[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117391-en":3,"doc-seo-117391-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117391,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","Sports Betting Optimization - with Machine Learning Algorithms","A machine-learning driven framework automates sports data retrieval, maintains a SQLite database, and compares model-predicted outcomes with daily moneyline betting odds to optimize wager decisions. The approach experiments with multiple feature sets and algorithm types, then couples predictive performance with a strategy designed to generate positive profits over a multi-day evaluation window. Initial scope targets NBA games and moneyline markets, with modular expansion to other bet formats. Experiments include user simulations, runtime efficiency goals, and model-accuracy benchmarking.","Sports Betting Optimization  \nwith Machine Learning Algorithms  \n■  \n■  \n■  \n■  \n■  \n■  \nOBJECTIVES  \nCreate an automated framework that retrieves data, builds/updates a database, and compares the predicted outcome of machine learning algorithms against daily betting odds to optimized moneyline wagers for sports games.  \nExperiment with different feature sets / types of machine learning algorithms to determine the best outcome predictions.  \nWe would like to couple this performance with a betting strategy that returns positive profits over a Ȃǿ day period.  \nModularity: Initial analysis will be limited to the NBA and moneyline wagers, but we would like to design a system that can be expanded to other types of sports or bets (spreads, over/under, etc.)  \nEfficiency: We want to design a system that minimizes the time it takes to predict / optimize betting tickets.  \nPerformance: Machine learning models have shown promising results when predicting the outcome of sports. We would like to strive for similar results, but achieve no worse than ȅȄ%  \nSYSTEM ARCHITECTURE  \nDATA COLLECTION 1   \nData  \n■ RAPID API → NBA Statistics (ȇ different endpoints)  \n■ Yahoo Sports → Moneyline Odds (web scrape with Python Package Selenium)  \nDatabase Schema  \n■ Shown are the Ȅ primary tables used for feature set building and subsequently model training/testing.  \nTECHNOLOGIES USED  \n■ The architecture will followed a client-server tier ȁ pattern, where the presentation layer/tier and application layer/tier are a standalone system (much like a client-server tier Ȁ pattern) whose only interaction beyond the client's machine is for data collection / accessing existing data servers online.  \n■ The underlying SQLite database  \n([https://www.sqlite.org/index.html](https://www.sqlite.org/index.html) ) can easily be viewed, explored, or queried with the free DB Browser application ([https://sqlitebrowser.org/](https://sqlitebrowser.org/) ) . SQLite also offers a helpful API and Python Package (sqliteȂ) that makes it easy to connect to and manage a database.  \n3  \nCLI  \n5  \nRESULTS  \nModel Accuracy (Top ȀȄ out of ȅ9) User Simulations Conclusions & Future Work  \nAvailable Parameters  \nRun w/ Random Parameters  \nTo test our system as well as evaluate the best feature-set/model/optimization strategy combinations we ran Ȃ different user simulations on the system.  \nEach simulation:  \n■ Lasted from Ȃ/Ȁȅ/ȁȂ to ȃ/ȀȈ/ȁȂ (consisting of ȁȀȀ games)  \n■ Began with a starting balance of $ȁȄ  \n■ Followed a set ofpredefined rules dictating how to manage its profits/remaining balance until depleted  \nNote, between the number of feature-sets, models, optimizer strategies, and user simulations- ȇȁȇ combinations were tested.  \nWith the right balance of systematic risk (user), unsystematic risk (optimizer), and model performance-a user can be profitable.  \nModel Accuracy doesn’t necessarily translate to higher profits, but it does help. Models with a better accuracy generally performed better in the user simulations, but models that better predicted upsets (possibly at the cost of accuracy) performed the best (the payoffs of selected upsets outweighs the payoffs of selecting multiple favorites) .  \nEnd ofthe day it is still gambling and depending when the simulation started the yielded results may have been different → Some models perform better/worse than others in different game spaces / periods of time-this observation would be interesting to explore further, especially when considering using multiple models in conjunction to make predictions on the “type of games” where they individually show the most success.  \nDatabase Update  \nOutput  \nKent Sullivan  \n|  \nDaffney Myers  \n|  \nAlicia Harman  \n|  \nDhiraj Srivastava","cbCaih6P2GYvJhj8","https://ap.wps.com/l/cbCaih6P2GYvJhj8","pdf",1464356,1,"English","en",105,"# Objectives\n## System Architecture\n## Data Collection\n## Technologies Used\n## Results","[{\"question\":\"What is the main goal of the proposed framework?\",\"answer\":\"Build an automated pipeline that retrieves sports data, updates a database, predicts outcomes using machine learning, and optimizes moneyline wagers by comparing predictions against daily betting odds.\"},{\"question\":\"Which sports and bet type are considered in the initial design?\",\"answer\":\"The initial analysis focuses on NBA games and moneyline wagers, with a modular design intended to expand to other bet types like spreads and over/under.\"},{\"question\":\"How are the models and strategies evaluated?\",\"answer\":\"They are tested through multiple user simulations over a defined date range and starting balance, applying predefined profit and bankroll-management rules while measuring model accuracy and simulation outcomes.\"}]","Sports Betting Optimization - with Machine Learning Algorithms | PDF",1785675599,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"sports-betting-optimization-with-machine-learning-algorithms","",{"@graph":35,"@context":84},[36,52,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/technology/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/sports-betting-optimization-with-machine-learning-algorithms/117391/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-09-04","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of the proposed framework?","Question",{"text":74,"@type":75},"Build an automated pipeline that retrieves sports data, updates a database, predicts outcomes using machine learning, and optimizes moneyline wagers by comparing predictions against daily betting odds.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which sports and bet type are considered in the initial design?",{"text":79,"@type":75},"The initial analysis focuses on NBA games and moneyline wagers, with a modular design intended to expand to other bet types like spreads and over/under.",{"name":81,"@type":72,"acceptedAnswer":82},"How are the models and strategies evaluated?",{"text":83,"@type":75},"They are tested through multiple user simulations over a defined date range and starting balance, applying predefined profit and bankroll-management rules while measuring model accuracy and simulation outcomes.","https://schema.org",{"og:url":50,"og:type":86,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":88,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,112,117,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":110,"slug":111},50,"technology",{"id":113,"doc_module":4,"doc_module_name":45,"category_name":114,"show_sort_weight":115,"slug":116},7,"Healthcare",40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":45,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]