Search Results (19)

We found statistical evidence for a mismatch between the (global) spatial curvature parameter K in the geodesic equation for incoming photons and the corresponding parameter in the Friedmann equation that determines the time evolution of the background spacetime and its perturbations. The mismatch, hereafter referred to as ‘curvature slip’, was especially evident when the SH0ES prior of the current expansion rate was assumed. This result is based on joint analyses of cosmic microwave background (CMB) observations with the PLANCK satellite (P18), the first year results of the Dark Energy Survey (DES), baryonic oscillation (BAO) data, and at a lower level of significance, the Pantheon SNIa (SN) catalog as well. For example, the betting odds against the null hypothesis were greater than ${10}^7$:1, 1400:1 and 1000:1 when P18+SH0ES, P18+DES+SH0ES and P18+BAO+SH0ES were considered, respectively. Datasets involving SNIa weakened this curvature slip considerably. Notably, even when the SH0ES prior was not imposed, the betting odds for the rejection of the null hypothesis were 70:1 and 160:1 in cases where P18+DES and P18+BAO were considered. When the SH0ES prior was imposed, the global fit of the modified model (that allows for a nonvanishing ‘curvature slip’) strongly outperformed that of $\Lambda$CDM, being manifested by significant deviance information criterion (DIC) gains ranging between 7 and 23, depending on the dataset combination considered. Even in comparison with K$\Lambda$CDM, the proposed model resulted in significant, albeit smaller, DIC gains when SN data were excluded. Our finding could possibly be interpreted as an inherent inconsistency between the (idealized) maximally symmetric nature of the FRW metric and the dynamical evolution of the GR-based homogeneous and isotropic $\Lambda$CDM models. As such, this implies that there is apparent tension between the metric curvature and the curvature-like term in the time evolution of the redshift. Full article

The association between pollen food allergy syndrome (PFAS) and allergic march remains unclear. In this prospective cohort study of the general population in Tokyo (T-Child Study), we found that sensitization to Cry j 1 and Fel d 1 at ages 5 and 9 years was associated with an increased risk of PFAS at 13 years old (at 5 years, Cry j 1: adjusted odds ratio aOR, 2.74; 95% confidence interval CI, 1.53–4.91; Fel d 1: aOR, 2.61; 95% CI, 1.31–5.19; at 9 years, Cry j 1: adjusted odds ratio aOR, 4.28; 95% confidence interval CI, 1.98–9.25; Fel d 1: aOR, 2.40; 95% CI, 1.33–4.32). In particular, sensitization to Bet v 1 at ages 5 and 9 years was associated with a strong risk of PFAS at the age of 13 years (at 5 years: aOR, 10.6; 95% CI, 2.64–42.5; at 9 years: aOR, 9.1; 95% CI, 4.71–17.6). PFAS risk by age 13 years was increased by any allergic symptom at 5 or 9 years, a combination of wheezing, eczema, and rhinitis, and Bet v 1 sensitization. Our findings suggest that PFAS may be associated with allergic march. Full article

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction. Full article

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. Full article

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. Full article

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. Full article

This paper studies efficient market hypothesis in prediction markets and the results are illustrated for the in-play football betting market using the quoted odds for the English Premier League. Our analysis is based on the martingale property, where the last quoted probability should be the best predictor of the outcome and all previous quotes should be statistically insignificant. We use regression analysis to test for the significance of the previous quotes in both the time setup and the spatial setup based on stopping times, when the quoted probabilities reach certain bounds. The main contribution of this paper is to show how a potentially different distributional opinion based on the violation of the market efficiency can be monetized by optimal trading, where the agent maximizes logarithmic utility function. In particular, the trader can realize a trading profit that corresponds to the likelihood ratio in the situation of one market maker and one market taker, or the Bayes factor in the situation of two or more market takers. Full article

A large body of literature on the favorite–longshot bias finds that sports bettors in a variety of markets appear to have irrational biases toward either longshots (which offer a small chance of winning a large amount of money) or favorites (which offer a high chance of winning a small amount of money). While early studies in horse racing led to an impression that longshot bias is dominant, favorite bias has also now been found in a variety of sports betting markets. This review proposes that the evidence is consistent with both biases being present in the average sports bettor. Sports betting markets with only two potential outcomes, where the favorite therefore has a probability >0.5 of happening, often produce favorite bias. Sports betting markets with multiple outcomes, where the favorite’s probability is usually <0.5, appear more consistent with longshot bias. The presence of restricted odds ranges within any given betting market provides an explanation for why single studies support, at most, one bias. This literature review highlights how individual sports bettors might possess biases toward both highly likely, and highly unlikely, events, a contradictory view that has not been summarized in detail before. Full article

The possibility to use hedging strategies is an often neglected aspect in the literature on prediction/betting markets, as most papers assume that bettors will bet according to their beliefs about the probability of the outcome of the event, as opposed to the direction in which the odds will move. This ignores strategies that try to buy low and sell high through exploiting price changes, which is an important aspect to incorporate to fully understand market pricing. In this paper, we derive the key mathematical results in using hedging strategies through taking opposite positions to an initial bet after the market odds have changed and show that a profit can be made without explicitly speculating on the probability of the outcomes. We also discuss two sources of inefficiency that can arise when using hedging strategies in practice: (i) the need to pay a fee when using a betting exchange and (ii) the lack of a lay option (the possibility to bet against outcomes) on some markets, and we analyze how they affect the possibilities to hedge. Many of the results have interesting properties when expressed in terms of the naive probabilities implied by the odds. Full article

