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 conversations, 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 game start, allowing for bets informed by its predictions. We first discuss the rationale behind our approach, then 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 (10 sample), and real-time Twitter data (full stream) collected by monitoring the conversations about all soccer matches of the four major European tournaments (FA Premier League, Serie A, La Liga, and Bundesliga), and the 2014 UEFA Champions League, during the period between October, 25th 2014 and November, 26th 2014.
This work, to the best of our knowledge, is the first to exploit social media streams to predict soccer matches. However, various recent studies have approached related problems @cite_29 , such as predicting the outcome of political elections @cite_18 @cite_11 , talent shows @cite_7 , movies success @cite_3 @cite_31 , stock-market fluctuations @cite_24 @cite_6 , political protests @cite_19 @cite_23 @cite_15 @cite_21 , and diffusion of information @cite_26 @cite_16 .
@cite_18 @cite_26 @cite_7 @cite_15 @cite_29 @cite_21 @cite_3 @cite_6 @cite_24 @cite_19 @cite_23 @cite_31 @cite_16 @cite_11
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Sports analytics in general, and football (soccer in USA) analytics in particular, have evolved in recent years in an amazing way, thanks to automated or semi-automated sensing technologies that provide high-fidelity data streams extracted from every game. In this paper we propose a data-driven approach and show that there is a large potential to boost the understanding of football team performance. From observational data of football games we extract a set of pass-based performance indicators and summarize them in the H indicator. We observe a strong correlation among the proposed indicator and the success of a team, and therefore perform a simulation on the four major European championships (78 teams, almost 1500 games). The outcome of each game in the championship was replaced by a synthetic outcome (win, loss or draw) based on the performance indicators computed for each team. We found that the final rankings in the simulated championships are very close to the actual rankings in the real championships, and show that teams with high ranking error show extreme values of a defense attack efficiency measure, the Pezzali score. Our results are surprising given the simplicity of the proposed indicators, suggesting that a complex systems' view on football data has the potential of revealing hidden patterns and behavior of superior quality. Using social media for political discourse is becoming common practice, especially around election time. One interesting aspect of this trend is the possibility of pulsing the public’s opinion about the elections, and that has attracted the interest of many researchers and the press. Allegedly, predicting electoral outcomes from social media data can be feasible and even simple. Positive results have been reported, but without an analysis on what principle enables them. Our work puts to test the purported predictive power of socialmedia metrics against the 2010 US congressional elections. Here, we applied techniques that had reportedly led to positive election predictions in the past, on the Twitter data collected from the 2010 US congressional elections. Unfortunately, we find no correlation between the analysis results and the electoral outcomes, contradicting previous reports. Observing that 80 years of polling research would support our findings, we argue that one should not be accepting predictions about events using social media data as a black box. Instead, scholarly research should be accompanied by a model explaining the predictive power of social media, when there is one. Using social media for political discourse is increasingly becoming common practice, especially around election time. Arguably, one of the most interesting aspects of this trend is the possibility of ''pulsing'' the public's opinion in near real-time and, thus, it has attracted the interest of many researchers as well as news organizations. Recently, it has been reported that predicting electoral outcomes from social media data is feasible, in fact it is quite simple to compute. Positive results have been reported in a few occasions, but without an analysis on what principle enables them. This, however, should be surprising given the significant differences in the demographics between likely voters and users of online social networks. This work aims to test the predictive power of social media metrics against several Senate races of the two recent US Congressional elections. We review the findings of other researchers and we try to duplicate their findings both in terms of data volume and sentiment analysis. Our research aim is to shed light on why predictions of electoral (or other social events) using social media might or might not be feasible. In this paper, we offer two conclusions and a proposal: First, we find that electoral predictions using the published research methods on Twitter data are not better than chance. Second, we reveal some major challenges that limit the predictability of election results through data from social media. We propose a set of standards that any theory aiming to predict elections (or other social events) using social media should follow. The "wisdom of crowds" refers to the phenomenon that aggregated predictions from a large group of people can rival or even beat the accuracy of experts. In domains with substantial stochastic elements, such as stock picking, crowd strategies (e.g. indexing) are difficult to beat. However, in domains in which some crowd members have demonstrably more skill than others, smart sub-crowds could possibly outperform the whole. The central question this work addresses is whether such smart subsets of a crowd can be identified a priori in a large-scale prediction contest that has substantial skill and luck components. We study this question with data obtained from fantasy soccer, a game in which millions of people choose professional players from the English Premier League to be on their fantasy soccer teams. The better the professional players do in real life games, the more points fantasy teams earn. Fantasy soccer is ideally suited to this investigation because it comprises millions of individual-level, within-subject predictions, past performance indicators, and the ability to test the effectiveness of arbitrary player-selection strategies. We find that smaller, smarter crowds can be identified in advance and that they beat the wisdom of the larger crowd. We also show that many players would do better by simply imitating the strategy of a player who has done well in the past. Finally, we provide a theoretical model that explains the results we see from our empirical analyses.