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A 220M Sprint Test Predicts Anaerobic Performance in Elite Youth Soccer Players

  † These authors contributed equally to this work.

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28 July 2026

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30 July 2026

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Abstract
The gold standard for assessing anaerobic performance is the laboratory Wingate cycle-ergometer test; however, cycling is not a sport-specific movement pattern for soccer players. The aim of this study was to determine whether mechanical variables from a 220-m field sprint test, combined with anthropometrics, could predict maximal anaerobic capacity in elite youth male soccer players. Forty-three elite Brazilian players were evaluated according to their competitive division: under-16 (n = 22) and under-18 (n = 21). Anaerobic capacity was assessed using the laboratory Wingate test and a 220-m sprint analyzed via kinematic modeling. Raw maximum sprint velocities did not differ significantly between categories (p = 0.322). Multiple linear regression modeling optimized through the Akaike Information Criterion (AIC) and influence diagnostics revealed that Average Power (AVG_Watts) was robustly predicted by a parsimonious model integrating Body Mass (B = 5.13, p = 0.004) and Mean Velocity (V_m, B = 94.74, p = 0.021). The final model was highly significant (p < 0.001), achieving an adjusted R2 of 0.414. The proposed mathematical model provides a feasible, robust, and cost-effective method for estimating laboratory-derived anaerobic performance using accessible field metrics on the soccer pitch.
Keywords: 
sprint test
;  
wingate test
;  
adolescents
;  
soccer
Subject: 
Public Health and Healthcare  -   Physical Therapy, Sports Therapy and Rehabilitation

Introduction

Football involves great demand of strength, speed and high intensity efforts in short period times (Stølen, et al, 2005; Tumilty, 1993; Al-Hazzaa H. M. et al, 2001). In soccer, relative strength (i.e. strength in relation to body mass) is considered a kay variable influencing maximum anaerobic performance (Andersen E. et al, 2018). Moreover, football players display exceptionally high capability to perform fast displacements, especially with acceleration and deceleration during training and competitive activities (Faude, et al, 2012; Carling, et al, 2012; Wisloff et al, 1998). Therefore, considering the relevance of anaerobic performance in soccer players, it is crucial to determine new feasible maneuvers to study power anaerobic capacities (Capranica et al, 1992).
There are multiple factors that influence anaerobic performance, such as: body mass, lean mass, body fat proportion and age (Armstrong et al, 2001). Wingate cycle-ergometer test is the most widely used test to assess power anaerobic capacity, including children and adults (Bouchard et al, 1991; Armstrong et al, 2001). However, there are several sports where pedaling capacity is not the outcome performance (i.e. soccer players). Suggesting the need to innovate in new statistical models and filed tests to determine anaerobic performance. In addition, it has been shown that mechanical power production could be affected by age, in absolute and relative to body mass or lean body mass (Inbar and Bar-Or, 1986). However, something interesting to note is that there are no differentiated protocols or mathematical models to assessed/estimate power anaerobic performance in elite soccer players from different division categories (different ages).
On the other hand, while Wingate cycle-ergometer test is the gold standard to estimate maximum power anaerobic capacity, there are controversial results when has been correlate with field assessment (Jacobs, 1979, Baker et al, 1980; Chamari et al, 2005; Wragg et al, 2000; Taunton et al, 1981 apud Inbar et al, 1996). Indeed, it has been shown moderate to high correlations between anaerobic performance variables obtained through the Wingate cycle-ergometer test and field tests (Jacobs, 1979, Baker et al, 1980; Chamari et al, 2005). Contrarily, other research showed low or very low correlations (Wragg et al, 2000; Taunton et al, 1981 apud Inbar et al, 1996). Then, considering all this evidence, it is necessary to determine new mathematical models and/or field test to estimate maximum power anaerobic performance, specifically in sports where pedaling capacity is not the outcome performance, such as soccer players. Therefore, the present research was designed to determine, through general stepwise regression model, the feasibility of using 220-m sprint run time in a football field as indirect measure of maximum anerobic performance, considering Wingate cycle-ergometer test as the reference criterion.

