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Dynamic factor structure of team performances in Liga MX
Francisco Corona1, Nelson Muriel2, Graciela González-Farías3
1Instituto Nacional de Estadística y Geografía, Mexico City, Mexico.
Journal of Applied Statistics
|June 16, 2022
Summary
This study uses Dynamic Factor Models (DFMs) to analyze Mexican Football League (Liga MX) team performance. Two common factors were identified, distinguishing top teams from emerging or relegated ones.
Area of Science:
- Sports Analytics
- Econometrics
- Statistical Modeling
Background:
- Team performance in professional sports leagues is crucial for competitive balance and fan engagement.
- The Mexican Football League (Liga MX) presents a dynamic environment for analyzing team performance metrics.
Purpose of the Study:
- To analyze team performance in Liga MX using Dynamic Factor Models (DFMs).
- To identify underlying common factors influencing team success and categorize teams based on performance dynamics.
Main Methods:
- Dynamic Factor Models (DFMs) were employed to analyze team performance, measured as the percentage of total points obtained per tournament.
- Principal Components Analysis was used for common component estimation.
- Panel Analysis of Non-stationarity in Idiosyncratic and Common Components investigated the stochastic nature of the model.
Main Results:
- Two significant common factors were identified influencing team performance in Liga MX.
- One of these factors exhibited non-stationary behavior, indicating evolving league dynamics.
- These factors effectively differentiated between top-performing teams and those facing relegation or emerging status.
Conclusions:
- The identified common factors provide valuable insights into the dynamic behavior of teams in Liga MX.
- The model successfully categorizes teams, offering a new perspective on competitive balance within the league.
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