Volume 5, Issue 4

This paper designs and implements an empirical test to discern whether the parties to a contract are able to commit not to renegotiate their agreement. We study optimal contracts with and without commitment and derive an exclusion restriction that is useful to identify the relevant commitment scenario. The empirical analysis takes advantage of a data set on Spanish soccer player contracts. Our test rejects the commitment hypothesis. We argue that our conclusions should hold a fortiori in many other economic environments.

The current study analyzed historical performance data in order to contribute to the understanding of the development of elite athletes. Data for elite adult and youth swimmers from 1962 to 2007 were employed to identify changes in American swimming records and in the performances of elite American youth swimmers. Over this period, 11 of the 12 men's and women's American records analyzed are improving at an ever slowing rate. This trend parallels the resultant analyses herein from elite youth swimmers, suggesting a possible limit to reaching elite athletic performance. Unique gender differences by event for both elite adult and youth swimmers were also revealed. Results imply that genotype plays a role in elite athletic performance. Additionally, possible reasons and ramifications of these findings are proffered.

Time series clustering is used to show that, relatively, the career progression of Australian legend Sir Donald Bradman's test career as a batsman was most similar to West Indian Brian Lara. Consequently, it is likely his peak performance would have occurred while the Second World War disrupted all international cricket.Data from the 20 international cricketers who played in at least 70 innings over more than 17 years and averaged more than 40 runs per dismissal (as at January 1, 2009) is used to create a number of global measures that indicate the ebb and flow of a career. As is shown in this paper, this clustering methodology, proposed by Wang et al. (2006), generates instinctive clustering results and can be applied on different length time series.Utilizing the framework created for clustering, Bradman's batting average is estimated to be 105 if his career had been uninterrupted.

The purpose of the present study was to examine the relationships among National Collegiate Athletic Association (NCAA) Division I football teams' 2002 recruiting rankings from the Rivals (RIV) and Scouts (SCO) recruiting services and the Jeff Sagarin end-of-season performance ratings from 2002–2006. The RIV and SCO recruiting services included rankings for 100 common NCAA Division I football teams for the 2002 recruiting season. Each recruiting service included a total point system rating (TOTPTS) and average star rating (AVESTAR). The Jeff Sagarin NCAA football ratings system was chosen as an indicator of the teams' performance. Pearson product moment correlation coefficients (R) and the corresponding predictive indices (R2) were used to examine whether the 2002 RIV & SCO TOTPTS and RIV & SCO AVESTAR ratings could predict the Jeff Sagarin end-of-season ratings and total number of wins for each football team for the 2002 through 2006 seasons. In addition, R and R2 values were computed to examine whether the 2002 Jeff Sagarin end-of-season ratings and total number of wins could predict the following season's recruiting rankings (2003 RIV & SCO TOTPTS and RIV & SCO AVESTAR). The results indicated that RIV & SCO TOTPTS and AVESTAR predicted up to 45% of the variances in the end-of-season ratings and total wins. Thus, other factors (besides recruiting rankings) must be contributing to the end-of-season ratings for the 100 NCAA football teams included in this study. In addition, up to 51% of the variance in RIV & SCO AVESTAR and TOTPTS was predicted by the previous year's end-of-season ratings or total wins, which suggests that more successful seasons tend to yield better subsequent recruiting classes.

We present a methodology to assess the effect of point deductions, which are widely applied as punishments to teams in European association football (soccer) leagues. In particular we outline a method to estimate the increased probability of relegation and the decreased probability of promotion that are associated with a certain penalty for a given team in a particular league. We illustrate our method in the context of two case studies involving points deductions: the relegation of Luton Town from English League Two in 2008--9, and the promotion of Juventus from Italian Serie B in 2006--7. By adapting the parameterisation of our model, we extend our analysis to the more challenging objective of designing standardised point deductions, and discuss how prior knowledge on performance influences the likely severity of any given punishment. We also investigate the relationship between the magnitude of any point deduction and the financial implications for the penalised team. Our techniques allow unambiguous quantification of the consequences of a form of punishment that has been widely, but not necessarily scientifically, implemented by a number of governing bodies in European association football.