The Effect of Competitive Level and Gender on the Interval Speed Characteristics and Pacing Strategies of High-Level 100 m Backstroke Athletes in China

Abstract

Background: This study was performed to investigate the influence mechanisms of competitive level and gender on the interval speed characteristics and pacing strategies of 100 m backstroke athletes. Methods: A total of 48 high-level male and female 100 m backstroke athletes were selected from the final rounds of three major competitions between 2023 and 2024. The athletes’ interval speed data across 16 segments were recorded and analyzed using the Dartfish software. Two-way ANOVA and Pearson correlation analysis were used to examine the correlation between interval speeds and total performance, as well as the pacing strategy characteristics of elite backstroke swimmers. Results: For male athletes, significant negative correlations were found between interval speeds and total performance in the 0–25 m, 40–45 m, 65–85 m, and 90–100 m segments (p < 0.05). For female athletes, significant negative correlations were observed in the 0–35 m, 40–45 m, 50–85 m, and 90–95 m segments (p < 0.05). The main effect of competitive level indicated that elite athletes achieved significantly higher interval speeds than sub-elite athletes in several race segments. Moreover, male athletes demonstrated significantly greater standardized speeds across multiple intervals (p < 0.05). Conclusion: (1) Elite athletes demonstrated significantly faster speeds in starts, turns, and the final 50 m sprint compared to sub-elite athletes, indicating higher power output during starts and turns during the race. (2) Male athletes generally exhibited a maximal-effort pacing strategy, as reflected in lower coefficients of variation in speed, while female athletes demonstrated a more balanced pacing pattern.

1. Introduction

In major swimming competitions, elite athletes frequently display closely matched performance levels, with the difference in completion times often being minimal. Therefore, adopting a rational interval pacing and rhythm strategy becomes crucial for achieving success [1]. As a crucial energy allocation strategy in timed events, optimizing pacing strategies plays a pivotal role in determining athletic performance [2]. Due to the unique biomechanical constraints of backstroke swimming—such as reduced propulsion efficiency and increased drag—pacing strategies require specific optimization [3].

Both an athlete’s competitive level and gender influence their pacing strategy. In Olympic events where pacing strategies play a key role, significant differences in pacing behaviors are observed between high-level and lower-level athletes. Previous studies have shown that in the 5000 m and 10,000 m long-distance races, gold medalists initially ran at a slower pace but significantly increased their speed in the second half, while athletes with lower competitive levels tended to maintain a more consistent pace [4,5]. In the 400 m freestyle event, the medal-winning group exhibited a larger difference in speed compared to the non-medalists, with the medalists being relatively faster in the final 50 m (p < 0.010) but slower in the 0–50 m and 50–100 m segments [6]. These studies suggest that higher-level athletes enhance their performance by strategically adjusting their pacing (e.g., accelerating in the second half), whereas lower-level athletes fail to make such adjustments. These differences are not only evident in speed distribution but also indirectly reflect the varying tactical decisions of coaches and energy distribution strategies of athletes during the competition [7]. Sex-related differences in physiological structure and metabolic mechanisms, which lead to biomechanical variations, also influence the selection of swimming pacing strategies [8]. Lu et al. [9] reported that female swimmers tend to adopt a parabolic pacing strategy in the 200 m freestyle event, where they swim slower in the first half and accelerate in the second half, while male swimmers are more inclined to accelerate throughout the race.

Despite notable progress in swimming research, studies focusing on the interval speed and pacing strategies of high-level backstroke athletes remain relatively limited. Compared with freestyle, the tactical choices and speed distribution patterns in backstroke have not been fully elucidated, highlighting the need for further in-depth investigation. Understanding these characteristics is crucial for optimizing training strategies and enhancing race performance in backstroke events. Moreover, research specifically addressing the pacing strategies and interval speed characteristics of 100 m backstroke athletes is still scarce. Therefore, this study aims to examine the mechanisms through which competitive level and gender influence the interval speed characteristics and pacing strategies in the 100 m backstroke.

