OLYMPIC ROWING: MODEL OF COMPETITIVE ACTIVITY
OF INTERNATIONAL LEVEL ELITE FEMALE ATHLETES
REMO OLÍMPICO: MODELO DE ATIVIDADE COMPETITIVA DAS ATLETAS DE ELITE INTERNACIONAL
REMO OLÍMPICO: MODELO DE ACTIVIDAD COMPETITIVA DE LAS ATLETAS DE ELITE INTERNACIONAL
Fábio Barreto Maia da Silva1
(Physical Education Professional)
João Paulo Reis Gonçalves Moreira
de Brito2
(Physical Education Professional)
Antonio Carlos Gomes3
(Physical Education Professional)
1. Club de Regata Vasco da Gama,
Sede Náutica, Rio de Janeiro,
RJ, Brazil.
2. Instituto Politécnico de Santarém,
Escola Superior de Desporto de
Rio Maior, Rio Maior, Santarém,
Portugal.
3. Instituto Sport Training-IST.
Academia Brasileira de Treinadores
(ABT), Rio de Janeiro, RJ, Brazil.
Correspondence:
Fábio Barreto Maia da Silva
Rua Visconde de Sepetiba, 50, apto.
803, Niterói, RJ, Brazil. 24020-206.
fbms78@hotmail.com
Original Article
Artigo Original
Artículo Original
ABSTRACT
Introduction: High-performance training should focus on motor capability determinants in competition to
make physical preparation effective. Objective: To analyze and draw up an elite model based on competitive
activity, to guide the theory and practice of female rowers, using data on the 42 finalists of the 2010-2018
world championships. Methods: Forty-two rowers from 2010-2018 world championships participated in the
study, final A, women’s single scull without weight restrictions. The statistical comparison was performed and
differences between 500 meter splits analyzed for each variable were discussed. Results: Strong correlation
was observed between time (r=-0.99, p<0.01), power (r=0.99, p<0.01), technical level (r=0.99, p<0.01) and
speed. In the competitive model, the relationship between speed at 2000 meters and time (r=-0.96, p<0.01),
speed (r=0.94, p<0.01) and power (r=0.96, p<0.01) showed a strong correlation with speed in the splits of the
third 500 meters of the race. In terms of stroke rate (r=-0.56, p<0.01) and stroke length (r=0.54, p<0.01), the
strongest correlation occurs at the start, in the first 100 meters of competition. Conclusion: Having performed
these analyses, it is possible to confirm the need to investigate competitive activity in order to supplement the
rower’s fitness preparation system. Level of evidence I; Diagnostic studies-Investigating a diagnostic test.
Keywords: Rowing; Performance; Sporting activity.
RESUMO
Introdução: O treinamento no alto rendimento deve focar a capacidade motora determinante na competição,
assim a preparação física se torna eficaz. Objetivo: Analisar e elaborar um modelo de elite, a partir da atividade competitiva, para orientar a teoria e a prática desportiva das remadoras, a partir dos dados das 42 finalistas dos mundiais de
2010 a 2018. Métodos: Participaram do estudo 42 remadoras de mundiais entre 2010 a 2018, final A, barco individual
feminino sem restrição de peso. Realizou-se a comparação estatistica e as diferenças entre as parciais de cada 500
metros analisadas para cada variável foram discutidas. Resultados: Observou-se o seguinte: forte correlação entre
o tempo (r=-0,99; p<0,01), a potência (r=0,99; p<0,01), o índice técnico e a velocidade (r=0,99; p<0,01). No modelo
competitivo, a relação entre a velocidade nos 2000 metros com o tempo (r=-0,96; p<0,01), a velocidade (r=0,94; p<0,01)
e a potência (r=0,96; p<0,01) apresentou forte correlação com a velocidade na parcial dos terceiros 500 metros da
prova. Já na quantidade (r=-0,56; p<0,01) e amplitude de remadas (r=0,54; p<0,01), a maior correlação apresenta-se
na partida, nos primeiros 100 metros de competição. Conclusão: Feitas essas análises, pode-se assegurar a necessidade de investigar a atividade competitiva com o objetivo de complementar o sistema de preparação da condição
física do remador. Nível de evidência I; Estudos diagnósticos–Investigação de um exame para diagnóstico.
Descritores: Remo; Desempenho; Atividades esportivas.
