[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118403-en":3,"doc-seo-118403-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},118403,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Multiple Machine Learning Algorithms-based NBA Team Playoffs Prediction - read online free","Rapid advances in sports analytics require machine learning models that can convert large, team-level statistics into reliable predictions. This study forecasts NBA playoff qualification by training multiple algorithms on team performance data spanning 1947–2024. Models include Logistic Regression, K-Nearest Neighbors, Random Forest, and Elastic Net Regression, with preprocessing via scaling, centering, and missing-value handling. Evaluation uses rigorous 5-fold cross-validation, and Random Forest achieves the best ROC-AUC of 0.841, capturing complex feature interactions for improved predictive accuracy and future sports analytics.","Multiple Machine Learning Algorithms-based NBA Team Playoffs Prediction  \nManho Yeung  \nStatistics and Data Science, University of California Santa Barbara, Santa Barbara, USA  \nAbstract. With the rapid development of data analytics in sports, it is vital to use machine learning methods to make decisions and predictions. This study focuses on predicting NBA playoff qualifications using machine learning techniques. By utilizing team-level statistics from 1947 to 2024, the paper implemented models such as Logistic Regression, K-Nearest Neighbors, Random Forest, and Elastic Net Regression. The data waspreprocessed by scaling, centering, and handling missing values, followed by rigorous 5-fold cross-validation to ensure robust evaluation. Among the models, Random Forest outperformed the others, achieving the highest ROC-AUC score of 0.841. Its ensemble approach allowed for the effective capture of complex feature interactions, making it the most accurate model for predicting whether a team would qualify for the playoffs based on team performance. The research demonstrates the power of machine learning in improving prediction accuracy, providing insights for future sports analytics, and offering a foundation for integrating more complex data like player metrics or strategic factors. This work contributes to advancing predictive modeling in sports.  \n1 Introduction  \nIn the past few months, the exciting NBA (National Basketbal Association) playoffs have already finished, which has attracted plenty of attention from basketball fans all around the world. Among the whole NBA season, playoffs are one of the most exciting and arousing phases. The importance of entering the playoffs is to win the title, which is often considered the greatest honor for the games of the NBA. The reason why machine learning method is used to predict the NBA playoffs qualification is that it can transform complex, large datasets into manageable models, which can be used to predict the outcome of NBA playoffs. Also with the development of machine learning and data mining, it becomes gradually appropriate to apply ML methods and its implications to analyze the sport data [1] . Furthermore, by following the trend of machine learning applications, more and more players or companies attempt to enhance their performance or profits on or off-court [2] .  \nThere are plenty of researches that apply machine learning to predict results in the field of sports. However, the accuracy of some previous research does not achieve an excellent level. Horvat uses the algorithms of logistic regression, naive bayes, decision tree, multilayer perceptron neural networks, K-nearest neighbours and logit boost to predict basketball  \n[Corresponding author:](Corresponding author: myeung@ucsb.edu)[ myeung@ucsb.edu](Corresponding author: myeung@ucsb.edu)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \ngame outcomes. Among these algorithms, KNN has the best performance, which has the highest accuracy of 59%, while the decision tree has the worst performance, which has the lowest accuracy of 53.5%. Madhavan applies Hidden Markov models (HMMs) to predict the match results, then the model is able to reach an accuracy of 73%[3,4] .  \nThe purpose of this research is to develop a predictive model that can forecast whether an NBA team will enter the playoffs based on a dataset called “Team Stats Per Game.cvs”, and implement multiple machine learning techniques to yield the most accurate model for this binary classification problem.  \nThese variables will be used to predict the binary response variable, indicating playoff qualification (TRUE/FALSE). The paper will partition our data into training and test sets and create a recipe encapsulating all preprocessing steps. The dataset will be prepared for","cbCaiiILnOPS8mWj","https://ap.wps.com/l/cbCaiiILnOPS8mWj","pdf",263460,1,6,"English","en",105,"# 1 Introduction\n# 2 Methods\n## 2.1 Data Collection and Preparation","[{\"question\":\"What is the main objective of this study?\",\"answer\":\"To predict whether an NBA team qualifies for the playoffs using team statistics and multiple machine learning techniques for a binary classification task.\"},{\"question\":\"Which machine learning models are compared in the research?\",\"answer\":\"Logistic Regression, K-Nearest Neighbors, Random Forest, Elastic Net Regression, and additional models such as LDA and Boosting Trees are used and compared.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"The study applies preprocessing steps and uses 5-fold cross-validation to obtain robust evaluation results, reporting metrics including ROC-AUC.\"}]","Multiple Machine Learning Algorithms-based NBA Team Playoffs Prediction - read online free | PDF",1785683454,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"multiple-machine-learning-algorithms-based-nba-team-playoffs-prediction-read-online-free","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/multiple-machine-learning-algorithms-based-nba-team-playoffs-prediction-read-online-free/118403/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of this study?","Question",{"text":75,"@type":76},"To predict whether an NBA team qualifies for the playoffs using team statistics and multiple machine learning techniques for a binary classification task.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the research?",{"text":80,"@type":76},"Logistic Regression, K-Nearest Neighbors, Random Forest, Elastic Net Regression, and additional models such as LDA and Boosting Trees are used and compared.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated?",{"text":84,"@type":76},"The study applies preprocessing steps and uses 5-fold cross-validation to obtain robust evaluation results, reporting metrics including ROC-AUC.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]