Freestyle skiing aerials is a technical scoring event that combines gymnastics and skiing techniques. It is a dominant snow event for the Chinese delegation in the Winter Olympics. China received its first Winter Olympic medal in snow sports, first Winter Olympic gold medal in snow sports, and first gold medal in a world-class snow sports competition in this event. During the 2022 Beijing Winter Olympics, the Chinese freestyle skiing aerials team achieved their best result with gold medals in the individual men’s and women’s events, and a silver in the mixed team event, demonstrating their strong competitive ability.

The action structure of the event consists of four technical segments: inrun, takeoff, aerials, and landing¹. It requires athletes to complete multiple complex flips and spins while airborne after takeoff, demanding high execution control ability². In this sport, technical moves are categorized into single, double, and triple maneuvers. The takeoff height for the athletes is determined by the difficulty factor of the chosen technical move. In the Winter Olympics, longer inrun tracks, steeper takeoff ramps, and greater heights of takeoff contribute to higher technical scores, particularly when performing challenging triple movements, which are key to winning gold medals³. Currently, the mainstream high-difficulty triple maneuvers used in women’s events include bLFF (Back Lay Full Full), bLTF (Back Lay Tuck Full), bFFF (Back Full Full Full), and bLdFF (Back Lay Double Full Full), while in men’s events, moves such as bdFFdF (Back Double Full Full Double Full) and bFdFdF (Back Full Double Full Double Full) are predominant. In these maneuvers, “Back” indicates that the athlete performs a backward flip during takeoff, “Lay” denotes a layout position, “Tuck” denotes a tuck position, “Full” denotes a 360-degree twist, and “Double Full” denotes a 720-degree twist⁴.

Freestyle skiing aerials is a high-risk event in the Olympics. It requires athletes to descend a ramp at speeds of 50 to 70 km/h and launch from a takeoff platform that is 6 to 8 m high⁴. The aerial maneuvers can result in a maximum vertical drop of up to 18 m from takeoff to landing⁵. Mistakes in any technical segment can potentially lead to sports injuries^6,7. Among these, landing failures are identified as a key factor in injury occurrence^8,9,10. Research statistics show that among injuries sustained by athletes due to training or competition, knee injuries are the most common¹¹. Given the challenges of landing stability and sports injuries faced by athletes during training and competitions, along with the developmental trends in freestyle skiing aerials, physical fitness has emerged as a crucial factor influencing their technical and tactical performance^3,12,13,14.

Currently, the Chinese freestyle skiing aerials team is at a critical juncture of generational transition, featuring both seasoned veterans and highly promising newcomers¹⁵. During this period, maintaining and enhancing the overall competitive level poses a significant challenge. Although the team has garnered notable achievements in international competitions, research on the physical fitness model of champions is still relatively lacking. Establishing a scientific physical fitness model for champions can better guide athletes’ training, enhance their competitiveness in international competitions, and reduce the occurrence of sports injuries. Therefore, there is an urgent need to study the physical fitness model of champion during freestyle skiing aerials event. The so-called champion physical fitness model refers to the physical fitness characteristics of world-class athletes who possess the ability to contend for championship in international competitions.

This study aims to enhance the competitive performance of freestyle skiing aerials athletes and provide scientific training and monitoring support for the 25th Milan Winter Olympics. Based on this objective, we hypothesize that a comprehensive champion physical fitness model can accurately reflect the physical fitness characteristics of champion athletes and effectively predict their performance. To test this hypothesis, we systematically investigate and construct the indicator model, the weight model, and the quantitative model.

