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The present study has made an attempt to discover the relationship between selected Anthropometric variables with the performance of Ethiopian junior sprinters and middle-distance athletes across Genders. The subjects for the present study consisted of 240 sprinters 120 male and 120 female athletes) and middle-distance athletes 15-20 years of age. For the purpose of the study, the total population have been included from three different athletics centres. To achieve the objectives of the present study, moments of Pearson correlation have been used. From the results it has been found that, there was a negative relationship between body mass and 100m best performance of male sprinters (r=-0.376). The anthropometric variables such as leg length, calf length and thigh length are correlated positively with best time of 800m male athletes. Besides, 1500m male athletes’ performance has a positive relationship with body weight. Furthermore, the Anthropometric variables of 100m female sprinters such as age and calf circumference are correlated negatively with best time. However, Height was correlated positively with 400m female sprinters best performance. Similarly, there was a positive relationship between height and best performance of 1500m female athletes. Other Anthropometric variables have no correlation. However, none of the anthropometric variables used in this study have a relationship with the best performance of 800m female athletes and 400m male sprinters.

The objective of this paper is to study The Relationship between Selected Anthropometric Variables with the Performance of Ethiopian Junior Sprinters and Middle-Distance Athletes across Gender.

Physical fitness has been defined as measure of how well one performs physical activity. In other words, it can also be labelled as body movements produced by muscle action that increases energy expenditure (Kyrolainen et al., 2010). Physical fitness can be divided into health-related physical fitness and motor related physical fitness. Health related physical fitness includes muscular strength, muscular endurance, cardio respiratory endurance and flexibility. Motor related physical fitness consists of agility, power and balance (Heyward, 2002;So and choi,2010) Besides, Tillman and fournier ( 2005) also indicated that muscular strength is one of the elements of physical fitness.

 Moreover, Habibi et.al. (2010) found that the jump assessment of a single leg hop for distance is strongly related to the sprinting performance(r=0.76). in addition, pinero et.al (2010) indicated that standing long jump test as a predictor to assess the lower body muscular strength is better than the vertical jump test. Furthermore, some researcher’s showed that the standing long jump ability with both sprinting acceleration and sprinting velocity have significant correlation (Almuzaini& fleck, 2008;Kale et.al,2009). Moreover, Jenkins and Beazell(2010) stated that flexibility is an individual variable, joint specific, inherited characteristics that influences by age, muscle size and warm up are the factors contributing to flexibility. The flexibility of females in hip abduction, flexion and extension are better than males associating with anatomy factors. In addition (Kumar.H andTewari.R(2022) indicated that sprint acceleration run as well as 6x10m shuttle run. Pearson Correlation of peak velocity with short sprint run and agility run was found as r=-0.542 and r=-0.457 respectively for all subjects, while male hockeytrainees demonstrated the value of r=-0.794 and -0.6999, whereas the female hockey traineesindicated the value of r=-0.632 and r=-0.575 with short sprint acceleration run and agility run respectively. Comparison of nutrient intake with RDA values was made, and one sampled t test was applied to the significance. The total energy intake of the subjects was found to be significantly lesser as compared to the RDA values. The findings of the present study indicated the inadequate total energy, carbohydrate and fibre intake of female soccer players. Fat and protein intake was greater than RDA values. Jain,R  et. al (2021)

In this particular research, the researcher has intensively collected the data in person from a total of 240 junior sprinters (120male athletes and 120female athletes) (100m and 400m) and middle-distance runners (800m and 1500m) aged 15-20. The total population of the research makes up athletes found in three different athletics centres in Ethiopia namely Ethiopian youth sports academy, Tirunesh Dibaba sports academy, and Bokoji sports academy exclusively. For the purpose of the research, the total populations have been included in this study. The researcher has collected the data from the respondents at the specific point of time as a result; cross-sectional research design was employed.

The instruments used to collect the data include measurements such as a wall-mounted stadiometer, electronic weighing scale, and flexible tape. In order to undertake the statistical analysis, the general moments of Pearson correlation have been used using statistical package for social sciences software version twenty. The result of the analysis has been presented in the form of tables.

The Pearson correlation between the males and female athlete’s performance and the measured variables were computed and shown in table form below.

Table- 1. Pearson correlation between males 100m sprint performance and the measured and calculated variables (N=30)

** Correlation is significant at 0.01 levels (2-tailed).

* Correlation is significant at 0.05 levels (2-tailed).

From above tables it is clear that body weight has significant and negative correlation with performance.  It suggests that body weight contributes to the performance at this age group. No other measurements have a significant correlation with the performance of the male sprinters.

Table- 2. Pearson correlation between males 400m sprint performance and the measured and calculated variables (N=30)

** Correlation is significant at 0.01 levels (2-tailed).

* Correlation is significant at 0.05 levels (2-tailed).

None of the anthropometric variables were significantly correlated with the performance of 400m male athletes. Therefore, other anthropometric variables such as ankle circumference, shoulder width, arm length etc. must be considered to get a wide-ranging result.

Table- 3. Pearson correlation between males 800m sprint performance and the measured and calculated variables (N=30)

** Correlation is significant at 0.01 levels (2-tailed).

* Correlation is significant at 0.05 levels (2-tailed).

Table 3 reveals that, performance in 800m race has a significant and positive correlated with leg length, calf length and thigh length. It means that, with the increase in leg, calf and thigh length, the time taken to finish the event also increases, so, the performance decreases. 

