Depuis les 10 dernières années, l’intelligence d’affaires et l’analytique (IA&A) sont devenues des sujets d’intérêts pour la recherche en systèmes d’information (SI) ainsi que pour les professionnels du domaine. Les initiatives en IA&A ont généré des bénéfices pour de nombreuses organisations dans plusieurs secteurs tels que la finance, les assurances, le divertissement ou encore les communications. Un domaine relativement nouveau où l’IA&A a fait son apparition est le sport compétitif. Les institutions, organisations sportives et athlètes d’élite tentent de mettre à profit l’utilisation des données et des technologies pour améliorer leurs performances, et ce, à différents niveaux. Alors que l’utilisation d’outils en IA&A dans les sports compétitifs a joui d’une médiatisation plus importante ces derniers temps, la recherche académique dans le domaine reste encore à un stade primaire. Dans cette étude, nous utiliserons deux situations dans le football universitaire pour mettre en évidence les composantes d’un cadre conceptuel de création de valeur dans les sports compétitifs. Nous présenterons une méthodologie afin d’optimiser le processus de recrutement collégial d’une organisation universitaire, ainsi qu’une autre méthodologie afin d’optimiser la prise de décision dans une situation de match précise.

Sports analytics arrived in the mainstream media through the novel and film Moneyball. However, its origins date back to operations researchers following World War II. Often considered a subdiscipline of statistics, sports analytics draws from statistics but also includes concepts from data science, communication, and marketing. As a passionate fan of sports, I have pursued statistics in my undergraduate and graduate education with the dream of working in sports for my career. However, educational opportunities in sports analytics are limited nationwide, and more specifically, there is no educational opportunity at my university, California Polytechnic State University in San Luis Obispo. This thesis investigates the sports analytics discipline, aiming to explain what sports analytics is, how it differs from statistics, how sports analytics is used in various organizations, what sports analysts do, and how sports analytics should be taught at the undergraduate level here at Cal Poly. To accomplish this, I have taken three online sports analytics courses, conducted interviews with professors of sports analytics and sports analysts of professional and college teams, done extensive online research and literature review, and gauged interest campus-wide in a potential sports analytics course. Ultimately, this thesis led me to conclude that sports analytics differs from statistics, and there should be a course in sports analytics at Cal Poly offered by the Statistics Department. Skills including SQL and Tableau, communication to various sports constituents, data collection and data management, machine learning methods such as classification trees and clustering, advanced statistical methods such as General Additive Models and spatial analysis, and visualization techniques are all prominent in sports analytics. Statistics students at Cal Poly do not gain a firm foundation in all of these ideas and could benefit from a course which teaches these skills. The significance of this work is that I have created a course proposal for a sports analytics course. If this course were to be adopted by the Statistics Department, students would learn essential skills to prepare them for a career in sports or any data related career. This work can advance sports analytics education and lead to the creation of other courses in the discipline down the line.

Sports and technology have always co-operated to bring better and more specific sports statistics. The collection of sports game data as well as the ability to generate valuable sports statistics of it is growing. This thesis investigates the development of a sports statistics application that should be able to collect sports game data, structure the data according to suitable data models and show statistics in a proper way. The application was set to be a web application that was developed using modern web technologies. This purpose led to a comparison of different software stack solutions and web frameworks. A theoretical study of sports analytics was also conducted, which gave a foundation for how sports data could be stored and how valuable sports statistics could be generated. The resulting design of the prototype for the sports statistics application was evaluated. Interviews with persons working in sports contexts evaluated the prototype to be both user-friendly, functional and fulfilling the purpose to generate valuable statistics during sport games.

Recent advancements in sports information and technology systems have ushered in a new age of applications of both supervised and unsupervised analytical techniques in the sports domain. These automated systems capture large volumes of data points about competitors during live competition. As a result, multi-relational analyses are gaining popularity in the field of Sports Analytics. We review two case studies of dimensionality reduction with Principal Component Analysis and latent factor analysis with Non-Negative Matrix Factorization applied in sports. Also, we provide a review of a framework for extending these techniques for higher order data structures. The primary scope of this thesis is to further extend the concept of tensor decomposition through the use of function spaces. In doing so, we address the limitations of PCA to vector and matrix representations and the CP-Decomposition to tensor representations. Lastly, we provide an application in the context of professional stock car racing.