In the process of constructing this comprehensive model, a multi-stage approach was adopted, with each step being interrelated and advancing progressively to form an integrated whole. First, through extensive literature review and expert interviews, physical fitness indicators related to freestyle skiing aerials were collected. The literature review helped identify key indicators from existing research, providing a theoretical foundation for the model’s construction. Expert interviews further verified and refined the practical applicability of these indicators, ensuring that the model met actual training needs. Subsequently, based on the indicators obtained from the previous two steps, an indicator selection questionnaire was designed. A two-round expert survey was conducted, and the selected indicators were revised and confirmed, which enhanced the validity and practicality of the indicators. However, after the questionnaire screening, a relatively large number of indicators remained, many of which showed potential correlations. Therefore, physical fitness tests were conducted on the athletes to obtain specific data for each indicator. This step provided an empirical basis for subsequent data analysis. Through these tests, the actual impact of each indicator on athletic performance was evaluated. Following this, factor analysis was conducted on the collected data to reduce the dimensionality of the indicators and optimize the model structure, thus constructing the most representative indicator model. Furthermore, by integrating expert ratings and statistical methods, a weighting model was established to quantitatively analyze the importance of each indicator. This process combined subjective evaluation with objective data, enhancing the accuracy and credibility of the model. Finally, based on the selected and weighted indicators, a quantitative model of champion physical fitness characteristics and performance prediction was constructed.

The study collected and analyzed data from 29 national freestyle skiing aerials athletes to construct a comprehensive champion physical fitness model. The research process is illustrated in Fig. 1, which clearly demonstrates the logical relationships and interactions between each step.

Research flowchart.

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Participants

The study established a champion physical fitness model by selecting 29 athletes from the Chinese national team in freestyle skiing aerials, all of whom were preparing for the 2022 Beijing Winter Olympics, as the test sample. This group included 15 male athletes with an average age of 21.67 ± 4.24 years, an average training experience of 10.60 ± 3.44 years, an average height of 174.98 ± 3.36 cm, and an average weight of 68.60 ± 4.15 kg; 14 female athletes with an average age of 22.07 ± 4.41 years, an average training experience of 10.86 ± 3.18 years, an average height of 159.90 ± 3.81 cm, and an average weight of 55.68 ± 5.06 kg. The sample population encompassed athletes who participated in major domestic and international competitions, including Olympic medalists, national medalists, and World Cup and World Championship winners, reflecting their high level of competitive ability. Among them, some of the participants in the tests were top world-class athletes in freestyle skiing aerials, including Olympic champions and grand slam winners.

A comprehensive literature review was conducted to gain a thorough understanding of the current trends and advancements in freestyle skiing aerials and related disciplines. This review, encompassing both print and digital sources, focused on key topics such as sports training and talent identification. The insights gained informed the development of the physical fitness model, structured into indicator, weight, and quantitative models, and categorized the fitness test indicators into body morphology, physiological function, and athletic quality.

We consulted experts in the field of this discipline from institutions such as Beijing Sport University and Shenyang Sport University (n = 4), as well as coaches from national and provincial teams (n = 6). The emphasis was placed on seeking expert insights regarding the choice of physical fitness test metrics, the specifics of conducting these tests, and the methodologies for assessment. Specifically, the interviews covered various aspects such as the components of competitive ability in freestyle skiing aerials champions, the importance of physical fitness, key physical fitness indicators, critical factors for success, energy metabolism characteristics, ideal body composition, essential physiological function, and athletic quality. The primary methods of this study were face-to-face and telephone interviews. Each interview lasted approximately 0.5 to 1 h (see Supplementary Information 1 for the interview outlines).

Based on the main indicators extracted from the first two sections, the “Freestyle Skiing Aerials Champion Physical Fitness Indicator Selection Questionnaire” was designed (Supplementary Information 2). The questionnaire consists of three parts. The first part collects basic information on the experts, including name, age, gender, institution, position, professional title, education level, research field, and years of experience in teaching or training. The second part involves rating the importance of physical fitness indicators for freestyle skiing aerials using a 5-point Likert scale (1 = very unimportant, 2 = unimportant, 3 = neutral, 4 = important, 5 = very important). Experts can also provide modification suggestions. The primary indicators include body morphology, physiological function, and athletic quality. The secondary indicators further refine the primary indicators, such as length, anaerobic capacity, and strength. The tertiary indicators further detail the secondary indicators, such as lower limb length/height×100, maximum anaerobic power, and 1RM barbell clean. The third part asks experts to rate their familiarity with the indicators on a 5-point scale (very familiar to unfamiliar) and evaluate the basis of their judgment (practical experience, theoretical analysis, literature references, personal intuition) using a three-level scale (high, medium, low).

