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How to Analyze Breaststroke Swimming

As a take home message:
1. In this piece we did the analysis of three age-group breaststrokers.
2. Modeled data sometimes does not fit completely the individual data of a specific swimmer.
3. After benchmarking the swimmers against data retrieved from the literature we have learned that one of the swimmers is able to reach a high speed (strong point), but with a high speed fluctuation (main concern).
4. Several analyses were carried out to run a full diagnosis, prescribe what to change and predict the outcome of such improvements. He would be able to increase the speed by 0.1 m/s (faster) and decrease the speed fluctuation by 10% (more efficient).

In my latest piece eventually I shared a picture that depicts modeled and real data. At that time, it was explained that most textbooks and even research papers share modeled data because: (i) it will be easier to understand a concept having “smoothed” data and; (ii) the modeled curve aims to represent the main trend across all subjects assessed.

Unfortunately, there is a huge drawback of this. Most of the times the theoretical model does not fit in the data of one particular subject. In academia, this falls under the topic “universal versus individual data analysis” (Barbosa et al., 2010). I.e., data from a pooled sample of swimmers does not represent what is the best for my swimmer. Indeed, the inter-individual variability is a concern for several researchers (Seifert et al., 2011).

A very good example is the assessment of the speed fluctuation. The academic jargon for speed fluctuation is “intra-cyclic variation of the velocity”. Meaning, it is how speed changes over one fig1single stroke cycle. As you are aware the swim speed is the result of the balance between thrust (propulsion) and resistance (drag). So, over one single stroke cycle the speed goes up whenever the thrust is higher than the resistance. The speed goes down if the thrust is lower than the resistance.

Let’s apply these two concepts (universal v. individual analysis; assessment of the speed fluctuation) to breaststroke. Figure 1 (top panel) depicts what would be the typical speed fluctuation at breaststroke reported in a textbook. At the begging of the stroke cycle the speed goes up due to the kicking and eventually reaches a first peak (thrust by the kick is higher than the water resistance). After the kick the swimmer will glide in the streamlined position. Because there is no thrust, only passive drag is acting upon the body, the speed decreases and we can find that “valley”. After the glide it is performed the arms’ stroke and the speed increases once again (second peak). With the legs’ recovery the resistance increases significantly and speed goes south sharply.

Now, that we did the re-cap of the stroke cycle at breaststroke, I must share with you that this curve is the modeled data of three age-group breaststrokers. Figure 1 (bottom panel) depicts the variations in the modelled curve considering the individual differences among the three swimmers. For instance, at the beginning of the stroke cycle (the first upward slope, i.e., kicking) the vertical lines are rather small. That means that the three swimmers are quite similar, little difference can be found among them. But, the vertical lines on the second peak are big. So, it seems that the three are doing thing in a different way at this phase of the stroke cycle.

So far, we did the analysis of the pooled and modeled data of three swimmers (i.e. universal analysis). Moving on to the individual analysis. Figure 2 depicts the individual curves of each subject. One seems to be a top-tier age-group breaststroker (blue line), the other two a mid- and a low-tier swimmers (red and green lines, respectively). Now, you understand why I told you at the beginning that unfortunately the modeled curves most of the times do not fit the individual curves. Figure 1 was computed based on the data of these three swimmers represented in Figure 2. We can see that at the beginning the curves of the three match almost perfectly, but then start to drift away from each other. This is why the variation is low at the beginning of the cycle and high at the end as shared earlier (vertical lines, i.e. standard deviation, in figure 1 – bottom panel).



Now, we are ready to do the quantitative analysis. For the kinematic analysis, I selected the average swimming velocity (v), stroke frequency (SF), stroke length (SL), maximal velocity (v-max) and minimal velocity (v-min) within the stroke cycle. Two other variables were selected as efficiency estimators, and this includes the stroke index (Costill et al., 1985) and the speed fluctuation (e.g., Barbosa et al., 2005).

