OddAssist - An eSports betting recommendation system

It is globally accepted that sports betting has been around for as long as the sport itself. Back in the 1st century, circuses hosted chariot races and fans would bet on who they thought would emerge victorious. With the evolution of technology, sports evolved and, mainly, the bookmakers evolved. Due to the mass digitization, these houses are now available online, from anywhere, which makes this market inherently more tempting. In fact, this transition has propelled the sports betting industry into a multi-billion-dollar industry that can rival the sports industry. Similarly, younger generations are increasingly attached to the digital world, including electronic sports – eSports. In fact, young men are more likely to follow eSports than traditional sports. Counter-Strike: Global Offensive, the videogame on which this dissertation focuses, is one of the pillars of this industry and during 2022, 15 million dollars were distributed in tournament prizes and there was a peak of 2 million concurrent viewers. This factor, combined with the digitization of bookmakers, make the eSports betting market extremely appealing for exploring machine learning techniques, since young people who follow this type of sports also find it easy to bet online. In this dissertation, a betting recommendation system is proposed, implemented, tested, and validated, which considers the match history of each team, the odds of several bookmakers and the general feeling of fans in a discussion forum. The individual machine learning models achieved great results by themselves. More specifically, the match history model managed an accuracy of 66.66% with an expected calibration error of 2.10% and the bookmaker odds model, with an accuracy of 65.05% and a calibration error of 2.53%. Combining the models through stacking increased the accuracy to 67.62% but worsened the expected calibration error to 5.19%. On the other hand, merging the datasets and training a new, stronger model on that data improved the accuracy to 66.81% and had an expected calibration error of 2.67%. The solution is thoroughly tested in a betting simulation encapsulating 2500 matches. The system’s final odd is compared with the odds of the bookmakers and the expected long-term return is computed. A bet is made depending on whether it is above a certain threshold. This strategy called positive expected value betting was used at multiple thresholds and the results were compared. While the stacking solution did not perform in a betting environment, the match history model prevailed with profits form 8% to 90%; the odds model had profits ranging from 13% to 211%; and the dataset merging solution profited from 11% to 77%, all depending on the minimum expected value thresholds. Therefore, from this work resulted several machine learning approaches capable of profiting from Counter Strike: Global Offensive bets long-term.É globalmente aceite que as apostas desportivas existem há tanto tempo quanto o próprio desporto. Mesmo no primeiro século, os circos hospedavam corridas de carruagens e os fãs apostavam em quem achavam que sairia vitorioso, semelhante às corridas de cavalo de agora. Com a evolução da tecnologia, os desportos foram evoluindo e, principalmente, evoluíram as casas de apostas. Devido à onda de digitalização em massa, estas casas passaram a estar disponíveis online, a partir de qualquer sítio, o que torna este mercado inerentemente mais tentador. De facto, esta transição propulsionou a indústria das apostas desportivas para uma indústria multibilionária que agora pode mesmo ser comparada à indústria dos desportos. De forma semelhante, gerações mais novas estão cada vez mais ligadas ao digital, incluindo desportos digitais – eSports. Counter-Strike: Global Offensive, o videojogo sobre o qual esta dissertação incide, é um dos grandes impulsionadores desta indústria e durante 2022, 15 milhões de dólares foram distribuídos em prémios de torneios e houve um pico de espectadores concorrentes de 2 milhões. Embora esta realidade não seja tão pronunciada em Portugal, em vários países, jovens adultos do sexo masculino, têm mais probabilidade de acompanharem