A two-round survey using the Delphi method was conducted with domestic experts (including coaches, referees, and researchers, totaling 16 individuals) to evaluate the importance of these indicators. Following the first round of expert questionnaires, some indicators were deleted or modified based on the scoring and feedback, leading to the development of the second-round questionnaire. 16 questionnaires were distributed and effectively recovered. The authority coefficient measures the level of expertise of the experts, thereby assessing the reliability of the questionnaire. This coefficient is calculated using the formula: authority coefficient = (judgment coefficient + familiarity degree) / 2. The higher the value of the authority coefficient is, the greater the level of expertise. Typically, an authority coefficient > 0.7 is considered to indicate a high level of expertise. For this survey, authority coefficients attained values of 0.87 and 0.91 in the first and second rounds, respectively, underscoring the high authority of the selected experts for the study.

The coefficient of variation (CV) indicates the degree of difference in experts’ understanding of the relative importance of an indicator. Under normal circumstances, a CV < 0.25 is considered to indicate an acceptable range; the smaller the CV, the greater the degree of consensus among experts^16,17,18. After the second round of questionnaires, indicators with a CV < 0.25 and an average questionnaire score ≥ 4 were selected as the final indicators and used for physical fitness testing.

For determining the weights of representative indicators, this study developed the “Freestyle Skiing Aerials Champion Physical Fitness Indicator Weight Assessment Questionnaire” which was then disseminated among relevant experts to assign weights to the physical fitness indicators. 14 questionnaires were disseminated and subsequently all retrieved, as detailed in Supplementary Information 3. The weight assessment questionnaire requires experts to rate the primary indicators using a 5-point Likert scale. Since the weights of the secondary and tertiary indicators are determined by the contribution rates from factor analysis, experts need to indicate their agreement or disagreement with these weights calculated through the contribution rates. The detailed process of factor analysis is elaborated in the statistical analysis section.

Reliability and validity of the questionnaires

To ascertain the two questionnaires’ efficacy, eight experts were engaged for validation purposes (see Supplementary Information 4). The results of the validation showed that for the “Physical Fitness Indicator Selection Questionnaire”, 75% of the experts expressed that they were “very satisfied”, 25% expressed that they were “satisfied”. For the “Physical Fitness Indicator Weight Assessment Questionnaire”, 62.5% of the experts indicated they were “very satisfied”, while 37.5% of the experts indicated they were “satisfied”. These responses indicate that the structure and content of both questionnaires were well received by the experts, demonstrating good validity.

This study employed the test-retest method to assess the reliability of the questionnaires. After the initial distribution and completion of the questionnaire, eight experts were randomly selected from the original respondents to complete the questionnaire again after an interval of half a month. Upon completion of both rounds of responses, statistical software was used to analyze the correlation between the two sets of results. The test-retest reliability coefficients for the two questionnaires were 0.82 and 0.84, respectively, indicating that the questionnaires demonstrated high reliability.

The physical fitness tests were conducted at the Qinhuangdao Training Base of the General Administration of Sport of China. Prior to testing, it was ensured that the athletes were in optimal condition without any sports injuries. All participants were briefed about the experimental risks and benefits of the study and signed informed consent forms. The study was approved by the Ethics Committee of Shenyang Sport University and conformed to the Declaration of Helsinki. To ensure the reliability of the test data, a retest method was used on the 29 athletes, with a two-month interval between tests. Correlation analysis was performed on the data from the two tests, and the correlation coefficient of the tested indicators was 0.89.

In evaluating the body morphology of aerials athletes, this study strictly adhered to the guidelines outlined in “Sports Measurement and Evaluation”¹⁹. Measurements were conducted using standardized equipment by professionally trained personnel, ensuring the accuracy and reliability of data collection. For body composition measurement, we utilized an advanced bioelectrical impedance analysis device (model: InBody270, produced by InBody Co., China), enhancing the precision of our assessments.

