[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119569-en":3,"doc-seo-119569-105":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},119569,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",6,"Technology","Machine Learning Models for Nba Game Prediction","Sports analytics increasingly relies on machine learning to forecast match outcomes in high-variance leagues. This study focuses on NBA games using key team indicators and individual player performance metrics. It evaluates multiple approaches, including Random Forest, Decision Trees, K-Nearest Neighbors, and Linear Regression, and compares them through performance metrics covering prediction power, robustness, and computational efficiency. Ensemble methods deliver higher accuracy and generalizability, while linear models suit structured datasets with faster training. Future work may incorporate deep learning and richer external data such as biometrics and psychological profiles.","Machine Learning Models for Nba Game Prediction  \nWeichen Peng  \nComputer Science and Technology, Changsha University of Science & Technology, Changsha, China  \nAbstract. In the realm of sports analytics, the application of machine learning in predicting match outcomes has attracted considerable academic and practical interest. This study specifically examines NBA match data, encompassing key game indicators, team statistics, and individual player performance metrics. This work intends to investigate how many machine learning approaches—such as Random Forest, Decision Trees, K-Nearest Neighbors (KNN), and Linear Regression—contribute to determine game results by means of their respective strengths and algorithmic characteristics. Several performance evaluations will help to assess the prediction power of these models, and numerous measurements are used to allow a comprehensive comparison of their dependability, robustness, and computational efficiency. The results show that since ensemble learning approaches can capture complicated interactions among variables, they show higher prediction accuracy and generalizability. Linear models, meantime, do well with organized statistical datasets and require less training time. For sports bettors, team strategists, and sports analysts, these revelations are priceless. By using deep learning models, sophisticated statistical approaches, and more extensive outside data sources—such as player biometrics, psychological profiles, and real-time game conditions—future research might further hone predicted accuracy and practical applicability.  \n1 Introduction  \nSports analytics has been somewhat well-known in recent years because to developments in machine learning methods and the availability of massive data. Predicting game results in professional sports—especially the National Basketball Association (NBA)—is a difficult chore given their complicated and often changing character. Still, accurate projections maybe rather important for improving strategic decision-making, game strategy optimization, and audience involvement enhancement. Teams, coaches, sports broadcasters, and the betting business among other stakeholders gain from these realizations.  \nUsing individual data, team performance measures, and game-specific variables, several research have investigated many machine learning techniques to forecast sports results. For example, used artificial neural networks and decision trees to forecast NBA game results,  \n[weichenpeng@stu.csust.edu.cn](weichenpeng@stu.csust.edu.cn)  \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/)).  \nso proving that defensive rebounds, three-point percentage, and free throws produced much impact match results [1] . Another research using many machine learning approaches examined past game data in order to find important performance variables influencing game outcomes [2] . Furthermore, stressing the efficiency of modern algorithms in collecting complex patterns in basketball data [3], comparing logistic regression, support vector machines, deep neural networks, and random forests. Recent research utilizing combined XGBoost and SHAP models shows that field goal %, defensive rebounds, and turnovers are regularly correlated to game outcomes [4] .  \nBy means of player data, team records, and game indications, this paper fills in holes in NBA match prediction by pointing out important elements. To identify the best successful method it contrasts machine learning models including linear regression, K-Nearest Neighbors, decision trees, random forests, XGBoost, LightGBM, and Support Vector Regression. Key performance measures are examined using feature selection and statistical evaluation—including the R² coefficient—using NBA STATS data. This research advances predictive analyt","cbCaikTpT4Nvcp8o","https://ap.wps.com/l/cbCaikTpT4Nvcp8o","pdf",370268,1,8,"English","en",105,"# Introduction\n## Prior research and motivation\n# Methodology\n## Data collection\n## Dataset specifics\n## Prediction models\n## Linear Regression (LR)\n## K-Nearest Neighbors (KNN)","[{\"question\":\"Which NBA data sources and indicators are used for the prediction task?\",\"answer\":\"The study uses data collected from the NBA STATS official website, built by web scraping and organized into CSV. It includes player and team statistics such as points, assists, rebounds, shooting efficiency, and player efficiency metrics.\"},{\"question\":\"What machine learning models are compared in the study?\",\"answer\":\"The paper contrasts linear regression, K-Nearest Neighbors, decision trees, random forests, XGBoost, LightGBM, and Support Vector Regression, along with discussions of their algorithmic characteristics and expected strengths.\"},{\"question\":\"How are model performances evaluated and compared?\",\"answer\":\"Evaluation relies on multiple performance measures, including statistical assessment using coefficients such as R², together with feature selection to compare prediction power, robustness, and computational efficiency.\"}]","Machine Learning Models for Nba Game Prediction | PDF",1785725019,20,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-models-for-nba-game-prediction","",{"@graph":36,"@context":86},[37,54,69],{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-models-for-nba-game-prediction/119569/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which NBA data sources and indicators are used for the prediction task?","Question",{"text":76,"@type":77},"The study uses data collected from the NBA STATS official website, built by web scraping and organized into CSV. It includes player and team statistics such as points, assists, rebounds, shooting efficiency, and player efficiency metrics.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What machine learning models are compared in the study?",{"text":81,"@type":77},"The paper contrasts linear regression, K-Nearest Neighbors, decision trees, random forests, XGBoost, LightGBM, and Support Vector Regression, along with discussions of their algorithmic characteristics and expected strengths.",{"name":83,"@type":74,"acceptedAnswer":84},"How are model performances evaluated and compared?",{"text":85,"@type":77},"Evaluation relies on multiple performance measures, including statistical assessment using coefficients such as R², together with feature selection to compare prediction power, robustness, and computational efficiency.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,114,119,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},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":21,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":29,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":29,"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":107,"slug":137},19,"General","general"]