Predicting the Outcome of a Football Game: A Comparative Analysis of Single and Ensemble Analytics Methods

As analytical tools and techniques advance, increasingly large numbers of researchers apply these techniques on a variety of different sports. With nearly 4 billion followers, it is estimated that association football, or soccer, is the most popular sports for fans across the world by a large margin. The objective of this study is to develop a model to predict the outcomes of soccer (or association football) games (win-loss-draw), and determine factors that influence game outcomes. We used 10 years of comprehensive game-level data spanning the years 2007-2017 in the Turkish Super League, and tested a variety of classifiers to identify the most promising methods for outcome predictions

Smart Sports Predictions via Hybrid Simulation: NBA Case Study

Increased data availability has stimulated the interest in studying sports prediction problems via analytical approaches; in particular, with machine learning and simulation. We characterize several models that have been proposed in the literature, all of which suffer from the same drawback: they cannot incorporate rational decision-making and strategies from teams/players effectively. We tackle this issue by proposing hybrid simulation logic that incorporates teams as agents, generalizing the models/methodologies that have been proposed in the past. We perform a case study on the NBA with two goals: i) study the quality of predictions when using only one predictive variable, and ii) study how much historical data should be kept to maximize prediction accuracy. Results indicate that there is an optimal range of data quantity and that studying what data and variables to include is of extreme importance.Comment: Sent to the Winter Simulation Conference 202

Markov and Semi-markov Chains, Processes, Systems and Emerging Related Fields

This book covers a broad range of research results in the field of Markov and Semi-Markov chains, processes, systems and related emerging fields. The authors of the included research papers are well-known researchers in their field. The book presents the state-of-the-art and ideas for further research for theorists in the fields. Nonetheless, it also provides straightforwardly applicable results for diverse areas of practitioners

Talento esportivo no basquetebol brasileiro: o efeito da idade relativa, progressão na carreira e modelagem do potencial esportivo

There is still a wide path to be explored scientifically regarding the portrait, understanding and propositions of actions to the phenomena of sports talent in Brazilian basketball. Therefore, it is important to understand the behavior of talent emerging from the interaction between three factors: individual, task and environment. Considering that the phenomenon of sports talent cannot be observed directly, but rather, from aspects that may lead to its interpretation, the general aim of this study was to investigate the relative age effects (RAE) on Brazilian basketball and other factors associated with the career progression until the high performance and propose a modeling of the sports potential of youth basketball players. To meet this general aim, specific aims were established, in the realization of five studies: 1) to describe over time a portrait of the EIR in Brazilian basketball; 2) to better understand the career progression of youth Brazilian basketball players and the influence of the RAE; 3) to know the coaches' opinion regarding the importance attributed to the determinants factors of the development of youth basketball players, 4) to evaluate the classification of the multidimensional profile of the athletes and the classification of the sport potential made by the coaches; 5) to create a model for identifying talents in Brazilian basketball. Three sample groups were used to meet the nature of each study: 1) 10856 elite Brazilian basketball athletes who competed in national tournaments over 15 years in the U15, U17, U22 and New Brazilian Basketball (NBB) categories; 2) 94 basketball coaches with outstanding variability in terms of gender, age group, region, time of experience, competitive level, and performance; and 3) 178 youth athletes of regional / state level from 12 to 17 years old, who carried out a holistic assessment of their sporting potential over two years. The results found in study 1, say that RAE is present in athletes participating in Brazilian championships, with a predominance of those born in the first quartile. When observing this distribution over time, there is a predominance of those born in the first half of the year over the entire period observed, that is, 15 years of competition. Height is considered a determining factor in those born in the 1st semester in the U15 category, plus the tallest athletes in the NBB are born in the 2nd semester. In study 2, the results demonstrate that the search for universal learning by positions, migrating to the Southeast region, remaining in the development process over time, participating in the last category (U22) of access to the NBB, being tall and not being selected early, it is the characteristics that determine the athletes reach the NBB and that the RAE is present over time in Brazilian basketball but does not determine success in the career. In study 3, the results show a significant difference between the determining factors of the young athlete's development in the order of importance attributed, being the most important to the least important: physicalmotor and technical, anthropometric, tactical and psychological, finally the environmental. The positioning and decision-making indicator achieved 82% of extreme importance. The anthropometric factor was associated with the centers and the point guards importance was attributed to all factors. International coaches value the anthropometric factor, compared to national and regional coaches. In study 4, it was observed that in the high-potential group, the athletes were chronologically older, with a higher % of expected adult height, greater competitive and determined sports orientation, greater body size, smaller sum of skinfolds and greater physical performance. In comparison with the other athletes, the high potential basketball players had greater height, greater wingspan, greater length of the lower limbs, greater predicted adult height and a higher Z score of the % PAH. In Study 5, the Gold Score Basketball was created, a hybrid and weighted index for estimating sport potential, composed of 26 objective indicators and two subjective indicators. The model classified 5.1% of youth athletes as an excellent sports potential (Gold Score> 90). The internal consistency of the model was moderate (r = 0.59) and the stability of the diagnosis was high (r = 0.82) after one year. Construct validity and criterion validity were satisfactory. The athletes with the highest competitive level (62.9 ± 14.4 vs. 50.7 ± 15.6, p90). A consistência interna do modelo foi moderada (r = 0,59) e a estabilidade do diagnóstico foi elevada (r = 0,82), após um ano. A validade de construto e a validade de critério foram satisfatórias. Os atletas com maior nível competitivo (62,9 ± 14,4 vs. 50,7±15,6, p<0,001) e que venceram campeonatos estaduais/nacionais (64,3 ± 15,4 vs. 52,1 ± 15,6, p<0,001) apresentaram maior Gold Score. Os estudos sobre o talento esportivo no basquetebol brasileiro ainda apresentam muitas questões a serem investigadas. Observar o desempenho em competições associadas aos aspectos do EIR (especialmente em relação aos agentes sociais envolvidos e à tarefa), progressão na carreira, avaliações multidimensionais e observações dos treinadores, é um caminho importante a ser seguido. O basquetebol brasileiro deveria investir mais em programas de identificação e seleção de talentos, para otimizar as possibilidades de desenvolvimento de jogadores; melhorar a capacitação de treinadores; reorganizar os processos competitivos nas categorias de base e descentralizar as opções de progressão na carreira – propiciando assim, um impacto ainda maior na/da modalidade

