[pt] Em meio ao avanço expressivo da tecnologia e às evoluções contínuas observadas no ramo de inteligência artificial, esta última se mostrou ter potencial para ser aplicada a diferentes setores da sociedade. No contexto de extrema competitividade e relevância crescente nos esportes mais famosos ao redor do mundo, o basquete se apresenta como um esporte interessante para a aplicação de mecanismos de apoio à decisão capazes de aumentar a eficácia e consistência de vitórias dos times nos campeonatos. Diante desse contexto, este estudo propõe o desenvolvimento de sistemas de apoio à decisão baseados em modelos de redes neurais e k-Nearest Neighbors (kNNs). O objetivo é avaliar, para cada substituição durante um jogo de basquete, qual grupo de jogadores em quadra, conhecido por quinteto, apresenta mais chances de ter uma maior vantagem sobre o adversário. Para tal, foram treinados modelos para classificar, ao final de uma sequência de posses de bola, a equipe que conseguiria vantagem, e prever a magnitude dessa vantagem. A base de dados foi obtida de partidas do Novo Basquete Brasil (NBB), envolvendo estatísticas de jogadores, detalhes de jogo e contextos diversos. O modelo apresentou uma acurácia de 76,99 por cento das posses de bola nas projeções de vantagem entre duas equipes em quadra, demonstrando o potencial da utilização de métodos de inteligência computacional na tomada de decisões em esportes profissionais. Por fim, o trabalho ressalta a importância do uso de tais ferramentas em complemento à experiência humana, instigando pesquisas futuras para o desenvolvimento de modelos ainda mais sofisticados e eficazes na tomada de decisões no âmbito esportivo. / [en] In light of the recent significant growth in technological capabilities andthe observed advancements in the field of computational intelligence, the latterhas demonstrated potential for application in various sectors of society. Inthe context of extreme competitiveness and increasing relevance in the mostfamous sports around the world, basketball presents itself as an interestingsport for the application of decision-support mechanisms capable of enhancingthe efficacy and consistency of team victories in championships. In this context,this study proposes the development of decision-support systems, such asneural networks and k-Nearest Neighbors (kNNs). The goal is to evaluate, foreach substitution during a match, which group of players in the field, knownas lineup, presents the most probability to be superior to their opponent. Forthis, models were trained to predict, during a sequence of possessions, theteam that would have advantage and the magnitude of this advantage. Thedatabase was obtained from Novo Basquete Brasil (NBB) matches, involvingplayers statistics, match details and different contexts.. The model achieved anaccuracy of 76,99 percent in projections of superiority between the playing lineups,demonstrating the potential of using computational intelligence methods indecision-making applied to professional sports. Finally, the study highlightsthe importance of using such tools in conjunction with human experience,encouraging future research for the development of even more sophisticatedand effective models for decision-making in the sports field.

Sports analytics is an area that is growing at a rapid rate. It can be described as the use of data and analytics to gain an advantage in sports. It can include scouting, recovery, tactics and so on. This thesis uses data about association football passes to analyse players passing attributes. The dataset that has been used is acquired from the company Football Analytics Sweden AB and is produced by a subcontractor. The dataset includes event data for all games played in the Swedish division 1 during the 2020 season. In total there are 48 different types of events, but only the category passes accurate are analysed. Accurate passes are represented by x and y coordinates for the start and end point of the pass. The thesis's intention is the study how passes can be categorized in order to differentiate different players passing attributes, and how to identify types of players in regard to passing from that categorization. The categorization has been made possible by dividing the football pitch into different zones. The zones have been chosen on sports science and football grounds. Every pass is assigned a category based on which zone the pass starts in and what zone the pass ends up in. The proportion of passes a player hits for every category is then calculated and is used to perform factor analysis. The factor analysis identifies 13 underlying factors that can describe players different passing attributes. These factors have been interpreted and given names. The factors describe both general attributes as well as more specific attributes. Factor scores are then used to compare and identify which or what type of passing player a particular player is. / Sports analytics är ett begrepp som används mer och mer. I allmänhet kan det beskrivas som användningen av data och dataanalys för att få en fördel inom sport. Det kan vara allt från scouting (rekrytering av spelare), återhämtning och taktik. Den här studien använder information om fotbollspassningar för att undersöka fotbollsspelares passningsegenskaper. Datamaterialet som använts är erhållet från företaget Football Analytics Sweden AB och är framtaget av en underleverantör. Datamaterialet innehåller händelsedata för alla matcher som spelades i division 1 under säsongen 2020. Totalt finns det 48 olika typer av händelser, men endast händelsen lyckade passningar tas i beaktande. Lyckade passningar representeras av x och y koordinater för start- och slutpunkten för passningen. Studiens syfte är att undersöka hur passningar kan kategoriseras för att göra det möjligt att differentiera olika spelares passningsförmågor och hur man kan identifiera typ av passningsspelare utifrån kategoriseringen. Kategorisering av passningar utfördes med hjälp av en zonuppdelning av fotbollsplanen. Zonuppdelning är grundad på sportvetenskapliga och fotbollsmässiga grunder.  Varje passning tilldelas en kategori beroende på vilken zon passningen startar och slutar i. Andelen av passningar en spelare slår inom varje kategori beräknas, för att sedan användas i en faktoranalys. Faktoranalysen identifierar bakomliggande faktorer som påverkar en spelares andelar av passningar inom varje kategori. Totalt identifierades 13 bakomliggande faktorer och dessa kan beskriva en spelares passningsegenskaper. Dessa faktorer har verklighetstolkas och namngetts. Faktorerna beskriver både allmänna egenskaper och mer specifika egenskaper.  Faktorpoäng som beräknats från dem bakomliggande faktorerna används sedan för att jämföra och identifiera vilka eller vilken typ av passningsspelare en spelare är.