eSports que desportos tradicionais. Este fator, aliado à digitalização das casas de apostas, tornam o mercado de apostas em eSports muito apelativo para a exploração técnicas de aprendizagem automática, uma vez que os jovens que acompanham este tipo de desportos têm facilidade em apostar online. Nesta dissertação é proposto, implementado, testado e validado um sistema de recomendação de apostas que considera o histórico de resultados de cada equipa, as cotas de várias casas de apostas e o sentimento geral dos fãs num fórum de discussão – HLTV. Deste modo, foram inicialmente desenvolvidos 3 sistemas de aprendizagem automática. Para avaliar os sistemas criados, foi considerado o período de outubro de 2020 até março de 2023, o que corresponde a 2500 partidas. Porém, sendo o período de testes tão extenso, existe muita variação na competitividade das equipas. Deste modo, para evitar que os modelos ficassem obsoletos durante este período de teste, estes foram re-treinados no mínimo uma vez por mês durante a duração do período de testes. O primeiro sistema de aprendizagem automática incide sobre a previsão a partir de resultados anteriores, ou seja, o histórico de jogos entre as equipas. A melhor solução foi incorporar os jogadores na previsão, juntamente com o ranking da equipa e dando mais peso aos jogos mais recentes. Esta abordagem, utilizando regressão logística teve uma taxa de acerto de 66.66% com um erro expectável de calibração de 2.10%. O segundo sistema compila as cotas das várias casas de apostas e faz previsões com base em padrões das suas variações. Neste caso, incorporar as casas de aposta tendo atingido uma taxa de acerto de 65.88% utilizando regressão logística, porém, era um modelo pior calibrado que o modelo que utilizava a média das cotas utilizando gradient boosting machine, que exibiu uma taxa de acerto de 65.06%, mas melhores métricas de calibração, com um erro expectável de 2.53%. O terceiro sistema, baseia-se no sentimento dos fãs no fórum HLTV. Primeiramente, é utilizado o GPT 3.5 para extrair o sentimento de cada comentário, com uma taxa geral de acerto de 84.28%. No entanto, considerando apenas os comentários classificados como conclusivos, a taxa de acerto é de 91.46%. Depois de classificados, os comentários são depois passados a um modelo support vector machine que incorpora o comentador e a sua taxa de acerto nas partidas anteriores. Esta solução apenas previu corretamente 59.26% dos casos com um erro esperado de calibração de 3.22%. De modo a agregar as previsões destes 3 modelos, foram testadas duas abordagens. Primeiramente, foi testado treinar um novo modelo a partir das previsões dos restantes (stacking), obtendo uma taxa de acerto de 67.62%, mas com um erro de calibração esperado de 5.19%. Na segunda abordagem, por outro lado, são agregados os dados utilizados no treino dos 3 modelos individuais, e é treinado um novo modelo com base nesse conjunto de dados mais complexo. Esta abordagem, recorrendo a support vector machine, obteve uma taxa de acerto mais baixa, 66.81% mas um erro esperado de calibração mais baixo, 2.67%. Por fim, as abordagens são postas à prova através de um simulador de apostas, onde sistema cada faz uma previsão e a compara com a cota oferecia pelas casas de apostas. A simulação é feita para vários patamares de retorno mínimo esperado, onde os sistemas apenas apostam caso a taxa esperada de retorno da cota seja superior à do patamar. Esta cota final é depois comparada com as cotas das casas de apostas e, caso exista uma casa com uma cota superior, uma aposta é feita. Esta estratégia denomina-se de apostas de valor esperado positivo, ou seja, apostas cuja cota é demasiado elevada face à probabilidade de se concretizar e que geram lucros a longo termo. Nesta simulação, os melhores resultados, para uma taxa de mínima de 5% foram os modelos criados a partir das cotas das casas de apostas, com lucros entre os 13% e os 211%; o dos dados históricos que lucrou entre 8% e 90%; e por fim, o modelo composto, com lucros entre os 11% e os 77%. Assim, deste trabalho resultaram diversos sistemas baseados em machine learning capazes de obter lucro a longo-termo a apostar em Counter Strike: Global Offensive

