European Football Player - Statistical Data Analysis (R)

Project Overview

• Objective: This project aims to conduct a comprehensive statistical analysis of European Football Player data, emphasizing data quality analysis, exploratory data analysis (EDA), and statistical modeling.
• The analysis provides insights into factors influencing a player's potential and their likelihood of earning a weekly wage above 8000 Euro.
• Key statistical analysis include:
  • ANCOVA Analysis: Assessing factors influencing a player's potential.
  • Logistic Regression Analysis: Investigating factors affecting a player's likelihood to earn a high weekly wage.
• R libraries used: dplyr, ggplot2, gridExtra, grid, corrplot, reshape2.
• Access the document in RMarkdown, HTML, and PDF formats.

Data Quality Analysis (Section #1)

• Objective: Ensure data accuracy, consistency, and reliability.
• Key steps:
  • Data summary
  • Identifying numerical and categorical variables
  • Handling outliers
  • Addressing missing data
  • Visualizing data anomalies

Examples of Data Cleaning

Exploratory Data Analysis (EDA) (Section #2)

• Objective: Summarise dataset characteristics through visualisations.
• Visualisations include histograms, box plots, heatmaps, and hypothesis testing (Shapiro-Wilk normality test and Pearson's correlation coefficient).
• The heatmap illustrates correlations between attributes.

Statistical Modeling (Section #3 and #4)

• Objective: Address two research questions.

ANCOVA Analysis: "What factors influence a football player's potential?"

• ANCOVA tests variables affecting player potential.
• Hypotheses:
  • $H_0$: No variables affect player potentials.
  • $H_1$: Variables influence player potentials.
• Linear equation:
  • $y = (4.626e+01) + (3.02e-13) \times (wage) + (6.560e-05) \times age - (5.718e-01) \times height + (6.509e-02) \times shooting + (1.085e-01) \times (2.769e-01) \times dribbling (6.416e-02) \times defending + (7.931e-02) \times physics - (1.021e-01) \times power long shots + (3.154e+00) \times high.wage.indTRUE

Diagnosis Plots

• Conclusion: The alternative hypothesis is accepted, but this model needs improvement due to inconsistency and multicollinearity.

Logistic Regression: "What factors affect the likelihood of a football player earning a weekly wage above 8000 Euro?"

• Logistic Regression models factors influencing a player's likelihood to earn a high wage.
• Hypotheses:
  • $H_0$: No factors affect the likelihood of high wages.
  • $H_1$: Factors influence high wages.
• Logistic transformation equation:
  • $log(\frac{p}{1-p})=a + b1 \times x1 + b2 \times x2 ....bk \times xk$

Diagnostic Measures

Diagnostic Measures

• Conclusion: The Null Hypothesis is rejected, and the minimal adequate model is a good fit.

Conclusion:

• In conclusion, this project underscores the significance of data quality, exploratory data analysis, and statistical modeling in extracting valuable insights from European football player data.
• By following these analytical steps, the report ensures reproducibility and provides valuable insights into the world of football statistics.
• These findings can be invaluable for clubs, analysts, and enthusiasts looking to understand the factors influencing a player's potential and earnings in the dynamic world of European football.