Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.


43rd Annual Convention; Denver, CO; 2017

Event Details

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Symposium #401
The Matching Law: Alive and Kicking
Monday, May 29, 2017
9:00 AM–9:50 AM
Hyatt Regency, Centennial Ballroom B/C
Area: EAB/PRA; Domain: Basic Research
Chair: Holly Seniuk (University of Nevada, Reno)
Discussant: Jason C. Bourret (New England Center for Children)
Abstract: Over the past two decades, research has demonstrated the generalized matching law (GML) to be a useful tool in describing choice in sports including baseball, basketball, football, and hockey. However, this area of research has failed to gain much ground, despite its potential for contributing to our understanding of behavior in sports. It is important to extend this area of research beyond a simple demonstration of matching in order to make broader inferences regarding factors that influence choice in sports. Some recent research has risen to this challenge and begun to examine the intricacies and utility of applying quantitative analyses to sports, including analyses of risk tolerance, situational bias, and moving from a team-based analysis to individual analyses. The presentations in this symposium will discuss recent research in the application of the GML to sports, the importance of qualitative analyses of behavior in sports, and how behavior analysts can extend this line of work from demonstrations to application.
Instruction Level: Intermediate
Keyword(s): matching law, quantitative analysis, sports

An Examination of Matching in Professional Soccer Penalty Shoot-Outs: Using Archival Data to Advance Our Understanding of Choice in Sports

ALBERT MALKIN (Southern Illinois University ), Holly Seniuk (University of Nevada, Reno), Derek D. Reed (The University of Kansas), Mark R. Dixon (Southern Illinois University)

The generalized matching law (GML) provides a parsimonious account of choice during game-play in sports. The GML, when log transformed, suggests that selection amongst multiple alternatives is a linear function of the relative rate of reinforcement produced; research has demonstrated that the GML is predictive of behavior allocation across a variety of professional sports (e.g. baseball, basketball, football, and hockey). The current study made use of a data set of soccer penalty shootouts published by Chiappori, Levitt, and Groseclose (2002) and analyzed the data using the generalized matching equation. Analyses were conducted given a choice paradigm for both kickers and goalies, entailing three alternatives (e.g. kicking or blocking to the left, center, and right). Results indicate a bias away from kicking toward the center for kickers, and a similar bias in goalies responding, for staying out of the center. Additionally, goalies results suggest poor matching for single response alternatives, but a strong relationship when responses were aggregated. Implications of the results will be discussed related to the parameters of the use GML, the applied use of archival data, and the influence of rule-governed behavior on side selection in penalty shootouts.

Applications of the Generalized Matching Law to Professional Mixed Martial Arts Competition
HOLLY SENIUK (University of Nevada, Reno), Janie Vu (University of Guelph)
Abstract: Over the past 15 years various studies have demonstrated that the generalized matching equation (GME) can be used to describe choice in sports such as basketball, football, baseball, and more recently, hockey. Most of these studies aggregated the data from all of the players on the team, and very few have examined the data at the individual level. None of the studies to date have used the matching law to describe choice behavior in individual sports. The current study examined applications of the (GME) to strike selection for thirty-two professional mixed martial arts fighters. The results demonstrate that the GME is a strong descriptor of strike selection, and may be used to describe choice behavior in individual sports as well team sports. This presentation will discuss the relevance of individual analyses, as well as use previous studies to suggest implications for expanding the literature on the GME and sports from demonstration to application.



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Modifed by Eddie Soh