Football analytics is a rapid growing area which utilizes conventional data analysis and computational methods on gathered data from football matches. The results emerging out of this can give insights of performance levels when it comes to individual football players, different teams and clubs. A difficulty football analytics struggles with daily is to translate the analysis results into actual football qualities and knowledge which the wider public can understand. In this master thesis we therefore take on the ball event data collected from football matches and develop a model which classifies individual football player’s playing styles, where the playing styles are well known among football followers. This is carried out by first detecting the playing positions: ’Strikers’, ’Central midfielders’, ’Outer wingers’, ’Full backs’, ’Centre backs’ and ’Goalkeepers’ using K-Means clustering, with an accuracy of 0.89 (for Premier league 2021/2022) and 0.84 (for Allsvenskan 2021). Secondly, we create a simplified binary model which only classifies the player’s playing style as "Offensive"/"Defensive". From the bad results of this model we show that there exist more than just these two playing styles. Finally, we use an unsupervised modelling approach where Principal component analysis (PCA) is applied in an iterative manner. For the playing position ’Striker’ we find the playing styles: ’The Target’, ’The Artist’, ’The Poacher’ and ’The Worker’ which, when comparing with a created validation data set, give a total accuracy of 0.79 (best of all positions and the only one covered in detail in the report due to delimitations).  The playing styles can, for each player, be presented visually where it is seen how well a particular player fits into the different playing styles. Ultimately, the results in the master thesis indicates that it is easier to find playing styles which have clear and obvious on-the-ball-actions that distinguish them from other players within their respective position. Such playing styles, easier to find, are for example "The Poacher" and "The Target", while harder to find playing styles are for example " The Box-to-box" and "The Inverted". Finally, conclusions are that the results will come to good use and the goals of the thesis are met, although there still exist a lot of improvements and future work which can be made.  Developed models can be found in a simplified form on the GitHub repository: https://github.com/Sommarro-Devs/avatar-playing-style. The report can be read stand-alone, but parts of it are highly connected to the models and code in the GitHub repository.

Not all goals in the game of ice hockey are created equal: some goals increase the chances of winning more than others. This thesis investigates the result of constructing and using a reward function that takes this fact into consideration, instead of the common binary reward function. The two reward functions are used in a Markov Game model with value iteration. The data used to evaluate the hockey players is play-by-play data from the 2013-2014 season of the National Hockey League (NHL). Furthermore, overtime events, goalkeepers, and playoff games are excluded from the dataset. This study finds that the constructed reward, in general, is less correlated than the binary reward to the metrics: points, time on ice and, star points. However, an increased correlation was found between the evaluated impact and time on ice for center players. Much of the discussion is devoted to the difficulty of validating the results from a player evaluation due to the lack of ground truth. One conclusion from this discussion is that future efforts must be made to establish consensus regarding how the success of a hockey player should be defined.

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