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