In-play forecasting in football using event and positional data

Two highly relevant aspects of football, namely forecasting of results and performance analysis by means of performance indicators, are combined in the present study by analysing the value of in-play information in terms of event and positional data in forecasting the further course of football matches. Event and positional data from 50 matches, including more than 300 million datapoints were used to extract a total of 18 performance indicators. Moreover, goals from more than 30,000 additional matches have been analysed. Results suggest that surprisingly goals do not possess any relevant informative value on the further course of a match, if controlling for pre-game market expectation by means of betting odds. Performance indicators based on event and positional data have been shown to possess more informative value than goals, but still are not sufficient to reveal significant predictive value in-play. The present results are relevant to match analysts and bookmakers who should not overestimate the value of in-play information when explaining match performance or compiling in-play betting odds. Moreover, the framework presented in the present study has methodological implications for performance analysis in football, as it suggests that researchers should increasingly segment matches by scoreline and control carefully for general team strength.

Random forest model identifies serve strength as a key predictor of tennis match outcome

Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of particular interest to bettors and betting companies alike is potential use of sports records to predict tennis match outcomes prior to match start. We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods. Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real-time predictions. We were able to predict match outcomes with upwards of 80%accuracy, much greater than predictions using betting odds alone, and identify serve strength as a key predictor of match outcome. By combining prediction accuracies from three models, we were able to nearly recreate a probability distribution based on average betting odds from betting companies, which indicates that betting companies are using similar information to assign odds to matches. These results demonstrate the capability of relatively simple machine learning models to quite accurately predict tennis match outcomes.

The Use of Social Media in Research on Gambling: a Systematic Review

Purpose of Review Social media enables a range of possibilities in the way gamblers and gambling operators interact and content communicate with gambling. The purpose of this systematic review was to synthesise the extant literature to identify the ways in which social media has been investigated in the context of gambling. Recent Findings A systematic review of the literature identified 41 papers that collected primary data pertinent to gambling and social media from multiple disciplines. These papers broadly fell into three themes: communication, community and calculation (of sentiment). Papers on communication focused on the content of gambling advertising on social media and the impact on people exposed to it. Studies of gambling communities studied the activity and structures of discussion groups on social media concerning recreational or problematic gambling. Papers on calculation collated social media data to assess sentiment and compared it against betting odds. Summary There is an emerging multidisciplinary literature that has looked at the use of social media in relation to gambling. There is preliminary evidence that the content and the reach of gambling advertising on social media is a source of concern, particularly for younger people. The themes discussed on gambling support forums appear to be common across communities, focusing on negative emotions, recovery, addictive products and financial support. Using social media to assess sentiment appears to be particularly effective at identifying potential upsets in sporting matches. Future suggestions for research are explored.

Soccer Scores, Short-Term Mood and Fertility

Previous research has shown that seemingly irrelevant events such as unexpected outcomes in sporting events can affect mood and have relevant consequences for episodes of crime and violence, investing behavior and political preferences. In this article, we test whether mood shocks associated with unexpected results in soccer matches in Spain affect fertility. We use data on betting odds and actual scores to define mood shocks and link them to births by month and province in Spain, between 2001 and 2015. We find that unexpected losses of local teams lead to a small decrease in the number of births nine months thereafter. The effect is larger for more unexpected losses, in those provinces with the largest amount of support for the local team and robust to a number of placebo tests. We argue that these results are consistent with the gain–loss asymmetry predicted by prospect theory.

The Importance of Betting Early

We evaluate the impact of timing on decision outcomes when both the timing and the relevant decision are chosen under uncertainty. Sports betting provides the testing ground, as we exploit an original dataset containing more than one million online bets on games of the Italian Major Soccer League. We find that individuals perform systematically better when they place their bets farther away from the game day. The better performance of early bettors holds controlling for (time-invariant) unobservable ability, learning during the season, and timing of the odds. We attribute this result to the increase of noisy information on game day, which hampers the capacity of late (non-professional) bettors to use very simple prediction methods, such as team rankings or last game results. We also find that more successful bettors tend to bet in advance, focus on a smaller set of events, and prefer games associated with smaller betting odds.