Material and Method

Ethical aspects and subjects

Forty-three male Brazilian soccer elite players between 15- and 18-year-old were recruited (trained 30 hours per week, 3 hrs AM y 3 PM x 5 per week, whit a minimum of 6 months of stay in training regime). The present research was a cross-sectional study. Institutional review board approval for our study was obtained from Ethical Committee of Blinded for peer-review purposes University – School of 1714/06). Medicine (Registration N° All participants were carefully informed about the experiment procedures, and about the possible risk and benefits associated with their participation in the study, and an appropriate signed informed consent document has been obtained in accordance with the Declaration of Helsinki (2008).
Participants were allocated to Group A (15–16-year-old, n= 22) and Group B (17–18-year-old, n= 21). according to their competitive age division (junior and juvenile, respectively). Baseline anthropometric measurements are shown in Table 1. Exclusion criteria considered (i) potential medical problems or a history of ankle, knee, or back injury, (ii) any lower extremity reconstructive surgery in the past two years or unresolved musculoskeletal disorders and (iii) inactive in the past 3 months. Inclusion criteria were: i) be part of elite soccer team; ii) regular competition in the correspond soccer division; and iii) regular training in the last 6 months. In addition, athletes were instructed not to drink coffee, smoke, nor take a sauna 24 hours before being assessed, as well as not to eat 3 hours before, making any intense physical activity or drink alcohol, in addition to drinking water ad libitum. All were requested to run at full speed during the field test and make their maximum effort during the Wingate Test.
Anthropometric Measures
Height was measured using a wall-mounted stadiometer, with the subject barefooted, back slightly against the wall while standing with feet approximately 10 centimeters apart from one another. Body mass was measured in each subject using wearing sports trousers and 0.1 kilogram was rounded off in the gauged scale. Body mass index (BMI) was calculated as body mass/height2.
Body fat was estimated in the right half of the body with a caliper applied on 9 skinfolds pinched between the index finger and the thumb of the left hand of the tester following a standard procedure.
Three measurements were taken, estimating the body density according to Thorland et al, (1984).
Two multiple regression models were used to assess the feasibility of using 220-m running-based test as power anaerobic performance related to Wingate cycle-ergometer test.
Sprint Test
220-m sprint test was performed during the morning on the same surface of the regular play and training field that was prepared by marking the distance every 10 meters with poles, and every meter of the first 10 m with colored cones to facilitate reading and timing. After 10 minutes of standard warm-up session (Bergh and Ekblom, 1979) and 3-minute recovery time, the test was started. All subjects adopted a starting position with their arms hanging naturally relaxed close to the body, while trunk slightly bent forward and the head held looking ahead parallel to the track, with the dominant foot (determined by the player himself) immediately before the start line and the other 30 to 40 cm behind. This position was adopted because it is the position closest to specificity of field sports (Brown, Weir, 2001).
The test started with a clapperboard and an energetic oral command. A single test was performed. Each subject ran 220m and was recorded by a videocam with 29.97 pictures per second placed 50 m far from the starting point, and with the possibility to turn 360º. The use of aftereffects and the time code generator were activated to record the time. The film data were recorded in DVD, AVI format to allow reading the information.
Sprint Test calculation variables
Calculation of the times was obtained from the picture by picture of the video and by approximation of the number of pictures gone between the poles (a set of criteria for reading was validated by agreement between two assessors with kappa=0,403 and median of time difference was 0,0 [sec]).
To calculate speed and acceleration, two criteria were followed:
a) Discrete method: consisted in estimating the speed as the quotient of the distance difference and the time difference between two contiguous poles, which was associated to the mean point between the poles. Acceleration was calculated as the quotient of two speeds and the time difference between two contiguous speeds. In the results section, sub-indices “d” indicates discrete model.
b) Continuous method: consisted in a linear model for distance according to the formula (d = b0 + b1 x t + b2 x t2 + b3 x t3 + b4 x e-t), estimating speed and acceleration by the first and second derivatives, respectively. In the results section, sub-indices “c” indicates continuous model.
Wingate cycle-ergometer test
Wingate cycle-ergometer test was performed as previously shown (Inbar et al, 1996). Briefly, a standard 10-minute warm-up was performed previously to testing. The test consisted in pedaling during 30-s at maximum speed with constant resistance equivalent to a 1/10 of the body weight of the participant (Yáñez-Silva, 2003; Inbar O, et al, 1996). A computerized Monark CYBEX cycloergometer (New York) was used. Five seconds before the beginning of the test, the subject intensified the speed of pedaling just as the resistance of the flywheel gradually increased to 100% of the work load The participant was given verbal encouragement to perform maximal effort throughout the test. The maximum power anaerobic performance was express as watts (W) and relative to body mass (W⋅kg-1).

