nep-spo New Economics Papers
on Sports and Economics
Issue of 2023‒12‒11
two papers chosen by
Humberto Barreto, DePauw University
  1. The allocation of FIFA World Cup slots based on the Elo method and pairwise comparisons By L\'aszl\'o Marcell Kiss; L\'aszl\'o Csat\'o; Zsombor Sz\'adoczki
  2. Predicting Market Value in Professional Soccer: Insights from Explainable Machine Learning Models By Chunyang Huang; Shaoliang Zhang
  1. By: L\'aszl\'o Marcell Kiss; L\'aszl\'o Csat\'o; Zsombor Sz\'adoczki
    Abstract: Qualifications for several world championships in sports are organised such that different sets of teams play in their own tournament for a predetermined number of slots. This paper provides a reasonable approach to allocate the slots based on matches between these sets of teams. We focus on the FIFA World Cup due to the existence of an official rating system and its recent expansion to 48 teams. Our proposal adapts the methodology of the FIFA World Ranking to compare the strengths of five confederations. Various allocations are presented depending on the length of the sample, the set of teams considered, as well as the frequency of rating updates. The results show that more European and South American teams should play in the FIFA World Cup. The ranking of continents by the number of deserved slots is different from the ranking implied by FIFA policy. We recommend allocating at least some FIFA World Cup slots transparently, based on historical performances, similar to the access list of the UEFA Champions League.
    Date: 2023–10
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2310.19100&r=spo
  2. By: Chunyang Huang; Shaoliang Zhang
    Abstract: This study presents an innovative method for predicting the market value of professional soccer players using explainable machine learning models. Using a dataset curated from the FIFA website, we employ an ensemble machine learning approach coupled with Shapley Additive exPlanations (SHAP) to provide detailed explanations of the models' predictions. The GBDT model achieves the highest mean R-Squared (0.8780) and the lowest mean Root Mean Squared Error (3, 221, 632.175), indicating its superior performance among the evaluated models. Our analysis reveals that specific skills such as ball control, short passing, finishing, interceptions, dribbling, and tackling are paramount within the skill dimension, whereas sprint speed and acceleration are critical in the fitness dimension, and reactions are preeminent in the cognitive dimension. Our results offer a more accurate, objective, and consistent framework for market value estimation, presenting useful insights for managerial decisions in player transfers.
    Date: 2023–11
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2311.04599&r=spo
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This page is being maintained by Thomas Krichel. It was last updated on 2023‒12‒11 at 14:52.