Mike Wright (Wright, M. OR analysis of sporting rules -A survey. European Journal of Operational Research, 232(1):1-8, 2014 ) recently presented a survey of sporting rules from an Operational Research (OR) perspective. He surveyed 21 sports, which consider the rules of sports and tournaments and whether changes have led to unintended consequences. The paper concludes: "Overall, it would seem that this is just a taster and there may be plenty more such studies to come". In this paper we present one such study. This is an interdisciplinary paper, which cuts across economics, sport and operational research (OR). We recognize that the paper could have been published in any of these disciplines but for the sake of continuity with the paper that motivated this study, we wanted to publish this paper in an OR journal. We look at specific examples where the rules of sports have led to unforeseen and/or unwanted consequences. We hope that the paper will be especially useful to sports administrators, helping them to review what has not previously worked and also encouraging them to engage with the scientific community when considering making changes.We believe that this is the first time that such a comprehensive review of sporting rules, which have led to unexpected consequences, has been published in the scientific literature.

We systematically explore the time-series properties of life insurance demand using a novel statistical procedure that allows multiple unobservable (but interpretable) components to be extracted. This methodology allows the data to be modelled in new and innovative ways. We find univariate series decomposition allows us to more easily explain the behaviour of life insurance demand over the sample period , than would otherwise be possible. A multivariate model (including a number of variables thought to influence demand) produces quite pleasing results overall. A SUTSE model involving demand and each of the explanatory variables in turn shows evidence of common components in all cases but one. Finally, an out-of-sample forecast comparison shows the univariate model to outperform the multivariate model for accuracy.

The conference and divisional system has long been a staple part of tournament design in the major pro-sports leagues of North America. This popular but highly rigid system determines on how many occasions all bilateral pairings of teams play each other during the season. Despite the virtues of this system, it necessitates removing the biases it generates in the set of win ratios from the regular season standings prior to calculating within-season measures of competitive balance. This article applies a modified version of a recent model, an extension that is generalizable to any unbalanced schedule design in professional sports leagues worldwide, to correct for this inherent bias for the NFL over the seasons 2002-2011, the results of which suggest the NFL is even more competitively balanced than thought previously.

Since the season ending in 2001, the Scottish Premier League (SPL) has, unlike other European football leagues, utilised an unbalanced schedule, by which the strongest teams in a given season play each other an extra time, mutatis mutandis for the weakest teams. While this approach may make sense for several reasons, it also has implications for within‐season measures of competitive balance, because it creates biases in the set of win ratios from the end‐of‐season league table. This paper applies a simple log‐probability rule to calculate a set of adjusted win ratios correcting for this inherent bias. Such an adjustment is necessary if one wishes to compare within‐season competitive balance of the SPL with other European leagues. It is shown that by correcting for the unbalanced schedule, the SPL is consistently less competitive over the sample period. The implications of this finding are discussed at length.

A structural time‐series model is estimated to investigate the relation between competitive balance, measured by the actual‐to‐idealised standard deviation ratio, and average match attendance in the Australian Football League from 1945 to 2005. The unobserved components approach allows the data to be modelled in ways new to the literature on this topic. A seemingly unrelated time‐series version shows much of the explanatory power of the data to be in the irregular (fast‐moving) component. An OLS regression produces robust goodness‐of‐fit and diagnostic results, and the coefficient estimates produce inferences in contrast to those of Schmidt and Berri (2001), with persistent shocks.