INTRODUCTION

In competitive sports, seeking constant performance improvement is the primary objective of any athlete. When considering swimming, this performance is measured by the shortest time spent performing a certain distance (^{Garcia-Hermoso et al., 2013}). Although different factors influence the swimmers’ sports performance, establishing the best pacing during a competition is a crucial strategy for the success of these athletes, regardless of the context in which the swimmer is involved, being at the swimming pool (^{Barroso, Crivoi, Foster & Barbosa, 2021}; ^{Maglischo, 2010}), open water (^{Rodriguez & Veiga, 2018}) or triathlon (^{Wu et al., 2016}).

The literature presents different strategies coaches and athletes have used in the pool. ^{Maglischo (2010)}, for example, shows three different types of rhythm, uniform (constant), positive (with high-intensity block start and subsequent drop in speed) and negative (with the 2^nd half of the race with a stronger intensity than the 1^st). However, when specifically considering some distances, such as 800 and 1,500 m freestyle distances, athletes assume a parabolic rhythm, which results in a start and end of the race with stronger intensity and with a gradual slowing of pace in the middle laps (^{Oliveira et al., 2019}; ^{Lara & Del Coso, 2021}). Another characteristic of long-distance events is the low stability in the pace of swimmers, as analysed in the study by ^{Morais et al. (2020)} in the 1,500 m event and ^{Morais, Barbosa, Neiva & Marinho (2019)}, 800 m. It seems that elite swimmers are showing a sinusoidal (increase/decrease) profile in their pace. In both studies, in the analysis of the two races, it was observed that swimmers swim faster in the 1^st half of the race. As observed by ^{Morais et al. (2020)}, for European 1,500 m swimmers, variables such as the stroke frequency and stroke lenght seem to be the main predictors linked to rhythm, so the increase in the stroke frequency throughout the race allows for an improvement of total time. For the 800 m, the decrease in the turn speed variation can contribute to improving the performance (^{Morais et al., 2019}).

^{Veiga, Rodriguez, González-Frutos, and Navandar (2019)}, when comparing and relating the total race time with the pacing of elite swimmers in events of 5, 10 and 25 km in open water, realised that the most efficient strategy is the negative one, that the maximum distance between the leaders could not exceed 20 seconds and that an increase in speed characterises the end of the race. It is also required, based on what was exposed by ^{McGibbon, Pyne, Shephard, and Thompson (2018)}, that for sprint and middle-distance events, the maintenance of speed is crucial for a better result, while for long-distance events, what determines success is maintaining a low variability at each lap, in addition to the ability to increase the speed at the end of the race. Taken together, these pieces of evidence support that the strategies used to achieve success must consider the stroke and event. Regarding faster tests, a more detailed analysis seems to be important to provide objective feedback. In the 100m freestyle events, according to the analysis by ^{Morais, Barbosa, Lopes and Marinho (2022)}, the swimmers who perform better seem to execute a faster exit from the block, turn and finish, in addition to the stroke length being a factor- key to increased speed.

Specifically for the 200 m races, the higher metabolic demand on the glycolytic system imposes, in athletes, great difficulty in withstanding fatigue and maintaining the time performance between laps (^{Campos et al., 2017}). In this way, it becomes difficult to maintain the effort in a homogeneous way, which evokes the need to analyse the existing relationship between the partials of each lap and the total time. ^{Robertson, Pyne, Hopkins, and Anson (2009)}, when analysing the relationship of specific lap times to the final time of different strokes and distance, including the 200 m freestyle, of the finalist athletes in nine international competitions, concluded that there was a strong and positive correlation for the partials that corresponded to half of the 200 meters events (third partial). In general, the study by ^{Robertson et al. (2009)} reinforces the importance of knowing the influence of lap time on the result of 200 m races , serving as a parameter for organising training. However, it is still unclear whether, in these events, other types of analysis, such as the relative time of the laps in relation to the total race time, the relative analysis between the 1^st placed and the subsequent positions (Transition α), also would be interesting for decision making in training and competitions.

The aim of the present research was to examine the pacing strategies of elite swimmers of the 200 m freestyle.

METHODS

RESULTS

Concerning the analyses conducted on the male athletes (Table 1), the Spearman coefficients presented a negative correlation between the race time and the 1^st lap (r_s= -.38; p< .001; df= 35) and a tendency for 2^nd lap (r_s= -.19; p> .05; df= 35). On the other hand, a positive correlation was observed between the total race time and the 3^rd and 4^th laps. In the 1^st and 2^nd lap times, the winning swimmers (fastest time) spent %Time higher than the median of the respective lap times, and in the 3^rd and 4^th lap times, they spent %Time lower than the median. Additionally, a significant difference in the 1^st lap was found with better interdependence in relation to the total race time (p= .0008*). Furthermore, the Transition α from 1^st to 2^nd place is performing the 1^st lap time spending 23.75% of the total race time.

Regarding the analysis of female athletes and the correlation with the total race time, a negative correlation with 1^st lap (r_s= -.47; p< .001; df= 35) and a tendency for 2^nd lap (r_s= -.07; p> .05; df= 35) and positive with 3^rd and 4^th were identified (Table 1). Like the male athletes, the winning female swimmers in the 1^st and 2^nd lap spent %Time higher than the median of the respective laps and in the 3^rd and 4^th laps spent %Time lower than the median of the respective laps. A significant difference was also identified in the 1^st lap, showing better interdependence in relation to the final race time (p< .0001*). Furthermore, the Transition α from 1^st to 2^nd place performed the 1^st lap spending 23.68% of the total race time.

The percentiles (P20, P35, P50, P65 and P80) of the 4 lap times of the 200 m freestyle, for men and women, are illustrated in Figure 1. It was found that the 1^st lap is performed at a higher speed and different from the other laps for both sexes. The choice of dividing the percentiles was aimed at identifying the behaviour of the rhythm before and after the turns.