This research assessed the physiological function of freestyle skiing aerials athletes using both anaerobic and aerobic testing equipment, including an anaerobic power bicycle (MONARK837 model, Switzerland) for assessing anaerobic capacity, and a testing system (MAXII model, USA) for evaluating the athletes’ aerobic capacity and overall cardiopulmonary function. Additionally, specific blood analyzers, such as a hemoglobin analyzer (XF-IB model, China), a blood urea semiautomatic biochemical analyzer (BT-1904 C model, China), and a cortisol analyzer (DSL-10–67100 model, USA), were utilized to determine key biochemical indices, providing insights into the athletes’ physiological states.

To accurately assess the athletic quality required for freestyle skiing aerials, this study employed specific training and testing equipment. This included barbells and plates precisely manufactured by Zhangkong Barbell Manufacturing Co., Ltd., China, and auxiliary training equipment such as medicine balls, marker discs provided by Jiayou Sports & Leisure Goods Co., Ltd., China, as well as additional tools like balance pads and yoga mats. The accuracy of measurements was further enhanced by advanced timing devices, including the SEIKO SVAJ007 electronic stopwatch and the SmartSpeed optical gate timing system supplied by Fusion Sport Ltd., Australia. Detailed protocols for these assessments are available in Supplementary Information 5.

Statistical analysis

In this study, data from physical fitness tests were stored, categorized, and summarized using Microsoft Excel software. All statistical analyses and data visualizations were conducted with SPSS software version 22.0 and GraphPad Prism version 8.0.

Indicator model

To establish a champion physical fitness indicator model, factor analysis was conducted on the physical fitness test data. The main steps were as follows: (1) The Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity was conducted on the indicator data; factor analysis was deemed appropriate only when KMO > 0.5 and P < 0.01; otherwise, the data were considered unsuitable. (2) The eigenvalues, variance contributions, and cumulative contributions of each principal component factor were calculated. The criterion for effective factor extraction was set as each factor’s eigenvalue exceeding 1 and cumulative contribution rate above 60%. (3) The varimax method was applied for orthogonal rotation on the initial factor matrix, and a factor loading of 0.6 was used as the threshold. The rotated factor loading matrix was then further organized to derive the basic structure factors and their specific naming. (4) The indicators with the highest loadings in each principal component were selected based on their closeness to the physical fitness characteristics of the aerials event, the size of the factor loading, and the convenience of indicator testing.

Weight model

This study employed a combination of expert scoring and factor analysis to construct the champion physical fitness weight model. For the primary indicators, expert scoring was conducted through a questionnaire survey. The specific process included the following steps: (1) Developing an expert consultation form for weighting the primary indicators. (2) After the questionnaires were returned, the frequency of each score was tallied for every indicator. (3) The score for each indicator was calculated by multiplying the number of times each level was scored by the scores at each level and then summing the scores. (4) Finally, the weights of each primary indicator were calculated using the sum normalization method, as shown in Formula (1).

(:text{W}text{i}) represents the weight of the structural factor (:text{I}); (:text{P}text{i}text{j}) represents the value assigned by expert (:text{j}) to factor (:text{i}).

To determine the weight values of the secondary and tertiary indicators, this study primarily employed mathematical statistical methods. The steps were as follows: (1) First, the contribution rates of each principal factor were obtained through factor analysis as described previously. (2) The weight coefficients of each secondary and corresponding tertiary indicator were determined by the ratio of each factor’s contribution rate to the cumulative contribution rate. (3) The final total weight was obtained by multiplying the respective weight coefficients by the corresponding primary indicator weights.

Quantitative model

The champion physical fitness quantitative model consists of two parts: the champion physical fitness characteristics model and the performance prediction model. The characteristic model was based on 11 representative indicators (Table 1, Results section) selected through factor analysis, which serve as the basis for displaying physical fitness data. It employs data from champion athletes as a representative sample to elucidate their physical fitness attributes. To visually display the differences in physical fitness characteristics between the champions and other athletes, box plots were created for comparative analysis. Stepwise multiple linear regression analysis was performed to examine the relationship between test parameters and the performance of freestyle skiing aerials athletes, thereby establishing the performance prediction model. The independent variables included training experience and 11 representative indicators. The dependent variable was the average score of each athlete’s highest three results from national or higher-level competitions during the 2020–2021 season. The significance level was set at p < 0.05 to identify variables that have a significant predictive effect on performance.

