc. Biomech. Sports 2011, 10, 197–218. [CrossRef]
Fintelman, D.M.; Sterling, M.; Hemida, H.; Li, F.X. Optimal cycling time trial position models: Aerodynamics
versus power output and metabolic energy. J. Biomech. 2014, 47, 1894–1898. [CrossRef]
Garcia-Lopez, J.; Rodriguez-Marroyo, J.A.; Juneau, C.E.; Peleteiro, J.; Martinez, A.C.; Villa, J.G. Reference
values and improvement of aerodynamic drag in professional cyclists. J. Sports Sci. 2008, 26, 277–286.
[CrossRef]
Rampinini, E.; Connolly, D.R.; Ferioli, D.; La Torre, A.; Alberti, G.; Bosio, A. Peripheral neuromuscular
fatigue induced by repeated-sprint exercise: Cycling vs. running. J. Sports Med. Phys. Fit. 2016, 56, 49–59.
Millet, G.P.; Vleck, V.E.; Bentley, D.J. Physiological differences between cycling and running: Lessons from
triathletes. Sports Med. 2009, 39, 179–206. [CrossRef]
Giandolini, M.; Arnal, P.J.; Millet, G.Y.; Peyrot, N.; Samozino, P.; Dubois, B.; Morin, J.B. Impact reduction
during running: Efficiency of simple acute interventions in recreational runners. Eur. J. Appl. Physiol. 2013,
113, 599–609. [CrossRef] [PubMed]
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