Peak performance characteristics and key competition factors of elite speed skaters based on longitudinal data

Abstract

This study aimed to investigate the peak performance characteristics of world elite speed skaters and the key competition factors involved in the journey towards achieving better peak performance. Data from 651 elite skaters (361 males and 290 females) across the 2003/2004 to 2022/2023 seasons were collected from the international skating Union database. Individual quadratic trajectories were used to track progression to peak performance. K-means clustering grouped competitive feature variables, and key competition factors were analyzed through a binary logistic regression model, with the odds ratio (OR) indicating whether a factor was favorable (OR > 1) or unfavorable (OR < 1). Differences (P < 0.01) in the peak age between medalists and non-medalists were noticed, while no differences (P > 0.05) were noticed between sexes or distances. Differences (P < 0.05) were observed between sexes, as well as between medalists and non-medalists in the peak-performance window. Binary logistic regression revealed that medium (OR =  8.070) or high (OR  =  28.242) first participation performance, high first major competition performance (OR = 4.818), medium (OR = 10.661) or early (OR = 51.592) first major competition age, medium career-best performance age (OR = 40.333), participation frequency above 2.8 times/year in phase-3 (2.8–4.7 times/year: OR = 5.132; 4.8-8.0 times/year: OR = 8.171), and a career length of 9 years or more (9–13 years: OR = 2.538; 14–21 years: OR = 4.647) were favorable factors for achieving better peak performance. In conclusion, peak age range for world elite speed skaters was 25–28 years, with a duration of peak performance lasting 4–5 years. Certain competition factors were key conditions for world elite speed skaters to enhance their peak performance.

Introduction

Speed skating holds the highest gold-medal count in the Winter Olympic Games. Understanding the peak performance characteristics and key competition factors of athletes was crucial for coaches to design effective training programs and plan long-term training cycles¹. In the training field of China, competitive level refers to the measure of competitive ability displayed². While in other countries, the term “performance progression” was often used to describe the continuous changes in athletes’ competitive level as their age increased³. As a racing sport, speed skating performance times was relatively less influenced by opponents’ performances and competition results^4,5. It relied more on the athletes’ own intrinsic competitive abilities during the competition⁴. Therefore, performance time can be used to accurately measure the performance of speed skaters.

Since Schulz et al.⁶ published a comprehensive study on the peak performance of elite athletes in 1988, this topic has attracted significant attention in competitive sports. Researchers globally then examined peak performance by analyzing athletes’ competition results and ages over specific periods^7,8. Though this method provided valuable insights, it could not accurately reflect the characteristics compared with longitudinal studies focusing on individual performance development. To overcome this, Hollings et al.³ introduced the quadratic curve model to explore performance trajectories in track-and-field athletes, identifying peak performance age and window through specific formulas. This model was later adopted by scholars like Solberg et al.⁹, Boccia et al.¹⁰, and Weippert et al.¹¹ to study athletes’ peak performance across various sports. In racing sports, peak ages were identified as follows: swimmers between 22 and 24 years, track-and-field athletes around 25, triathletes at 27, marathon runners between 27 and 31, and cyclists around 30 years old^12–15. These findings underscored the significant variations in characteristics of peak performance across different sports. Identifying characteristics of peak performance for elite athletes in various sports provided a comprehensive framework for their developmental goals. However, for speed skating, a key Winter Olympic event, the characteristics of peak performance for elite skaters remained unknown, potentially hindering the sport’s global development.

For athletes, achieving outstanding performance in major competitions during their first participation was particularly challenging, especially with limited competition experience¹⁶. Studies suggested that repeated successes and failures in competitions acted as catalysts, enhancing athletes’ psychological maturity and stability¹⁷. Compared to specialized athletic abilities, competition experience often held greater significance¹⁷. This was because repeated participation allowed athletes to adapt to various aspects of competition, including regulations, environments, officials, opponents, and spectators¹⁸. Consequently, the extensive competition experience accumulated by speed skaters in their careers could positively influence their peak performance^19,20. Based on relevant research, this paper categorized competition periods according to key milestones in speed skaters’ performance progression^12,21,22,25. It primarily examined the impact of four competition factors: age, performance, participation frequency, and length of athletic career, on their peak performance.

