{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/real-time-esports-match-result-prediction","title":"Real-time eSports Match Result Prediction","arxiv_id":"1701.03162","date":"2016-12-10","proceeding":null,"authors":["Yifan Yang","Tian Qin","Yu-Heng Lei"],"abstract":"In this paper, we try to predict the winning team of a match in the\nmultiplayer eSports game Dota 2. To address the weaknesses of previous work, we\nconsider more aspects of prior (pre-match) features from individual players'\nmatch history, as well as real-time (during-match) features at each minute as\nthe match progresses. We use logistic regression, the proposed Attribute\nSequence Model, and their combinations as the prediction models. In a dataset\nof 78362 matches where 20631 matches contain replay data, our experiments show\nthat adding more aspects of prior features improves accuracy from 58.69% to\n71.49%, and introducing real-time features achieves up to 93.73% accuracy when\npredicting at the 40th minute.","url_abs":"http://arxiv.org/abs/1701.03162v1","url_pdf":"http://arxiv.org/pdf/1701.03162v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"real-time-esports-match-result-prediction","repo_url":"https://github.com/yang1fan2/Dota2-Prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"dota-2","task_name":"Dota 2"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}