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Anthropometry tries to measure thoroughly and accurately human physical characteristics and thereby to choose the practical limitations and benefits owing to them. The sports scientists have tried to encourage a lot and a lot of application of anthropometric techniques through systematic analysis that has been conducting through this field so these days, anthropometric in physical education and sports studies are widely used, (Leila Jaafari, 2012). Anthropometry has begun a fundamental tool within the hands of physical educationist and sports scientists to analyse the dimensions, shape and body composition of sports person on their performance. Anthropometry, at the essential level, involves identification of a sports person. Varied anthropometric measurements and indices, and their bearings on motor skill performance are extensively studied (Chauhan M.S., 2003; Bhola G, 2004; Gopinathan and Helina, 2009). Thus, sports anthropometry has developed as exceptional area, not solely as a measure of selective diagnostic technique but also conjointly as a performance prediction tool. However, mere identification isn't enough, there's a necessity to find out the special area of sports (specialization), wherein that person would match best so specialized coaching could also be necessary for this direction. This is often likely if an advanced study is designed to the dimensions, shape, proportion, body composition (fat, muscle, bone mass etc.) of the involved person (Sunil Sen, K. R. Bhagat and Kamlesh Sen, 2014).

Moreover, specific anthropometric measurements are pre-requisites for a better athletic performance in varied sports (Habibi et al., 2010). Similarly, Mirkov, Kukolj, Ugarkovic, Koprivica and Jaric (2010) noticed that anthropometric measurement is vital for early talent selection. There have been varieties of researchers making an attempt to search out the correlation between the anthropometric measures of lower legs and sprinting performance. In a very recent study, Lee and piazza (2009) investigated the anthropometric characteristics of the sprinters and non-sprinters of university students. It had been found that sprinters had thirty millimetres shorter lower legs (p=0.026) than that of the non-sprinters (Lee & piazza, 2009). These anthropometric measures were disturbing the speed of the sprinters, the products of stride length and stride rate. Either or both improvements in good spirits length and stride rate would cause greater sprinting performance by speed elevation. Watts, Coleman, and Nevill (2012) researched that anthropometric variable characterized the most effective world- class sprinters. Results suggest that whereas body mass index is related to success in each male and female world class sprinters, that suggests an influence of muscle mass on sprinting performance, the reciprocal ponderal index has emerged as an additional vital issue for success. Taller, more linear sprinters attain superior success, measured by sprint speed. Surinder Kaur, Dolly and Rajesh Kumar (2016), were conjointly tried to assess various variables with the running performance of 800m athletes. Among the varied variables that are assessed, shoulder, hip, and thigh girth are significantly correlated to performance. On the contrary, a number of the anthropometric measures were found to be not significant with sprinting performance. Within the analysis of Lee and Plaza (2009), the distance among the heel, first metatarsal head, lateral malleolus, and toe had nothing to do with the sprinting performance. Likewise, Yiu Tak, (2011) conducted a study on the association between selected anthropometric measures of lower limbs and 60meter sprinting performance and also the selected variables were thigh length, lower leg length, mid-thigh circumference, calf circumference and ankle circumference. The results urged that the selected variables were poor predictors of sprinting performance. However, from Ethiopia’s perspective, there have been no similar researches so far administrated this before on junior sprinters and middle-distance runners solely. Therefore, this study aimed to concentrate in exploring whether there is a significant relationship between anthropometric parameters 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).

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