Search results

This paper aims to investigate how predictions of football league standings and efficiency measures of teams, obtained through frontier estimation technique, evolve compared to actual results.

The study is based on data from the Turkish first division football league. Historical data for five seasons, from 2011 to 2016, are used to compare weekly estimates to de facto results. Data envelopment analysis efficiency measures are used to estimate team performances. After each week, a data envelopment analysis is run using available data until then, and final team standings are estimated via computed efficiencies. Estimations are improved by using a data envelopment analysis model that incorporates expert knowledge about football.

Results indicate that deductions can be made about the league’s future progress. Model incorporating expert knowledge tends to estimate the performance better. Although the prediction accuracy starts out low in early stages, it improves as the season advances. Scatter of individual teams’ performances show fluxional behaviour, which attracts studying the impact of uncontrollable factors such as refereeing.

While all previous studies focus on season performance, this study handles the problem as a combination of weekly performance and how it converges to reality. By tracking weekly performance, managers get a chance to confront their weak performance indicators and achieve higher ranking by improving on these inefficiencies.

Reliability evaluation of healthcare services has been a challenging task for both operations managers and system engineers working in the respective field. The purpose of this paper is to develop a data envelopment analysis-based reliability allocation model.

A two-phase optimization scheme for the reliability evaluation and allocation of homogeneous system entities, namely, hospitals, operating in a healthcare network is proposed. First, reliability evaluation is performed nonparametrically through the frontier estimation technique data envelopment analysis by considering several failure modes and failure free discharged patients as the inputs and output of the service system. Subsequently, optimal reliability allocation that maximizes the overall network reliability subject to a budget constraint is carried out by utilizing weights of the inputs and output calculated on the Pareto optimal frontier, which is constructed from the most reliable hospitals operating in the network.

The popular performance assessment methodology DEA is found to be an invaluable reliability assessment and allocation tool, where optimal weights of the associated envelopment model, under certain budget restrictions, are used to maximize overall network reliability.

An empirical illustration of the proposed model is presented on a set of hospital network data from Turkey. Modeling implications can be carried out on similar service operations where identification of the critical performance indicator costs is possible.