Statistical Analysis

Data analysis was performed using multiple linear regression models. In the first instance, a descriptive characterization of the sample was conducted through the creation of a Table 1, where explanatory variables were compared based on performance level (segmented by the median average power). For this descriptive analysis, continuous variables were expressed as mean ± standard deviation and categorical variables as frequencies and percentages, using hypothesis testing (t-test or chi-square) to identify crude baseline differences.
Subsequently, a data cleaning criterion was applied, excluding cases that did not have a complete set of values for the variables under study (Vm, V10, V100, Weight, Race, AVG_WATTS, WATTS, and Total Work). The construction of the regression model followed a hierarchical approach, first introducing potential confounding variables (Race and Weight) and subsequently incorporating mediating variables related to mechanical efficiency (velocities).
Compliance with critical regression assumptions was verified by evaluating linearity, homoscedasticity through residual analysis, and normality via standardized residuals. Multicollinearity was monitored using the Variance Inflation Factor (VIF), ensuring the independence of predictors. To guarantee model robustness, an influential case diagnostic was conducted, identifying records with a Cook's Distance exceeding the critical cutoff point (4/n); this resulted in the technical exclusion of one case (subject 24) that significantly distorted the regression slope.
Finally, the selection of the definitive model was based on the principle of parsimony, comparing different configurations using the Akaike Information Criterion (AIC), Adjusted R2, and Root Mean Square Error (RMSE). All analyses were executed using R statistical software, utilizing the performance and gtsummary packages for validation and results presentation.

Use of Generative AI in the Preparation of the Manuscript

During the preparation of this work, the author used the Gemini (Google) large language model to optimize the biostatistical analysis and technical drafting. Specifically, AI was employed for: (1) generating and validating R scripts for multiple regression analysis, model comparison using AIC, and diagnostics; (2) grammatical refinement and technical translation of the results and discussion sections; and (3) structuring the data according to scientific publishing standards. The author declares that human oversight was maintained throughout the process; all statistical outputs were manually verified against the original Wingate test data, and the author takes full responsibility for the accuracy, originality, and conclusions presented in the manuscript.

Results

Baseline anthropometric variables.
Regarding to baseline anthropometric variables (Group A vs. Group B, respectively): body mass (68.35 ± 5.118 vs. 74.55 ± 7.079 kg, p=0.002), BMI and (21.7 ± 1.6 vs. 23.6 ± 2.25 kg∙m-2, p=0.004) fat percentage (7.16 ± 2.512 vs. 13.09 ± 4.194 %, p=0.000) were significant different between Group A compared to Group B (table 1). However, height was not different between groups (Table 1).
Wingate cycle-ergometer test
Following Wingate cycle-ergometer test maximum power, relative maximum power, mean power and fatigue index were not significant difference between Group A compared to Group B (Table 2). However, Group A (junior) showed a significant increase of relative mean power compared to the Group B (juvenile) (9.30 ± 0.802 vs. 8.240 ± 1.095 W kg-1, respectively, p =0.029) (Table 2).
220-m Sprint test.
The discrete method to estimate speed and acceleration did not show any significant difference between both groups for any variable analyzed (Table 3).
In addition, regarding to continuous method different variables to speed (initial speed; speed at 65-m; speed at 105-m; mean speed and maximum speed) and acceleration (median acceleration) that were obtained by the multiple regression models for the distance (R2 was significant and was approximately 1 for each subject) were not significant different between both groups (Table 4).

