When Do Elite Speed Skaters Reach Peak Performance and How Can It Be Enhanced? A Longitudinal Data Study.

doi:10.21203/rs.3.rs-4860135/v1

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When Do Elite Speed Skaters Reach Peak Performance and How Can It Be Enhanced? A Longitudinal Data Study.

https://doi.org/10.21203/rs.3.rs-4860135/v1

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This study aimed to investigate the peak performance characteristics of world elite speed skaters and the key factors involved in the journey towards achieving better peak performance. Data from 651 elite skaters 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 developmental 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). Significant differences (P<0.01) in the peak age between medalists and non-medalists were noticed, while no significant differences (P>0.05) were noticed between sexes or distances. Significant differences (P<0.05) were observed between genders, 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 year. Certain developmental factors were key conditions for world elite speed skaters to enhance their peak performance in the future.

Biological sciences/Physiology/Ageing

Biological sciences/Developmental biology/Ageing

competition experience

developmental factors

peak performance

speed skating

Speed skating holds the highest gold-medal count in the Winter Olympic Games. Understanding the peak performance traits and key developmental factors of elite speed skaters was crucial for coaches to design effective training programs and plan long-term training cycles.[1] Competitive level was a core determinant of athletic performance, significantly impacting outcomes.[2] In China, this term typically referred to the measure of an athlete's competitive ability.[3] Conversely, in other countries, "performance progression" was often used to describe how an athlete's competitive level evolved with age.[4] As a racing sport, speed skating performance depended more on an athlete's individual ability and its display in competitions than on opponents' performances or competition outcomes.[2,5] Thus, speed skating results provided a precise measure of a skater's competitive level.

Since Schulz et al.[7] 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] While this approach provided valuable insights, it was limited by sample size and lacked the precision of longitudinal studies focusing on individual performance development. To overcome this, Hollings et al.[4] 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' 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] However, for speed skating, a key Winter Olympic event, the peak age and window 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 experience.[16] Studies suggested that repeated successes and failures in competitions acted as catalysts, enhancing athletes' psychological maturity and stability.[17] Compared to specialized athletic abilities, competition experience often held greater significance.[17] This was because repeated participation allowed athletes to adapt to various aspects of competition, including regulations, environments, officials, opponents, and spectators.[18] Consequently, the extensive competition experience accumulated by speed skaters early 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.[12,21-23] It primarily examined the impact of four factors: age, performance, participation frequency, and athletic career length, on their peak performance.

Analyzing the peak performance characteristics and key developmental factors of world elite speed skaters allowed coaches and researchers to systematically identify skaters with potential, optimizing their performance development stages. Hence, this study aimed to provide a comprehensive analysis of these factors to serve as a global reference for speed skating professionals.

Research Variables

This paper defined peak performance characteristics as the age of peak performance and the window of peak performance. The age of peak performance indicated the age at which an athlete achieved their career-best performance.[4] The window of peak performance described the period during which the athlete maintained their career-best performance.[4]

The study investigated the key factors in development 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 athletic career length (ACL). The descriptive statistical results and their definition for the above variables were presented in Table 1.

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 due to 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 distances,[24] this study categorized speed skating events into 3 groups: sprint events (500-1000m for both males and females), middle-distance events (1500-m for both males and females), and distance events (5000-10000m for males; 3000-5000m 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.[25], were removed. 2) Athletes whose performance trajectories did not follow a parabolic curve, rising to a peak and then declining, were excluded.[23] 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 bias.[4] 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 analysis.[12] 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, Figure 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 competitions.[27] 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 10000-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 females's events, they were 0.24%, 0.21%, 0.27%, and 0.33%, respectively. Additionally, the Mann-Whitney U test was conducted to investigate differences in characteristics of peak performance. In brief, when the effect size (Z-value) of the significance test lay within -2.58≤Z<-1.96 and Z<-2.58, the corresponding significance levels of the differences were P<0.05 and P<0.01, respectively. 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.

To explore the key developmental factors, competitive feature variables were clustered using k-means, with those showing significant differences between clusters selected as independent variables and career-best performance (CBP) as the dependent variable.[12] The nine competitive feature variables were grouped into three categories, while the CBP was divided into two. A one-way analysis of variance revealed significant differences among the groups (P<0.01). Consequently, the nine variables, along with gender, nationality, and competition distance as control variables, were included as independent variables, with CBP as the dependent variable. To further analyze key developmental 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 developmental key 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 comparisons.[12] 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 excluded.[28] Subsequently, we utilized the formula to convert the transformed points for the analysis of speed skaters' peak performance characteristics.