2. Methods

2.1. Participants

This study focuses on 48 male and female athletes who participated in the 100 m backstroke finals of the 2023 China National Championship, 2023 China National Grand Prix, and 2024 China National Grand Prix. These athletes were the top competitors in their respective events, ensuring that the data collected is of high competitive value and research significance. To compare performance differences between athletes of different levels, the 48 male and female athletes were ranked in ascending order based on their total scores within their respective genders, with the median used as the cutoff. Athletes with times below the median (i.e., faster swimmers) were classified into the elite group, whereas those with times above the median (i.e., slower swimmers) were classified into the sub-elite group [8,10]. Since the data for this study were obtained from on-site recordings without any artificial interventions on the athletes, no ethical issues were raised, and therefore, ethical committee approval was not necessary.

2.2. Data Collection

The entire process of the three competition finals was recorded using a Sony FX6 camera (Sony, Tokyo, Japan) at a video capture frequency of 50 Hz [11]. To ensure the accuracy of the pacing analysis, Dartfish 4.5.1.0 video analysis software (Dartfish, Switzerland) was used, taking advantage of its precise time markers and data extraction capabilities to capture each athlete’s performance at different stages [12,13]. The competition was divided into 16 analytical segments, with the start and turn phases represented by 15 m intervals, while the remaining segments were evenly divided into 5 m intervals (see Figure 1). Using this approach, each athlete’s total performance and the data for the 16 segments were precisely recorded and extracted, and the average speed of each segment was calculated as the representative speed for that interval. The analysis was conducted from the moment of take-off from the starting block until wall contact at the finish. This precise segmentation and data analysis provided reliable evidence for revealing the pacing characteristics of athletes at different stages of the race. To ensure digitization accuracy, an experienced sports scientist with five years of expertise in motion analysis performed manual digitization of each athlete five times, ensuring data reliability. Each performance was analyzed five times, and intraclass correlation coefficients (ICC) were calculated to determine test–retest reliability [14]. According to established thresholds, an ICC value < 0.50 indicates poor reliability, 0.50–0.75 indicates moderate reliability, 0.75–0.90 indicates good reliability, and >0.90 indicates excellent reliability [15].

2.3. Study Variables

This study first investigates the linear relationship between segment results and total performance across different genders, and then analyzes the speed differences between athletes of varying competitive levels and genders throughout the race.

Pacing strategy characteristics are typically measured by phase time. However, given the study’s focus on different competitive levels and genders, phase time may introduce issues with standardization and internal validity. Therefore, the study adopts the method proposed by Lara and Del Coso, using standardized speed to represent pacing strategy characteristics (Equation (1)) [16].

$${\mathit{V}}_{\mathit{S}\mathit{t}\mathit{a}\mathit{n}\mathit{d}\mathit{a}\mathit{r}\mathit{d}\mathit{i}\mathit{z}\mathit{e}\mathit{d} \mathit{S}\mathit{p}\mathit{e}\mathit{e}\mathit{d}}={\displaystyle \frac{ {\mathit{V}}_{\mathit{P}\mathit{h}\mathit{a}\mathit{s}\mathit{e} \mathit{S}\mathit{p}\mathit{e}\mathit{e}\mathit{d}} }{ {\mathit{V}}_{\mathit{A}\mathit{v}\mathit{e}\mathit{r}\mathit{a}\mathit{g}\mathit{e} \mathit{S}\mathit{p}\mathit{e}\mathit{e}\mathit{d} }}}$$

(1)

Additionally, to assess the stability of athletes’ speed during the race, the study uses the coefficient of variation in speed (Equation (2)). The coefficient of variation reflects the degree of speed fluctuation across different segments, providing a quantifiable basis for analyzing the pacing control abilities of athletes during the competition.

$$\mathrm{CV} ={\displaystyle \frac{{\mathit{S}\mathit{D}}_{\mathit{S}\mathit{p}\mathit{e}\mathit{e}\mathit{d}}}{{\mathit{V}}_{\mathit{A}\mathit{v}\mathit{e}\mathit{r}\mathit{a}\mathit{g}\mathit{e}}}}$$

(2)

2.4. Statistical Analysis

This study used R Studio 4.3.2 (Posit PBC, Boston, MA, USA) as the integrated development environment and R language (version 4.3.2) for data analysis. The data were expressed as mean ± standard deviation (M ± SD). The Shapiro–Wilk test was first used to assess the normality of the distribution of the complete data. To explore the interval speed characteristics of male and female 100 m backstroke athletes, Pearson correlation analysis was conducted to examine the linear relationship between the speeds in each segment and the total performance. The Pearson correlation coefficient (r) ranges from −1 to +1, where an r value of +1 indicates perfect positive correlation, an r value of −1 indicates perfect negative correlation, and an r value of 0 indicates no linear relationship. Based on the magnitude of r, the correlation can be categorized as: weak (r = 0.1 to 0.3), moderate (r = 0.3 to 0.5), or strong (r = 0.5 to 1.0) [17].

