[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127995-en":3,"doc-seo-127995-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127995,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Enhancing the Prediction of Shot Success in NBA Basketball Games Using Machine Learning Techniques - LSTM Neural Network","Data-driven decision-making is reshaping sports, making predictive accuracy central to team performance and financial outcomes. This work project studies machine learning and deep learning approaches to predict NBA shot success, implementing Random Forest, XGBoost, feedforward neural networks, and recurrent neural network variants. The recurrent neural network, including an LSTM setup, achieves the highest predictive performance and is highlighted as a previously underused approach for shot prediction. Findings support future use in sports strategy and business analytics.","A Work Project, presented as part ofthe requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economics.  \nENHANCING THE PREDICTION OF SHOT SUCCESS IN NBA BASKETBALL GAMES USING MACHINE LEARNING TECHNIQUES – LSTM NEURAL NETWORK  \nCELINA KOLLWITZ  \nWork project carried out under the supervision of:  \nYufei Shen  \n20/12/2023  \nAbstract  \nThe advent of data-driven decision-making has sparked a transformation in the sports industry, where the precision of predictive models now serves as a pivotal factor in both team success and financial viability. This thesis examines Machine Learning and Deep Learning models for predicting NBA shot success, with team members developing Random Forest, XGBoost, Feedforward and Recurrent Neural Network models. Notably, the Recurrent Neural Network, previously unapplied in this context, emerged with superior predictive accuracy. This study's primary contribution is unveiling the RNN's potential for shot prediction, paving the way for its future integration into sports strategic planning and business analytics.  \nKeywords: Predictive Modeling, Machine Learning, Deep Learning, NBA Basketball, shot success, Random Forest, XGBoost, Feedforward Neural Network, LSTM Neural Network Supported by Nova School of Business and Economics.  \nAcknowledgements  \nSpecial thanks to our advisor, Yufei Shen, whose dedication and insightful advice greatly enhanced this thesis. His enthusiasm and commitment made this journey an enjoyable experience. We also extend our gratitude to Paulo Marques, representing the Nova Data Science Knowledge Center, for providing crucial computational resources.  \nThis work used infrastructure and resources funded by Fundação para a Ciência e a Tecnologia (UID/ECO/00124/2013, UID/ECO/00124/2019 and Social Sciences DataLab, Project 22209), POR Lisboa (LISBOA-01-0145-FEDER-007722 and Social Sciences DataLab, Project 22209) and POR Norte (Social Sciences DataLab, Project 22209) .  \nTable of Contents  \n1. Introduction ................................................................................................................................4  \n1.1 Background.............................................................................................................................4  \n1.2 Problem Statement..................................................................................................................5  \n1.3 Research Contribution and Business Implications .................................................................6  \n1.4 Thesis Structure ...................................................................................................................... 8  \n2. Literature Review.......................................................................................................................9  \n2.1 Overview of Predictive Models ..............................................................................................9  \n2.2 The Emergence and Rise of Machine Learning in Sports Analytics.................................... 11  \n2.3 Predictive Modeling in Basketball ....................................................................................... 13  \n2.4 Shot Prediction in Basketball ............................................................................................... 15  \n2.5 The Strategic Importance of Shot Prediction ....................................................................... 16  \n2.6 Models Predicting Shot Success in NBA ............................................................................. 17  \n2.7 Research Gaps and Limitations ............................................................................................26  \n2.8 Discussion.............................................................................................................................28  \n3. Data and Context.........................................................................................","cbCairCkvVtw34Jw","https://ap.wps.com/l/cbCairCkvVtw34Jw","pdf",1504613,1,82,"English","en",105,"# 1. Introduction\n## 1.1 Background\n## 1.2 Problem Statement\n## 1.3 Research Contribution and Business Implications\n## 1.4 Thesis Structure\n# 2. Literature Review\n## 2.1 Overview of Predictive Models\n## 2.2 The Emergence and Rise of Machine Learning in Sports Analytics\n## 2.3 Predictive Modeling in Basketball\n## 2.4 Shot Prediction in Basketball\n## 2.5 The Strategic Importance of Shot Prediction\n## 2.6 Models Predicting Shot Success in NBA\n## 2.7 Research Gaps and Limitations\n## 2.8 Discussion\n# 3. Data and Context\n## 3.1 Introduction to the Data\n## 3.2 Data Descriptions and Statistics\n## 3.3 Exploratory Data Analysis (EDA)\n## 3.4 Data Cleaning\n## 3.5 Data Preprocessing\n## 3.6 Data Limitations\n## 3.7 Data Management and Reproducibility\n# 4. Methods\n## 4.1 Evaluation Metrics\n## 4.2 Neural Networks","[{\"question\":\"Which machine learning and deep learning models are used to predict NBA shot success?\",\"answer\":\"The project develops Random Forest, XGBoost, feedforward neural networks, and recurrent neural network models, including an LSTM neural network approach.\"},{\"question\":\"Why is the recurrent neural network emphasized in the study?\",\"answer\":\"The recurrent neural network previously not applied in this context shows the best predictive accuracy, making it a strong candidate for shot prediction.\"},{\"question\":\"What does the work include in terms of data preparation and analysis?\",\"answer\":\"The thesis covers data descriptions and statistics, exploratory data analysis (EDA), data cleaning, preprocessing, limitations, and data management to support reproducibility.\"}]","Enhancing the Prediction of Shot Success in NBA Basketball Games Using Machine Learning Techniques - LSTM Neural Network | 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