STATISTICAL ANALYSIS ON FACTORS THAT HINDERS THE EFFECTIVENESS OF JUNIOR ATHLETICS PROJECTS IN NORTH WOLLO ZONE, ETHIOPIA

Background: This study was conducted to assess factors that affect the effectiveness of junior athletics projects in north Wollo zone. The main objective of the study was identifying factors affecting the effectiveness of junior athletics projects in north Wollo zone. Methods : The study used stratified sampling method to select the samples. Both primary and secondary source of data were used to gather reliable data. Mixed types of research approach (quantitative and qualitative) were employed. To achieve the objective of the study, cross-sectional study designed were employed. The collected data was organized, tabulated, and analyzed using descriptive and inferential method of data analysis. Results: The results of Proportional Odds Model reveal that training year or duration on the training of the athlete, that are trained between 2.1 to 3 year and 3.1 to 4 years are 0.855 and 0.985 times respectively smaller in performance than those athletes who trained in the project for less than 1 year. The result showed that athletes who haven’t the training field are 0.792 times less in performance than those athletes who have training field. In this study, those athletes who have well, bad and very bad relationship with their coach are 0.707, 0.989 and 0.979 times respectively lower in performance than athletes having very good relationship with their coach. In addition athletes who don’t participate in planning with their coach are 0.849 times lower in performance as compare to those athletes who participate. Those athletes who don’t get payment in the project are 0.952 times low in performance than athletes who get payment. Conclusion: The study identified factors related to sport offices related, coach related factors, athlete related. Regarding to sport office related factors such as shortage of training equipment and facilities, non-availability of training fields, lack of supervision and follow up for coaches and athletics projects, shortage of competition opportunities for athletes. Among coach related factors inability to prepaid peridized and scientific training plan, lack of providing proper demonstration for athletes, not recruiting the athletes based on talent identification. Considering athlete related factors lack of motivation on athletics sport.

its name from the former province of Wollo. There are 10 woredas and 3 city administrations in the zone.

Study Design and Study Population
For this study a cross sectional study design was applied. The population frame includes 150 athletes from 5 athletics projects, 30 athletes each 15 male and 15 female, 5 project coaches, 5 woreda sport commission experts and one zone sport commission expert.

Sampling Technique and Sample Size Determination
The sample size determination formula by [4] was adopted for this study after considering the sight of the project as stratification. The reason why the researchers use the sight of the project as stratification is there is heterogeneity between the projects in training capacity.

Allocation of Sample Size
To determine the size of the sample from each stratum, equal allocation has been used which resulted in a sample of 18 athletes from each stratum.

Data
Data were obtained from both primary method of data collection (structured questionnaire, interviews as well as observation) and secondary method of data collection (athlete's portfolio, coaches training plan).

Study variables
The response/outcome variable in study is athlete's performance(High, medium and low) and The predictor (independent) variables considered were Athlete's age, training age(year), training day per week, training hour per day, Availability of training field, coaches' qualification, coaches' ability to plan, coaching experience, availability of training facility, support and follow-up by sport experts, getting competition opportunities, talent identification, performance test, Family and peer support, Motivation of athletes, practice of demonstration by the coach.

Methodology
Data on a sample of 90 athlete's which were from the junior athletics projects were examined using descriptive statistics and Ordinal logistic regression model.

Descriptive Statistics
Descriptive statistics refers to the techniques and methods for organizing and summarizing information obtained from the sample. Descriptive statistics is a kind of statistics, which describe the data using different measurements, like tables.

Ordinal logistic regression model
The general form of ordinal regression model may be written as follows Where j indexes the cut-off points for all categories (k) of the response variable, the function ( ( )) is the link function that connects the systematic components. (i.e + ) of the linear model, the alpha represents a separate intercept or threshold for each cumulative probability and represents the regression coefficient [5].If multiple explanatory variables are applied to the ordinal regression model, βX is replaced by the linear combination of ( + 1 1 + 2 2 + ⋯ + )). In this study we used proportional odds The proportional odds model (POM) [6] details that the proportional odds model is used as a tool to model the ordinal nature of a dependent variable by defining the cumulative probabilities instead of considering the probability of an individual event. Consider a collection of p explanatory variables denoted by the vector X= ( 1 , 2 , … , ). The relationship between the predictors and response variable is not a linear function in logistic regression; instead, the logistic regression function is used, which is the logit transformation of .