Based on the average speed over the cycle it is easy to follow that we are assessing swimmers of different competitive levels. The green swimmer has a lower speed, SF, SL, dv and v-max. What makes the difference between the blue and red swimmers? The SF is slightly higher, but the SL shorter for the later breaststroker. The red swimmer has a lower speed fluctuation than the blue counterpart. So, this deserves some further investigation.


First things first, we should benchmark the swimmers against other subjects in our database or data retrieved from the literature. Today I will benchmark the swimmer against the data reported in a research paper (Barbosa et al., 2013). The black dots are the data reported in the paper, the coloured dots are our swimmers. We are benchmarking the relationship between speed fluctuation and swim velocity. Now, we are sure that the green swimmer is an average breaststroker, the red a mid-tier-almost-top-tier and the blue a top-performer. The main concern is that the blue swimmer despite reaching a high speed also shows a big speed fluctuation in comparison with two other counterparts that race at similar paces (1.2-1.3 m/s, black dots). These two reach the same speed with speed fluctuations lower than 40% though. Being the bigger picture, now we should do the analysis of the individual curve of the blue swimmer.


The top-left panel (figure 4) depicts the kick. The blue swimmer is the one reaching the highest speed (2.01m/s). The rate of speed development is the same for the three, but the kicking action is 0.03s longer for the blue swimmer (the kick took 0.28s). So, one might say that the kick power (or mechanical impulse) is quite nice for the three, the main difference might be in the legs’ insweep. This is the end of the kick, when the swimmer squeezes up the water between the calf and feet, the plantar surface almost touches each other and toes point backwards-inwards.fig4

The top-right panel (figure 4) is related to the glide. This seems to be a major drawback for the blue swimmer. The speed drops 0.99m/s in 0.32s. Probably the glide is a little bit too long and the body position not the best. For instance, the red swimmer does not have such a significant decrease in speed. One should have arms fully extended and horizontal, head in a neutral position between the upper-arms and looking downward-forward, hips high close to the surface, legs fully extended and horizontal with no sinking of the feet.

The bottom-left panel (figure 4) reports the arms’ action. That took 0.26s and he reached a maximal speed of 2.09m/s. The blue swimmer is very balanced because the ratio between the maximal speed reached by kick and arms is 2.01 v. 2.09m/s. Several breaststrokers are more kick-driven and neglect the arms’ stroke. Both the red and green swimmers are not able to reach the same speed over the arms’ stroke they did at the kicking. I.e., the second peak (arms) is smaller than the first (kick). If they improve the arms’ action, the average speed would increase even though at the cost of the efficiency (i.e. probably speed fluctuation would increase). With that, they would reach the performance level of the blue swimmer. After reaching such level, would be time to think about the speed fluctuation (which is what we are doing right now to the blue swimmer).

The bottom-right panel (figure 4) help us to have a deeper understanding of the legs’ recovery, when the knees and hips bend. This should be another concern for the blue swimmer. In 0.34s he loses 1.83m/s reaching the lowest speed (0.25m/s). I.e., for tenths of a second he almost stopped in the water. Avoid dropping the thighs. Less bend by the hip and keep the knees high. The end of the arms’ insweep and legs’ recovery happens at the same time. Head moves as one with the torso (spine completely aligned). Do not bend or extend the neck. This phase is all about tempo and sync between upper and lower limbs.

An analyst must be able to answer a few questions:

  1. What is happening?
  2. What can we do to improve?
  3. How much will be the improvement?

Well, the diagnosis is done. So we can try to help the blue swimmer to be one of the finest breaststrokers.

Time to re-cap what we’ve learned so far: his propulsion is very good. Let’s keep this good work. The resistance after the glide and legs’ recovery are two concerns and deserve special attention.

