Página 1 dos resultados de 3 itens digitais encontrados em 0.004 segundos

Language competences in the new on-line sports information and betting business: Runningball (Aveiro); Competências linguísticas no novo negócio on-line de informação e apostas desportivas: Runningball (Aveiro)

Maksimova, Valentina
Fonte: Universidade de Aveiro Publicador: Universidade de Aveiro
Tipo: Dissertação de Mestrado
Português
Relevância na Pesquisa
100.04031%
This project evaluates the role of languages and intercultural competence in the new and growing sports business, namely one of the biggest sports events real-time data providers – Runningball. The company provides over 30 000 football games a year in more than 70 countries worldwide as well as basketball, volleyball, and ice-hockey games. The dissertation investigates how multicultural diversity is accepted and received in everyday work at this new type of sports data business and what role the languages play in helping to provide real-time data while using the second or third language to get the best instant results. Dimensions of business are explored, and data analysis made in order to realize how the company runs its business and how languages have become a crucial asset of Runningball. The field study reflected on the role of a lingua franca in the multilingual society of today. Furthermore it has been revealed in a field study that not only English has taken the place of the most important language in international business, but a mixture of various languages is the key to greater success.; O projeto apresentado para esta dissertação teve como principal objectivo analisar a influência global de várias línguas e a capacidade de mediar a comunicação intercultural num mercado desportivo on-line em ascenção. O objetivo foi alcançado em parceria com uma das maiores empresas fornecedoras de dados de eventos desportivos em tempo real - Runningball. Esta empresa oferece um enorme portofólio de eventos desportivos...

Predicting the NFL using Twitter

Sinha, Shiladitya; Dyer, Chris; Gimpel, Kevin; Smith, Noah A.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 25/10/2013 Português
Relevância na Pesquisa
28.493208%
We study the relationship between social media output and National Football League (NFL) games, using a dataset containing messages from Twitter and NFL game statistics. Specifically, we consider tweets pertaining to specific teams and games in the NFL season and use them alongside statistical game data to build predictive models for future game outcomes (which team will win?) and sports betting outcomes (which team will win with the point spread? will the total points be over/under the line?). We experiment with several feature sets and find that simple features using large volumes of tweets can match or exceed the performance of more traditional features that use game statistics.; Comment: Presented at ECML/PKDD 2013 Workshop on Machine Learning and Data Mining for Sports Analytics

TwitterPaul: Extracting and Aggregating Twitter Predictions

UzZaman, Naushad; Blanco, Roi; Matthews, Michael
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
18.898468%
This paper introduces TwitterPaul, a system designed to make use of Social Media data to help to predict game outcomes for the 2010 FIFA World Cup tournament. To this end, we extracted over 538K mentions to football games from a large sample of tweets that occurred during the World Cup, and we classified into different types with a precision of up to 88%. The different mentions were aggregated in order to make predictions about the outcomes of the actual games. We attempt to learn which Twitter users are accurate predictors and explore several techniques in order to exploit this information to make more accurate predictions. We compare our results to strong baselines and against the betting line (prediction market) and found that the quality of extractions is more important than the quantity, suggesting that high precision methods working on a medium-sized dataset are preferable over low precision methods that use a larger amount of data. Finally, by aggregating some classes of predictions, the system performance is close to the one of the betting line. Furthermore, we believe that this domain independent framework can help to predict other sports, elections, product release dates and other future events that people talk about in social media.; Comment: Check out the blog post with a summary and Prediction Retrieval information here: http://bitly.com/TwitterPaul