A Multidimensional Framework to Uncover Insights of Group Performance and Outcomes in Competitive Environments With a Case Study of FIFA World Cups

TY - JOUR

T1 - A Multidimensional Framework to Uncover Insights of Group Performance and Outcomes in Competitive Environments With a Case Study of FIFA World Cups

AU - Martinez-Mejorado, Denisse

AU - Ramirez-Marquez, Jose Emmanuel

N1 - Publisher Copyright: © 2013 IEEE.

PY - 2023

Y1 - 2023

N2 - Research on the performance of groups in competitive environments has traditionally focused on studying context-specific or collaboration factors without considering a multidimensional systems view integrating both. Additionally, there is limited research considering the co-dependence between the performance of a group and its adversaries. This paper proposes a framework to address these limitations by incorporating context-specific, network-based, and individual attributes to identify patterns and attributes of successful (and unsuccessful) groups. The framework provides a method to characterize performance patterns by searching for the dominant attributes that distinguish one pattern from another - relevant for decision-makers when dealing with many features. This analysis finds the different group behavior, both internal to the group and external, based on competition. The approach also identifies winning attributes through a machine-learning classification model. These factors allow differentiating a successful group and weighting context-specific network and opponent attributes. The framework is complemented with a visualization component illustrating competition with context-specific and network attributes at the player level. A case study is presented with data from FIFA World Cups in 2014 and 2018 to demonstrate the applicability of the proposed framework.

AB - Research on the performance of groups in competitive environments has traditionally focused on studying context-specific or collaboration factors without considering a multidimensional systems view integrating both. Additionally, there is limited research considering the co-dependence between the performance of a group and its adversaries. This paper proposes a framework to address these limitations by incorporating context-specific, network-based, and individual attributes to identify patterns and attributes of successful (and unsuccessful) groups. The framework provides a method to characterize performance patterns by searching for the dominant attributes that distinguish one pattern from another - relevant for decision-makers when dealing with many features. This analysis finds the different group behavior, both internal to the group and external, based on competition. The approach also identifies winning attributes through a machine-learning classification model. These factors allow differentiating a successful group and weighting context-specific network and opponent attributes. The framework is complemented with a visualization component illustrating competition with context-specific and network attributes at the player level. A case study is presented with data from FIFA World Cups in 2014 and 2018 to demonstrate the applicability of the proposed framework.

KW - Performance analytics

KW - competition

KW - group performance

KW - networks

KW - team performance

UR - http://www.scopus.com/inward/record.url?scp=85151556967&partnerID=8YFLogxK

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U2 - 10.1109/ACCESS.2023.3263043

DO - 10.1109/ACCESS.2023.3263043

M3 - Article

SN - 2169-3536

VL - 11

SP - 32776

EP - 32791

JO - IEEE Access

JF - IEEE Access

ER -