Good coaches will suggest a set of drills in order to improve the gliding position, the tempo (notably the duration of the glide), the thigh and shank positions over the legs’ recovery and timing between arms and legs. Follow the link for a comprehensive list of drills (WARNING: the original paper is in Portuguese. Sorry for the inconvenience. If you speak a Latin language it is easy to understand. Alternatively I have a nerdy solution for you: 1. Install the “google translate” in your smartphone or tablet; 2. Set the app to translate from Portuguese to English; 3. Below the field to type the word to be translated you will find the camera icon. Press that icon/button. 4. Point the camera to the text and the app will automatically translate the text for you. 5. To clarify: you need a hardcopy of the piece or softcopy being displayed in a second device. E.g., display the piece on a laptop and use the phone for the automatic and real-time translation).

A good analyst will try to predict what happens with the changes advised. He will have to work the math, do some modelling, signal processing, chunk the numbers and provide a result. My prediction for the blue swimmer is as follows. If over the legs’ recovery the speed does not decrease up to 0.25m/s but 0.33m/s, the speed fluctuation improves to 36.03% and the speed to 1.36m/s (i.e. 0.1m/s faster). Figure 5 includes the real glide (blue line) and the “optimal” glide (magenta line). If the swimmer improves his glide, the speed fluctuation reduces to 35.29% and the speed to the same 1.36m/s.

So, if the swimmer and the coach embark in only one of these two solutions, the dv-v will be 35%-1.36m/s. End of the day, shifting the blue dot in the figure 3 to the coordinates (35; 1.36) one can learn that the swimmer not only becomes the fastest but also the most efficient.


1. Barbosa TM, Keskinen KL, Fernandes RJ, Colaço C, Lima AB, Vilas-Boas JP. (2005). Energy cost and intra-cyclic variations of the velocity of the centre of mass in butterfly stroke. Eur J Appl Physiol. 93: 519-523.
2. Barbosa TM, Morouço P, Jesus S, Feitosa W, Costa MJ, Marinho DA, Silva AJ, Garrido ND (2013). Interaction between speed fluctuation and swimming velocity in young competitive swimmers. Int J Sports Med. 34(2): 123-130
3. Costill D, Kovaleski J, Porter D, Fielding R, King D (1985). Energy expenditure during front crawl swimming: predicting success in middle-distance events. Int J Sports Med. 6: 266-270
4. Seifert L, Leblanc H, Herault R, Komar J, Button C, Chollet D. (2011). Inter-individual variability in the upper-lower limb breaststroke coordination (2011). Hum Mov Sci 3:550-65

By Tiago M. Barbosa PhD degree recipient in Sport Sciences and faculty at the Nanyang Technological University, Singapore

The post How to Analyze Breaststroke Swimming appeared first on Swimming Science.

Comparison of the Short Course Meters Woman’s 100 Breaststroke World Record

Take Home Message:

  1. The aim was to: (i) compare Ruta Meilutyte (LTU) WR in Moscow (October 2013) and Alia Atkinson (JAM) in Doha (November 2014), both with a time of 1:02.36; (ii) learn the effect of the taper on Alia´s performance (Singapore vs. Doha races, 5 weeks apart).
  2. Water entry and water break was not different comparing Ruta and Alia.
  3. Alia Atkinson showed a shift in the stroke kinematics between the Singapore and Doha events (decrease in the clean speed, stroke length and efficiency but increase in the stroke rate).
  4. Alia’s breakout was around the 8-9m and 9-10m distances in Singapore and Doha, respectively. She not only stayed underwater longer, but the turning speed was also higher (10.6% and 6.9% faster in the first and last turns).