Next, a two-way analysis of variance (ANOVA) was used (competitive level × gender) to examine the main effects and interaction effects between pacing strategies at different stages [8]. If the main effects were significant, post hoc multiple comparisons were performed using the Least Significant Difference (LSD) method. If the interaction effect was significant, simple effect analysis was conducted. The effect size index used for analysis was ${\eta }_p^2$, where 0.01 represents a small effect, 0.06 represents a moderate effect, and 0.14 represents a large effect [18]. The significance level was set at α = 0.05.

3. Results

3.1. Participant Information

This study included 48 male and female athletes who participated in the 100 m backstroke finals of the 2023 China National Championship, 2023 China National Grand Prix, and 2024 China National Grand Prix (Table S1). The athletes were ranked within each gender group in descending order, with those scoring above the median classified as ‘elite’ and those scoring below the median categorized as ‘sub-elite’. After classification, the mean and standard deviation of the total 100 m backstroke scores for each group are shown in

Table 1. Total Performance Scores by Gender and Speed of Athletes (n = 48).

	Male		Female
Result/s	Elite	Sub-Elite	Elite	Sub-Elite
	54.15 ± 0.89	55.74 ± 0.56	59.79 ± 0.62	61.88 ± 0.49
Sample size per group	12	12	12	12
Height/m	1.87 ± 0.04	1.85 ± 0.03	1.74 ± 0.07	1.73 ± 0.06
Weight/kg	78.5 ± 2.4	76.8 ± 2.6	65.5 ± 1.8	63.5 ± 2.6

(Table 1).

3.2. Interval Speed Characteristics

The results of this study demonstrated that the intraclass correlation coefficients (ICCs) of the interval speeds showed excellent reliability, ranging from 0.934 to 0.942 (0.934 ± 0.004). Figure 2 presents the Pearson correlation analysis results between the interval speeds and total performance for male athletes. Significant negative correlations were found between interval speeds and total performance in the 0–15 m (r = −0.88, p = 0.001), 15–20 m (r = −0.44, p = 0.031), 40–45 m (r = −0.50, p = 0.013), 65–70 m (r = −0.47, p = 0.021), 70–75 m (r = −0.53, p = 0.008), 75–80 m (r = −0.50, p = 0.013), 80–85 m (r = −0.61, p = 0.002), 90–95 m (r = −0.49, p = 0.015), and 95–100 m (r = −0.50, p = 0.012) segments. However, no significant correlation was found between the interval speeds and total performance in the 20–25 m, 25–30 m, 30–35 m, 35–40 m, 45–50 m, 50–65 m, and 85–90 m segments.

Figure 3 presents the Pearson correlation analysis results between the interval speeds and total performance for female athletes. Significant negative correlations were found in the 0–15 m (r = −0.72, p = 0.001), 15–20 m (r = −0.49, p = 0.015), 20–25 m (r = −0.51, p = 0.010), 25–30 m (r = −0.71, p = 0.001), 30–35 m (r = −0.58, p = 0.003), 40–45 m (r = −0.73, p = 0.001), 50–65 m (r = −0.49, p = 0.015), 65–70 m (r = −0.43, p = 0.036), 70–75 m (r = −0.61, p = 0.001), 75–80 m (r = −0.57, p = 0.004), 80–85 m (r = −0.59, p = 0.002), and 90–95 m (r = −0.51, p = 0.010) segments. However, no significant correlation was found in the 35–40 m, 85–90 m, and 95–100 m segments.

3.3. Pacing Strategy Characteristics by Competitive Level and Gender

Regarding interval speed characteristics, a significant interaction between gender and competitive level was found in the 25–30 m segment (p = 0.029). Simple effects analysis revealed that the elite female group had significantly higher speeds in this interval compared to the sub-elite female group (p < 0.05). At the same time, no significant differences were found between other groups. Furthermore, no significant interaction effects between gender and competitive level were found at other interval speeds.