Analyzing the peak performance characteristics and key competition factors contributing to better peak performance among world elite speed skaters enabled coaches and researchers to systematically identify skaters with potential and optimize their performance development stages. Hence, this study analyzed competition data from 651 elite skaters spanning the 2003/2004 to 2022/2023 seasons to investigate the peak performance characteristics of world elite speed skaters and the key competition factors contributing to achieving better peak performance. The aim was to provide a comprehensive analysis of these elements, offering a valuable global reference for professionals in the field of speed skating.

Methods

Research variables

This paper defined characteristics of peak performance as the age of peak performance and the window of peak performance. The age of peak performance refers to the age at which an athlete reaches their best performance in their performance trajectory³. The window of peak performance refers to the period of time around the age of peak performance when performance predicted by each quadratic trend is within the smallest worthwhile change in performance from the peak³. During this window, athletes achieved their peak performance of their athletic career¹².

The study investigated the key competition factors for achieving better peak performance among world elite speed skaters through various competitive feature variables. The competitive feature variables included first participation age (FPA), first major competition age (FMA), career-best performance age (CPA), first participation performance (FPP), first major competition performance (FMP), career-best performance (CBP), participation frequency in phase-1 (PF1), participation frequency in phase-2 (PF2), participation frequency in phase-3 (PF3), and length of athletic career (LAC). The descriptive statistical results and their definition for the above variables were presented in Table 1.

Table 1.

Descriptive statistical results of competitive feature variables.

Variable	Variable definition	Unit of measurement	Mean	Standard deviation
First participation performance (FPP)	Best performance in first compet- ition in ISU database	Points*	80.4	6.1
First major competition performance (FMP)	Best performance in first World Single Distance Speed Skating Championships or Winter Olym- pic Games	Points*	80.8	6.4
Career-best performance (CBP)	Best performance achieved by the skater throughout their career	Points*	91.7	4.2
First participation age (FPA)	Age of skater’s first competition in ISU database	Years	21.8	3.9
First major competition age (FMA)	Age of skater’s first participation in World Single Distance Speed Skating Championships or Win- ter Olympic Games	Years	23.9	3.9
Career-best performance age (CPA)	Age of skater’s personal best performance achievement	Years	27.0	4.1
Participation frequency in phase-1 (PF1)	Frequency of participation from FPA to FMA intervals	Times/year#	4.2	1.4
Participation frequency in phase-2 (PF2)	Frequency of participation from FMA to CPA intervals	Times/year#	6.2	1.6
Participation frequency in phase-3 (PF3)	Frequency of participation after CPA	Times/year#	3.8	1.6
Length of athletic career (LAC)	Duration from FPA to the end of the athlete’s career	Years	9.4	3.7

^*Competition scores were calculated by converting performance times using a specific formula, which was detailed in the Data Processing section.

^#Indicated the participation frequency per year of age.

Data sample and outliers

The world elite speed skaters were defined as athletes who had achieved top-10 position in individual events at the Winter Olympic Games or World Single Distance Speed Skating Championships. The competition performance data of the speed skaters were sourced from the official database of the International Skating Union (ISU), which provided detailed records of each speed skater’s name, age, competition date, competition name, and venue where the result was set. The dataset covered the seasons from 2003/2004 to 2022/2023 and included results generated by timekeeping system. Excluded from the dataset were the World Sprint Speed Skating Championships and World Allround Speed Skating Championships as they had differing competition rules compared to the Winter Olympic Games. Consequently, the dataset comprised 4 Winter Olympic Games (Due to the absence of competition data from the 2006 Turin Winter Olympic Games in the database, the data were excluded), 151 World Cup, and 37 World Championships (including the World Single Distance Speed Skating Championships, ISU Four Continents Speed Skating Championships, ISU European Speed Skating Championships, and World Junior Speed Skating Championships), totaling 192 competitions. Based on the competition distances²³, this study categorized speed skating events into 3 groups: sprint events (500–1000 m for both males and females), middle-distance events (1500-m for both males and females), and distance events (5000–10000 m for males; 3000–5000 m for females), covering a total of 10 individual events across these categories.