Reclassification Based on Power Output

The initial descriptive analysis (Table 1) revealed significant differences in the physical profile of the subjects according to their performance level. Accordingly, individuals categorized as "High Power" (>649 W) exhibited a significantly greater mean body mass (75.39 ± 5.32 kg) compared with the "Low Power" group (67.76 ± 5.48 kg) (Table 5). Likewise, a higher mechanical cadence was observed in the high-performance group (Vm: 7.36 vs. 7.14 m/s). No differences were found in the distribution of the ethnicity variable between the two power levels.
Model Selection and Fitting Using AIC
The selection of the definitive model was based on a comparative performance analysis among different velocity predictors. Using the Akaike Information Criterion (AIC), the model based on Mean Velocity (Vm) demonstrated clear statistical superiority, obtaining an AIC weight of 0.719. This indicates a probability of 71.9% that this is the most informative model, significantly outperforming the variants based on final velocity (V100, AIC weight = 0.190) and initial velocity (V10, AIC weight = 0.091). The selected model achieved an adjusted R2 of 0.414, explaining 41.4% of the variability in mean power with the lowest residual error (RMSE = 51.64 Watts).

Analysis of the Regression Coefficients

The multiple linear regression model proved to be highly significant (F (2, 25) = 10.53, p < 0.001). The coefficients obtained show a logical and positive relationship with physical performance:
  • Body Mass: Acted as the most robust predictor (B = 5.13, p = 0.004). For each additional kilogram of body mass, an estimated increase of 5.13 Watts in mean power is expected.
  • Mean Velocity (Vm): Showed a significant relationship (B = 94.74, p = 0.021), indicating that each unit of increase in mean velocity translates into an increase of 94.74 Watts.

Assumption Validation and Influence Diagnostics

The assumption diagnostics confirmed the validity of the inferences. The Variance Inflation Factor (VIF) remained at optimal levels (~1.07), ruling out multicollinearity problems. The influence analysis using Cook’s Distance allowed the identification and exclusion of one critical case (Subject 24, D > 4/n), whose removal optimized the stability of the coefficients and reduced the prediction error. The Race variable did not show a significant contribution to the model and was excluded to maximize the parsimony of the final fit.
Statistical Summary of the Final Model
Variable Coefficient (B) Standard Error t p-value
(Intercept) -419.35 274.98 -1.52 0.139
Mean Velocity (Vm) 94.74 38.54 2.45 0.021*
Body Mass 5.13 1.63 3.14 0.004**

