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This work identifies elite five-person lineups using positive plus-minus per minute (PMM), even when the five players have never shared the court. A model combines seven classification tools into a conservative unanimous-consent ensemble, prioritizing high precision. Training and testing use 2017–18 player and lineup data, then predictions evaluate 2018–19 rosters across all 30 teams.",{"@graph":63,"@context":123},[64,80,102],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/predicting-elite-nba-lineups-using-individual-player-order-statistics-v1-paper/564381/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/predicting-elite-nba-lineups-using-individual-player-order-statistics-v1-paper/564381.png","ImageObject",442,249,{"name":88,"@type":89},"Jake","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-31","2026-10-12",true,{"@type":98,"interactionType":99,"userInteractionCount":101},"InteractionCounter",{"@type":100},"ViewAction",10,{"@type":103,"mainEntity":104},"FAQPage",[105,111,115,119],{"name":106,"@type":107,"acceptedAnswer":108},"How does the model define an “elite” five-person lineup?","Question",{"text":109,"@type":110},"An elite lineup is defined as one with a positive plus-minus per minute (PMM), indicating positive point differential impact while on the court.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"What makes the approach capable of evaluating lineups that have never played together?",{"text":114,"@type":110},"It uses individual player order statistics so the ensemble can predict a lineup’s PMM sign even when the specific five players have no prior shared lineup history.",{"name":116,"@type":107,"acceptedAnswer":117},"Why does the paper combine seven classifiers into a unanimous-consent classifier (ANC)?",{"text":118,"@type":110},"The ANC predicts a lineup as elite only if all seven classifiers agree, making the method conservative while achieving high positive predictive value (precision).",{"name":120,"@type":107,"acceptedAnswer":121},"How were predictions generated for the NBA season teams?",{"text":122,"@type":110},"The model is trained and tested on 2017–18 individual and lineup data, then used to predict elite performance for lineups drawn from all 30 teams’ 2018–19 regular-season rosters.","https://schema.org",{"og:url":78,"og:type":125,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":127,"canonical":78},"index,follow",{"doc_id":129,"site_id":56},564381,1792083807,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":88,"user_avatar":134,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":101,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":140,"language":141,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":61,"update_tm":145,"read_time":101},962084928904,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","arXiv :2303 .04963v1 [ stat .AP] 9 Mar 2023  \nPredicting Elite NBA Lineups Using Individual Player Order Statistics  \nSusan E. Martonosi 1 , Martin Gonzalez 1 , Nicolas Oshiro2  \n1: Harvey Mudd College  \n2: University of San Francisco  \nAbstract: NBA team managers and owners try to acquire high-performing players. An important consideration in these decisions is how well the new players will perform in combination with their teammates. Our objective is to identify elite five-person lineups, which we define as those having a positive plus-minus per minute (PMM) . Using individual player order statistics, our model can identify an elite lineup even if the five players in the lineup have never played together, which can inform player acquisition decisions, salary negotiations, and real-time coaching decisions. We combine seven classification tools into a unanimous consent classifier (all-or-nothing classifier, or ANC) in which a lineup is predicted to be elite only if all seven classifiers predict it to be elite. In this way, we achieve high positive predictive value (i.e., precision), the likelihood that a lineup classified as elite will indeed have a positive PMM. We train and test the model on individual player and lineup data from the 2017-18 season and use the model to predict the performance of lineups drawn from all 30 NBA teams' 2018-19 regular season rosters. Although the ANC is conservative and misses some high-performing lineups, it achieves high precision and recommends positionally balanced lineups. Keywords: Basketball; point differential; lineups; classification.  \n1 Introduction  \nThe strategy of professional basketball continues to evolve, leading NBA team managers and coaches to continuously seek new players who have the relevant skills to win the annual championship. While individual player performance is asignificant contributor towards team success, we are interested in the overarching factors contributing to high-performing teams and the collection of players that comprise them. This leads us to develop a model to predict high-performing five-person lineups, including those which have never played together before, using individual player order statistics. This model can help general managers considering trades and free agents evaluate how potential incoming players might perform in combination with existing team members. Coaches can also use this model as an in-game aid to see which player sitting on the bench may have a positive impact as a substitute.  \nMuch of the work in the literature focuses on methods for predicting the outcomes of basketball games, tournaments, or seasons. ¨Ozmen quantifies the incremental change in win probability per unit change in several game statistics [23] . Loeffelholz et al. use neural networks to predict the outcomes of NBA games more successfully than expert prediction [17] . Lin uses historical team data with real-time updating during a game to predict the game's outcome [16] . Shen et al. and Hua present methods for predicting the results of the NCAA “March Madness” collegiate tournament [28, 13], as do several articles in a 2015 special issue of this journal [11] . Gumm et al. and Zimmerman et al. use machine learning approaches on historical data to predict outcomes of NCAA and NBA playoffs [12, 35] . Vaz de Melo et al. forecast NBA outcomes using network analysis [30], while Cheng et al. use the maximum entropy principle to predict the outcome of NBA playoffs [7] . Ruiz and Cruz present a model to predict the win probability of teams participating in the NCAA March Madness Tournament by combining a Poisson factorization method with a model borrowed from soccer which quantifies a team's attack and defense coefficients [27] .  \nA body of work examines the role of individual players in determining team success. Several papers use centrality metrics and other methods from the field of complex social networks to identify prominent players and evaluate team performance [8, 25, 31] . Deshpa","cbCaispDUGLAWQ0q","https://ap.wps.com/l/cbCaispDUGLAWQ0q","pdf",433281,29,"English","# Abstract\n# 1 Introduction","[{\"question\":\"How does the model define an “elite” five-person lineup?\",\"answer\":\"An elite lineup is defined as one with a positive plus-minus per minute (PMM), indicating positive point differential impact while on the court.\"},{\"question\":\"What makes the approach capable of evaluating lineups that have never played together?\",\"answer\":\"It uses individual player order statistics so the ensemble can predict a lineup’s PMM sign even when the specific five players have no prior shared lineup history.\"},{\"question\":\"Why does the paper combine seven classifiers into a unanimous-consent classifier (ANC)?\",\"answer\":\"The ANC predicts a lineup as elite only if all seven classifiers agree, making the method conservative while achieving high positive predictive value (precision).\"},{\"question\":\"How were predictions generated for the NBA season teams?\",\"answer\":\"The model is trained and tested on 2017–18 individual and lineup data, then used to predict elite performance for lineups drawn from all 30 teams’ 2018–19 regular-season rosters.\"}]","Predicting Elite NBA Lineups Using Individual Player Order Statistics - v1 - paper | PDF",1791841860]