Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

Overview of CLEF NEWSREEL 2014: News Recommendations Evaluation Labs

This paper summarises objectives, organisation, and results of the first news recommendation evaluation lab (NEWSREEL 2014). NEWSREEL targeted the evaluation of news recommendation algorithms in the form of a campaignstyle evaluation lab. Participants had the chance to apply two types of evaluation schemes. On the one hand, participants could apply their algorithms onto a data set. We refer to this setting as off-line evaluation. On the other hand, participants could deploy their algorithms on a server to interactively receive recommendation requests. We refer to this setting as on-line evaluation. This setting ought to reveal the actual performance of recommendation methods. The competition strived to illustrate differences between evaluation with historical data and actual users. The on-line evaluation does reflect all requirements which active recommender systems face in practise. These requirements include real-time responses and large-scale data volumes. We present the competition’s results and discuss commonalities regarding participants’ approaches

The influence of data analysis on football teams to increase sports´ performance

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceAs we know, football is the most popular sport among the fans all over the world and, in today’s world a very lucrative business for club owners and stakeholders, and sometimes its own supporters. With the board and supporters’ expectations being higher with the money spent on new players and conditions to attract valuable assets for the clubs, the teams tend to invest their money on infrastructures and other type of conditions for their players, including a better staff. The teams’ staff normally gather many data during the training sessions, other teams’ observation, and post-match observations, meaning that the investment is now increasing on hiring new data analysts. Additionally, there are scouting teams that gather data as well. With that, the question that arises is how can football teams increase their performance, using data analysis? The goal of this dissertation is to understand how the existing tools are helping teams improving their performance in and off the pitch and propose new ways on how future analysis can be conducted. To meet this goal, an extended systematic literature review will be taken, to present a discussion and conclusions on how data analysis can influence football clubs and players’ performance