Predicting the value of football player with the impact of covid-19 on the market value of the player

The unprecedented onset of the Covid-19 pandemic has pervaded countless industries worldwide, with football being a major one to bear its brunt. This global health crisis ushered in a new era where normalcy was challenged and socio-cultural constructs were altered, leading to profound implications on football, both as a sport and an industry. Football, a nexus of economic and cultural intersections, witnessed several changes, not least among which was the effect on player valuations in the market. The pandemic brought life to a standstill, ensnaring societies in its clutches and forcing them into isolation. This sudden cessation had a ripple effect on football players, amplifying feelings of anxiety, tension, and uncertainty, thus altering their psychosocial dynamics. Consequentially, this shift in psychological state, combined with the disruption of regular football activities, contributed to fluctuations in the market prices of these athletes. Traditional approaches and methodologies, once relied upon for gauging player prices, particularly in the five major European football leagues (English, Spanish, Italian, German, and French), began showing signs of obsolescence in the face of these pandemic-induced challenges. So, this study presents the performance assessment of five machine learning algorithms—Linear Regression, Lasso Regression, Random Forest, Gradient Boosting, and K-Nearest Neighbors—on a regression problem. The evaluation metrics utilized are Mean Absolute Error (MAE), Root Mean Absolute Error (RMAE), and R-squared (R2). Linear Regression demonstrates a high R2 of 0.97 but falls short in MAE and RMAE. Lasso Regression surpasses with an R2 of 0.99 but exhibits the highest prediction errors, hinting at possible overfitting. Random Forest provides a balanced outcome with a R2 of 0.91 and moderate errors. Gradient Boosting stands out in terms of prediction accuracy, having the smallest MAE and RMAE, despite an R2 of 0.90. Meanwhile, K-Nearest Neighbors parallels Linear Regression's performance. The study suggests that Gradient Boosting might be the most accurate for predictions based on MAE and RMAE. However, the ideal model selection is contingent upon the specific objectives of the regression problem, which could prioritize explaining variance or minimizing prediction errors.