Discussion

The primary objective of this study was to determine the feasibility of utilizing anthropometric metrics and mechanical field performance variables to predict maximal anaerobic power in junior and juvenile elite male soccer players, using the laboratory Wingate cycle-ergometer test as the reference standard. The cornerstone finding of this research demonstrates that Average Power can be robustly and parsimoniously predicted by a model integrating Body Mass and Mean Velocity obtained from a 220-m sprint test, achieving an adjusted coefficient of determination (R2adjusted = 0.414). This indicates that a considerable portion of the variance in laboratory-derived anaerobic capacity can be accurately estimated through non-invasive, accessible, and highly applicable field measures on the soccer pitch.
When analyzing the sample characteristics by competitive categories (Group A vs. Group B), significant differences emerged in body mass, BMI, body fat percentage, and relative mean power, which highlights the expected biological and anthropometric evolution between adolescent age divisions. Interestingly, when executing the 220-m sprint test, neither the discrete nor the continuous kinematic modeling methods for instantaneous speeds and accelerations yielded statistically significant differences between both age groups. This lack of statistical differentiation between competitive categories implies that raw field performance variables alone might lack the sensitivity required to evaluate absolute physical capacities unless they are properly adjusted by systemic variables. Conversely, the selected regression model overcame these limitations by successfully integrating absolute body mass and multi-stage mechanical efficiency (Vm), demonstrating superior predictive and discriminatory power independent of the player's chronological division.
The prominent influence of anthropometric variables within our predictive framework aligns with established exercise physiology paradigms. Components of body mass are widely recognized as critical determinants of absolute anaerobic output (Laurin et al., 2024; Bonilla et al., 2022). In the present study, body mass emerged as the most stable predictor (B = 5.13, p = 0.004), contributing an estimated increase of 5.13 Watts per additional kilogram of mass. This phenomenon corroborates previous findings by Campeiz (2006) and Chamari et al. (2004), who observed that body mass-related metrics serve as critical components to discriminate absolute anaerobic power between junior and juvenile categories. However, a notable limitation shared by both Campeiz (2006) and the current investigation is the inability to isolate the independent contribution of pure lean body mass from overall body composition. Because lean mass drives absolute glycolytic capacity, future investigations should employ Dual-energy X-ray Absorptiometry (DEXA) or advanced multi-compartmental anthropometric models to further elucidate the exact role of fractional body composition in elite adolescent soccer performance.
From a metabolic and physiological perspective, the statistical superiority of Mean Velocity (V_m) over instantaneous or explosive markers—such as initial velocity (V_10) or final velocity (V_100)—is highly revealing. Backed by an Akaike Information Criterion (AIC) weight of 0.719, this model supports the notion that absolute average power during a 30-second maximal effort reflects total systemic capacity and fatigue resistance rather than isolated explosive power. The duration and intense nature of both the Wingate protocol and the 220-m sprint demand an optimal utilization of endogenous energy substrates. As described by Romijn et al. (1995) and Achten & Jeukendrup (2003), substrate oxidation during strenuous exercise depends strictly on the intensity and length of the physical exertion, involving a complex interplay between intramuscular triacylglycerols and glycogen depletion (Van Loon, 2004; Achten et al., 2002). The complex relationship between exercise and substrate used (Schrauwen-Hinderling et al., 2003) suggests that Vm successfully captures the overall efficiency of the glycolytic system and the capacity to maintain high mechanical outputs throughout the duration of the test, reinforcing its inclusion as the optimal predictor.
Furthermore, the statistical rigor applied during the construction of this model ensures its internal validity and generalizability. While early field tests or historical procedures like the Margaria Step-Running Test reported poor correlations due to cross-modal bias or a lack of motor pattern specificity (Taunton et al., 1981), our optimization protocol mitigated these biases. First, although body mass is inherently influenced by age, adding chronological age to our stepwise configuration yielded no statistical significance, confirming that the predictive equations remain stable across the evaluated adolescent cohorts. Second, the execution of rigorous diagnostics, including the monitoring of a low Variance Inflation Factor (VIF = 1.07) and the removal of influential outliers via Cook's Distance (Subject 24, D > 4/n), effectively stabilized the regression slopes. Finally, the mathematical exclusion of non-contributing confounding factors, such as race, maximized model parsimony, ensuring a reliable mathematical tool that accounts for the sport-specific differences between pedaling and sprinting mechanics (Taunton et al., 1981; Baker et al., 1993).
In summary, this study provides new mathematical and physiological evidence demonstrating that laboratory-based anaerobic power can be accurately forecasted using basic anthropometric metrics combined with mean velocity field data. These results offer an innovative, cost-effective, and highly generalizable method to monitor athletic performance directly on the training ground, bridging the gap between sophisticated laboratory gold standards and everyday coaching workflows in professional youth soccer.

Conclusion

This study suggests that body mass-related variables are fundamental to understanding the 220-meter sprint test as a valid index of anaerobic performance. The models developed for initial velocity and median acceleration were structured based on the interaction between body mass variables and the power obtained using the Wingate test on a cycle ergometer, always considering their statistical and physiological relevance.
Consequently, our results suggest the need to further develop these models, incorporating new parameterization methods and additional variables that allow for a more precise elucidation of the composition of maximal anaerobic performance in young elite soccer players. These findings not only validate the use of mathematical tools for performance prediction but also offer a low-cost, highly applicable method for monitoring training on the field.

Author Contributions

Conceptualization, AYS, MUS, and IDCP; Methodology, AYS and MUS; Validation, AYS, MUS, and IDCP; Formal Analysis, AYS and MUS; Investigation, AYS and MUS; Data Curation, AYS and MUS; Writing—Original Draft Preparation, AYS and MUS; Writing—Review & Editing, AYS, MUS, and IDCP; Supervision, IDCP. AYS and MUS contributed equally to this work. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by: Programa de Formación de Capital Humano Avanzado – Becas de Postgrado CONICYT, Concurso Magíster y Doctorado 2004, folio N.º 96032748.

