[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117611-en":3,"doc-seo-117611-105":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},117611,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",6,"Technology","Machine Learning for Sports Betting - Sports betting odds improvement","Machine learning applied to sports betting to improve the odds of successful wagers as legal acceptance expands. Focuses on the over/under bet, where the combined teams’ final score determines outcomes (Over, Under, or Push) with profitability dependent on achieving sufficient predictive accuracy. Examines key challenges: forecasting future games, collecting reliable data, and unifying inputs from multiple sources. Compares a neural network with a null baseline and presents results showing a modest accuracy and profitability edge plus a confidence-based option to skip low-probability bets. Outlines next steps using more comprehensive data such as starters and injuries.","Machine Learning for Sports Betting  \nHank Rugg, Data Science  \nAdvisor: Ted Wendt  \nIntroduction  \nWith sports betting becoming more widely legal, the use of machine learning algorithms for improving an individual’s odds of placing successful sport bets have increased.  \nThis is directly applicable to the over/under bet – over if you believe the combined total score will be above the number set by the sports book and under if you believe the combined score will be less than the number set by the sports book.  \nOver/Under Bet  \n●Combined score of both teams at the end of the game  \n●Three outcomes: Over, Under, or Push  \n●Bet $110 to make $100  \n○ Must be more than 53% accurate to be profitable  \nChallenges  \n●Predicting outcome of a future game ●Collecting relevant and accurate data ●Ingesting data from multiple sources  \n\n| Neural Network Model\u003Cbr>Null Model\u003Cbr>● Mode of training data target\u003Cbr>○ Predict everything is “Over”\u003Cbr>○ 50.66% accurate Neural Network Model\u003Cbr>● Sequential Neural Network\u003Cbr>○ 53.53% accurate |  | \u003Cbr>Results\u003Cbr>The machine learning model beats the null model by 2.87% although it is only slightly profitable. If we want to be more profitable, we can look at the maximum probability of each prediction as a representation of how confident we are in each prediciton. Taking into account the possibility of not betting will increase our profitability as shown below. |\n| --- | --- | --- |\n|  |  |  |\n|  |  | \u003Cbr>The graph above shows the confidence in each prediction vs the percent accuracy. This can be used to improve the percent of accurate bets by only |\n| Neural Network Output\u003Cbr>The neural network outputs a probability based on which class is predicted. This means that we have the option to not bet if the probability isnot high enough. |  |  |\n| Data Layer | Hidden Layers\u003Cbr>Over Under Push |  |\n| \u003Cbr>Data\u003Cbr>● Game stats from 2021-2022 and 2022-2023 season\u003Cbr>○ Points\u003Cbr>○ Rebounds\u003Cbr>○ Field Goal Percentage\u003Cbr>○ Over/Under Line for each game\u003Cbr>● Used two most recent games for each team along with over/under line for current game |  | \u003Cbr>betting on those game that high probability of being correct.\u003Cbr>Moving Forward\u003Cbr>To improve this project, I would consider using more data . I was restricted to two years of data due to the availability of historical odds, but to find a different way to retrieve this data would improve the project. Using different data such asthe game starters, injuries, recent updates, or other related data could also improve the model. |","cbCairiCZ3Xqd4Th","https://ap.wps.com/l/cbCairiCZ3Xqd4Th","pdf",426572,1,"English","en",105,"# Introduction\n## Over/Under Bet\n# Challenges\n# Models and Results\n## Neural Network vs Null Model\n## Confidence and Optional No-Bet\n# Data Used\n# Moving Forward","[{\"question\":\"How is an over/under bet defined in this presentation?\",\"answer\":\"It uses the combined final score of both teams. If the total exceeds the sports book line it’s an Over, if it’s below it’s an Under, and if it matches it’s a Push.\"},{\"question\":\"What were the main challenges addressed?\",\"answer\":\"Predicting outcomes for future games, collecting relevant and accurate data, and ingesting data from multiple sources are highlighted as core obstacles.\"},{\"question\":\"How did the neural network perform compared with the null model?\",\"answer\":\"The neural network beats the null model by 2.87%, though the improvement is only slightly profitable overall.\"}]","Machine Learning for Sports Betting - Sports betting odds improvement | PDF",1785677271,3,{"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},"machine-learning-for-sports-betting-sports-betting-odds-improvement","",{"@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/machine-learning-for-sports-betting-sports-betting-odds-improvement/117611/",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},"How is an over/under bet defined in this presentation?","Question",{"text":74,"@type":75},"It uses the combined final score of both teams. If the total exceeds the sports book line it’s an Over, if it’s below it’s an Under, and if it matches it’s a Push.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What were the main challenges addressed?",{"text":79,"@type":75},"Predicting outcomes for future games, collecting relevant and accurate data, and ingesting data from multiple sources are highlighted as core obstacles.",{"name":81,"@type":72,"acceptedAnswer":82},"How did the neural network perform compared with the null model?",{"text":83,"@type":75},"The neural network beats the null model by 2.87%, though the improvement is only slightly profitable overall.","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"]