Construction of the champion physical fitness indicator model

The two-round indicator selection questionnaire identified 3 primary, 10 secondary, and 25 tertiary indicators (Table 2). Given the large number of indicators, the tertiary indicators were chosen as the test items, and physical fitness tests were conducted to collect data from athletes. These data served as the basis for subsequent factor analysis, which ultimately led to the selection of representative indicators.

Factor analysis was conducted on the body morphology indicators, yielding a correlation coefficient among the variables of 0.640, with a KMO value greater than 0.5. This indicates that the extracted factors effectively represent the information of the original variables. Additionally, the significance level of Bartlett’s test was 0.000, which was below the threshold of 0.01, further confirming the suitability of this dataset for factor analysis. The eigenvalues and contribution rates of each principal component are shown in Table 3. Based on the factor loadings of each indicator, these three types of factors were named body composition factors, limb circumference factors, and limb length factors (see Table 4).

After conducting KMO and Bartlett tests for the physiological function indicators, the correlation coefficient among the variables was found to be 0.708. The KMO value was > 0.5 and the Bartlett test was highly significant, and both indicated suitability for factor analysis. As shown in Table 5, the first three factors had eigenvalues greater than 1, with a cumulative contribution rate of 79.383%, effectively reflecting the basic structure of physiological function. Based on the factor loadings in the analysis, these three types of factors were named anaerobic capacity factors, cardiopulmonary function factors, and functional recovery factors (Table 6).

After conducting KMO and Bartlett tests on the athletic quality indicators, the results showed a KMO value of 0.682, and the test of sphericity was highly significant (p < 0.01). This indicates that the selected indicators are suitable for factor analysis. In the analysis, principal components with a larger cumulative contribution were retained, and indicators with higher factor loadings were selected as common factor indicators for naming each principal component. This study identified five main factors, for a total contribution of 86.829% (Table 7). Based on the factor loadings of each indicator within the components, these five categories of factors were named limb strength factors, core stability factors, explosive power factors, speed agility factors, and aerobic endurance factors (Table 8).

In factor analysis, the factor loading coefficient represents the degree of correlation between indicators and common factors. The larger the coefficient value, the greater the importance of the indicator to the common factor. Based on the magnitude of the factor loadings and the specific characteristics of the event, 11 representative tertiary indicators were ultimately selected. Thus, the physical fitness indicator model for freestyle skiing aerials champions was established, comprising 3 primary indicators, 11 secondary indicators, and 11 tertiary indicators (see Table 1).

Construction of the champion physical fitness weight model

The results of the weight survey for primary physical fitness indicators are as follows: For body morphology, 2 experts rated it as important, 7 were neutral, and 5 considered it unimportant. For physiological function, 5 experts rated it as important, 8 were neutral, and 1 considered it unimportant. Notably, no experts rated either of these two indicators as very important. In contrast, for athletic quality, 12 experts rated it as very important, 2 as important, with no neutral or unimportant ratings. For secondary and tertiary indicators, weights were determined through factor analysis. The survey results show that 12 experts (85.7%) agreed with the assigned weights, considering them accurate and reasonable. Building on these results, Table 9 presents the weight model for the primary, secondary, and tertiary indicators.

Construction of the champion physical fitness quantitative model

In this study, field tests were conducted on 29 national team athletes using indicators selected via expert questionnaires. Factor analysis was performed to statistically screen the indicators, and ultimately, 11 indicators with the highest factor loading coefficients were chosen as representative. These indicators established the champion physical fitness characteristics model, as detailed in Table 10. The indicator values of the male and female champions from the 2022 Beijing Winter Olympics, identified as athletes Qxx and Xxx, were used as references for the champion characteristics model.