RESUMEN
Introducción: El entrenamiento en el alto rendimiento debe enfocar la capacidad motora determinante en la competición, así la preparación física se vuelve eficaz. Objetivo: Analizar y elaborar un modelo de élite, a partir de la actividad
competitiva, para orientar la teoría y la práctica deportiva de las remadoras, a partir de los datos de las 42 finalistas de
los mundiales de 2010 a 2018. Métodos: Participaron en el estudio 42 remadoras de mundiales entre 2010 a 2018, final A,
barco individual femenino sin restricción de peso. Se realizó la comparación estadística y fueron discutidas las diferencias
entre las parciales de cada 500 metros analizadas para cada variable. Resultados: Se observó lo siguiente: fuerte correlación
entre el tiempo (r=-0,99, p<0,01), la potencia (r=0,99, p<0,01), el índice técnico y la velocidad (r=0,99, p<0,01). En el modelo
competitivo, la relación entre la velocidad en los 2000 metros con el tiempo (r=-0,96, p<0,01), la velocidad (r=0,94, p<0,01)
y la potencia (r=0,96, p<0,01) presentó fuerte correlación con la velocidad en la parcial de los terceros 500 metros de la
prueba. Ya en la cantidad (r=-0,56, p<0,01) y amplitud de remadas (r=0,54, p<0,01) la mayor correlación se presenta en
la partida, en los primeros 100 metros de competición. Conclusión: Hechos estos análisis, se puede asegurar la necesidad
de investigar la actividad competitiva con el objetivo de complementar el sistema de preparación de la condición física
del remador. Nivel de evidencia I; Estudios de diagnósticos-Investigación de un examen para diagnóstico.
Descriptores: Remo; Rendimiento; Actividades deportivas.
DOI: http://dx.doi.org/10.1590/1517-869220202602218337
Rev Bras Med Esporte – Vol. 26, No 2 – Mar/Abr, 2020
Article received on 00/00/0000 accepted on 00/00/0000
145
INTRODUCTION
The stroke action in Olympic rowing is defined as a cyclic movement.1 In the force application phase, the lower and upper limbs act
simultaneously while rowing to propel the scull2 and achieve competitive
speed. In competitions, the rowers or scullers present reaction velocity
after the visual signal at the start and consequent acceleration to 500
meters reaching maximum speed, while between 500 and 1000 meters
there is a speed reduction and from 1000 meters velocity resistance up
to 2000 meters. Accordingly, high-performance training should focus
on the motor capability determinants in competition, with preparation
being the determining factor in the athlete’s result.3 Therefore, knowing
the characteristics related to time and strength interaction4 is of considerable benefit in improving rowing comprehension2 and scull speed.
In high performance sport, some advances are indicated in the training methodology and theory that are based on information from and
statistical analyses of the competitions that add significant value to the
sport.5,6 Thus, the act of analyzing the competitive data of athletes is an
effective tool for developing the training plan that produces adequate
and specific performance. Moreover, it directs the process of constructing
the competitive activity model proposed by Platonov.7 This model not
only reflects the structure and composition of the preparation process,
but also the concrete set of athletes for a particular sport as a standard
sample. Therefore, it is advisable not only to know the competitive properties to identify the difference between an average athlete and a world
elite athlete, based on the dynamics and percentage of performance of
both, but also to improve each stage of improvement and monitoring.
Despite the importance of high-level performance monitoring, there
is still no definite tool or precise variable for predicting performance.8
Some training strategies are implemented to promote favorable performance by reducing poorly adapted training responses.9 To achieve
these goals, training should be periodized from the training plan to
coincide with competitions.10 Thus, the preparation of the elite model can
minimize errors in the rower’s fitness preparation, define a competitive
split or variable as the most important, and also provide a comparative
database with competitive differences, according to category, type of
vessel and type of individual world championship. Thus, the variables or
splits that influence high-level sport performance should attract more
attention from the coaching staff, as this will enable them to know
patterns, athlete characteristics and individual comparisons.11
The specialized bibliography highlights the biological aspects and
performance of Olympic and world champions.12,13 It also points out
some performance prediction models between categories and types
of vessel.14,15 This article aims to analyze and prepare the elite model of
competitive activity to guide the theory and practice of female rowers,
based on data from 42 world finalists between 2010 and 2018, the
world rowing elite.
MATERIALS AND METHODS
We used the results of 42 female rowers, in the adult single scull
category, without weight restrictions. Data refer to final A of the 2010,
2011, 2013, 2014, 2015, 2017 and 2018 world rowing championships.
However, in Olympic Games such as those held in 2012 and 2016, the
single scull category is not included in the world championship. Thus,
the Olympic years were excluded, as they do not present the results
in the International Rowing Federation (FISA). All the data used in this
research project are available to the public, exhibited on the FISA website
http://www.worldrowing.com.
Data collected from the worldrowing website: date of birth, weight,
height, split, speed, 500 meter stroke rate and total race time for the
2000 meters. Calculated data: age (event date-date of birth/365.25),
146
body mass index (weight/height^2), time of first 100 meters and last
250 meters (distance/speed), number of strokes (stroke rate*time in
seconds/60 seconds), stroke cycle time (60 seconds/stroke rate), stroke
length (speed*time of each stroke cycle), power of each stroke (2.8/
pace^3) and technical level (100*(record in seconds/race time)^3).
Statistical analysis
Statistical procedures were used to characterize the values of the
different variables in terms of central tendency and dispersion. In the
inferential analysis, the normality of variables was tested using the
Kolmogorov-Smirnov test. Pearson’s correlation was used to determine
the relationship between performance and all the parameters. The
significance level was set at p <0.01. Data were analyzed using SPSS 16
statistical software (Chicago, IL, USA). To construct the competitive and
elite models, line and radar charts were used, respectively. The competitive model was the basis for the construction of the elite model, since
the results of the world championships were established as a reference.
