Contest success functions: an extension

AbstractThis paper is concerned with methods for analysing patterns of conflict. We survey dynamic games, differential games, and simulation as alternative ways of extending the standard static economic model of conflict to study patterns of conflict dynamics, giving examples for each type of model.It turns out that computational requirements and theoretical difficulties impose tight limits on what can be achieved using the first two approaches. In particular, we appear to be forced to model the outcome of conflict as being decided in a single final confrontation if we employ non-linear contest success functions.A simulation study based on a new model of adaptive, boundedly rational decision making, however, is shown not to be subject to this limitation. Plausible patterns of conflict dynamics emerge, which we can link to both historical conflict and standard tenets of military theory.

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Generalized contest success functions

Economic Theory ◽

10.1007/s00199-007-0328-2 ◽

2008 ◽

Vol 40 (1) ◽

pp. 139-149 ◽

Cited By ~ 45

Author(s):

Birendra K. Rai ◽

Rajiv Sarin

Keyword(s):

Contest Success Functions

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An Application of Contest Success Functions for Draws on European Soccer

Journal of Sports Economics ◽

10.1177/1527002517716973 ◽

2017 ◽

Vol 19 (8) ◽

pp. 1191-1212 ◽

Cited By ~ 4

Author(s):

Anil Yildizparlak

Keyword(s):

Home Bias ◽

Likelihood Method ◽

Probit Regression ◽

Data Set ◽

Main Determinant ◽

High Performing ◽

Market Values ◽

Success Function ◽

Contest Success Functions ◽

The Difference

A contest success function (success function) maps the level of efforts into winning and losing probabilities in contest theory. We aim to assess the empirical performance of success functions for draws and analyze the differences between European soccer leagues in terms of home bias, return on talent (ROT), and talent inequality. We use a data set with 10,569 matches acquired manually from transfermarkt.co.uk containing club-based average market values of the lineup of teams for each match played through 12 seasons from 7 major European soccer leagues. The results are obtained estimating the parameters of the success functions with a general maximum-likelihood method, and the hypotheses suggested by success functions are controlled with a probit regression. Two of the success functions outperform one conclusively. The difference in the performance between these two groups results from the contrast in the main determinant of the success function in allocating the probability of a draw. The high-performing success functions take difference in aggregate talent levels as the main determinant in drawing, while the other takes the aggregate talent as the main determinant. The results also show that there are major differences across leagues in terms of ROT, home bias, and talent inequality, despite the similarities in economic environment and the homogeneity in the rules of the game imposed across leagues. Our analysis sheds light on the contributions and implications of microeconomic theory to model sports and presents the differing characteristics of the European soccer leagues that impact match results significantly.

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