To further enhance the practicality of the model, this study constructed a performance prediction model. Using the physical fitness test results and performance data of all 29 athletes, the model predicts future competitive performance through stepwise regression analysis. Table 11 presents the results of the multiple linear regression analysis performed using the stepwise method. The goodness of fit for the model is R²＝0.789, indicating that the four independent variables introduced by the stepwise regression model can explain 78.9% of the performance variance. The model equation is as follows: performance = -39.667＋training experience×1.389＋relative maximum anaerobic power×3.786＋1RM barbell squat×0.444＋overhead barbell squat on balance pads×1.797. Statistical tests showed that the regression effect of the equation is significant. The Student’s T-test indicates that the variables training experience, 1RM barbell squat, and overhead barbell squat on balance pads have a highly significant impact on performance (p < 0.01), while relative maximum anaerobic power also has a significant impact on performance (p < 0.05).

The aim of this study is to enhance the competitive ability of freestyle skiing aerials athletes by establishing a champion physical fitness model and to provide a reference for training and monitoring for the 2026 Milan Winter Olympics. Through systematic analysis, a comprehensive physical fitness model has been successfully constructed, including an indicator model, a weight model, and a quantitative model. The application of these models provides an effective scientific basis for training and monitoring and helps to improve athletes’ performance. Specifically, the indicator model includes three body morphology indicators, three physiological function indicators, and five athletic quality indicators. The weight model assigns weights to these representative indicators, highlighting the importance of different indicators. The quantitative model conducts a detailed analysis from the perspective of champion physical fitness characteristics and performance prediction, providing quantitative data for the formulation of scientific training and competition strategies.

Analysis of the champion physical fitness indicator model

The performance of individuals during winter sports events is related to the makeup of their body composition²⁰. Lean body mass, which is primarily composed of muscles, has a significant impact on athletic ability^21,22,23. Freestyle skiing aerials require athletes to have considerable muscular strength, particularly in the lower limb muscles. Actions such as closing legs during aerial maneuvers, and eccentric squatting for buffering upon landing impose high demands on athletes’ leg strength^6,24. Systematic strength training increases the proportion of lean body mass, thereby enhancing muscle strength and explosive power²⁵. Evidence suggests that lean body mass is correlated with landing stability. A greater lean body mass in the lower limbs can increase energy absorption during landing²⁶. Waist/height×100 is a significant indicator of core strength in athletes, reflecting the development level of abdominal and waist muscles¹². Lower limb length/height×100 is primarily used for assessing the body proportions of aerials athletes. Athletes with elongated body proportions often excel in competitions due to the superior visual impact and rhythmic movement of their specialized technical maneuvers⁵. However, excessively long lower limbs may hinder the execution of technical maneuvers and balance⁵. A harmonious and proportionate ratio of lower limb length to height is advantageous for enhancing competitive performance.

The relative maximum anaerobic power, a key indicator of an athlete’s anaerobic metabolism level, is measured by the maximum watt output per kilogram of body weight. This measurement quantitatively represents an athlete’s ability to exert maximum force instantly under anaerobic conditions. For aerials athletes, excellent anaerobic power enables them to perform technical maneuvers rapidly and accurately at critical moments, enhancing the execution of aerial skills and the safety of landings, while also reducing the risk of injuries due to insufficient strength¹⁵. Relative maximum oxygen uptake evaluates cardiorespiratory function. In competitive sports with strict weight control, its value affects athlete performance. During freestyle skiing aerials training or competitions, athletes need to attempt and complete technical maneuvers multiple times²⁷. Good cardiorespiratory function aids in fatigue recovery, providing ample energy for training or competition¹⁵. Blood urea reflects the metabolic state of muscle breakdown and synthesis, indicating an athlete’s nutritional status and training load. High-intensity training increases blood urea concentration, signifying increased muscle breakdown. Conversely, during recovery periods or low-intensity training, blood urea levels decrease, indicating muscle repair. This indicator provides coaches with valuable information for adjusting training plans, enhancing athlete recovery, and taking necessary nutritional supplementation measures²⁸.