Can Social Media Predict Soccer Clubs’ Stock Prices? The Case of Turkish Teams and Twitter

Finance literature in sports focuses on three main methods of stock price prediction in soccer: based on match results, pre-match expectations, or match importance. For pre-match expectations, betting odds is commonly used as the indicator of investors’ sentiments. We propose to include Twitter data as another indicator of this variable, and analyze the links among soccer match results, sentiments, and stock returns of the four major Turkish soccer teams. Our results show that social media can be a strong indicator of pre-match expectations and investors’ sentiments in stock price prediction.

Professional Clubs as Platforms in Multi-Sided Markets in Times of COVID-19: The Role of Spectators and Atmosphere in Live Football

In-stadium spectators affect the emotional value and atmosphere of sport live broadcasts. Due to the COVID-19 pandemic, in Europe, the presence of in-stadium spectators, however, was suspended until further notice. Conceptualizing professional clubs as economic platforms, network effects due to the lack of in-stadium spectators may affect stakeholders’ utility. Thus, the main aims of this study are to examine the influence of missing in-stadium spectators for professional clubs by investigating network effects on (1) TV viewers’ emotional arousal and (2) TV viewers’ attention towards sponsor messages during live football broadcasts. Using a quantitative research design, a controlled lap was conducted, and broadcasts were presented to n = 26 highly involved participants. Heart rate, eye-tracking, and betting odds data served as measurements of arousal, attention, and game outcome uncertainty and were aggregated on a second-by-second basis (k = 140,400). Multilevel regression analysis showed significant differences in viewers’ arousal and attention to sponsors, contingent on the presence of in-stadium spectators and game outcome uncertainty. The presence of in-stadium spectators increased arousal, while attention towards sponsor messages decreased, depending on game outcome uncertainty. Based on the presence of network effects, implications to sustainably adapting professional football clubs’ business models based on stakeholders’ different interests can be given.

Construction of a Predictive Model for MLB Matches

The main purpose of this article was to define a model that could defeat the online bookmakers’ odds, where the betting item considered was the first five innings of major league baseball (MLB) matches. The betting odds of online bookmakers have two purposes: first, they are used to quantify the amount of profit made by the bettors; second, they are regarded as a market equilibrium point between multiple bookmakers and bettors. If the bettors have a more accurate prediction model than the system used to produce betting odds, it will create a positive expected return for the bettors. In this article, we used the Markov process method and the runner advancement model to estimate the expected runs in an MLB match for the teams based on the batting lineup and the pitcher.

How well do Elo-based ratings predict professional tennis matches?

This paper examines the performance of five different measures for forecasting men’s and women’s professional tennis matches. We use data derived from every match played at the 2018 and 2019 Wimbledon tennis championships, the 2019 French Open, the 2019 US Open, and the 2020 Australian Open. We look at the betting odds, the official tennis rankings, the standard Elo ratings, surface-specific Elo ratings, and weighted composites of these ratings, including and excluding the betting odds. The performance indicators used are prediction accuracy, calibration, model discrimination, Brier score, and expected return. We find that the betting odds perform relatively well across these tournaments, while standard Elo (especially for women’s tennis) and surface-adjusted Elo (especially for men’s tennis) also perform well on a range of indicators. For all but the hard-court surfaces, a forecasting model which incorporates the betting odds tends also to perform well on some indicators. We find that the official ranking system proved to be a relatively poor measure of likely performance compared to betting odds and Elo-related methods. Our results add weight to the case for a wider use of Elo-based approaches within sports forecasting, as well as arguably within the player rankings methodologies.

Profiting from overreaction in soccer betting odds

Betting odds are generally considered to represent accurate reflections of the underlying probabilities for the outcomes of sporting events. There are, however, known to be a number of inherent biases such as the favorite-longshot bias in which outsiders are generally priced with poorer value odds than favorites. Using data from European soccer matches, this paper demonstrates the existence of another bias in which the match odds overreact to favorable and unfavorable runs of results. A statistic is defined, called the Combined Odds Distribution (COD) statistic, which measures the performance of a team relative to expectations given their odds over previous matches. Teams that overperform expectations tend to have a high COD statistic and those that underperform tend to have a low COD statistic. Using data from twenty different leagues over twelve seasons, it is shown that teams with a low COD statistic tend to be assigned more generous odds by bookmakers. This can be exploited and a sustained and robust profit can be made. It is suggested that the bias in the odds can be explained in the context of the “hot hand fallacy”, in which gamblers overestimate variation in the ability of each team over time.