"i have a feeling trump will win..................": Forecasting Winners and Losers from User Predictions on Twitter

Social media users often make explicit predictions about upcoming events. Such statements vary in the degree of certainty the author expresses toward the outcome:"Leonardo DiCaprio will win Best Actor" vs. "Leonardo DiCaprio may win" or "No way Leonardo wins!". Can popular beliefs on social media predict who will win? To answer this question, we build a corpus of tweets annotated for veridicality on which we train a log-linear classifier that detects positive veridicality with high precision. We then forecast uncertain outcomes using the wisdom of crowds, by aggregating users' explicit predictions. Our method for forecasting winners is fully automated, relying only on a set of contenders as input. It requires no training data of past outcomes and outperforms sentiment and tweet volume baselines on a broad range of contest prediction tasks. We further demonstrate how our approach can be used to measure the reliability of individual accounts' predictions and retrospectively identify surprise outcomes.Comment: Accepted at EMNLP 2017 (long paper

Brazil or Germany - who will win the trophy? Prediction of the FIFA World Cup 2014 based on team-specific regularized Poisson regression

In this article an approach for the analysis and prediction of soccer match results is proposed. It is based on a regularized Poisson regression model that includes various potentially influential covariates describing the national teams' success in previous FIFA World Cups. Additionally, similar to Bradley-Terry-Luce models, differences of team-specific effects of the competing teams are included. It is discussed that within the generalized linear model (GLM) framework the team-specific effects can either be incorporated in the form of fixed or random effects. In order to achieve variable selection and shrinkage, we use tailored Lasso approaches. Based on the three preceding FIFA World Cups, two competing models for the prediction of the FIFA World Cup 2014 are fitted and investigated

Win-stay lose-shift strategy in formation changes in football

Managerial decision making is likely to be a dominant determinant of performance of teams in team sports. Here we use Japanese and German football data to investigate correlates between temporal patterns of formation changes across matches and match results. We found that individual teams and managers both showed win-stay lose-shift behavior, a type of reinforcement learning. In other words, they tended to stick to the current formation after a win and switch to a different formation after a loss. In addition, formation changes did not statistically improve the results of succeeding matches.The results indicate that a swift implementation of a new formation in the win-stay lose-shift manner may not be a successful managerial rule of thumb.Comment: 7 figures, 11 table