RESULTS
Data on physical characteristics and competitive activity and their
correlation with speed in the 2000 meter race are presented in Table 1.
The variables: time, power, stroke length and technical level alone had
significant influence on scull speed. Power and technical level were (r
= 0.99; p <0.01), time (r = -0.99; p <0.01), number of strokes (r = -0.54; p
<0.01) and stroke length (r = 0.53; p <0.01). The other variables presented correlations below (r = 0.14), but still had no statistical significance.
In the formation of the competitive activity model, the variables
were shown in splits: start initial 100 meters and finish line the last
250 meters; the first, second, third and fourth 500 meters. Time, speed,
number of strokes, length of strokes and power presented medium and
strong positive and negative correlation, and also statistical significance
in all splits. Conversely, stroke rate and cycle presented weak positive
and negative correlation, and still had no statistical significance in the
splits. All variables displayed in splits were correlated with speed over
the 2000 meters of the race. Moreover, in the third 500 meter split,
time (r = -0.96; p <0.01), speed (r = 0.94; p <0.01) and power (r = 0.96;
p <0.01) indicated a strong correlation and statistical significance with
scull speed over 2000 meters. However, in terms of the number (r = -0.56;
p <0.01) and length of strokes (r = 0.54; p <0.01), there was a higher
correlation and statistical significance at the start and initial 100 meters.
In addition, the first and second 500 meters revealed the shortest time
(110.91; 114.86s), respectively. Thus, the first 500 meters and the fastest
(4.38 m/s) and most powerful (240.57W), consequently has the highest
stroke rate in the race (40.85; 35.90 spm), while the highest number
Table 1. Physical characteristics and competitive activity of the 42 finalists of the
2010-2018 world 2000-meter rowing championships.
Standard
Minimum Maximum r(2000m) p-value
deviation
Age (year)
29.28
4.60
23.00
41.00
-0.03
0.83**
Weight (kg)
74.26
5.01
69.00
86.00
-0.11
0.45**
Height (cm)
181.00 5.40
171.00
193.00
-0.13
0.39**
BMI (kg/m2)
16.88
1.95
14.53
20.93
-0.07
0.65**
Time 2000m (s)
456.24 11.65
434.95
484.60
-0.99 <0.01**
Average speed (m/s) 4.38
0.11
4.13
4.60
1.00
<0.01**
Stroke rate (spm)
34.03
1.75
31.20
40.50
-0.14
0.35**
Power (W)
236.75 17.75
196.83
272.23
0.99
<0.01**
Number (nos)
258.86 16.06
230.87
322.27
-0.54 <0.01**
Length (m)
7.75
0.44
6.21
8.66
0.53
<0.01**
Cycle time (s)
1.76
0.08
1.48
1.92
0.13
0.40**
Technical level (%) 82.69
6.20
68.75
95.09
0.99
<0.01**
Variables
Mean
**p<0.01; BMI (body mass index), spm (strokes per minute), nos (number of strokes), m (meters).
Rev Bras Med Esporte – Vol. 26, No 2 – Mar/Abr, 2020
of strokes was in the first and fourth 500 meters (66.37; 65.48 number
of strokes), the greatest length of strokes in the second and third 500
meters (7.99; 7.95 m), and the shortest stroke time at the start and in
the first 500 meters (1.47 s; 1.67 s), respectively (Table 2). Figure 1 show
competitive activity in chart form with the dynamics of the variables in
each 500 meter split of the race.
The competitive activity of the rowers was used as a basis to create a
model designed to compare athletes with international elite rowers. For
this comparison the authors generated fictitious data on an imaginary
rower for demonstrative purposes. The data created were: age 31 years,
weight 61 kg, height 173 cm, splits of 183, 185, 186 and 188 seconds,
and partial paces of 29, 26, 26 and 28 strokes per minute in every 500
meters, respectively. Soon after this, the radar chart served to visualize
comparisons with the global mean. The black chart contains the model
data and the gray graph the researchers’ data (Figure 2).
DISCUSSION
The results showed a strong correlation between time and power
over 2000 meters with the average speed over 2000 meters, confirming
that the force applied in each stroke has an influence on performance
in international rowing competitions. According to Warmenhoven et
al.,2 the greater the efficiency in the application of force in each stroke
cycle phase, in which the oar is in the water, the greater the propulsion
of the scull, and consequently the shorter the race time. It is suggested,
according to the data submitted, that an emphasis on special strength
training in competitive exercise may help improve performance.
A classical experimental research project16 evaluated the stroke
force of 71 athletes on 21 vessels in each phase of the stroke during a
competition, and found that power is the main factor affecting boat
speed. The authors continue by stating that power was significantly
higher in the smaller boats, with the female single scull reaching 247
watts,16 close to the average presented by the world elite, 236.75 watts
in the 2000 meter race. The number and length of strokes achieved a
mean correlation and statistical significance with speed in the 2000-meter race, indicating that the greater the force applied in each stroke,
the greater the boat displacement and stroke length, and the lower
the number of strokes in the 2000-meter race in world championships.
