TCC - Bacharelado em Ciência da Computação (UAG)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/2952
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Item Aplicativo para sorteio de jogadores na formação de equipes em esportes coletivos(2019) Almeida, Luiz Alberes Bispo de; Carvalho, Tiago Buarque Assunção de; http://lattes.cnpq.br/7150833804013500In this report is presented the Sem Panelinha, an application for adraw of play-sports teams, with anemphasis on football. With Sem Panelinha,the user can choose three types of sweepstakes: Random, which distributes the players to teams without any criteria; By Key Heads, which does not allow the players to defined as Keyheads remain on the same team; and By Stars, where possible to give a note through the amount of stars for each of the players. The from the selection of one of these draws the participating players are registered and the end of the registration process, the teams will be formed. Among the benefits of the system, the following stand out: practicality, agility and power of choice. The application has been deployed using Android Studio,with the local data base made by SQLite. Besides that, techniques of Software and Data bases were used for better project development. During the modeling process, meetings with users and visits to the event sites enabled the creation of requirements, meeting the needs of of users. A unit test was performed that indicated a good functioning of the system. Finally, all the proposed requirements have been met and the system is in operation, through usability testing.Item Previsão de resultados de jogos do campeonato brasileiro de futebol utilizando aprendizagem de máquina(2019-02-05) Almeida, Luiz Alberes Bispo de; Carvalho, Tiago Buarque Assunção de; http://lattes.cnpq.br/7150833804013500Over the past few years, the demand for sports betting has grown, and several people started living off this market. Using Machine Learning with the goal of making game analysis for betting more comfortable as well as measuring profits, a database for the Brazilian Soccer Championship Série A 2017 was created, encompassing features from two opposing teams and the match. For testing in an evaluation model, the Naive Bayes technique was chosen, simulating a Brazilian Championship round, which contains ten matches. The evaluation model was executed both with and without correct probability restrictions, with the goal of reducing errors. Three scenarios were used on the evaluation model, the first having two classes that consider goals by both teams, the second regarding the total match goals (over or below 2.5 goals) and the third considering all three possible match results (a victory for one side, a tie, or a victory for the other side). The results without probability restriction reached its higher value at 5.51% of average and total profit. However, regarding the results with probability restriction, the best average gain was 36.05%, and the best total profit was 39.13%, both using 99% correct probability restriction.