Descriptive Statistics
This research was conducted in north wollo Zone and collected data on the performance of youth athletes. As reported by north wollo zone, there were ten athletics projects, but now a days there are only 5 projects, namely Kobo town, Robit, Kone, Gohe and Lalibela. The analysis presented in the study is based on 86 athletes who are found in north wollo zone athletics projects.
As we have got the information from north wollo zone sport office, we were planned to collect the data from one coach from each athletics projects a total of ten. However, practically we have got only five coaches. The data that we collect from coaches shows that 60% of the coaches have firs level coaching license and the remaining 40% have second level, only 20% of the coaches were selected the athletes by using talent identification, but the rest 80% of the athletes were selected without considering their talent, 80% of the coaches replied that they were not got any support from any stake holders, 40% of the coaches respond that they have an awareness on periodization, however as we have seen only 40% of the coaches were try to designed training plan, the coaches responded that 60% of the athletes had low motivation for training and the remaining 40% of the athletes had medium motivation for training, as 100% the coaches responded that they could not get competition opportunities for their athletes. And the detail statistics is attached in the table 1. In this study we try to incorporate the data from sport expert through interview regarding to this, we have incorporate 5 woreda and one zonal sport office experts on why the performance of the athletes becoming low? Based on this, the Woreda experts themselves were respond that they are not interested to support and supervise day today activities of the training, because of the fear that the coaches had knowledge on scientific training, Dearth of training facility, equipment and training field. Athlete's family need more support from the athlete in labor work and athletes missed training days (the athlete did not have enough time to practice). The Zone sport office did not give any support, the community did not give more attention for athletics like football and other sport, Lack of budget because there is low support form stockholders, and finally, according to the zonal sport expert response the report that sends from woreda sport office was fake.   Table 3). To identify the factors that affect performance of the athlete and to estimate their effect, the study fitted proportional odds model. The results of the proportional odds model are given in Table 4. Having fitted proportional odds model, a test procedure [7] was run to see whether the fitting of a proportional odds model is appropriate for the data. Brant's (1990) test procedure produced a significant chi-square value of 142.62 (p-value=0.075) indicating that a parallel lines assumption is fulfilled. The results of Brant tests were shown in the last column of Table 4, which reveals that all the variables were found insignificant.

Determinants of Athlete's Performance Status
In POM, around 10 independent variables in the model were found to be significant which were significant in this study, were availability of training equipment and Support from stakeholders. Athletes who did not have training equipment are 0.563 times lower in performance but did not Supported by stakeholders are 3.105 times higher.

DISCUSSIONS
In this study, an attempt has been made to develop a method that can help to identify factors that affect the performance of the athlete. Accordingly, Proportional odds model was fitted.
The POM becomes appropriate model for analyzing the considered data since the p-value of In this study training year (duration on training) was statistically significant on performance of the athletes, as the training year increased the performance of the athlete decrease implies that the training that delivered for the athlete do not bring progression on athletes performance. According to [8] if the training loads are too far apart the athlete's fitness level will keep returning to original levels. Widely spaced loading will produce little or no fitness improvement. So from this we can understand that the athletes were not doing their training based on a regular base or they were missing their training days.
Regarding to training hours per day athletes who trained less than 30 minutes and more than 2 hour per day have less in performance. This result is supported by [8] If the training load is not great enough there is little or no overcompensation and if loading is too great, will cause the athlete to have problems with recovery and he may not return to original levels of fitness.
Athletes who got training field and equipment have better performance than those do not get training field and equipment this finding is supported by [9].
Most of the coaches have not any form of training plan. Only few number of coaches were prepared training plan and as we refer their plan the designed plan is not scientifically designed, on the same issue, similar result on study that conducted by [10] stated that most of youth project coaches were not prepared weekly, monthly and annual plan.
Athlete who had better coach athlete relationship had better training performance, when we compare with those did not have good coach athlete relationship. This result is similar with [11]. Identifying the talent of the trainees during athlete's recruitment had great impact on the performance of the athletes. This finding is supported by [12] talent identification is a knowledge based task in which the coach should be capable of applying scientific tests in measuring psychological, physiological, social, and technical abilities when identifying talented athletes with a potential of becoming elite.
Most of the athletes were not motivated for athletics training because they did not get support from family and friends. These supported by [10] parents of the athletes did not support their children's in order to get training in the project.
Considering sport experts ability to support coaches in athletics training, experts have not the ability to support coaches on training issues. Similar with this study, [13] stated the sport organization has not qualified human resource such as qualified office experts, losing education and modern coaching science .In addition to this [10] zone sport office and the regional sports commission does not provide regular technical support.

Conclusions
Generally, in the study we assessed different factors that affect effectiveness of junior athletics projects this factors can be categorized as sport office related factors, coach related factors, athlete related factors, parental related factors, and community related factors. When we compare the impact, those factors sport office related factors have a great impact on athlete's performance, which is followed by coach related factors and thirdly athlete related factors affect athlete's performance.
Regarding to sport office related factors, shortage of training equipment and facilities, nonavailability of training fields, absence of reward for best performers in the competitions, lack of capacity building training for coaches and sport experts, lack of supervision and follow up for coaches and athletics projects, shortage of competition opportunities for athletes, giving unequal emphasis for each sports, organizing false report by woreda experts.
Among coach related factors, inability to prepaid peridized and scientific training plan, inability to create awareness for athletes and families about athletics training and its benefits, dearth of providing the training without interruption, lack of providing physical fitness test to assess training effectiveness, lack of providing proper demonstration for athletes, lack of organizing true age portfolio for the athletes, not recruiting the athletes based on talent identification.
Considering athlete related factors lack of motivation on athletics sport, being material driven when they came to raining area. In addition, Stockholders related factors include lack of supporting athletics projects morally and financially are the basic determinant factors that affect the athletics projects.

Declarations
Abbreviation: POM: Proportional Odds Model