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The purpose of this paper is to examine the social impact of an initiative (Hockey FIT) aimed at improving the health and well-being of sport fans and their community.

Fans (n=80) participated in 12 weekly health promotion sessions hosted in local hockey club facilities. Objective health measurements, diet and physical activity levels of fans were measured at baseline, 12 weeks and 12 months, to determine the intermediate, long-term, individual and community impact. Furthermore, one-on-one interviews with 28 program participants were conducted to further understand the program’s social impact.

The intermediate impact was noticed as improvements in weight loss, body mass index, waist circumference, systolic blood pressure (BP), steps per day, healthful eating, self-reported overall health and fatty food scores at 12 weeks. The long-term individual impact of Hockey FIT was realized as participants maintained or continued to improve their weight loss, waist circumference, healthful eating, systolic BP and diastolic BP 12 months after the program had been offered. The program was also reported to increase family bonding time and improved the diet, daily physical activity, and general awareness of health promotion programs and components for friends, family members and coworkers.

The positive health-related results from this study contradict prior research that has suggested there is minimal evidence of any substantial contributions from social programs in sport. Through a collective approach to corporate social responsibility, this research demonstrates the ability for sport organizations to contribute to meaningful social change and the positive role that they play within the community.

Ranked preference data arise when a set of judges rank, in order of their preference, a set of objects. Such data arise in preferential voting systems and market research surveys. Covariate data associated with the judges are also often recorded. Such covariate data should be used in conjunction with preference data when drawing inferences about judges.

To cluster a population of judges, the population is modeled as a collection of homogeneous groups. The Plackett-Luce model for ranked data is employed to model a judge's ranked preferences within a group. A mixture of Plackett- Luce models is employed to model the population of judges, where each component in the mixture represents a group of judges.

Mixture of experts models provide a framework in which covariates are included in mixture models. Covariates are included through the mixing proportions and the component density parameters. A mixture of experts model for ranked preference data is developed by combining a mixture of experts model and a mixture of Plackett-Luce models. Particular attention is given to the manner in which covariates enter the model. The mixing proportions and group specific parameters are potentially dependent on covariates. Model selection procedures are employed to choose optimal models.

Model parameters are estimated via the ‘EMM algorithm’, a hybrid of the expectation–maximization and the minorization–maximization algorithms. Examples are provided through a menu survey and through Irish election data. Results indicate mixture modeling using covariates is insightful when examining a population of judges who express preferences.