A lot was already said about Alia´s WR and gold medal at the SCM World Championships held last December in Doha. It is great for her, for Jamaica and for the World swimming according to the reasons pointed out in a very comprehensive way in the specialized media. Let´s go back one month, November 2014. Early that month, a few weeks before the Championships, it was held here in Singapore the last leg of the 2014 FINA World Cup Series. Overall, the leg was fairly entertaining considering that: (i) most swimmers were away from home at several weeks to compete at the legs of the Asian cluster; (ii) each leg is a two-days meet packed with a lot of events; (iii) most swimmers race more than two events per day; (iv) there are claims that some of them still have training sessions between the morning and evening races; (v) probably they are looking forward to the World Championships in 4-5 weeks time. However, a couple of athletes posted very promising races, swimming at world record paces.

That time, my comment to a few friends and peers was that if Chad and Alia can race at WR pace 4-5 weeks before Doha, after a good taper, probably they will smash some records in December. So, we must keep an eye on them. Surprisingly, at least for some people, that did happen. So this bring us to today´s post: (i) compare Ruta Meilutyte (LTU) WR in Moscow (October 2013) and Alia Atkinson (JAM) in Doha (November 2014), both with a time of 1:02.36; (ii) learn the effect of the taper on Alia´s performance (Singapore vs. Doha, 5 weeks apart).

Race analysis was done as reported in my previous posts on Ruta Meilutyte’s 100 SCM World Record Race Analysis. The Doha race can be found on YouTube® and the one in Singapore I recorded on the stands.

Ruta is well known to be very quick on the blocks (i.e. reaction time). However the water entry and water break is not so different comparing RM and AA (table 1). Between Singapore and Doha, Alia covered one more meter fully immersed but only spent an extra 0.13s. Hence, one might consider that she improved the first and second glides in the start (RM: 2.43m/s; AA: 2.30m/s and 2.44m/s; an improvement of 5.8% in 5 weeks).

table 1

AA was slightly faster in the first split than RM (AA: 29.46s; RM: 29.56s) but that paid-off even though she was slower by 0.1s in the following one (Table 2). In Singapore, AA did the first split at the WR pace (29.58s). I am not sure if she was only testing paces, really wanted to break the World record but was too tired, saving energy for the remaining events of the session because she raced back-to-back two finals: the W100Br (at 06:24pm) and the W200IM (at 06:53pm). Only she and her coach have the right answer to that.

table 2

Surprisingly the Atkinson´s stroke kinematics were slightly lower than the one performed by RM (table 3). Clean speed, stroke length and efficiency (i.e. stroke index) are lower, but the stroke rate higher. Interestingly, the same trend can be verified comparing the Singapore leg with Doha´s final. In Singapore, 81.8% of the speed was related to the stroke length, while in Doha only 35.34%. So, it seems that she had a strategy based on the stroke rate in Doha, a nice and “smoother” technique in Singapore.

So far, we learned that Alia Atkinson start was quite good, and there was a shift in the stroke kinematics. This lead us to the question on how did she performed during the turns and the finish.
table 3

Over the three turns, AA increased the distance to the water break (table 4). She was doing the water break around the 8-9m and 9-10m distances in Singapore and Doha, respectively. Not only she stayed longer fully immersed but the turning speed was also higher (10.6% and 6.9% faster in the first and last turns). Regarding the finish, the difference between RM and AA is 0.06s. AA showed a slight improvement by 0.04s (1.2%) between November and December. Therefore, it seems that the turns were determinant for Alias Atkinson World Record.
table 4

table 5

To wrap-up, comparing RM and AA WR at the W100Br by the same time of 1:02.36, it seems that the start and the turns were determinants for the later swimmer´s performance. Over that race, the clean swimming relied more on the stroke rate than the stroke length or swimming efficiency. That improvement on the start and turns did happen between the race delivered in Singapore and the final in Doha. Moreover, there was a slight shift in the swimming mechanics (higher SR, lower SL).

Can’t wait for the long course meters woman’s 100 breaststroke world record showdown, any predictions?

By Tiago M. Barbosa PhD degree recipient in Sport Sciences and faculty at the Nanyang Technological University, Singapore

The post Comparison of the Short Course Meters Woman’s 100 Breaststroke World Record appeared first on Swimming Science.