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 peak age of females (26.48/5.65) was slightly higher than that of males (26.11/4.35), there was no significant difference between them (Z=-0.864, P>0.05). Furthermore, there were no significant differences in peak age between males and females across different distance and individual events (P>0.05). However, a gender 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).

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.

Key Factors in Development for Better PeakPerformance

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).

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 developmental 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).

For elite athletes, characteristics of peak performance were critical for their success.[12] Similar to triathletes, elite speed skaters tended to reach their peak age (25-28 years) at a moderate to late stage in racing sports.[14] 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 age,[30] 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 had demonstrated significant differences in peak age between males and females.[30] 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 males.[23] However, the findings of Hollings et al.[4] were consistent with the results of this study. The lack of difference in peak age between males and females might have been related to the rate of performance development.[29]It was well-known that speed skaters typically trained in multi-year intervals leading up to the Winter Olympic Games, organized in cycles of four years. 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 weightlifting.[9] 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 skaters.[32] 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 skating.[33] Bartlett et al.[34] 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 abilities,[33] they had greater opportunity to maintain these capabilities over a longer period,[4] thereby extending their window of peak performance.

This study utilized a binary logistic regression model and found that participating in world-class competitions between the ages of 16 and 26 was one of the key factors in development 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 had a less than 59% chance of reaching elite levels in the future,[35] 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 aspects.[36] 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 efficiency.

Medium or high first participation performance and high first major competition performance enhanced peak performance for elite speed skaters. Research indicated that elite speed skaters' performance began to improve rapidly (2.9%-6.4%) between the ages of 13 and 16.[29] However, this improvement slowed to just 1.2%-1.9% between the ages of 16 and 19 and stabilized between the ages of 19 and 24 (0.1%-0.6%).[29] Given that females typically reached peak height velocity between the ages of 11 and 13, and males between 13 and 15, speed skaters' performance tended to stabilize after age 16, reflecting their future performance.[38] 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 attained higher career-best performance. This age range coincided with the period when elite speed skaters' performance generally stabilized,[29] 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 specialization, 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.

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 represented a limitation. Future research could include these data to explore results that might differ from those obtained in this study. Additionally, the study observed instances of elite speed skaters winning Olympic Games gold medals before reaching their peak performance or outside the observed peak-performance windows. For example, the age range associated with the peak performance of the former world champion in the 5000-m event was 25 to 31 years old. Interestingly, he won Olympic Games gold medals not only at age 28 but also at ages 24 and 32. The reasons for this included the performance of his rivals and the stability of his own performance. Therefore, caution had to be advised when applying these findings to this speed skaters.

The peak age range for world elite speed skaters was typically 25-28 years old, with no significant differences observed between genders or distances. Unlike in other racing sports, elite speed skaters tended to reach their peak performance at a later age, with a peak-performance window lasting 4-5 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. Hence, the relatively late peak age may have played a vital role in elite speed skaters' success in winning medals, while the longer peak-performance window offered them more opportunities to achieve podium finishes. 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 developmental factors were key conditions for world elite speed skaters to achieving better peak performance in the future. We aspire for this study to play a role in fostering a sustainable competitive advantage for the world's speed skating program.

Data availability

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

Author contributions

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

Competing interests

The authors declare that they have no competing interests.

Additional information

Correspondence and requests for materials should be addressed to L.C.

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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
Athletic career length (ACL)	Duration from FPA to the end of the athlete's career	Years	9.4	3.7

Note: ^*Competition scores were obtained by converting performance times using a specific formula. ^#Indicated the participation frequency per year of age.

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
Event	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)
10000-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)

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

Table 3. Binary Logistic Regression of Key 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
ACL (0=Short, 3-8 Years)		8.836	2	0.012^*
ACL (1=Medium, 9-13 Years)	0.932	6.327	1	0.012^*	2.538	1.228	5.245
ACL (2=Long, 14-21 Years)	1.536	6.067	1	0.014^*	4.647	1.369	15.776

Abbreviations: CBP, career-best performance; FPP, first participation performance; FMP, first major competition performance; FPA, first participation age; FMA, firstmajor 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; ACL, athletic career length.

Note: ^*P<0.05, ^**P<0.01, ^***P<0.001. Due to space limitations, the coefficients of the control variables related to the gender, nationality, race distance, and constant term, were omitted in Table 3.

No competing interests reported.

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When Do Elite Speed Skaters Reach Peak Performance and How Can It Be Enhanced? A Longitudinal Data Study.

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