Verkhoshansky17 supports the idea that the main biomotor ability responsible for performance in sports is speed, which should be decisively
recognized when focusing on the training of the various other abilities,
depending on the sport to be trained. Gomes et al.18 claim that speed
capacity development is essential for competitive success. The classic
study by Steinacker19 maintains that power is greater in the initial phase
in order to reach high speed in a shorter time. In view of the above, we
recommend paying attention to the development of power, alactic
and lactic capacity, as the stimuli reach up to 120 seconds7,18,20 through
Table 2. Competitive activity in the 2000 meter race in splits, average speed, stroke rate, power, number and length of strokes at the start, 500m split and finish line, of the 42
finalists of the 2010-2018 world rowing championships.
Variables
Start time 100m (s)
Time 500m_1 (s)
Time 500m_2 (s)
Time 500m_3 (s)
Time 500m_4 (s)
Finish time 250m (s)
Average start speed 100m (m/s)
Average speed 500m_1 (m/s)
Average speed 500m_2 (m/s)
Average speed 500m_3 (m/s)
Average speed 500m_4 (m/s)
Average speed finish line 250(m/s)
Stroke rate start _100m (spm)
Stroke rate 500m_1 (spm)
Stroke rate 500m_2 (spm)
Stroke rate 500m_3 (spm)
Stroke rate 500m_4 (spm)
Stroke rate finish line_250m (spm)
Power 100m (W)
Power 500m_1 (W)
Power 500m_2 (W)
Power 500m_3 (W)
Power 500m_4 (W)
Power finish line_250m (W)
Number of strokes start 100m (nos)
Number of strokes 500m_1 (nos)
Number of strokes 500m_2 (nos)
Number of strokes 500m_3 (nos)
Number of strokes 500m_4 (nos)
Number of strokes finish_250m (nos)
Stroke length start 100m (m)
Stroke length 500m_1 (m)
Stroke length 500m_2 (m)
Stroke length 500m_3 (m)
Stroke length 500m_4 (m)
Stroke length finish line_250m (m)
Cycle time start 100m (s)
Cycle time 500m_1 (s)
Cycle time 500m_2 (s)
Cycle time 500m_3 (s)
Cycle time 500m_4 (s)
Cycle time finish line_250m (s)
Mean
22.96
110.91
114.86
115.38
115.08
58.01
4.38
4.51
4.35
4.32
4.34
4.31
40.85
35.90
32.84
32.83
34.15
34.17
240.57
257.47
231.96
229.14
231.16
225.82
15.59
66.37
62.87
63.16
65.48
33.03
6.44
7.55
7.99
7.95
7.65
7.59
1.47
1.67
1.83
1.83
1.76
1.75
Standard Deviation
1.90
2.83
3.06
3.65
3.90
2.11
0.36
0.10
0.11
0.13
0.15
0.15
2.72
1.82
2.21
2.18
1.87
1.91
60.00
18.83
19.09
21.15
23.32
24.27
1.15
4.03
4.71
5.07
3.79
2.09
0.46
0.45
0.54
0.56
0.41
0.45
0.09
0.08
0.11
0.11
0.09
0.09
Minimum
19.61
107.64
108.15
108.60
108.76
54.35
3.60
4.30
4.20
4.00
4.00
4.00
35.70
31.50
29.80
29.30
30.90
31.20
130.64
215.64
203.87
181.24
184.06
179.20
13.22
57.07
54.08
55.96
57.74
29.64
5.22
6.53
6.17
6.04
6.09
6.17
1.23
1.52
1.46
1.46
1.48
1.44
Maximum
27.78
117.52
119.74
124.53
123.89
62.50
5.10
4.60
4.60
4.60
4.60
4.60
48.80
39.40
41.20
41.00
40.50
41.80
371.42
280.64
276.69
273.26
272.06
272.54
19.17
76.56
81.05
82.84
82.07
40.50
7.56
8.76
9.25
8.93
8.66
8.43
1.68
1.90
2.01
2.05
1.94
1.92
r(2000m)