During data collection and sample selection, four types of exceptional cases were excluded: (1) Outliers with performance times exceeding 4.5 standard deviations, as per Hopkins et al.²⁴, were removed. (2) Athletes whose performance trajectories did not follow a parabolic curve, rising to a peak and then declining, were excluded²⁵. This was determined by fitting developmental curves, and samples deviating from this pattern were discarded. Additionally, samples with peak ages outside the athletes’ competition years were removed to reduce research bias³. (3) Athletes still active at the end of the 2020/2021 season, when international events were suspended due to the pandemic, were excluded from participation frequency analysis¹². (4) Athletes whose first major competition occurred before their first participation age or whose career-best performance predated their first major competition were excluded from the phase-1 and phase-2 participation frequency analyses, respectively. After processing, the final sample comprised 651 athletes and 46,986 competition records.

Statistical analysis

All statistical analyses in this study were conducted using SPSS ver. 25. For characteristics of peak performance, Fig. 1 illustrated how a quadratic curve model was used to determine an athlete’s peak age and calculate the duration of peak-performance window. The quadratic curve model used to fit the performance trajectory of an athlete’s was: y = a×Age²+b×Age+c. Each athlete’s performance trajectory was individually derived. The formula to describe the peak age was -b/(2a), and the formula to describe the duration of the peak-performance window was 2(√∆/a), where ∆ was the smallest worthwhile change in performance. The calculation of ∆ value was based on the study by Noordhof et al.²⁶, which multiplied the coefficient of variation of each event by their corresponding smallest worthwhile effects. The smallest worthwhile effects was calculated as 0.3 of the within-athlete variation in performance between international competitions²⁷. For males, values for within-athlete variation were 0.6% for ≤ 1000-m events, 0.5% for 1500-m, 0.8% for 5000-m, and 1.2% for 10,000-m. For females, the coefficient of variation were 0.8% for ≤ 1000-m events, 0.7% for 1500-m, 0.9% for 3000-m, and 1.1% for 5000-m. Consequently, the ∆ values for male’s events were 0.18%, 0.15%, 0.24%, and 0.36%, respectively, while for female’s events, they were 0.24%, 0.21%, 0.27%, and 0.33%, respectively. Additionally, when investigating differences in peak performance characteristics, an independent samples t-test was applied if the data passed the Shapiro-Wilk test for normality, while the Mann-Whitney U test was used for non-normally distributed data. Based on whether elite speed skaters had won medals at the Winter Olympic Games or World Single Distance Speed Skating Championships, this study divided the sample into two subgroups: medalists and non-medalists, to explore differences in peak age and window of peak performance between them.

Fig. 1.

The performance progression trajectory, age of peak performance and duration of the window of peak performance for a former world champion in the male’s 500-m event. The solid line represented the age of peak performance (27.8 years); The stage between two vertical dotted lines showed the duration of peak-performance window (4.5 years); ∆ was the smallest worthwhile effect.

To explore the key competition factors, competitive feature variables were clustered using k-means, with those showing differences between clusters selected as independent variables and career-best performance (CBP) as the dependent variable¹². The nine competitive feature variables were grouped into three categories, while the CBP was divided into two. A one-way analysis of variance revealed differences among the groups (P < 0.01). Consequently, the nine variables, along with sex, and competition distances as control variables, were included as independent variables, with CBP as the dependent variable. To further analyze key competition factors, the study incorporated the odds ratio (OR) into the regression model. OR, calculated as the exponential of the coefficient (OR = e^β), measured the correlation between variables: OR > 1 indicated a favorable factor, OR < 1 an unfavorable one, and OR = 1 no association between the variables.

Data processing

During the statistical analysis, it was found that most elite speed skaters had participated in multiple events. When analyzing individual events separately, the data for secondary-events (individual events aside from the primary-event) were often insufficient, making it difficult to accurately reflect the characteristics of peak performance and key competition factors of the athletes. Therefore, the study concluded that it was inappropriate to analyze secondary-events separately. Additionally, this study focused on athletes who had ranked in the top-10 at the Winter Olympic Games or World Single Distance Speed Skating Championships. However, the dataset included all performance data from the athletes’ careers, which might have meant that some athletes’ secondary-events performances did not reach elite levels. Consequently, for athletes participating in multiple events, the event in which they achieved their best performance was considered their primary-event, while other events were considered secondary-events. Although secondary-events were not analyzed separately in the statistical process, their data were retained and included in the analysis of primary-event.

