Estimating the parameters of the logistic regression model using the swam algorithm for the handball team of the College of Physical Education and  Sports Sciences Al-Mustansiriyah University

Ons Edin Musa

doi:10.62383/bilangan.v2i6.336

Authors

Ons Edin Musa College of Physical Education and Sports Science, Mustansiriyah University, Baghdad, Iraq.

DOI:

https://doi.org/10.62383/bilangan.v2i6.336

Keywords:

Biomechanic, Kinematic variables, Akaike Criterion, Bird Swarm algorithm, Artificial intelligence, logistic regression model

Abstract

This research investigated the biomechanical variables of movement analysis and its essential
components for handball players at the College of Physical Education and Sports Sciences, Al-Mustansiriya
University, during a league match and university qualifiers. The data was analyzed using binary logistic
regression, a mathematical model that defines the link between a dependent variable, which takes the value
of one when a goal is scored against the opposing team and zero when no goals are scored, and independent
factors.
The bird swarm algorithm will be used in this research. It is one of the artificial intelligence
algorithms that rely on the intelligence of living flocks by monitoring their movements, such as birds, bees,
cats, chickens, and many other swarm algorithms.
The conclusions we reached from this study are as follows: When using logistic regression, we found
only four explanatory variables that affect the dependent variable. They are detailed as follows: The two
explanatory variables (The maximum height of the hip and Flight time until leaving the ball) and the
dependent variable (shooting) have an inverse relationship and affect it. The two variables (Knee angle at the
moment of thrust and The instantaneous speed of the ball) have a positive relationship with aiming and affect
it.
When we used the Bird Swarm algorithm, we found that all the explanatory variables included in the
study had a significant effect on the dependent variable. The variables (Knee angle at the moment of thrust,
Rising angle, Flight angle, The instantaneous speed of the ball, and the horizontal distance of the
performance) have a positive relationship, with the dependent variable (shooting). In contrast (The maximum
height of the hip and Flight time until leaving the ball) have an inverse relationship with the dependent
variable.
Using the logistic model helps sports coaches and researchers to estimate and predict models,
especially when the dependent variable takes values (one or zero). In contrast, we noticed that the results
were more accurate and objective when using the bird swarm algorithm. It further helps academics, those
interested in sports, and coaches benefit from these results.

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