Related Experiment Video
Updated: Jun 20, 2025

Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
Comparing Accuracy of the Final Height Prediction Models for Elite Football Players and Developing a New Model
Objective:
This study aimed to assess the accuracy of previously developed height prediction models in male Japanese football players and create new height prediction models.
Materials:
The participants were elite academy male football players. We collected current height, parent's height, calendar age and bone age in 6th grade of primary school and obtained actual final height at 20 to 28 years old.
Methods:
We compared the accuracy of two conventional models for predicting final height. These used current height, calendar age and either bone age (Model 1) or parental height (Model 2). We then developed a new model to optimize the coefficients of Model 1 (Model 3). The final model added parental height to Model 3 and optimized the coefficients (Model 4).
Results:
Prediction accuracy was higher for Model 2 (R = 0.52, P < 0.001) than Model 1 (p = 0.33, P < 0.001). The equation of Model 3 was final height = 0.63229313×actual measured height-8.2541327×calendar age-2.3009853×bone age (TW2)+206.627184. The R-square was 0.49 (P < 0.0001). The equation of Model 4 was final height = 0.32156081×actual measured height - 4.6652063×calendar age+0.41903909×father's height+0.34952508×mother's height-0.740469×bone age(TW2)+62.1007751. The R-square was 0.61 (P < 0.0001).
Conclusions:
In the two previous conventional models, a formula using parental height had better predictive accuracy. We developed a new height prediction model using current height, calendar age, father's and mother's height and bone age.
Related Concept Videos
Variation: Normal Distribution, Range, and Standard Deviation
Applications of Normal Distribution
The heights of 15 to 18-year-old males from Chile from 1984 to 1985 followed a normal distribution. The mean height is 172.36...
Polygenic Traits
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Typical Model Studies