Figure 1. Evolution of Percentiles (P20, P35, P50, P65, and P80) in the men’s (blue panel) and women’s (red panel) 200m freestyle at the Olympic Games and World Championships in swimming (2009 to 2021). 

Finally, when analysing Figure 2, it is possible to observe the dispersion graphs of both sexes for the 200m freestyle with the interdependence between the 1^st lap in %Time and the total race time. The 1^st lap was selected, as it was the only one that exhibited a significant difference for both males and females (Table 1). From the analysis of the data dispersion obtained from the relationship between the total race time and the time only in lap 1, it is possible to observe that regardless of gender, the fastest athletes in this lap are not the winners of this race (Figure 2).

Figure 2. Interdependence between lap 1 (% of time) and the time that men’s (blue panel) and women’s (red panel) performed the 200m freestyle at the Olympic Games and World Championships in swimming (2009 to 2021). 

DISCUSSION

The aim of the present research was to examine the pacing of elite swimmers of the 200 m freestyle, checking the strategies used by them. In general, both for males and females, the 1^st lap presented a significant difference in relation to the total race time. Additionally, concerning the total race time, a negative correlation was observed in the 1^st half (100 meters) of the race and a positive correlation in the 2^nd half. Finally, the Transition α showed that the winning athletes presented a different strategy when compared with the analysed median.

The results were obtained from the analysis of the lap times and total race times of athletes of both sexes in the 200 m freestyle. Such results were represented as much by the median of the time (laps and total race time) as by the %Time. It was observed that, for both sexes, there is a significant difference only for the 1^st lap (Table 1) and for a strategy in which there is a race division characterised by a faster 1^st half and a slower 2^nd half (Figure 1). Regarding the 1^st lap, we could infer that the block start assumes great responsibility for the swimming speed, as suggested in the study by ^{McGibbon et al. (2018)}. ^{Campos et al. (2017)} highlight that this trial presents a high glycolytic metabolic demand, making it difficult to maintain performance. In this way, an inadequate choice of speed to perform the 1^st half of the race faster than 2^nd half (Figure 2) may represent a failure, as it negatively impacts the performance in the 2^nd half.

The W-Pace is the percentage that the swimmer must be faster than the 2nd half of the race in terms of the MD-Time (median times of winners). From Table 1, the MD-Time was the median time of the analysed group, while the W-Time was the winners’ time. High-performance swimmers start with a faster 1^st lap, from block start, followed by a decrease in speed during the 2^nd half, and a progressive increase (3^rd and 4^th laps) until the end of the race (final sprint), characterised as a positive strategy (^{Maglischo, 2010}; ^{Abbiss & Laursen, 2008}; ^{Menting, Elfernink-Gemser, Huijgen & Hettinga, 2019}). In this study, this strategy using the W-Pace (how effective an athlete must be compared to the median time to win a race), which identified that winners spent the 2^nd half of the race 3.23% (men) and 2.50% (women) faster compared to the 1^st half (Table 1). In practical terms, although the athletes maintain a high speed throughout the race, knowing which lap more swimming power should be applied can be a decisive factor for success (^{Figueiredo, Zamparo, Sousa, Vilas-Boas & Fernandes, 2011}).

Moreover, when considering the Transition α, winning athletes performed the 1^st partial at a slower pace in comparison with those who finished 2^nd and 3^rd, besides being slower than the median. This pattern was also found among the athletes who came 3^rd compared to those who did not achieve a podium position. They swan the 1^st partial slower but with a higher partial time than the winners.

Our results partially correspond to ^{Nikolaidis and Knechtle (2017)}, who investigated lap times in the 100 to 800 m freestyle events in different age groups. They concluded that in the 200 m race, there was a greater decline in speed during the 2^nd lap, but the increase in speed was only observed in the last lap. ^{De Koning et al. (2011)} noticed that the world record holders of the 200 m freestyle swam at a less uniform pace, according to our findings, as well as ^{Moser, Sousa, Olher, Nikolaidis, and Knechtle (2020)}, who found that a positive strategy was adopted, in addition to the fastest 1^st lap.

Interpreting the correlations from %Time can be a method for the coach to instruct the athletes, in a practical way, to focus the optimisation of their performance in a specific partial or part of the race (^{Robertson et al., 2009}; ^{De Koning et al., 2011}). In addition, it estimates how much the athlete needs to improve performance to have a chance of reaching the podium, also considering the Transition α. In other words, this approach combines analysis of the athlete’s performance with themself and with other swimmers. Using only the time variable of the lap times restricts the analysis to that group of athletes. It is therefore assumed that any 200m athlete can benefit from such a proposal based on the distribution of pacing through the percentage of effort to be performed. The W-Pace shows how the winners swim the race. It allows the athlete to know a more refined strategy that enables them to improve their performance. This way, using %Time allows the athlete to train like the best, even if they are not in the same pool.

In fact, using the correct pace is an important strategy in swimming. However, research in this area has focused on analysing only competition data to characterise athletes’ most used race strategies, leaving their reproducibility limited to competitions. Our study presents a training proposal for athletes to adapt to such race strategies. Future research must analyse other events and swimming strokes and the practical applicability of the proposal herein.

CONCLUSIONS

For both men and women, the 1^st lap is the one that presents better interdependence in relation to the total race time, although the block start influences the time. The 2^nd part shows a slight decrease in speed, which allows for a faster pace in the 2^nd half of the race (3^rd and 4^th laps). The race winners (W-Time) use the W-Pace and are faster than the median in the 2^nd half of the race. Finally, %Time serves as an option for coaches and athletes concerning the possibility of working with different athletes’ pacing.