Due to significant performance differences between different speed skating events, a normalization method was adopted to standardize the performance time of primary- and secondary-events. Drawing from related research in swimming, the study converted performance time into competitive points for horizontal and vertical comparisons¹². The formula for calculating competitive points was P = 100×(B/T)³, where B was the benchmark time for the specific event (world record performance) and T was the athlete’s competition time for that event. Through the conversion formula, each points value corresponded to a specific performance in an event. Given the minimal performance improvements in speed skating from the 2003/2004 to 2022/2023 seasons, the potential impact of time series was excluded²⁸. Subsequently, we utilized the formula to convert the transformed points for the analysis of speed skaters’ peak performance characteristics.

Results

Characteristics of peak performance in elite speed skaters

The characteristics of peak performance in world elite speed skaters are shown in Table 2. Overall, while the median peak age of females (Me/Q: 26.48/5.65 years) was slightly higher than that of males (Me/Q: 26.11/4.35 years), there was no difference between them (Z=-0.864, P > 0.05). Furthermore, there were no differences in peak age between males and females across different distance and individual events (P > 0.05). However, a sex difference existed in the peak-performance window (Z=-3.140, P < 0.01). Specifically, the peak-performance window for females was significantly longer than that of males in the 500-m (Z=-3.954, P < 0.01) and sprint (Z=-3.208, P < 0.01).

Table 2.

Age (years) at peak performance and number of years in the peak performance window for each event of world elite speed skaters.

Event	Males			Females
	n	Age of peak performance	Window of peak performance	n	Age of peak performance	Window of peak performance
Sprint	158	26.08± 3.37 (25.55–26.61)	4.19/0.40 (4.15–4.23)	116	26.90± 3.67 (26.22–27.58)	4.32/0.48** (4.25–4.39)
500-m	77	26.26± 3.17 (25.54–26.98)	4.15/0.35 (4.06–4.23)	59	27.28± 3.79 (26.30−28.27)	4.37/0.49** (4.25–4.46)
1000-m	81	25.92± 3.56 (25.13–26.71)	4.20/0.47 (4.17–4.32)	57	26.51± 3.60 (25.56–27.47)	4.26/0.46 (4.17–4.37)
Middle-distance	–	–	–	–	–	–
1500-m	86	26.48/4.29 (25.80−26.98)	4.22/0.39 (4.16–4.31)	70	26.69± 4.14 (25.70−27.67)	4.25/0.38 (4.20–4.34)
Distance	117	26.04/5.35 (25.43–27.03)	4.21/0.49 (4.15–4.30)	104	25.84/6.05 (24.75–27.70)	4.27/0.37 (4.20–4.32)
3000-m	–	–	–	61	26.50± 4.43 (25.37–27.63)	4.26/0.35 (4.19–4.35)
5000-m	66	26.78±4.28 (25.72–27.83)	4.19/0.46 (4.13–4.34)	43	27.01± 4.53 (25.61–28.40)	4.28/0.57 (4.17–4.38)
10,000-m	51	26.25± 4.11 (25.10−27.41)	4.22/0.51 (4.16–4.43)	–	–	–
Overall	361	26.11/4.35 (25.77–26.50)	4.20/0.43 (4.17–4.23)	290	26.48/5.65 (25.50-27.14)	4.28/0.41** (4.24–4.32)

If the data followed a normal distribution, it was presented using the mean ± SD; otherwise, the median/interquartile range were utilized. Data in parentheses were 95% confidence interval.

^**indicated differences (P < 0.01) in window of peak performance between males and females within the same event; no differences (P > 0.05) were found in the age of peak performance between males and females within the same event; no differences were found in age of peak performance and window of peak performance among skaters across various distance events.

Comparative analysis between medalists and non-medalists

Figure 2 illustrates the differences in peak age and peak-performance window between medalists and non-medalists. It was evident that the peak age of male (Z=-2.314, P < 0.05) and female (Z=-2.898, P < 0.01) medalists was significantly higher than that of non-medalists. Additionally, compared to non-medalists (male: Z=-2.545, P < 0.05; female: Z=-2.460, P < 0.05), medalists had a longer peak-performance window.

Fig. 2.

The difference in peak performance of elite speed skaters with different competitive Levels. ^*P < 0.05, ^**P < 0.01 indicated differences between medalists and non-medalists in peak age and window of peak performance. (a) The difference of peak age between medalists and non-medalists. (b) The difference of window of peak performance between medalists and non-medalists.