The 1RM barbell squat is a key indicator for assessing the maximum strength of the lower limbs. It primarily assesses the strength of the athlete’s gluteal and thigh muscles, as well as testing the stability of their trunk. Evidence suggests that improving athletes’ squat strength positively impacts their athletic performance^29,30and injury prevention^31,32. The inclusion of this indicator emphasizes the need for athletes to focus on developing maximum leg strength in their daily training. During the landing phase, athletes need to cushion the impact force through active flexion and extension movements of the lower limb joints (especially the hip and knee joints), followed by rapid extension to return to the standing position, a process that places high demands on extensor muscle strength³³. Overhead barbell squat on balance pads and side throw balls are highly aligned with the demands on the core area during the aerials and landing phases of freestyle skiing aerials. Overhead barbell squat on balance pads assesses an athlete’s balance and stability control during rapid squatting in an unstable state. The side throw ball measures the explosive force of trunk rotation without lower limb support and the balance between the left and right sides of the trunk. A strong core muscle group is essential for athletes performing aerial maneuvers and ensuring stable landings, providing the necessary body control and balance to enhance the accuracy and success rate of their movements³⁴. The 30-meter sprint primarily relies on the phosphagen energy system, which supports high-intensity and short-duration bursts of activity. In freestyle skiing aerials, the critical phases are the aerial maneuvers and landing³⁵, typically requiring 2–3 s of rapid force exertion, aligning with the energy demands of a 30-meter sprint. During this brief technical execution period, athletes must demonstrate quick reactions and swift movement speeds to ensure precise and smooth execution of their maneuvers⁵. The 12-minute run test evaluates athletes’ aerobic metabolic capacity. During breaks in competitions or training, good aerobic metabolism can enhance the recovery rate of ATP and CP, preparing athletes for the next phase of high-intensity performance^36,37,38. Therefore, athletes should emphasize aerobic metabolism training. Additionally, freestyle skiing aerials is a snow sport with a high injury rate¹¹. Aerobic metabolic training is an important method for injury recovery (post-surgery rehabilitation) and can help athletes return to competition more quickly^39,40,41.

Analysis of the champion physical fitness weight model

Determining weights is essential for refining the champion physical fitness model. This study determined the weights for primary indicators and secondary and tertiary indicators through questionnaires and factor analysis. The weights assigned to each indicator highlight their relevance to the physical fitness components crucial for freestyle skiing aerials.

Among the primary indicator weights, athletic quality had the highest weight (0.45), followed by physiological function and body morphology. Therefore, athletic quality constitutes a critical component of physical conditioning in freestyle skiing aerials. Among the athletic quality indicators, 1RM barbell squat, overhead barbell squat on balance pads, and side throw balls had the highest weights, in the order of 0.31, 0.20, and 0.19, respectively; all of these are strength-type indicators. These findings indicate that physical fitness training for aerials athletes should primarily focus on strength. Among the physiological function indicators, the relative maximum anaerobic power had the highest weight (0.43), indicating a high demand for anaerobic metabolism capacity in these athletes. The next highest was the relative maximum oxygen uptake, suggesting the importance of cardiopulmonary function enhancement in regular training. Body morphology indicators were as follows: lean body mass (0.37) > waist/height×100 (0.35) > lower limb length/height×100 (0.28), emphasizing the importance of muscle mass development and core muscle strengthening in athletes. Lower limb length/height×100 is predominantly determined by genetic factors, with limited potential for improvement through training. This underlines the importance of prioritizing individuals with innate athletic physiques in the process of talent identification.

Analysis of the champion physical fitness quantitative model

The physical fitness differences among freestyle skiing aerials athletes significantly impact competitive results³⁵. Compared to other athletes, champions have demonstrated superior traits in multiple physical fitness indicators, which are critical factors contributing to their competitive success (Figs. 2, 3 and 4).