Table- 4. Pearson correlation between males 1500m sprint performance and the measured and calculated variables (N=30)

Variables	R	P
Age	-0.283	0.129
Height	0.153	0.419
Weight	0.386*	0.035
Leg length	0.214	0.255
Calf length	0.218	0.248
Thigh length	0.202	0.284
Calf circumference	0.295	0.113
Thigh circumference	0.019	0.919
BMI	0.155	0.413

** Correlation is significant at 0.01 levels (2-tailed).

* Correlation is significant at 0.05 levels (2-tailed).

Table 4 indicates that, there was no significant relationship between 1500m best performance and most of the measured and calculated variables such as age, height, leg length, calf length, thigh length, calf circumference, thigh circumference and BMI. Nevertheless, weight was significantly correlated positively with 1500m best performance (r=.386, p<.05).

Table- 5. Pearson correlation between females 100m sprint performance and the measured and calculated variables (N=30)

** Correlation is significant at 0.01 levels (2-tailed).

* Correlation is significant at 0.05 levels (2-tailed).

From the above table 5, it is clear that age and calf circumference of an athlete has a significant contribution to the best performance of 100m female sprinters due to their negative relationship. But, for the rest of the variables such as, height, weight, leg length, calf length, thigh length, thigh circumference and BMI were not significantly correlated with the 100m best performance of female sprinters.

Table- 6. Pearson correlation between females 400m sprint performance and the measured and calculated variables (N=30)

** Correlation is significant at 0.01 levels (2-tailed).

* Correlation is significant at 0.05 levels (2-tailed).

As we can see in the above table 6, the widely held measured and calculated variables including, age, weight, leg length, calf and thigh length, calf and thigh circumference and BMI, were not significantly correlated with the performance of 400m female sprinters. However, there was a positive relationship between height and 400m best performance of female sprinters (r=.446, p<.05), meaning that, as the height of an athlete decreases, the time to finish 400m also decreases as a result performance is enhanced.

Table -7. Pearson correlation between males 800m sprint performance and the measured and calculated variables (N=30)

** Correlation is significant at 0.01 levels (2-tailed).

* Correlation is significant at 0.05 levels (2-tailed).

None of the selected anthropometric variables in this study have a significant relationship to the best performance of 800m female athletes. Therefore, taking other factors in to consideration, it is may be pertinent to deal on other variables to get a comprehensive result.

Table- 8. Pearson correlation between females 400m sprint performance and the measured and calculated variables (N=30)

** Correlation is significant at 0.01 levels (2-tailed).

* Correlation is significant at 0.05 levels (2-tailed).

Table 8 reveals that, there was a positive relationship between 1500m best performance and height (r=.339, p<.05). The height decrease leads to deducts the timing or improve the performance of an athlete. However, the rest selected variables have insignificant role to the best performance of 1500m female athletes. 

Table-9. Pearson correlation between males 800m best performance and the measured and calculated variables (N=30). 

                   ** Correlation is significant at 0.01 levels (2-tailed).

                    * Correlation is significant at 0.05 levels (2-tailed).

The measured and calculated variables explicitly age; height, weight, calf circumference, thigh circumference, BMI, broad jump, wall squat sit, systolic and diastolic blood pressure, and Vo2 max were not significantly correlated with 800m best performance. However, there was a positive relationship between leg length and 800m best performance (r=.458, p<.05). Besides, calf length and 800m best performance were positively correlated (r=.444, p<.05). Furthermore, thigh length was significantly correlated positively with best performance of 800m runners (r=.366, p<.05).

Among the muscular fitness variables, speed and 800m best performance were negatively correlated (r= -.596, p<.05). In addition, there was a positive relationship between sit and reach flexibility test and 800m best performance of an athlete (r=.369, p<.05). Also, a negative significant correlation between speed endurance and 800m best performance were found (r= -.370, p<.05). Furthermore, there was a negative relationship between RHR and the best performance of 800m athletes (r= .376, p<.05). 

Table-10. Pearson correlation between males 1500m best performance and the measured and calculated variables (N=30).

                 ** Correlation is significant at 0.01 levels (2-tailed).

                    * Correlation is significant at 0.05 levels (2-tailed).

There was no significant relationship between 1500m performance and most of the measured and calculated variables such as age, height, leg length, calf length, thigh length, calf circumference, thigh circumference, BMI, broad jump, sit and reach, speed, speed endurance, systolic and diastolic blood pressure, and RHR. Nevertheless, weight was significantly correlated positively with 1500m best performance (r=.386, p<.05). Similarly, there was a negative relationship between wall squat sit and the performance 1500m athletes (r= -.374, p<.05). Furthermore, a negative significant correlation among Vo2 max and 1500m best performance were found (r= -.430, p<.05).

1. A Pearson correlation of female athletes at p<.05 level

i. There was a negative relationship between body mass and 100m best performance of sprinters (r=-0.376).

ii. The anthropometric variables such as leg length, calf length, and thigh length are correlated with the best time of 800m athletes.

iii. There was a positive relationship between weight and best performance of 1500m athletes.

2.  A Pearson correlation of female athletes at p<.05 level reveals that

i. The Anthropometric variables of 100m sprinters age and calf circumference are correlated negatively with the best time.

ii. Height was correlated positively with 400m best performance.

iii. There was a positive relationship between height and best performance of 1500m athletes. Conversely, no significant relation has been obtained between the performance of 800m female and 400m male athletes with the selected anthropometricvariables.

3. A Pearson correlation of male athletes at the p<05 level reveals that.

i. There was a negative relationship between body mass and 100m best performance of sprinters (r=0.376)

ii. Sprint speed (40m) was positively correlated to 100m best performance of sprinters (r=0.595)

iii. The anthropometric variables such as leg length, calf length and thigh length are correlated with best time of 800m athletes.

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