Abstract

The objective of this thesis is to analyse the psychometric data using statistical and machine learning methods. Psychological data are analysed to predict illness and injury of athletes. Regression technique, one of the statistical processes for estimating the relationship among variables is used as basis of this thesis. We apply the linear regression, time series and logistics regression to predict illness and well-being. Our linear regression simulation results are mainly used, to understand the data well. By reviewing the results of linear regression, time series model is developed which predicts sickness one day ahead. The predicted values of this time series model are continuous. However, logistic regression can be used, to provide a probabilistic approach to predict the future levels as a categorical value. Hence we have developed a binomial logistics regression model, when observation variable is the type of dichotomous. Our simulation results show that this prediction model performs well. Our empirical studies also show that our method can act as early warning system for athletes.

Highlights

  • 1.1 Motivation and ObjectivesPrediction of athletes injury/illness is an important issue in the sports field

  • Method of stepwise regression used in MATLAB [24], is bidirectional elimination, it performs both forward and backward selection to determine the final model.At each step, the function searches for terms to add or remove from the model based on the value of cut-off thresholds

  • This domain has 208 athletes. 33 athletes reported more than 450 days. 49 athletes are reported more than 300 days. 8 athletes reported more than 250 days

Read more

Summary

Introduction

1.1 Motivation and ObjectivesPrediction of athletes injury/illness is an important issue in the sports field. Predicting an individual athlete injury/illness based upon his/her past record can be critical in the selection of team members in international competitions. This process is highly subjective usually requires much expertise and negotiating decision making. This project deals foremost with predictions of future wellness of athlete based upon historical data. It enables sports organizations and trainers to monitor wellness and health risk of their athletes It acts as an early warning system which gives support staff and supervisors actionable insights that they can use to prevent health problems from serious effects, effectively stopping such issues in their tracks

Objectives
Methods
Results
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.