Box plot comparisons of body morphology revealed that both male and female participants exceeded the upper quartile for lean body mass and waist/height×100, indicating relatively better muscle mass and stronger core strength. The lower limb length/height×100 value for the male champion exceeded the median value of other athletes, while for the female champion, it surpassed the upper quartile, indicating a relatively elongated physique. Physiologically, the male champion’s relative maximal anaerobic power exceeded the upper quartile, while the female champion’s value approached the maximum, highlighting the importance of anaerobic capacity in this event. The relative maximum oxygen uptake of the male champion was slightly less than the median, while that of the female champion was greater than the upper quartile. This finding indicates that the cardiopulmonary function of the male champion is at an average level, while that of the female champion is at a higher level. Blood urea levels were within the normal range in all the athletes (male, normal range: 1.7–8.3 mmol/L; female, normal range: 1.7–8.3 mmol/L). In terms of athletic quality, the study found that the champion athletes’1RM barbell squat and overhead barbell squat on balance pads were among the top in the entire team. Particularly in the 1RM barbell squat, the performance of these individuals was significantly superior to that of other athletes, surpassing the maximum value of the box plot, which highlights their advantage in maximal leg strength. In the overhead barbell squat on balance pads test, the scores of both male and female champion were close to the maximum, reflecting their exceptional lower limb balance and core stability. Their performance in the side throw ball test and 30-meter sprint also surpassed the upper quartile, showcasing excellent trunk rotation and speed agility. In the 12-minute run, the result of the male champion was above the median, while that of the female champion exceeded the upper quartile. This result is similar to that of the relative maximum oxygen uptake, which reflects cardiopulmonary function. Given that the two Olympic champions at the 2022 Beijing Winter Olympics were elite athletes older than 30 years and that their physical fitness traits were well established through years of standardized periodized training, the physical fitness characteristics model established in this study holds high reference value.

Comparison of representative body morphology test scores between champions and other athletes.

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Comparison of representative physiological function test scores between champions and other athletes.

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Comparison of representative athletic quality test scores between champions and other athletes.

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This study identified key variables related to the performance of freestyle skiing aerials, including training experience, relative maximum anaerobic power, 1RM barbell squat, and overhead barbell squat on balance pads. Stepwise regression analysis revealed that these variables could explain 78.9% of the variance in athletes’ performance. According to relevant studies^4,15,42, training experience is a critical factor for the performance of aerials athletes, as only those with extensive training experience can tackle high-difficulty technical maneuvers. Therefore, this study combined training experience and physical fitness factors to analyze athletes’ performance. The results indicated that training experience has a significant predictive effect. Compared to athletes with less training experience, those with extensive experience not only can perform more challenging technical maneuvers but also can quickly adjust their posture during competitions. This ability relies on advanced spatiotemporal perception. Experienced athletes excel in perceiving external stimuli, extracting information from the environment, and making quick decisions. These skills help them adapt to complex environmental conditions and facilitate the integration of multisensory information, forming optimized response patterns². The three physical fitness factors in the regression model (relative maximum anaerobic power, 1RM barbell squat, and overhead barbell squat on balance pads) reflect the importance of anaerobic capacity, lower limb strength, core stability and body balance control in aerials performance. The performance prediction model can provide coaches and athletes with more forward-looking training guidance and competitive strategy suggestions.

(1) Through initial indicator selection, expert screening, and statistical optimization, a physical fitness indicator model for freestyle skiing aerials champions was constructed. This model includes three primary indicators: body morphology, physiological function, and athletic quality; 11 secondary indicators: body composition, limb circumference, limb length, anaerobic capacity, cardiopulmonary function, functional recovery, limb strength, core stability, explosive power, speed agility, and aerobic endurance; and 11 corresponding tertiary indicators.

(2) Based on the expert questionnaire survey, the weights of each primary indicator were calculated as follows: body morphology (0.25), physiological function (0.30), and athletic quality (0.45). The weights of the tertiary indicators determined through mathematical statistical methods were as follows: lean body mass (0.37), waist/height×100 (0.35), lower limb length/height×100 (0.28), relative maximum anaerobic power (0.43), relative maximum oxygen uptake (0.32), blood urea (0.25), 1RM barbell squat (0.31), overhead barbell squat on balance pads (0.20), side throw ball (0.19), 30-meter sprint (0.18), and 12-minute run (0.12).

(3) Based on factor analysis and stepwise regression analysis, this study established a quantitative model for champion physical fitness in freestyle skiing aerials. The model is composed of two parts: the champion physical fitness characteristic model and the performance prediction model. Through comparative analysis, the champion physical fitness characteristic model provides a more intuitive representation, offering scientific support for understanding the physical fitness characteristics of champion athletes and optimizing their training. The performance prediction model offers forward-looking training guidance and competitive strategy suggestions for coaches and athletes.