-0.58
-0.73
-0.85
-0.96
-0.87
-0.75
0.56
0.67
0.84
0.94
0.85
0.75
0.12
-0.20
0.25
-0.17
0.26
-0.06
0.53
0.73
0.84
0.96
0.86
0.75
-0.56
-0.48
-0.54
-0.54
-0.45
-0.49
0.54
0.47
0.52
0.52
0.45
0.48
-0.09
0.21
0.22
0.14
-0.07
0.03
p-value
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
0.44**
0.18**
0.10**
0.26**
0.08**
0.69**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
<0.01**
0.54**
0.17**
0.15**
0.37**
0.63**
0.84**
**p<0.01. spm (strokes per minute), nos (number of strokes), m (meters), r(2000m) correlation with average speed.
Rev Bras Med Esporte – Vol. 26, No 2 – Mar/Abr, 2020
147
500m_1
EC
C
500m_3
500m_4
Quantidade
PotênciaPower
68,00
270,00
270.00
67,00
260,00
260.00
66,00
250,00
250.00
65,00
Remadas
Watts
Watts
66,38
257,47
257.47
240,00
240.00
64,00
63,00
230,00
230.00
62,00
220,00
220.00
61,00
210.00
210,00
60,00
200.00
200,00
59,00
63,16
229,15
229.15
62,88
231,96
231.96
500m_1
500m_1
500m_1
500m_2
500m_2
500m_2
64,00
231,17
231.17
500m_3
500m_3
500m_3
500m_4
500m_4
500m_4
Metros
Meters
Metros
F
Frequencia
Length
Amplitute
FD
37,00
8,20
8.20
36,00
8,00
8.00
35,00
7.80
7,80
34,00
7.60
7,60
33,00
7.40
7,40
32,00
7,99
7.99
35,90
7,96
7.96
7,56
7.56
Metros
Meters
Reamdas
Remadas
Strokes
Watts
Strokes
Reamdas
Metros
Metros
Meters
500m_2
Metros
Metros
Metros
Metros
Meters
Seconds
Watts
Segundos
Rem
Rema
64,00 500m_1
500m_2 62,88500m_3
500m_4 64,00
106,00
63,00
63,00
500m_1
500m_2
500m_3
500m_4
62,00
Metros
62,00
61,00
Metros
61,00
60,00
60,00
59,00
Averagemédia
Speed
Velocidade
Potência
Tempo
A
B B
A
C59,00
500m_1
500m_2 Time500m_3
500m_4
500m_1
500m_2
500m_3
500m_4
118.00
4.60
4,60
270,00
118,00
Metros 115.38
114,86 Metros115,38
114.86
4.55
4,55
260,00
116.00
116,00
257,47
4.51
4,51
4.50
4,50
115,08
115.08
250,00
114.00
4.45
4,45
114,00
FF
110,91
110,91 Amplitute
Amplitute
4,34
4.34
229,15
4.40
4,40
240,00
4,36
4.36
112,00
112.00
231,96
115.08
8,20
4.35
4,35
4,35
4.35
231,17
8,20
230,00
7,99
110,00
110.00
4.30
4,30
7,99
8,00
7,96
8,00
220,00
4.25
4,25
7,96
108,00
108.00
7,56
7,80
4.20
4,20
7,56
210,00
7,80
106,00
106.00
4.15
4,15
7,60
200,00
7,60 500m_1
500m_1
500m_2
500m_3
500m_4
500m_1
500m_2
500m_3
500m_4
500m_2
500m_3
500m_4
500m_1
500m_2
500m_3
500m_4
500m_1
500m_2
500m_3
500m_47,47
7,47
7,40
7,40
Metros
Meters
Metros
Meters
7,20
Metros
7,20
7,00
7,00
B
Velocidade média
Number of strokes
E E
Quantidade
Stroke rate
Frequencia
D 6,80
D6,80 500m_1
Potência
C
500m_2
500m_3
500m_4
4,60
500m_1
500m_2
500m_3
500m_4
68,00
68.00
37,00
37.00
4,55
270,00
Metros
67,00
Metros
4,51
67.00
4,50
36,00
36.00
35,90
35.90
66,38
260,00
66.38
66,00
257,47
66.00
4,45
35,00
35.00
4,34
250,00
4,40
65,00
65.00
32,83
32.83
4,36
34,16
34.16
63,16
34,00
34.00
63.16
Figure
1.
Competitive
model
of
the
500
meter
splits
of
female
rowers.
Time
in seconds
(A),
average
4,35 1. Competitive model
32,84
4,35 splits of female
229,15
32.84
64,00
62,88
Figure
of the 500 meter
seconds
(A),
average
64,00
64.00
240,00 rowers. Time in62.88
64.00
231,96 per minute (D),
33,00
33.00
speed
in in
meters
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quantity
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quantity
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220,00
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31.00
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– Meters
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rate
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61,00
61.00
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– Seconds
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– Average
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29,00
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59.00
500m_1
500m_2
500m_3
500m_4
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500m_2
500m_3
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500m_2
500m_4
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500m_2
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Metros 500m_3
500m_1
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7,47
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7,20
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7.00
30,00
7,00
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6.80
6,80
500m_1
500m_1
500m_1
500m_2
500m_2
500m_2
500m_3
500m_3
500m_3
500m_4
500m_4
500m_4
Metros
Metros
Meters
Metros
Meters
Metros
Reamdas
Watts
Potência
C
Abaixo:
First
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Technical
– Time – –Average
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Second500
500
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TechnicalLevel
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Frequencia
FDLevel – TimeAmplitute
Figure
1. (B),
Competitive
model in
of watts
the 500
Figure
1.- Stroke
Competitive
model
of the
500
meter– splits
ofAverage
female
rowers.