Regarding the main effect of gender on interval speed, significant gender main effects were observed in all segments except for the 25–30 m interval (p < 0.001). Post hoc multiple comparisons showed that male backstroke athletes demonstrated a significant gender advantage in these segments (p < 0.050).

Regarding the main effect of competitive level on interval speed, significant competitive level main effects were found in the 0–15 m (p < 0.001), 15–20 m (p = 0.020), 35–40 m (p = 0.046), 40–45 m (p < 0.001), 50–65 m (p = 0.02), 65–70 m (p = 0.03), 70–75 m (p < 0.01), 75–80 m (p = 0.01), 80–85 m (p < 0.01), and 90–95 m (p = 0.019) segments (

Table 2. Speed Characteristics by Competitive Level and Gender.

	Male		Female		Gender Effect		Competitive Level Effect		Interaction Effect
	Elite	Sub-Elite	Elite	Sub-Elite
	M ± SD	M ± SD	M ± SD	M ± SD	p	${\mathit{\eta }}_{\mathit{p}}^2$	p	${\mathit{\eta }}_{\mathit{p}}^2$	p	${\mathit{\eta }}_{\mathit{p}}^2$
0–15 m	2.40 ± 0.10	2.28 ± 0.05	2.1 ± 0.06	2.0 ± 0.05	<0.001	0.838	<0.001	0.406	0.603	0.006
15–20 m	1.90 ± 0.07	1.85 ± 0.08	1.72 ± 0.06	1.67 ± 0.06	<0.001	0.649	0.020	0.118	0.919	0.001
20–25 m	1.80 ± 0.07	1.8 ± 0.08	1.68 ± 0.04	1.64 ± 0.03	<0.001	0.597	0.213	0.035	0.185	0.04
25–30 m	1.78 ± 0.05	1.78 ± 0.05	1.65 ± 0.05	1.59 ± 0.03	<0.001	0.768	0.006	0.157	0.029	0.103
30–35 m	1.76 ± 0.08	1.74 ± 0.08	1.62 ± 0.04	1.57 ± 0.05	<0.001	0.609	0.061	0.077	0.364	0.019
35–40 m	1.78 ± 0.08	1.73 ± 0.09	1.62 ± 0.04	1.58 ± 0.07	<0.001	0.567	0.046	0.087	0.764	0.002
40–45 m	1.66 ± 0.09	1.61 ± 0.07	1.57 ± 0.05	1.47 ± 0.05	<0.001	0.458	<0.001	0.283	0.220	0.034
45–50 m	1.64 ± 0.06	1.65 ± 0.04	1.49 ± 0.07	1.44 ± 0.06	<0.001	0.715	0.216	0.035	0.153	0.046
50–65 m	1.75 ± 0.08	1.71 ± 0.07	1.61 ± 0.04	1.55 ± 0.08	<0.001	0.558	0.019	0.119	0.614	0.006
65–70 m	1.67 ± 0.06	1.65 ± 0.06	1.59 ± 0.05	1.53 ± 0.08	<0.001	0.402	0.030	0.103	0.387	0.017
70–75 m	1.72 ± 0.04	1.68 ± 0.04	1.58 ± 0.03	1.52 ± 0.05	<0.001	0.768	<0.001	0.286	0.446	0.590
75–80 m	1.70 ± 0.07	1.65 ± 0.08	1.56 ± 0.05	1.5 ± 0.05	<0.001	0.594	0.003	0.181	0.969	0.001
80–85 m	1.71 ± 0.05	1.64 ± 0.08	1.56 ± 0.05	1.49 ± 0.07	<0.001	0.620	<0.001	0.246	0.885	0.001
85–90 m	1.61 ± 0.03	1.59 ± 0.05	1.49 ± 0.05	1.45 ± 0.07	<0.001	0.631	0.077	0.070	0.598	0.006
90–95 m	1.58 ± 0.07	1.52 ± 0.06	1.43 ± 0.06	1.39 ± 0.06	<0.001	0.583	0.006	0.162	0.544	0.008
95–100 m	1.87 ± 0.08	1.83 ± 0.07	1.63 ± 0.05	1.63 ± 0.08	<0.001	0.726	0.346	0.020	0.337	0.021

). Post hoc multiple comparisons revealed that elite athletes had significantly higher speeds than sub-elite athletes in these segments (p < 0.050).

