Match score dataset for team ball sports.

In this data article, we present a dataset containing match scores from major international competitions for 12 popular team ball sports: basketball, cricket, field hockey, futsal, handball, ice hockey, lacrosse, roller hockey, rugby, soccer, volleyball, and water polo. The dataset was obtained by web scraping data available on Wikipedia pages and includes the following information related to individual matches: the year of the competition edition when a match occurred, the names of the two opposing teams, their respective scores, and the name of the winning team. Our match score dataset provides researchers in the field of sports analytics with valuable data that can be used to compute team statistics, develop team ranking and rating systems, infer patterns and trends in a team's performance across the edition years, build predictive models to forecast the outcome of future matches, and evaluate the performance of machine learning algorithms.

Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel.

This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acuity patients, with the aim of minimizing patient waiting times and ED operating costs. We formulate this problem as a general multiobjective simulation-based optimization problem where some of the objectives are expensive black-box functions that can only be evaluated through a time-consuming simulation. To efficiently solve this problem, we propose a metamodeling approach that uses an artificial neural network to replace a black-box objective function with a suitable model. This approach allows us to obtain a set of Pareto optimal points for the multiobjective problem we consider, from which decision-makers can select the most appropriate solutions for different situations. We present the results of computational experiments conducted on a real case study involving the ED of a large hospital in Italy. The results show the reliability and effectiveness of our proposed approach, compared to the standard approach based on derivative-free optimization.