Time
in seconds
(A),
average
speed
in meters per
second
power of strokes
(C),meter
stroke splits
rate inof female rowe
Pace
-270,00
Stroke
Length
– Number
ofof
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World
– Your
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speed in meters per second (B), power of strokes in watts (C), stro
37,00
8,20
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–
Age
–
Weight
–
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strokes
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500 260,00
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– Age
Weight
–
BMI
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Height
257,47
quantity of strokes in number of strokes (E) and length of strokes in
36,00
35,90
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250,00
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35,00
7,56
7,80
229,15
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240,00
34,16
Number of strokes – Strokes - Meters – Length – Meters 34,00
231,96
32,84
7,60
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First
500 meters
Segundo
500
metros
A 230,00
B
C
Primeiro
500
metros
7,47
Second
Segundo500
500meters
metros
Primeiro
500
metros
Third
500500
meters
7,40
Terceiro
metros
Terceiro 500 metros
B B 32,00
A A220,00
Quarto 500 me
Tempo (s)
Tempo (s)
Time
Tempo (s)
Time (s)
7,20
Time
120,00 Tempo
Tempo (s)
Tempo (s)
210,00
Tempo (s)
31,00
120,00
D
C
120,00
120.00
120,00
100,00
100.00
200,00
100,00
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Voga 500m_4
Indice 500m_1
Técnico
Technical
Level 80.00
Voga
500m_2
500m_3
80,00
Voga
Indice Técnico 60.00
60,00
60,00
40,00
40.00
40,00
Metros
20,00
20.00
20,00
0,00
0.00
0,00
Stroke
Length
Amplitude
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Amplitude de
VelocidadeSpeed
Média
Average
Velocidade Média
Remadas
Frequencia
D Remadas
37,00
36,00
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Pace
Quantidade
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Média
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Média Mundial
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35,00
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Watt
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Seu Atleta
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SeuAthlete
Atleta
32,83
34,16
32,84
33,00
32,00
TerceiroD500 metros
31,00
(s)
30,00
Voga
29,00
Velocidade
Média
120,00
120.00
100,00
100.00
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80.00
Indice
Técnico
Level 500m_3
500m_1 Technical
500m_2
60,00
60.00
40.00
40,00
Metros 20.00
20,00
0.00
0,00
Amplitude
de
Stroke
Length
Remadas
120.00
D
100,00
100.00
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80,00
Indice Técnico
Voga
Technical
Level 80.00
Voga
Indice Técnico
First 500 meters
– Second
meters – Technical
60,00
60.00
Voga Level
Indice
Técnico 500 80,00
40,00
Pace - Stroke Length
– Number of Strokes
– World Average – Your A
40.00
60,00
20,00
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500 meters – Age –20.00
Weight – BMI – Height
0,00
0.00
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Amplitude
de
Velocidade
Stroke
Length
Average
Speed
Amplitude de
Abaixo:
Voga
Remadas
Velocidade
Média
Amplitude de
Remadas
Média 0,00
Remadas
Velocidade
Média
de
Figure 1.Quantidade
Competitive
model
Time
in seconds (A), average
Quantidade de
Wattof the 500 meter splits of female rowers.
Quantidade
de
Stroke
Stroke
Pace
Watt
RemadasPace
Quantidade
Wattper minute (D),
Watt
Remadas
speed in meters
per second Watt
(B),
power of strokes in watts (C), stroke
rate in strokes
Remadas
Primeiro 500 metros
Remada
Quantidade
de
B
A
quantity of strokes
in
number
of
strokes
length
of
strokes
in
meters
(F).
Média Mundial
Seu
Atleta (E) andWatt
Quantidade
de
Média
Seu Atleta
WorldMundial
Average RemadasYour
Athlete
World
Average
Your
Athlete
Média Mundial
Seu Atleta
M
Watt
Tempo (s)
Remadas
120,00
Time – Seconds – Meters – Average Speed – Power – Watts – Meters – Stroke rate – Strokes - Meters
100,00
Indice
Técni
Number of strokes – Strokes - Meters
– Length – Meters
Média Mundial
Seu- Atleta Indice Técnico
Média Mundial
Seu Atleta
80,00
Voga
Fourth
Quarto500
500 meters
metros
Time
Tempo (s)
D
120,00
120.00
100,00
C
100,00
100.00
120,00
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Voga
Voga100,00
80,00
Indice
Técnico
6,80
29,00
60,00
60.00
40,00
60,00 500m_4
40,00
500m_1
500m_2
500m_3
500m_1
500m_2
500m_3
500m_4
40.00
20,00
20,00
40,00
20.00
0,00
0,00
0.00
20,00
Amplitude de
Metros
Metros
Amplitude
de
Velocidade Média
Stroke
Length
Velocidade
Média
Remadas
Average
0,00 Speed
Remadas
Amplitude de
Remadas
7,00
30,00Técnico
Indice
80,00
Technical
Level 80,00
Indice Técnico
80.00
60,00
E
E
Age
Idade
Amplitude
150,00
150.00 de
Remadas
60,00
40,00
20,00
0,00
E
Idade
150,00
120,00
90,00
Velocidade
60,00Média
30,00
Speed
– Stroke
IMC
0,00
120.00
120,00
Voga Abaixo: First 500 meters – Second 500 meters – Technical Level
– Time – Average
500m_4Voga
90.00
90,00
Pace - Stroke Length – Number of Strokes – World Average – Your
Athlete
60.00
Quantidade–deThird 500 meters – Fourth
60,00
Watt
500 meters – Age – Weight – BMI – Height
30.00 Remadas
30,00
Média
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0.00
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IMC
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Velocidade
Weight
Average Speed
Amplitude de
Remadas
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Quanti
Rem
M
Média
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Quantidade
de
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Watt
Remadas
Idademodel, comparing the
Figure150,00
2. Elite
meters120,00
of race (A), second 500 meters
Segundo 500 metros
Mundial
Primeiro 500 metros
Seu atleta
Height
B
Tempo (s)
Tempo (s)
120,00comparing the world average with a fictitious a
Figure 2. Elite Estatura
model,
120,00
100,00
100,00
80,00 in international
Indice
Técnico rowers
Voga
activity of
female
rowing competitions. First 500
80,00
Voga
Indice Técnico
60,00 Seu atleta
Mundial
60,00
meters
of
race
(B),
third
of
race (C), last
500
40,00 500 metros
world average with a fictitious athlete, based
on
the
competitive
activity
of
female
rowers
in
international
rowing
competitions.