Patient-physician discordance in assessment of adherence to inhaled controller medication: a cross-sectional analysis of two cohorts

We aimed to compare patient's and physician's ratings of inhaled medication adherence and to identify predictors of patient-physician discordance.(SFRH/BPD/115169/2016) funded by Fundação para a Ciência e Tecnologia (FCT); ERDF (European Regional Development Fund) through the operations: POCI-01-0145-FEDER-029130 ('mINSPIRERS—mHealth to measure and improve adherence to medication in chronic obstructive respiratory diseases—generalisation and evaluation of gamification, peer support and advanced image processing technologies') cofunded by the COMPETE2020 (Programa Operacional Competitividade e Internacionalização), Portugal 2020 and by Portuguese Funds through FCT (Fundação para a Ciência e a Tecnologia).info:eu-repo/semantics/publishedVersio

Identification of clusters of asthma control: A preliminary analysis of the inspirers studies

This work was funded by ERDF (European Regional Development Fund) through the operations: POCI- -01-0145-FEDER-029130 (“mINSPIRERS—mHealth to measure and improve adherence to medication in chronic obstructive respiratory diseases - generalisation and evaluation of gamification, peer support and advanced image processing technologies”) co-funded by the COMPETE2020 (Programa Operacional Competitividade e Internacionalização), Portugal 2020 and by Portuguese Funds through FCT (Fundação para a Ciência e a Tecnologia).© 2020, Sociedade Portuguesa de Alergologia e Imunologia Clinica. All rights reserved. Aims: To identify distinct asthma control clusters based on Control of Allergic Rhinitis and Asthma Test (CARAT) and to compare patients’ characteristics among these clusters. Methods: Adults and adolescents (≥13 years) with persistent asthma were recruited at 29 Portuguese hospital outpatient clinics, in the context of two observational studies of the INSPIRERS project. Demographic and clinical characteristics, adherence to inhaled medication, beliefs about inhaled medication, anxiety and depression, quality of life, and asthma control (CARAT, >24 good control) were collected. Hierarchical cluster analysis was performed using CARAT total score (CARAT-T). Results: 410 patients (68% adults), with a median (percentile 25–percentile 75) age of 28 (16-46) years, were analysed. Three clusters were identified [mean CARAT-T (min-max)]: cluster 1 [27(24-30)], cluster 2 [19(14-23)] and cluster 3 [10(2-13)]. Patients in cluster 1 (34%) were characterised by better asthma control, better quality of life, higher inhaler adherence and use of a single inhaler. Patients in clusters 2 (50%) and 3 (16%) had uncontrolled asthma, lower inhaler adherence, more symptoms of anxiety and depression and more than half had at least one exacerbation in the previous year. Further-more, patients in cluster 3 were predominantly female, had more unscheduled medical visits and more anxiety symp-toms, perceived a higher necessity of their prescribed inhalers but also higher levels of concern about taking these inhalers. There were no differences in age, body mass index, lung function, smoking status, hospital admissions or specialist physician follow-up time among the three clusters. Conclusion: An unsupervised method based on CARAT--T, identified 3 clusters of patients with distinct, clinically meaningful characteristics. The cluster with better asthma control had a cut-off similar to the established in the validation study of CARAT and an additional cut-off seems to distinguish more severe disease. Further research is necessary to validate the asthma control clusters identified.publishersversionpublishe

Cabbage and fermented vegetables : From death rate heterogeneity in countries to candidates for mitigation strategies of severe COVID-19