Large Scale Analysis of Offensive Performance in Football - Using Synchronized Positional and Event Data to Quantify Offensive Actions, Tactics, and Strategies

Offensive performances in football have always been of great focus for fans and clubs alike as evidenced by the fact that nearly all Ballon d’Or winners have been forwards or midfielders. With the increase in availability of granular data, evaluating these performances on a deeper level than just goals scored or gut instinct has become possible. The domain of sports analytics has recently emerged, exploring how applying data science techniques or other statistical methods to sports data can improve decision making within sporting organizations. This thesis follows the footsteps of other sports like baseball or basketball where, at first, offensive performances were analyzed. It consists of four studies exploring various levels of offensive performance, ranging from basic actions to team-level strategy. For that, it uses a dataset part of larger research program that also explores the automatic detection of tactical patterns. This dataset mainly consists of positional and event data from eight seasons of the German Bundesliga and German Bundesliga 2 between the seasons 2013/2014 and 2020/2021. In total this amounts to 4, 896 matches, with highly accurate player and ball positions for every moment of the match and detailed logs of every action that occurred, thus making it one of the largest football datasets to be analyzed at this level of granularity. In a first step, this thesis shows how the two different data sources can be synchronized. With this synchronized data it is possible to better quantify individual basic actions like shots or passes. For both actions new metrics (Expected Goals and Expected Passes) were developed, that use the contextual information to quantify the chance quality and passing difficulty. Using this improved quantification of individual actions, the subsequent studies evaluate offensive performance on a tactical pattern level (how goals are scored) and on a strategy level (what team formations are particular effective offensively). Besides their usage on the performance side, these metrics have also been adapted from broadcasters to enhance their data story telling: Expected goals and expected passes are shown during every Bundesliga match to a worldwide audience, thus bringing the field of sports analytics to millions of fans

Automated Detection of Complex Tactical Patterns in Football—Using Machine Learning Techniques to Identify Tactical Behavior

Football tactics is a topic of public interest, where decisions are predominantly made based on gut instincts from domain-experts. Sport science literature often highlights the need for evidence-based research on football tactics, however the limited capabilities in modeling the dynamics of football has prevented researchers from gaining usable insights. Recent technological advances have made high quality football data more available and affordable. Particularly, positional data providing player and ball coordinates at every instance of a match can be combined with event data containing spatio-temporal information on any event taking place on the pitch (e.g. passes, shots, fouls). On the other hand, the application of machine learning methods to domain-specific problems yields a paradigm shift in many industries including sports. The need for more informed decisions as well as automating time consuming processes—accelerated by the availability of data—has motivated many scientific investigations in football analytics. This thesis is part of a research program combining methodologies from sports and data science to address the following problems: the synchronization of positional and event data, objectively quantifying offensive actions, as well as the detection of tactical patterns. Although various basic insights from the overall research program are integrated, this thesis focuses primarily on the latter one. Specifically, positional and event data are used to apply machine learning techniques to identify eight established tactical patterns in football: namely high-/mid-/low-block defending, build-up/attacking play in the offense, counterpressing and counterattacks during transitions, and patterns when defending corner-kicks, e.g. player-/zonal- or post-marking. For each pattern, we consolidate definitions with football experts and label large amounts of data manually using video recordings. The inter-labeler reliability is used to ensure that each pattern is well-defined. Unsupervised techniques are used for the purpose of exploration, and supervised machine learning methods based on expert-labeled data for the final detection. As an outlook, semi-supervised methods were used to reduce the labeling effort. This thesis proves that the detection of tactical patterns can optimize everyday processes in professional clubs, and leverage the domain of tactical analysis in sport science by gaining unseen insights. Additionally, we add value to the machine learning domain by evaluating recent methods in supervised and semi supervised machine learning on challenging, real-world problems