Regarding standardized interval speed characteristics, significant gender main effects were observed in the 0–15 m (p < 0.001), 40–45 m (p = 0.038), 65–70 m (p = 0.005), and 95–100 m (p = 0.001) segments. Post hoc multiple comparisons revealed that male backstroke athletes had a significant gender advantage in these segments’ standardized speeds (p < 0.050).

Regarding the main effect of competitive level on interval speed, a significant gender main effect was found in the 0–15 m segment (p = 0.014). Post hoc multiple comparisons revealed that elite athletes had significantly higher speeds than sub-elite athletes in this segment (p < 0.050) (

Table 3. Standardized Speed Characteristics by Competitive Level and Gender.

	Male		Female		Gender Effect		Competitive Level Effect		Interaction Effect
	Elite	Sub-Elite	Elite	Sub-Elite
	M ± SD	M ± SD	M ± SD	M ± SD	p	${\mathit{\eta }}_{\mathit{p}}^2$	p	${\mathit{\eta }}_{\mathit{p}}^2$	p	${\mathit{\eta }}_{\mathit{p}}^2$
0–15 m	135.36 ± 4.51	131.87 ± 2.12	129.66 ± 3.27	128.07 ± 3.42	<0.001	0.343	0.014	0.13	0.341	0.021
15–20 m	107.32 ± 3.39	106.88 ± 4.64	106.09 ± 3.35	106.76 ± 3.48	0.535	0.009	0.914	0.001	0.609	0.006
20–25 m	101.39 ± 3.47	103.86 ± 4.48	103.93 ± 2.23	104.7 ± 2.13	0.076	0.070	0.089	0.064	0.367	0.019
25–30 m	100.62 ± 3.01	102.51 ± 2.96	102.26 ± 2.81	101.56 ± 1.96	0.664	0.004	0.454	0.013	0.105	0.059
30–35 m	99.23 ± 4.26	100.5 ± 4.65	100.25 ± 2.05	100.38 ± 3.23	0.676	0.004	0.510	0.010	0.594	0.007
35–40 m	100.6 ± 4.54	100.14 ± 4.61	100.11 ± 2.77	101.27 ± 4.17	0.787	0.002	0.769	0.002	0.493	0.011
40–45 m	93.78 ± 4.59	92.84 ± 3.9	97.1 ± 2.39	94.06 ± 3.53	0.038	0.094	0.068	0.074	0.329	0.022
45–50 m	92.74 ± 3.16	95.11 ± 2.78	91.93 ± 4.95	92.11 ± 3.45	0.080	0.068	0.236	0.032	0.306	0.024
50–65 m	98.91 ± 4.84	98.9 ± 4.03	99.4 ± 2.64	98.94 ± 5.19	0.833	0.001	0.851	0.001	0.857	0.001
65–70 m	94.32 ± 3.23	95.05 ± 3.3	97.99 ± 3.18	97.66 ± 4.73	0.005	0.167	0.853	0.001	0.617	0.006
70–75 m	97.24 ± 2.55	97.02 ± 2.09	97.72 ± 2.24	97.13 ± 3.41	0.695	0.004	0.595	0.006	0.812	0.001
75–80 m	96.18 ± 3.45	95.24 ± 4.07	96.44 ± 2.72	96.17 ± 3.06	0.541	0.009	0.534	0.009	0.731	0.003
80–85 m	96.72 ± 2.48	94.84 ± 4.11	96.21 ± 2.51	95.32 ± 4.11	0.986	0.001	0.165	0.043	0.614	0.006
85–90 m	90.76 ± 2.3	91.79 ± 3.26	91.94 ± 2.9	92.94 ± 4.67	0.242	0.031	0.308	0.024	0.983	0.001
90–95 m	89.11 ± 4.12	87.53 ± 3.13	88.21 ± 2.96	88.67 ± 4.06	0.909	0.001	0.596	0.006	0.334	0.021
95–100 m	105.72 ± 3.92	105.92 ± 3.31	100.75 ± 3.53	104.26 ± 4.6	0.005	0.166	0.105	0.059	0.147	0.047

).