Large differences in COVID-19 death rates exist between countries and between regions of the same country. Some very low death rate countries such as Eastern Asia, Central Europe, or the Balkans have a common feature of eating large quantities of fermented foods. Although biases exist when examining ecological studies, fermented vegetables or cabbage have been associated with low death rates in European countries. SARS-CoV-2 binds to its receptor, the angiotensin-converting enzyme 2 (ACE2). As a result of SARS-CoV-2 binding, ACE2 downregulation enhances the angiotensin II receptor type 1 (AT(1)R) axis associated with oxidative stress. This leads to insulin resistance as well as lung and endothelial damage, two severe outcomes of COVID-19. The nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is the most potent antioxidant in humans and can block in particular the AT(1)R axis. Cabbage contains precursors of sulforaphane, the most active natural activator of Nrf2. Fermented vegetables contain many lactobacilli, which are also potent Nrf2 activators. Three examples are: kimchi in Korea, westernized foods, and the slum paradox. It is proposed that fermented cabbage is a proof-of-concept of dietary manipulations that may enhance Nrf2-associated antioxidant effects, helpful in mitigating COVID-19 severity.Peer reviewe

Nrf2-interacting nutrients and COVID-19 : time for research to develop adaptation strategies

There are large between- and within-country variations in COVID-19 death rates. Some very low death rate settings such as Eastern Asia, Central Europe, the Balkans and Africa have a common feature of eating large quantities of fermented foods whose intake is associated with the activation of the Nrf2 (Nuclear factor (erythroid-derived 2)-like 2) anti-oxidant transcription factor. There are many Nrf2-interacting nutrients (berberine, curcumin, epigallocatechin gallate, genistein, quercetin, resveratrol, sulforaphane) that all act similarly to reduce insulin resistance, endothelial damage, lung injury and cytokine storm. They also act on the same mechanisms (mTOR: Mammalian target of rapamycin, PPAR gamma:Peroxisome proliferator-activated receptor, NF kappa B: Nuclear factor kappa B, ERK: Extracellular signal-regulated kinases and eIF2 alpha:Elongation initiation factor 2 alpha). They may as a result be important in mitigating the severity of COVID-19, acting through the endoplasmic reticulum stress or ACE-Angiotensin-II-AT(1)R axis (AT(1)R) pathway. Many Nrf2-interacting nutrients are also interacting with TRPA1 and/or TRPV1. Interestingly, geographical areas with very low COVID-19 mortality are those with the lowest prevalence of obesity (Sub-Saharan Africa and Asia). It is tempting to propose that Nrf2-interacting foods and nutrients can re-balance insulin resistance and have a significant effect on COVID-19 severity. It is therefore possible that the intake of these foods may restore an optimal natural balance for the Nrf2 pathway and may be of interest in the mitigation of COVID-19 severity

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure < 100 mmHg (n = 1127), estimated glomerular filtration rate < 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Youth soccer players, 11-14 years: Maturity, size, function, skill and goal orientation

Background: Participants in many youth sports are commonly combined into age groups spanning 2 years. Aim: The study compared variation in size, function, sport-specific skill and goal orientation associated with differences in biological maturity status of youth soccer players within two competitive age groups. Methods: The sample included 159 male soccer players in two competitive age groups, 11-12 years (n=87) and 13-14 years (n=72). Weight, height, sitting height and four skinfolds, four functional capacities, four soccer skills and goal orientation were measured. Skeletal maturity was assessed using the Fels method. Each player was classified as late, on time or early maturing based on the difference between skeletal and chronological ages. ANOVA was used to compare characteristics of players across maturity groups. Results: Late, on time and early maturing boys are represented among 11-12-year-olds, but late maturing boys are under-represented among 13-14-year-olds. Players in each age group advanced in maturity are taller and heavier than those on time and late in skeletal maturity, but players of contrasting maturity status do not differ, with few exceptions, in functional capacities, soccer-specific skills and goal orientation. Conclusion: Variation in body size associated with maturity status in youth soccer players is similar to that for adolescent males in general, but soccer players who vary in maturity status do not differ in functional capacities, soccer-specific skills and goal orientation

V diretriz da Sociedade Brasileira de Cardiologia sobre tratamento do infarto agudo do miocárdio com supradesnível do segmento ST

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V diretriz da Sociedade Brasileira de Cardiologia sobre tratamento do infarto agudo do miocárdio com supradesnível do segmento ST

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