{"title":"Predictive power of fantasy sports data for soccer forecasting","authors":"Erik Å trumbelj, Marko Robnik-Å ikonja","doi":"10.1504/IJDMMM.2015.069247","DOIUrl":null,"url":null,"abstract":"We analyse data from 5,000 competitors who participated in an online soccer managerial game which revolved around the English Premier League (EPL). We show that competitors incorporate into their decisions relevant information about the outcome of a soccer match. Furthermore, forecasts based on managerial game data are significantly better than random forecasts, forecasts based on relative frequency, and forecasts based on teams' attendance, but worse than bookmaker odds. Our work provides an evidence that crowds poses significant amount of information for the match outcome prediction.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"204 1","pages":"154-163"},"PeriodicalIF":0.4000,"publicationDate":"2015-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Data Mining Modelling and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJDMMM.2015.069247","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 2
Abstract
We analyse data from 5,000 competitors who participated in an online soccer managerial game which revolved around the English Premier League (EPL). We show that competitors incorporate into their decisions relevant information about the outcome of a soccer match. Furthermore, forecasts based on managerial game data are significantly better than random forecasts, forecasts based on relative frequency, and forecasts based on teams' attendance, but worse than bookmaker odds. Our work provides an evidence that crowds poses significant amount of information for the match outcome prediction.
期刊介绍：
Facilitating transformation from data to information to knowledge is paramount for organisations. Companies are flooded with data and conflicting information, but with limited real usable knowledge. However, rarely should a process be looked at from limited angles or in parts. Isolated islands of data mining, modelling and management (DMMM) should be connected. IJDMMM highlightes integration of DMMM, statistics/machine learning/databases, each element of data chain management, types of information, algorithms in software; from data pre-processing to post-processing; between theory and applications. Topics covered include: -Artificial intelligence- Biomedical science- Business analytics/intelligence, process modelling- Computer science, database management systems- Data management, mining, modelling, warehousing- Engineering- Environmental science, environment (ecoinformatics)- Information systems/technology, telecommunications/networking- Management science, operations research, mathematics/statistics- Social sciences- Business/economics, (computational) finance- Healthcare, medicine, pharmaceuticals- (Computational) chemistry, biology (bioinformatics)- Sustainable mobility systems, intelligent transportation systems- National security
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