Key competition factors for better peak performance

Table 3 presents the variable grouping and binary logistic regression results. The Omnibus test confirmed that the fitted equation was statistically significant (P < 0.001), and the Hosmer-Lemeshow test indicated a good model fit (R² = 0.560, P = 0.513).

Table 3.

Binary logistic regression of key competition factors of Career-Best performance of world elite speed skaters.

						OR(95%CI)
CBP (1 = High, 90.9–100.0 Points)	β	Wald	df	P	OR	Lower	Upper
FPP (0 = Low, 59.0–75.0 Points)		32.998	2	0.000***
FPP (1 = Medium, 75.2–82.6 Points)	2.088	14.606	1	0.000***	8.070	2.766	23.547
FPP (2 = High, 82.7–93.5 Points)	3.644	32.253	1	0.000***	28.242	10.874	134.491
FMP (0 = Low, 64.5–75.9 Points)		14.587	2	0.000***
FMP (1 = Medium, 76.0-83.7 Points)	-0.282	0.445	1	0.505	0.754	0.329	1.727
FMP (2 = High, 84.0-96.6 Points)	1.572	8.000	1	0.005**	4.818	1.621	14.321
FPA (0 = Late, 25–36 Years)		8.000	2	0.018*
FPA (1 = Medium, 20–24 Years)	-0.659	0.943	1	0.485	0.517	0.081	3.288
FPA (2 = Early, 15–19 Years)	0.785	1.110	1	0.479	2.192	0.249	19.297
FMA (0 = Late, 27–37 Years)		12.023	2	0.002**
FMA (1 = Medium, 22–26 Years)	2.367	5.584	1	0.018*	10.661	1.497	75.915
FMA (2 = Early, 16–21 Years)	3.943	10.888	1	0.000***	51.592	4.959	536.802
CPA (0 = Early, 18–24 Years)		20.926	2	0.000***
CPA (1 = Medium, 25–29 Years)	3.697	20.891	1	0.000***	40.333	8.263	196.869
CPA (2 = Late, 30–41 Years)	1.459	1.897	1	0.168	4.302	1.533	12.071
PF1 (0 = Low, 1.0-3.5 Times/year)		0.033	2	0.984
PF1 (1 = Medium, 3.7–5.6 Times/year)	-0.027	0.004	1	0.950	0.973	0.415	2.283
PF1 (2 = High, 5.7-9.0 Times/year)	-0.121	0.031	1	0.861	0.886	0.230	3.410
PF2 (0 = Low, 1.0-4.9 Times/year)		0.031	2	0.985
PF2 (1 = Medium, 5.0-7.2 Times/year)	0.053	0.012	1	0.914	1.054	0.405	2.743
PF2 (2 = High, 7.3–11.0 Times/year)	0.103	0.030	1	0.863	1.109	0.344	3.575
PF3 (0 = Low, 1.0-2.5 Times/year)		17.082	2	0.000***
PF3 (1 = Medium, 2.8–4.7 Times/year)	1.635	12.427	1	0.000***	5.132	2.067	12.740
PF3 (2 = High, 4.8-8.0 Times/year)	2.101	16.159	1	0.000***	8.171	2.934	22.754
LAC (0 = Short, 3–8 Years)		8.836	2	0.012*
LAC (1 = Medium, 9–13 Years)	0.932	6.327	1	0.012*	2.538	1.228	5.245
LAC (2 = Long, 14–21 Years)	1.536	6.067	1	0.014*	4.647	1.369	15.776
Competition distances (0 = sprint)		2.980	2	0.225
Competition distances (1 = middle-distance)	0.083	0.030	1	0.863	1.086	0.425	2.779
Competition distances (2 = distance)	0.694	2.681	1	0.102	2.001	0.872	4.592
Sex (1 = males)	0.101	0.080	1	0.778	1.107	0.548	2.236
Constant	-9.479	44.686	1	0.000***	0.000

CBP, career-best performance; FPP, first participation performance; FMP, first major competition performance; FPA, first participation age; FMA, first major competition age; CPA, career-best performance age; PF1, participation frequency in phase-1; PF2, participation frequency in phase-2; PF3, participation frequency in phase-3; LAC, length of athletic career.

^*P < 0.05, ^**P < 0.01, ^***P < 0.001. In Table 3, sex and competition distances were included as control variables.

