81,267 research outputs found
On predictability of rare events leveraging social media: a machine learning perspective
Information extracted from social media streams has been leveraged to
forecast the outcome of a large number of real-world events, from political
elections to stock market fluctuations. An increasing amount of studies
demonstrates how the analysis of social media conversations provides cheap
access to the wisdom of the crowd. However, extents and contexts in which such
forecasting power can be effectively leveraged are still unverified at least in
a systematic way. It is also unclear how social-media-based predictions compare
to those based on alternative information sources. To address these issues,
here we develop a machine learning framework that leverages social media
streams to automatically identify and predict the outcomes of soccer matches.
We focus in particular on matches in which at least one of the possible
outcomes is deemed as highly unlikely by professional bookmakers. We argue that
sport events offer a systematic approach for testing the predictive power of
social media, and allow to compare such power against the rigorous baselines
set by external sources. Despite such strict baselines, our framework yields
above 8% marginal profit when used to inform simple betting strategies. The
system is based on real-time sentiment analysis and exploits data collected
immediately before the games, allowing for informed bets. We discuss the
rationale behind our approach, describe the learning framework, its prediction
performance and the return it provides as compared to a set of betting
strategies. To test our framework we use both historical Twitter data from the
2014 FIFA World Cup games, and real-time Twitter data collected by monitoring
the conversations about all soccer matches of four major European tournaments
(FA Premier League, Serie A, La Liga, and Bundesliga), and the 2014 UEFA
Champions League, during the period between Oct. 25th 2014 and Nov. 26th 2014.Comment: 10 pages, 10 tables, 8 figure
Implementation and perceived benefits of an after-school soccer program designed to promote social and emotional learning: A multiple case study
Social and emotional learning (SEL) competencies such as self-awareness and relationship skills are predictors of academic success, overall well-being, and avoidance of problematic behaviors. Among school-aged children, research has demonstrated that well-implemented programs teach SEL competencies and life skills (e.g., leadership, responsible decision making) that can transfer to other settings. Similar claims have been made in the field of sport-based youth development (SBYD), however, the SEL framework has not been widely applied in sport programming. Implementation, student learning, and transfer of learning in SBYD programs designed to promote SEL require further exploration. Therefore, the current study examined the implementation and perceived benefits of an after-school soccer program designed to promote SEL. Participants were six coaches and 51 students from three different sites where this program is offered. A multiple case study design was used, integrating data from customized feedback surveys, interviews, systematic observation, and field notes. Results indicated the program reflects many SBYD best practices. Although implementation varied between sites, program culture and core values were consistent. Evidence indicated students learned and applied SEL lessons in the soccer program and that transfer beyond the program was promoted. Participants were most likely to report transfer to the school setting, therefore, future studies should examine this topic more directly. Other implications for research and program implementation are discussed
Using Monte Carlo Search With Data Aggregation to Improve Robot Soccer Policies
RoboCup soccer competitions are considered among the most challenging
multi-robot adversarial environments, due to their high dynamism and the
partial observability of the environment. In this paper we introduce a method
based on a combination of Monte Carlo search and data aggregation (MCSDA) to
adapt discrete-action soccer policies for a defender robot to the strategy of
the opponent team. By exploiting a simple representation of the domain, a
supervised learning algorithm is trained over an initial collection of data
consisting of several simulations of human expert policies. Monte Carlo policy
rollouts are then generated and aggregated to previous data to improve the
learned policy over multiple epochs and games. The proposed approach has been
extensively tested both on a soccer-dedicated simulator and on real robots.
Using this method, our learning robot soccer team achieves an improvement in
ball interceptions, as well as a reduction in the number of opponents' goals.
Together with a better performance, an overall more efficient positioning of
the whole team within the field is achieved
Evolution of a robotic soccer player
Robotic soccer is a complex domain where, rather than hand-coding computer programs to control
the players, it is possible to create them through evolutionary methods. This has been successfully
done before by using genetic programming with high-level genes. Such an approach is, however,
limiting. This work attempts to reduce that limit by evolving control programs using genetic
programming with low-level nodes
The Effect of Animation Teaching Materials on Soccer Game Tactics and Strategies Knowledge
This test is motivated by the problem of student learning outcomes, especially information about soccer strategies and tactics which are still relatively low. The motivation behind this review is to determine the impact of animation on knowledge of the strategy and tactics soccer games on 12th grade Pangkalpinang Senior High School 3. This test provides a reference for educators in expanding student information. This research is an experimental quantitative research. Determination of the sample by purposive sampling procedure. The sample used is 32 students. This experiment uses a Pre-Experimental Design with a trial plan of the One Group Pretest-Posttest Plan. The data testing method used One Sample t-test (Pretest-Posttest). After testing the presumption using the t-test, it is obtained that tcount = 3.893 and ttable with a degree of 5% = 1.696, then Ha is recognized and H0 is rejected. Thus, it can be concluded that there is an effect of animation teaching materials on the knowledge of strategy and tactics soccer game on 12th grade Pangkalpinang Senior High School 3
- …