The gender main effect significantly impacted the coefficient of variation in overall speed. Post hoc multiple comparisons revealed that male backstroke athletes experienced a significant gender disadvantage in overall speed variability (p < 0.050). No significant differences were observed in the other effects (Figure 4).

4. Discussion

This study aimed to investigate the interval speed characteristics and pacing strategies of high-level 100 m backstroke athletes in China, with a focus on different competitive levels and genders. By analyzing the total and segmented speed data of 48 athletes from the 2023 China National Championship, 2023 China National Grand Prix, and 2024 China National Grand Prix 100 m backstroke finals, we revealed the interval speed characteristics, pacing strategies, and overall speed variation in China’s elite backstroke athletes. This research offers valuable insights into the racing characteristics of athletes across different competitive levels and genders in the 100 m backstroke.

4.1. Correlation Between Interval Speed and Total Performance

Previous studies have found that the speed during the start phase (0–15 m) and the post-turn breakout phase (50–65 m) in the 100 m backstroke strongly associated with in total performance [19]. As the fastest phases of the race, the technical execution of the start and turn directly impacts whether an athlete can establish or maintain an advantage. A strong start technique can give an athlete a valuable initial speed, while an efficient turn minimizes speed loss and sets up the sprint in the latter part of the race [20,21]. This study found that during the start phase, the stage speed of both male and female athletes showed a significant negative correlation with total completion time, consistent with previous studies. However, in the post-turn breakout phase (50–65 m), female athletes’ stage speed showed a significant negative correlation with total performance, while male athletes did not exhibit a significant correlation. This may be attributed to differences in the competitive structure of male athletes, which are primarily reflected in their speed performance during the latter stages of the race [22]. Since the athletes’ technical levels were similar, the efficiency of the turn and push-off actions was also comparable, which may explain this result.

Additionally, the study observed gender differences in the correlation between interval speed and total performance in the first and last 50 m segments. Female athletes had a significant negative correlation between speed in most segments of the first 50 m (15–35 m, 40–45 m) and total performance, whereas male athletes did not show significant negative correlations in most segments of the first 50 m (25–40 m). Furthermore, in the second half of the race, female athletes did not show a significant correlation between their sprint speed in the 95–100 m interval and total performance. This suggests that for female athletes, establishing and maintaining a stable speed throughout the first half of the race is crucial. In contrast, male athletes may leverage their ability to generate explosive power even when fatigued during the final sprint, allowing for greater speed adjustment and compensation in the second half of the race, thus gaining a competitive advantage over their opponents [23].

4.2. Interaction Between Gender and Competitive Level on Pacing Strategy

This study found a significant interaction between gender and competitive level in the 25–30 m interval speed, with the elite female group demonstrating significantly higher speed in this segment compared to the sub-elite female group. The 25–30 m interval is the first part of the race after the start, reflecting the athlete’s acceleration and underwater propulsion efficiency in the early stage of the race [24]. The elite female group exhibited significantly higher speed in the 25–30 m segment, potentially reflecting superior underwater propulsion. From a practical perspective, targeted training for elite female swimmers should emphasize enhancing underwater kicking efficiency, optimizing body streamline during the breakout phase, and improving acceleration capacity immediately after the start. Specific training methods could include resisted underwater kicking, hypoxic underwater training, and breakout drills that combine start and transition techniques. These approaches may further consolidate the competitive advantage of elite female athletes in this crucial phase of the race.

4.3. Effect of Competitive Level on Speed Distribution and Pacing Strategy

In this study, athletes were categorized into elite and sub-elite groups based on the median of their total scores, revealing the influence of competitive level on pacing strategies. It was found that elite athletes were significantly faster in most intervals, particularly in the start phase (0–15 m), the turn phase (50–65 m), and the entire final 50 m sprint phase (65–100 m) compared to the sub-elite group. This result is consistent with previous findings in long-distance running and freestyle swimming, where higher-level athletes not only have faster overall speeds but also excel at accelerating in critical parts of the race (e.g., the second half) [25,26]. The speed advantage demonstrated by elite athletes in the final 50 m may suggest indicates superior lactate tolerance and energy system efficiency, allowing them to resist fatigue and either maintain or even increase their sprint speed. In contrast, sub-elite athletes exhibited more pronounced speed deterioration in the latter part of the race, a finding consistent with previous studies comparing finalists and semi-finalists, where finalists typically experience less speed decline [27].