The results revealed that athletes with high FPP had an OR of 4.818 (P < 0.001, 95% confidence interval: 1.621–14.321), meaning they were 4.818 times more likely to achieve high CBP compared to those with low FPP. Although athletes with medium FPP had an OR of 0.754, it was not statistically significant (P > 0.05), indicating no meaningful association. The interpretation of the other variables followed the same logic. Overall, the key competition factors for achieving better peak performance among world elite speed skaters were: medium (OR = 8.070) or high (OR = 28.242) FPP, high FMP (OR = 4.818), medium (OR = 10.661) or early (OR = 51.592) FMA, medium CPA (OR = 40.333), a PF3 of over 2.8 times/year (2.8–4.7 times/year: OR = 5.132; 4.8-8.0 times/year: OR = 8.171), and an ACL of 9 years or more (9–13 years: OR = 2.538; 14–21 years: OR = 4.647).

Discussion

For elite athletes, characteristics of peak performance were critical for their success¹². Similar to triathletes, elite speed skaters tend to reach their peak age (25–28 years) at a moderate to late stage in racing sports¹⁴. Stoter et al.²⁹ pointed out that top-level speed skaters typically demonstrated outstanding competitive abilities from the age of 15 onwards, surpassing their peers gradually. In contrast to swimming, which required early specialization and an early peak age³⁰, speed skaters were advised to undergo diversified training and acquire a variety of sports skills before puberty to increase their likelihood of achieving success in competitive sports.

The study results indicated that while the peak age of females was slightly higher than that of males, there was no substantial difference between them, a phenomenon not commonly observed in racing sports. Numerous studies have demonstrated differences in peak age between males and females³⁰. Research by Allen et al.³¹ had shown that, except for the 50-m freestyle, males peaked later than females in other individual events. In track-and-field, one doctoral thesis had found that the peak age of females was later than males²⁵. However, the findings of Hollings et al.³ were consistent with the results of this study. Although females entered puberty earlier²⁵, males demonstrated a faster rate of performance development²⁹. Studies identified factors that influenced speed skating performance, emphasizing technical attributes such as trunk angle, pre-extension knee angle, and effective directed push-off, rather than anthropometric variables like body mass and lean body mass^32–35. With ongoing advancements in female’s specialized techniques and training methods, these technical characteristics no longer differed between sexes³⁶. Considering the similar timeframes that males and females required to acquire specialized skills. Consequently, males and females were more likely to achieve peak performance at similar ages. It was well-known that speed skaters typically trained in multi-year intervals leading up to the Winter Olympic Games, which were organized in four-year cycles. Therefore, based on the results of this study, it was reasonable to infer that most elite speed skaters had only one or possibly multiple Olympic cycles (4–5 years) to demonstrate their peak performance.

This research found that medalists peaked in age 1–2 years later than non-medalists, similar to findings in weightlifting⁹. Research showed that factors such as starting speed, ice-pushing effectiveness, and body posture played pivotal roles in determining final competition results for elite speed skaters³⁷. For example, sprint speed skater necessitated the coordinated movement of the entire body and balanced development of the upper and lower limbs to ensure the rational connection of technical actions and the release of maximum power in lower limb skating³⁸. Bartlett et al.³⁹ suggested that stability in specialized abilities was a critical factor influencing performance. Even elite speed skaters had to continually enhance their specialized abilities to improve their chances of winning medals, a process that was long and continuous. Thus, a relatively later peak age might have been an important factor influencing the medal-winning potential of elite speed skaters. Furthermore, the study found that the peak-performance window for medalists was longer than that for non-medalists. Because medalists had spent more time developing stable specialized techniques and acquiring stronger specialized abilities³⁸, they had greater opportunity to maintain these capabilities over a longer period³, thereby extending their window of peak performance.