Background and aims: The current level of knowledge concerning the effect of socioeconomic status (SES) on internet use, gambling, and substance use in structurally disadvantaged regions is scarce. The objective of this study was an investigation of the relationship between SES and risky internet use, gambling and substance use in a structurally disadvantaged region in Central Europe. Methods: A cross-sectional survey was conducted among high school students (n = 1063) in a Czech structurally disadvantaged region in autumn 2017. Binary Logistic Regression models were applied to data from the modified Excessive Internet Use scale (mEIUS), a standard tool for measuring the risk of addictive behavior on the internet and the risk of excessive gaming. Other data were collected using the Lie/Bet (problematic gambling), CAGE (acronym of the key words: cut, angry, guilty and eye-opener), and the Cannabis Abuse Screening Test (CAST) (problematic alcohol/cannabis use) tools. Results: There were statistically significant differences between at-risk and not-at-risk groups in addictive behavior on the internet and gaming, while none were found in problematic gambling. Individual dimensions of SES showed significant effects on substance use. Regarding parenting styles, significant differences were found only in the risk of addictive behavior on the internet or gaming between the authoritarian and authoritative styles. Being engaged in behavioral addictions with one´s parents increased the odds of the behavioral addiction risk and decreased the odds of the substance addiction risk. Engagement with one´s parents in substance addictions decreased the odds of the behavioral addiction risk and increased the odds of the substance addiction risk. Discussion and Conclusions: The results point at specific relations between SES and the risk of addictive behaviors on the internet and gaming within structurally disadvantaged regions. The results of SES and/or structurally disadvantaged region measures obtained in research, policy-making, and care-provision may improve the focus of actions taken. Full article

Match outcome prediction is a challenging problem that has led to the recent rise in machine learning being adopted and receiving significant interest from researchers in data science and sports. This study explores predictability in match outcomes using machine learning and candlestick charts, which have been used for stock market technical analysis. We compile candlestick charts based on betting market data and consider the character of the candlestick charts as features in our predictive model rather than the performance indicators used in the technical and tactical analysis in most studies. The predictions are investigated as two types of problems, namely, the classification of wins and losses and the regression of the winning/losing margin. Both are examined using various methods of machine learning, such as ensemble learning, support vector machines and neural networks. The effectiveness of our proposed approach is evaluated with a dataset of 13261 instances over 32 seasons in the National Football League. The results reveal that the random subspace method for regression achieves the best accuracy rate of 68.4%. The candlestick charts of betting market data can enable promising results of match outcome prediction based on pattern recognition by machine learning, without limitations regarding the specific knowledge required for various kinds of sports. Full article

Point process models have made a significant contribution to the prediction of football association outcomes. It is conventionally the case that defence and attack capabilities have been assumed to be constant during a match and estimated against the average performance of all other teams in history. Drawing upon a Bayesian method, this paper proposes a dynamic strength model which relaxes assumption of the constant teams’ strengths and permits applying in-match performance information to calibrate them. An empirical study demonstrates that although the Bayesian model fails to achieve improvement in goal difference prediction, it registers clear achievements with regard to the prediction of the total number of goals and Win/Draw/Loss outcome prediction. When the Bayesian model bets against the SBOBet bookmaker, one of the most popular gaming companies among Asian handicaps fans, whose odds data were obtained from both the Win/Draw/Loss market and over–under market, it may obtain positive returns; this clearly contrasts with the process model with constant strengths, which fails to win money from the bookmaker. Full article

In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team. Full article

We consider a repeated betting market populated by two agents who wage on a binary event according to generic betting strategies. We derive new simple criteria, based on the difference of relative entropies, to establish the relative wealth of the two agents in the long-run. Little information about agents’ behavior is needed to apply the criteria: it is sufficient to know the odds traders believe fair and how much they would bet when the odds are equal to the ones the other agent believes fair. Using our criteria, we show that for a large class of betting strategies, it is generically possible that the ultimate winner is only decided by luck. As an example, we apply our conditions to the case of Constant Relative Risk Averse (CRRA) and quantal response betting. Full article

To date it is not clear what the role of dairy products is in metabolic diseases like diabetes, obesity, and hypertension. Therefore, the aim of this study is to test the association between dairy product consumption and those pathologies. A cross-sectional study was conducted with 5081 adults included in the di@bet.es study, from 100 health centers around Spain. Food frequency questionnaires were carried out concerning consumption habits, which included dairy product consumption. Logistic regression models were used for the association analyses between the variables controlling confounding variables. Women had a higher consumption of milk, cheese, or yogurt than men (p < 0.0001), but men consumed more sugar dairy products (p < 0.001). People who live in the North of Spain consume more dairy products than those who live in the East. Dairy product consumption was inversely associated with the presence of hypertension regardless of age, sex, geographical region, and body mass index (BMI) (Odds Ratio (OR) 0.743; p = 0.022). The presence of obesity was inversely associated with dairy consumption regardless of age, sex, and geographical region (OR 0.61; p < 0.001). Milk consumption was not associated with diabetes. Our results show that consuming dairy products is associated with a better metabolic profile in the Spanish population. Full article