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Table 1. Anthropometric measures for groups A and B.
Table 1. Anthropometric measures for groups A and B.
Variable 1 Group A Group B p value 2
Age (years) 16.55 ± 0.678 17.67 ± 0.500 0.000
Height (cm) 177.6 ± 6.40 178.0 ± 5.965 0.859
Body mass (kg) 68.35 ± 5.118 74.55 ± 7.079 0.002
BMI (kg∙m-2) 21.7 ± 1.6 23.6 ± 2.25 0.004
Fat % 7.16 ± 2.512 13.09 ± 4.194 0.000
1 All the variables are distributed according to a normal distribution (Kolmogorov-Smirnov Test). 2 p value for t-Student test for different or equal variance, as the case may be.
Table 2. Measures of the Wingate Test for Groups A and B. 
Table 2. Measures of the Wingate Test for Groups A and B. 
Variable 1 Group A Group B p2 value
Maximum power (W) 859.1 ± 117.75 880.3 ± 192.74 0.714
Relative max. power (Wkg1) 12.31 ± 1.364 11.71 ± 2.247 0.381
Mean power (W) 633.0 ± 73.29 619.0 ± 99.39 0.722
Relative mean power (W kg-1) 9.30 ± 0.802 8.240 ± 1.095 0.029
Fatigue index (%) 46.32 ± 11.304 45.6 ± 9.454 0.866
1 All the variables are distributed according to a normal distribution (Kolmogorov-Smirnov Test). 2 p value for t-Student test for different or equal variance, as the case may be.
Table 3. Measures of speed and acceleration per the discrete method for Groups A and B.
Table 3. Measures of speed and acceleration per the discrete method for Groups A and B.
Variable 1 Group A Group B p 2 value
Initial speed (m·s-1) 1.469 ± 0.378 1.668 ± 414 0.125
Speed at 65m (m·s-1) 7.957 ± 0.340 8.109 ± 0.247 0.128
Speed at 105m (m·s-1) 8.345 ± 0.463 8.472 ± 0.275 0.320
Mean speed (m·s-1) 6.820 ± 0.248 6.912 ± 0.196 0.218
Maximum speed (m·s-1) 8.975 ± 0.434 9.101 ± 0.317 0.322
Mean acceleration (m·s-2) 0.6752 ± 0.109 0.7036 ± 0.081 0.375
Maximum acceleration (m·s-2) 8.48 ± 2.184 7.918 ± 2.959 0.499
1 All the variables are distributed according to a normal distribution (Kolmogorov-Smirnov Test). 2 p value for t-Student test for different or equal variance, as the case may be.
Table 4. Measures of speed and acceleration by the continuous method. Groups A and B.
Table 4. Measures of speed and acceleration by the continuous method. Groups A and B.
Variable 1 Group A Group B p 2 value
Initial speed (m·s-1) 4.356 ± 1.474 -3.844 ± 2.383 0.444
Speed at 65m (m·s-1) 7.957 ± 0.340 8.109 ± 0.247 0.128
Speed at 105m (m·s-1) 8.345 ± 0.463 8.472 ± 0.275 0.320
Mean speed (m·s-1) 8.241 ± 0.291 8.374 ± 0.273 0.322
Maximum speed (m·s-1) 0.6752 ± 0.109 0.7036 ± 0.081 0.375
Median acceleration (m·s-2) 12.83 ± 1.550 12.490 ± 2.494 0.499
1 All the variables are distributed according to a normal distribution (Kolmogorov-Smirnov Test). 2 p value for t-Student test for different or equal variance, as the case may be.
Table 5. Sample Characterization according to Average Power. 
Table 5. Sample Characterization according to Average Power. 
Total Baja Potencia (<=649W) Alta Potencia (>=649W)
Variable N=28¹ N= 15¹ N= 14¹ p-value²
Peso Corp.(kg) 71.4 ± 6.6 67.8 ± 5.5 75.4 ± 5.3 <0.001
Vel. media (m/s) 7.25 ± 0.28 7.14 ± 0.27 7.36 ± 0.24 0.025
Vel. inicial (V10) 4.34 ± 0.27 4.29 ± 0.27 4.40 ± 0.26 0.3
Vel Final (V100) 7.40 ± 0.26 7.32 ± 0.29 7.50 ± 0.19 0.062
Etnia/Raza) >0.29
Blanca 19 (66%) 10 (67%) 9 (64%)
Negra 10 (34%) 5 (33%) 5 (36%)
1 Mean (±SD); n (%); 2 Welch Two Sample t-test; Fischer’s exact test.
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