This study employed a binary logistic regression model and found that participating in world-class competitions between the ages of 16 and 26 years was one of the key competition factors for achieving better peak performance among world elite speed skaters. This enhancement was particularly pronounced within the age range of 16–21 years. While some studies indicated that speed skaters who started competitions between the ages of 13 and 16 years had a less than 59% chance of reaching elite levels in the future⁴⁰, early accumulation of competition experience could contribute to better peak performance later on. The competition arena was a necessary testing ground for evaluating the effectiveness of daily training, as well as the best way for athletes to accumulate experience rapidly in technical, tactical, and psychological aspects¹⁹. Chen et al.¹² believed that participating in at least 4 competitions annually was a vital factor for the top-8 swimmers to achieve better peak performance. Hu et al.²⁰ concluded that participation in world-class competitions helped athletes gain experience and skills for Olympic Games, with a significant turning point occurring after 5 competition appearances. The studies mentioned above surpassed the findings of this research, which suggested that elite speed skaters should have engaged in competitions at least 2.8 times per year during PF3^12,20. This was because speed skating, being a winter sports, was only held between January and March and in November and December each year, resulting in lower overall competition frequencies. Due to speed skaters often competing in multiple events, it was essential to integrate event characteristics and athletes’ competitive abilities, fully utilizing the “training through competition” function to maximize efficiency by ensuring training enhanced performance in competitions and competition facilitated training. This approach created a virtuous and interactive closed-loop system, ensuring the maximization of efficiency⁴¹.

Medium or high first participation performance and high first major competition performance enhanced peak performance for elite speed skaters. Research has indicated that the performance of elite speed skaters began to improve rapidly (2.9–6.4%) between the ages of 13 and 16 years²⁹. However, this improvement slowed to just 1.2–1.9% between the ages of 16 and 19 years and stabilized between the ages of 19 and 24 years (0.1–0.6%)²⁹. Given that females typically reached peak height velocity between the ages of 11 and 13 years, and males between 13 and 15 years, speed skaters’ performance tended to stabilize after age 16, reflecting their future performance⁴². This largely explained why elite speed skaters needed to achieve high first major competition performance. It was noteworthy that athletes whose career-best performance age was between 25 and 29 years attained higher career-best performance. This age range coincided with the period when elite speed skaters’ performance generally stabilized²⁹, aligning with the previously mentioned peak age of elite speed skaters. Overall, athletes who began specialized training later and exhibited poor performance in the early stages, although capable of rapidly improving their performance through specialization⁴³, faced limitations in the development of certain athletic qualities due to late specialization. This delay could postpone the career-best performance age, preventing them from reaching better peak performance.

Practical applications and limitations

This research explored the peak ages and duration for achieving peak performance among world elite speed skaters. In training practice, the competitive development processes of individual athletes varied. Coaches and researchers could use the reference ranges provided by this study to scientifically design overall perennial training plans, avoiding negative impacts on athletes’ long-term development.

However, it was important to acknowledge the limitations of this study. The dataset excluded data from the World Sprint Championships and the World Allround Championships due to differing competition rules, which constitutes a limitation and may affect the generalizability of the findings. Future research could incorporate these data to examine whether results differ from those obtained in this study. Additionally, the study observed cases where elite speed skaters won Olympic gold medals before reaching their peak performance or outside the identified peak-performance windows. For example, the former world champion in the 5000-m event had a peak performance age range of 25 to 31 years but won Olympic Games gold medals not only at age 28 but also at ages 24 and 32. This phenomenon was attributed to factors such as the performance of his rivals and the stability of his own performance. Therefore, caution should be exercised when applying these findings to individual speed skaters. It should also be noted that although peak performance characteristics offered quantitative benchmarks for the long-term training and competition of speed skaters, success in winning medals or championships was influenced by a combination of physiological, psychological, and other factors. Future research is expected to further investigate the mechanisms through which these factors influenced peak performance in speed skaters.

Conclusions

The peak age for world elite speed skaters typically ranged from 25 to 28 years, with no differences between sexes or distances, and a peak-performance window of 4 to 5 years. Among elite speed skaters, medalists typically reached their peak age 1–2 years later than non-medalists, and they also tended to maintain their peak performance for a longer duration. At the same time, speed skaters who had medium or high FPP, high FMP, medium or early FMA, medium CPA, PF3 above 2.8 times/year, and ACL of 9 years or more, generally exhibited superior peak performance. This indicated that certain specific competition factors were key conditions for world elite speed skaters to achieving better peak performance. We aspire for this study to play a role in fostering a sustainable competitive advantage for the world’s speed skating program.

Author contributions

All author made equal contributions to every aspect of this study. All authors reviewed and endorsed the final version of the manuscript.

Funding

This work was supported by the National Social Science Fund of China (Grant No. 23BTY044).

Data availability

Availability of data and materials for this study, we have included official results from the official database of the International Skating Union (http://www.isu.org). The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.