First
500meters of race
40,00
20,00
20,00 meters of race and physical characteristics (E).
of race (B), third 500 metros of race (C), last 500
0,00
0,00
Amplitude de
Velocidade Média
Amplitude de
Remadas
Velocidade Média
Remadas
WorldMundial
Average
Média
Seu Atleta
E
Watt
Watt
A
Your
Athlete
Seu Atleta
Figure 2. Elite model, comparing the world average with a fictitious athlete, based on the comp
activity of female rowers in international rowing competitions. First 500 meters of race (A), secon
the interval workoutPeso
method, with special and competitive
exercises
always
within
the percentage
calculations
of competitive
speed,
sincecharacteristic
meters
of race (B),
third
500 metros
of race (C),
last 500 meters
of race and
physical
IMC
90,00
60,00
30,00
0,00
Quantidade de
distributed in the training plan.
Remadas
The technical level indicates how far the athlete is from the
world
Média
Mundial
record, and in this particular research project has strong correlation
Estatura
and statistical significance, indicating that the average speed of world
Mundial
atleta
championships
isSeu17.31%
away from the world record.
paring the world
average
with
a
fictitious athlete,
based
on theshowed
competitive
Regarding the competitive
model,
the study
that time, speed
nternational rowing competitions. First 500 meters of race (A), second 500
and power had strong correlation and statistical significance with speed
metros of race (C), last 500 meters of race and physical characteristics (E).
in all splits. Some authors explain that effective strength coordination
throughout the stroke cycle is a significant aspect of performance.21-23
Not only does the current study demonstrate that the highest correlation
is found in the third 500 meter split, but also proposes that the greater
the power over the 1500 meters of the race, the better the result. Also,
particular attention should be focused on speed resistance stimuli,
148
Quantidade de
the
highest correlation occurs inRemadas
the third split.Watt
Watt
In terms
length
of strokes, average
correlation
Mundial
Seu Atleta
Seu Atleta of the number and Média
and significance is shown in all splits. There is also the higher correlation
at the start, suggesting that the lower the number and the greater the
length of strokes, the higher the speed of the scull. Corroborating the
assumption that continuously applying force improves scull propulsion2
in the acceleration, speed, velocity resistance, and finish line phases, as
the greater the power the greater the scull’s displacement.
The model allows the comparison of any athlete with the world elite,
due to the analysis of competitive variables. It also drills deeply into the
theory and practice of the sport.7 Based on the analysis of this model, it
is evident that higher power in each stroke phase could improve performance in the 2000-meter single women’s scull race. Zamotin et al.23
Rev Bras Med Esporte – Vol. 26, No 2 – Mar/Abr, 2020
claim that competitive activity is the culmination of a long training
process and deserves an objective, systematic and thorough analysis.
Thus, knowing the competitive activity model of high performance
scullers gives us the exact notion that applied training beliefs need to
be reviewed. In other words, the current main guiding principle of the
training system is undoubtedly the competition system. We need to
train as close as possible to the competitive model. Training volumes,
which used to take up significant time in preparation programs, have
decreased in this new stage while speed/muscle power is taking on
greater importance, thus presenting new dynamics in terms of training
intensity. Parameters such as average competition speed, number of
strokes (stroke rate) and stroke execution power, as well as lactate concentration, made the elements most valuable to the coach.
It is now understood that despite being a sport that requires high
maximal oxygen uptake (VO2max), the winner is the athlete who has the
ability to produce fast strength most of the time, and this is acquired by
training at and above competitive speeds. Special strength becomes the
key element of the preparation of today’s rowers, which general theory
still resists, because the high volume and consequently low muscle power
of training has always been the coach’s strategy of choice.
As a limitation, this study is still far from clarifying all the details that
might help in a rowing-specific training theory. We still need to carry
out more invasive studies to verify the morphofunctional adaptations
that can produce workouts with less volume and more neuromuscular
and functional qualities. In any event, this study provided us with an
oversight of the competitive reality of high performance Olympic rowing.