Machine learning has recently made significant progress due to modern neural network architectures and training procedures. When neural networks learn a task, they create internal representations of the input data. The specific neural network architecture, training process, and task being addressed will influence the way in which the neural network interprets and explains the patterns in the data. The goal of representation learning is to train the neural network to create representations that effectively capture the overall structure of the data. However, the process by which these representations are generated is not fully understood because of the complexity of neural network data manipulations. This makes it difficult to choose the correct training procedure in real world applications. In this dissertation, we apply representation learning to improve the performance of neural networks in three different areas: NBA movement data, material property prediction, and generative protein modeling. First, we propose a novel deep learning approach for predicting human trajectories in sporting events using advanced object tracking data. Our method leverages recent advances in deep learning techniques, including the use of recurrent neural networks and long short-term memory cells, to accurately predict the future movements of players and the ball in a basketball game. We evaluate our approach using data from the NBA's advanced object tracking system and demonstrate improved performance compared to existing methods. Our results have the potential to inform real-time decision making in sports analytics and improve the understanding of player behavior and strategy. Next, we focused on group activity recognition (GAR) in basketball. In basketball, players engage in various activities, both collaborative and adversarial, in order to win the game. Identifying and analyzing these activities is important for sports analytics as it can inform better strategies and decisions by players and coaches. We introduce a novel deep learning approach for GAR in team sports called NETS. NETS utilizes a Transformer-based architecture combined with LSTM embedding and a team-wise pooling layer to recognize group activity. We test NETS using tracking data from 632 NBA games and found that it was able to learn group activities with high accuracy. Additionally, self- and weak-supervised training in NETS improved the accuracy of GAR. Then, study an application of neural networks on protein modeling. Recent work on autoregressive direct coupling analysis (arDCA) has shown promising potential to efficiently train a generative protein sequence model (GPSM) to adequately model protein sequence data. We propose an extension to this work by adding a higher order coupling estimator to build a model called autoregressive higher order coupling analysis (arHCA). We show that our model can correctly identify higher order couplings in a synthetic dataset and that our model improves the performance of arDCA when trained on real-world sequence data. Finally, we study material property prediction. Incorporation of physical principles in a machine learning (ML) architecture is a fundamental step toward the continued development of AI for inorganic materials. As inspired by the Pauling’s rule, we propose that structure motifs in inorganic crystals can serve as a central input to a machine learning framework. To demonstrate the use of structure motif information, a motif-centric learning framework is created by combining motif information with the atom-based graph neural networks to form an atom-motif dual graph network (AMDNet), which is more accurate in predicting the electronic structures of metal oxides such as bandgaps. The work illustrates the route toward fundamental design of graph neural network learning architecture for complex materials by incorporating beyond-atom physical principles. / Computer and Information Science

Health and fitness apps have become ubiquitous as smart devices become a major necessity in day-to-day life. However, an obvious issue with mobile health (mHealth) apps is that a substantial portion of them lack a scientific foundation and instead utilize  experiential  stratagems.  Hence,  the  acquired  data  becomes  unreliable.  In sports, where data collection is extensive, this becomes a vital factor for success due to  the  increasing  usage  of  mHealth.  Therefore,  the  Swedish  School  of  Sport  and Health Sciences has, in collaboration with other organizations, created the Perceived Load  and  Fitness  Scale  Questionnaire.  The  purpose  of  this  questionnaire  is  to function as a marker for overtraining, and thus injury prevention and intervention will become a simpler and more efficient task. A computer software was developed for the questionnaire; however, a mobile version was required, and thus requested. Consequently, the mHealth prototype app eValuate was developed. Research, in the form of literature studies, and dissection of other apps, for additional information, contributed  to  the  development  of  it.  The  prototype  was  developed  using  the programming language Java with Android Studio as the Integrated Development Environment  and  Cloud  Firebase  Firestore  as  a  database  solution.  The  finished prototype, eValuate, had to be trialled to ensure that it satisfies the criteria. Thus, the Mobile Application Rating Scale was employed as the most appropriate means of evaluation. A small-scale study was planned to trial the prototype by utilizing this scale.  However,  due  to  unforeseen  events,  only  four  respondents  could  provide feedback. The prototype performed admirably and scored 3.8 stars out of 5 stars. Nonetheless, the testing sample is too small to draw any real conclusions.

Yes / Amid much recent interest we discuss a Variance Gamma model for Rugby Union matches (applications to other sports are possible). Our model emerges as a special case of the recently introduced Gamma Difference distribution though there is a rich history of applied work using the Variance Gamma distribution – particularly in finance. Restricting to this special case adds analytical tractability and computational ease. Our three-dimensional model extends classical two-dimensional Poisson models for soccer. Analytical results are obtained for match outcomes, total score and the awarding of bonus points. Model calibration is demonstrated using historical results, bookmakers’ data and tournament simulations.

Sport clubs are always searching for innovative ways to improve performance and obtain a competitive edge. Sports analytics today is focused primarily on evaluating metrics thought to be directly tied to performance. Injuries indirectly decrease performance and cost substantially in terms of wasted salaries. Existing sports injury research mainly focuses on correlating one specific feature at a time to the risk of injury. This paper provides a multidimensional approach to non-contact injury prediction in Swedish professional ice hockey by applying machine learning on historical data. Several features are correlated simultaneously to injury probability. The project’s aim is to create an injury predicting algorithm which ranks the different features based on how they affect the risk of injury. The paper also discusses the business potential and strategy of a start-up aiming to provide a solution for predicting injury risk through statistical analysis. / Idrottsklubbar letar ständigt efter innovativa sätt att förbättra prestation och erhålla konkurrensfördelar. Idag fokuserar data- analys inom idrott främst på att utvärdera mätvärden som tros vara direkt korrelerade med prestation. Skador sänker indirekt prestationen och kostar markant i bortslösade spelarlöner. Tidigare studier på skador inom idrotten fokuserar huvudsakligen på att korrelera ett mätvärde till en skada i taget. Den här rapporten ger ett multidimensionellt angreppssätt till att förutse skador inom svensk elitishockey genom att applicera maskininlärning på historisk data. Flera attribut korreleras samtidigt för att få fram en skadesannolikhet. Målet med den här rapporten är att skapa en algoritm för att förutse skador och även ranka olika attribut baserat på hur de påverkar skaderisken. I rapporten diskuteras även affärsmöjligheterna för en sådan lösning och hur en potentiell start-up ska positionera sig på marknaden.