The main effect of competitive level was statistically significant in the 50–65 m segment, with elite athletes showing significantly higher interval speeds than sub-elite athletes. A previous study on swimming turn mechanics also supports this finding. The study, using a pool wall force platform, showed that elite male and female swimmers demonstrated 25–50% higher peak power in their push-off from the wall compared to sub-elite athletes [28]. This indicates that elite athletes generate significantly more acceleration from the energy converted during their turn push-offs, which results in a more substantial speed increase in the 50–65 m segment. The superiority of turn technique and explosive power during the push-off allows elite athletes to gain a significant lead in this phase, positively influencing their overall performance.

4.4. Effect of Gender on Speed Distribution and Pacing Strategy

The main effect of gender analysis revealed that male athletes were significantly faster than female athletes in several intervals (e.g., 0–15 m, 15–20 m, 35–40 m), which is attributed to greater absolute strength, larger muscle cross-sectional area, and higher explosive power in male athletes [8]. In addition, men generally have higher anaerobic capacity and a greater proportion of type II muscle fibers, which may facilitate rapid power output and contribute to their superior sprint speed in the early and middle segments of the race. However, this advantage was not fully realized in the latter part of the race (e.g., 50–65 m), which could be related to the stronger negative correlation between speed and total performance for female athletes in the turn phase (50–65 m).

Female athletes exhibited a more balanced pacing strategy throughout the race, with their coefficient of variation for overall speed significantly lower than that of male athletes. This pattern may be partly explained by women’s relatively greater reliance on aerobic metabolism and a higher proportion of type I muscle fibers, which support fatigue resistance and enable a steadier speed profile across race segments. Previous studies have shown that male athletes tend to adopt a maximal effort pacing strategy in the 100 m freestyle, whereas female athletes are more likely to use a balanced pacing strategy [29]. As a result, female athletes experienced smaller fluctuations in speed during the race, demonstrating a more stable pacing pattern, which contributed to their lower overall speed variation compared to male athletes.

4.5. Study Limitations

This study focused on the top athletes from the 2023–2024 national finals in China. While the participants in this study represent a high level of performance in the 100 m backstroke in China, the findings may not fully capture the entire spectrum of Chinese 100 m backstroke performance, as sub-elite swimmers were not included and the sample size was relatively small. Future research could address these limitations by expanding both the sample range and size to include athletes from various performance levels, thereby enhancing the generalizability and representativeness of the results.

Furthermore, the study relied on video analysis and manual labeling, which could introduce human error. Although efforts were made to minimize errors through strict labeling standards and multiple verifications, some bias may persist. To improve the accuracy and timeliness of data collection, future studies could consider using wearable devices (e.g., for monitoring heart rate variability and lactate concentration), motion capture systems, or other technologies to enable real-time data acquisition and automated analysis, thereby reducing human interference and enhancing the reliability and objectivity of the results [30,31,32].

Moreover, external environmental factors such as pool conditions (e.g., water temperature, lane turbulence) and competition schedule (e.g., race sequence, recovery intervals) were not controlled in this study. These factors may introduce additional variability in performance outcomes and should be more explicitly considered in future research to better account for their potential confounding effects.

Additionally, this study focused solely on the main effects of gender and competitive level, along with some interaction effects, without exploring other potential factors that may influence athletic performance. Future research could integrate physiological metrics and psychological factors to better understand the complex mechanisms through which gender and competitive level influence performance, delving into the underlying physiological and psychological processes. This could provide a more comprehensive theoretical basis for optimizing swimming training and competitive strategies.

5. Conclusions

This study revealed that elite athletes demonstrated significantly faster speeds in the start, turn, and final 50 m sprint phases compared to sub-elite athletes, indicating higher explosiveness during the race. Additionally, differences in pacing strategy were observed between male and female 100 m backstroke athletes in China: males generally adopted a maximum-effort pacing strategy, whereas females tended to use a more balanced approach. However, due to methodological limitations, such as the sample size and specific testing conditions, the generalizability of these findings may be constrained. Therefore, the conclusions of this study should be interpreted within the context of Chinese high-level 100 m backstroke athletes and may not be directly generalizable to swimmers from other countries or competitive environments.