Research Article
Analysis of the Attributes of a Left-Right Football Midfielder
Issue:
Volume 14, Issue 2, April 2026
Pages:
49-57
Received:
18 September 2025
Accepted:
17 April 2026
Published:
29 May 2026
DOI:
10.11648/j.sjams.20261402.11
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Views:
Abstract: The availability of player performance data has significantly enhanced the scope and depth of quantitative analysis in recent professional football; however, position-specific statistical investigations, especially for specialized roles such as wide midfielders, remain relatively unavailable. This study addresses this gap by carefully studying these correlations and picking out relevant determinants of players' performance for left and right midfield positions using adequate multivariate statistical techniques. A comprehensive dataset comprising physical, technical, and mental attributes of modern-day professional players occupying these wide midfield positions was collected and subjected to rigorous statistical procedures. The study also deployed Pearson correlation analysis to explore linear relationships between individual player attributes and overall performance ratings. Canonical correlation analysis (CCA) was used to determine the relative contribution of each attribute, as well as principal component analysis (PCA), to uncover latent performance dimensions and reduce redundancy among some of the highly correlated variables. The empirical results show that physical and mental attributes, especially ball control, dribbling, vision, short passing, and composure, show strong positive correlations with the general performance ratings of the players. In contrast, physical attributes such as sprint speed and acceleration show comparatively weaker and less consistent relationships. Furthermore, the presence of strong intercorrelations among technical variables suggests substantial overlap among performance indicators, therefore, justifying the use of dimensionality-reduction techniques in the study. Finally, the study highlights the importance of technical proficiency and decision-making ability in determining the effectiveness of wide midfielders. These revelations provide valuable empirical support for data-driven approaches to identification of talent, player development, tactical improvements and maximization, and scouting strategies in modern football analytics.
Abstract: The availability of player performance data has significantly enhanced the scope and depth of quantitative analysis in recent professional football; however, position-specific statistical investigations, especially for specialized roles such as wide midfielders, remain relatively unavailable. This study addresses this gap by carefully studying thes...
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Research Article
Comparative Distributional Analysis of Nigeria's GDP Using the Generalized Inverted Exponential Distribution
Chinedu Kingsley Nwankwo*
,
Dongnaan Godswill Apameh
Issue:
Volume 14, Issue 2, April 2026
Pages:
58-70
Received:
15 November 2025
Accepted:
9 February 2026
Published:
2 June 2026
DOI:
10.11648/j.sjams.20261402.12
Downloads:
Views:
Abstract: This study examines the distributional behavior of Nigeria’s Gross Domestic Product (GDP) using the Generalized Inverted Exponential Distribution (GIED) as a flexible parametric framework for modeling macroeconomic data. GDP series in developing economies are often characterized by skewness and departures from normality, which limits the adequacy of conventional distributions such as the Log-Normal and Pareto models in capturing their empirical properties. This motivates the use of more flexible alternatives for improved distributional modeling. The analysis is based on Nigeria’s GDP data spanning 1981–2022. Model parameters are estimated using Maximum Likelihood Estimation (MLE), while model adequacy is evaluated through log-likelihood values, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Kolmogorov–Smirnov (K–S) statistics, and graphical diagnostic tools. The results indicate that the GIED provides a superior overall fit compared to the competing distributions, as reflected in lower information criteria values and improved goodness-of-fit statistics, highlighting its flexibility in capturing skewed and asymmetric features of GDP data. In addition, a simple linear time-trend regression model is employed to assess the temporal evolution of GDP. The estimated time coefficient is positive and statistically significant, and its stability is confirmed through bootstrap resampling and Bayesian robustness checks, indicating consistency across estimation approaches. Overall, the findings suggest that the GIED is a suitable and flexible distribution for modeling GDP in contexts characterized by non-normality, while the regression results provide complementary evidence of a sustained upward trend in Nigeria’s GDP over the study period. This supports the usefulness of combining distributional and regression approaches in macroeconomic data analysis.
Abstract: This study examines the distributional behavior of Nigeria’s Gross Domestic Product (GDP) using the Generalized Inverted Exponential Distribution (GIED) as a flexible parametric framework for modeling macroeconomic data. GDP series in developing economies are often characterized by skewness and departures from normality, which limits the adequacy o...
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