Golf is a sport where the correct technical execution is important for performance and injury prevention. The existing feedback systems are often cumbersome and not readily available to recreational players. To address this issue, this thesis explores the potential of using 2D Human Pose Estimation as a mobile phone-based swing analysis tool. The developed system allows to identify three events in the swing movement (toe-up, top and impact) and to measure specific angles during these events by using an algorithmic approach. The system focuses on quantifying the knee flexion and primary spine angle during the address, and lateral bending at the top of the swing. By using only the wrist coordinates in the vertical direction, the developed system identified 37% of investigated events, independently of whether the swing was filmed in the frontal of sagittal frame. Within five frames, 95% of the events were correctly identified. Using additional joint coordinates and the event data obtained by the above-mentioned event identification algorithm, the knee flexion at address was correctly assessed in 66% of the cases, with a mean absolute error of 3.7°. The mean absolute error of the primary spine angle measurement at address was of 10.5°. The lateral bending angle was correctly identified in 87% ofthe videos. This system highlights the potential of using 2D Human Pose Estimation for swing analysis. This thesis primarily focused on exploring the feasibility of the approach and further research is needed to expand the system and improve its accuracy. This work serves as a foundation, providing valuable insights for future advancements in the field of 2D Human Pose Estimation-based swing analysis. / Golf är en sport där korrekt tekniskt utförande är avgörande för prestation och skadeförebyggelse. Feedbacksystem som finns är ofta besvärliga och inte lättillgängliga för fritidsspelare. För att åtgärda detta problem undersöker detta examensarbete potentialen att använda 2D mänsklig poseuppskattning som mobiltelefonsbaserat svinganalysverktyg. Det utvecklade systemet gör det möjligt att identifiera tre händelser i svingen (toe-up, top och impact) och att mäta specifika vinklar under dessa händelser genom en algoritmisk metod. Systemet fokuserar på att kvantifiera knäböjningen och primära ryggradsvinkeln under uppställningen, och laterala böjningen vid svingtoppen. Genom att endast använda handledskoordinater i vertikalriktning identifierade det utvecklade systemet 37% av de undersökta händelserna oavsett om svingen filmades från frontal- eller medianplanet. Inom fem bildrutor identifierades 95% av händelserna korrekt. Genom att använda ytterligare ledkoordinater och händelsedata som erhållits genom den tidigare nämnda algoritmen för händelseidentifiering, bedömdes knäböjningen vid uppställningen vara korrekt i 66% av fallen med en medelabsolutfel på 3.7°. Medelabsolutfelet för mätningen av primär ryggradsvinkel vid uppställningen var 10.5°. Laterala böjningen identifierades korrekt i 87% av tillfällena. Detta system belyser potentialen i 2D mänsklig poseuppskattning för svinganalys. Detta examensarbete fokuserade främst på att utforska tillvägagångssättets genomförbarhet och ytterligare forskning behövs för att utveckla systemet och förbättra dess noggrannhet. Detta arbete är grundläggande och ger värdefulla insikter för framtida forskning inom området för svinganalys baserad på 2D mänsklig poseuppskattning.

OBJECTIVE The purpose of this study was to determine how kinematic, big data can be evaluated using computational, comprehensive analysis of movement parameters in a diverse population. METHODS Retrospective data was collected, cleaned, and reviewed for further analysis of biomechanical movement in an active population using 3D collinear resistance loads. The active sample of the population involved in the study ranged from age 7 to 82 years old and respectively identified as active in 13 different sports. Moreover, a series of exercises were conducted by each participant across multiple sessions. Exercises were measured and recorded based on 6 distinct biometric movement parameters: explosiveness, velocity, power, deceleration, braking, consistency, endurance, and range of motion. Analysis and data visualization portrayed how 3D collinear resistance load impacted specific muscles and performance metrics. RESULTS The model with the highest accuracy rate was Naive Bayes and Fast Large Margin at 58.3% for future predictions considering impact for specific muscles, movement parameters, and performance metric data. The data visualization involved a proof-of-concept human-computer interface and presented each component in relation to one another within the active population database, movement parameters, and performance metrics. DISCUSSION Understanding the findings regarding 3D collinear resistance sets a precedence for future development for the active population and research in the sports analytics field. Additionally, the visual proof of concept interface promotes future development for a diverse, active population.