CONCLUSIONS
The elite model of competitive activity in this study demonstrates
the behavior of variables and power as a key factor in female rowers
in world championships. Therefore, the model presented in this study
can be seen as an important complement in female rowers’ fitness
preparation, and may also serve as a guide for the percentages and
characteristics of training loads.
Future studies should both verify which of these variables or splits are
predictors of performance, and analyze the competitive activity of each
category and gender, as the dynamics may differ from the model presented.
All authors declare no potential conflict of interest related to this article
AUTHORS’ CONTRIBUTIONS: Each author made significant individual contributions to this manuscript. FBMS: writing, statistical analysis, intellectual concept and creation of the
entire research project; JPRGMB: data collection, review and typing of data; ACG preparation of the graphs, writing, review and intellectual concept of the study. All authors reviewed
and approved the final version of the manuscript.
REFERENCE
1. Dawson RG, Lockwood RJ, Wilson JD, Freeman G. The Rowing Cycle: Sources of Variance and Invariance
in Ergometer and On-the-Water Performance. J Mot Behav. 1998;30(1):33-43.
13. Plews DJ, Laursen PB. Training Intensity distribution over a four-year cycle in Olympic champion
rowers: different roads lead to Rio. Int J Sports Physiol Perform. 2017;0(0):1-24.
2. Warmenhoven J, Cobley S, Draper C, Smith R. Over 50 years of researching force profiles in rowing:
what do we know? Sports Med. 2018;48(12):2703-14.
14. Akça F. Prediction of rowing ergometer performance from functional anaerobic power, strength
and anthropometric components. J Hum Kinet. 2014;41:133-42.
3. Korner T. Background and experience with long-term build-up programmes for high performance
rowers. FISA-Coach. 1993;49(3):1-6.
15. Silva FB, Brito JP, Reis VM. Predição do desempenho a partir das características antropométricas,
fisiológicas e de força no remo. Rev Bras Med Esporte. 2017;23(6):446-9.
4. Smith RM, Loschner C. Biomechanics feedback for rowing. J Sports Sci. 2002;20(10):783-91.
5. Passfield L, Hopker JG. A Mine of Information: Can Sports Analytics Provide Wisdom From Your
Data? Int J Sports Physiol Perform. 2017;12(7):851-5.
6. Liebermann DG, Katz L, Hughes MD, Bartlett RM, McClements J, Franks IM. Advances in the application
of information technology to sport performance. J Sports Sci. 2002;20(10):755-69.
16. Kleshnev V. Estimation of Biomechanical Parameters and Propulsive Efficiency of Rowing. Australian
Institute Sport Biomech Department. 1998.
17. Verkhoshansky Y. Teoría y metodología del entrenamiento deportivo. 1nd ed. Barcelona: Paidotribo; 2002.
18. Gomes AC, Souza J. Futebol: Treinamento desportivo de alto rendimento. Porto Alegre: Artmed, 2008.
7. Platonov VN. Tratado Geral de Treinamento Desportivo. 1nd ed. Brasil: Phorte Editora; 2008.
19. Steinacker JM. Physiological aspects of training in rowing. Int J Sports Med. 1993;14(Suppl 1):S3-10.
8. Halson SL. Monitoring training load to understand fatigue in athletes. Sports Med. 2014;44(Suppl 2):S139-47.
20. Craig NP, Pyke FS, Norton KI. Specificity of test duration when assessing the anaerobic lactacid capacity
of high-performance track cyclists. Int J Sports Med. 1989;10(4):237-42.
9. Bourdon PC, Cardinale M, Murray A, Gastin P, Kellmann M, Varley MC, et al. Monitoring athlete training
loads: consensus statement. Int J Sports Physiol Perform. 2017;12(Suppl 2):S2161-70.
10. Fox JL, Scanlan AT, Stanton R. A Review of player monitoring approaches in basketball: current
trends and future directions. J Strength Cond Res. 2017;31(7):2021-9.
21. Anderson R, Harrison A, Lyons GM. Accelerometry-based feedback--can it improve movement consistency
and performance in rowing? Sports Biomech. 2005;4(2):179-95.
11. Nevill A, Atkinson G, Hughes M. Twenty-five years of sport performance research in the Journal of Sports
Sciences. J Sports Sci. 2008;26(4):413-26.
22. Warmenhoven J, Smith R, Draper C, Harrison AJ, Bargary N, Cobley S. Force coordination strategies
in on-water single sculling: Are asymmetries related to better rowing performance? Scand J Med
Sci Sports. 2018;28(4):1379-88.
12. Mikulic P, Bralic N. Elite status maintained: a 12-year physiological and performance follow-up of
two Olympic champion rowers. J Sports Sci. 2018;36(6):660-5.
23. Zamotin T, Issurin V, Egorenko L. Examination of the competitive activity of the rowers of Russian
national rowing team. Uchenye zapiski universiteta imeni PF Lesgafta. 2014;7(113):69-73.
Rev Bras Med Esporte – Vol. 26, No 2 – Mar/Abr, 2020
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