{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T04:13:18Z","timestamp":1770523998911,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,12,18]]},"DOI":"10.1145\/3703323.3703743","type":"proceedings-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T12:03:28Z","timestamp":1750853008000},"page":"284-291","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9756-3864","authenticated-orcid":false,"given":"Shamik","family":"Bhattacharjee","sequence":"first","affiliation":[{"name":"Data Science, Dream11, Mumbai, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3438-8571","authenticated-orcid":false,"given":"Nilesh","family":"Patil","sequence":"additional","affiliation":[{"name":"Data Science, Dream11, Mumbai, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5752-8601","authenticated-orcid":false,"given":"Kamlesh","family":"Marathe","sequence":"additional","affiliation":[{"name":"Data Science, Dream11, Mumbai, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3200-0606","authenticated-orcid":false,"given":"Hitesh","family":"Kapoor","sequence":"additional","affiliation":[{"name":"Data Science, Dream11, Mumbai, India"}]}],"member":"320","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Ryan Beal Timothy\u00a0J Norman and Sarvapali\u00a0D Ramchurn. 2020. Optimising daily fantasy sports teams with artificial intelligence. International Journal of Computer Science in Sport 19 2 (2020) 21\u201335.","DOI":"10.2478\/ijcss-2020-0008"},{"key":"e_1_3_3_1_3_2","unstructured":"Nicholas Bonello Joeran Beel Seamus Lawless and Jeremy Debattista. 2019. Multi-stream data analytics for enhanced performance prediction in fantasy football. arXiv preprint arXiv:1912.07441 (2019)."},{"key":"e_1_3_3_1_4_2","unstructured":"Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang and Wojciech Zaremba. 2016. OpenAI Gym. arXiv:arXiv:1606.01540http:\/\/arxiv.org\/abs\/1606.01540"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Rory Bunker and Teo Susnjak. 2022. The application of machine learning techniques for predicting match results in team sport: A review. Journal of Artificial Intelligence Research 73 (2022) 1285\u20131322.","DOI":"10.1613\/jair.1.13509"},{"key":"e_1_3_3_1_6_2","unstructured":"Team CredAble. 2023. IPL to Drive a Surge in Revenue for Fantasy Gaming Platforms. https:\/\/www.credable.in\/insights-by-credable\/business-insights\/how-ipls-growing-popularity-has-transformed-indian-cricket-economics\/. Accessed: November 18 2024."},{"key":"e_1_3_3_1_7_2","unstructured":"Deloitte. 2023. India\u2019s Fantasy Sports Industry to Grow at 33% CAGR. https:\/\/www2.deloitte.com\/in\/en\/pages\/technology-media-and-telecommunications\/articles\/Fantasy-Sports-2023.html. Accessed: November 18 2024."},{"key":"e_1_3_3_1_8_2","unstructured":"Timothy\u00a0P Lillicrap Jonathan\u00a0J Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015)."},{"key":"e_1_3_3_1_9_2","unstructured":"Ian MacDow. 2018. Deep Reinforcement Learning for Drafting My Fantasy Football Team. web.stanford.edu\/class\/archive\/cs\/cs221\/cs221.1192\/2018\/restricted\/posters\/macdow\/poster.pdf. Stanford CS221 Class Archive."},{"key":"e_1_3_3_1_10_2","unstructured":"Niall McCarthy. 2017. The Most Popular Spectator Sports Worldwide. https:\/\/www.statista.com\/chart\/10042\/the-most-popular-spectator-sports-worldwide\/. Accessed: November 18 2024."},{"key":"e_1_3_3_1_11_2","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)."},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Andrei\u00a0A Rusu Joel Veness Marc\u00a0G Bellemare Alex Graves Martin Riedmiller Andreas\u00a0K Fidjeland Georg Ostrovski et\u00a0al. 2015. Human-level control through deep reinforcement learning. nature 518 7540 (2015) 529\u2013533.","DOI":"10.1038\/nature14236"},{"key":"e_1_3_3_1_13_2","unstructured":"Antonin Raffin Ashley Hill Adam Gleave Anssi Kanervisto Maximilian Ernestus and Noah Dormann. 2021. Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 268 (2021) 1\u20138."},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/JCSSE54890.2022.9836260"},{"key":"e_1_3_3_1_15_2","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)."},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"D Silver et\u00a0al. 2016. Mastering the game of go with deep reinforcement learning and tree search. Nature 529 7587 (2016) 484\u2013504.","DOI":"10.1038\/nature16961"},{"key":"e_1_3_3_1_17_2","volume-title":"Reinforcement learning: An introduction","author":"Sutton Richard\u00a0S","year":"2018","unstructured":"Richard\u00a0S Sutton and Andrew\u00a0G Barto. 2018. Reinforcement learning: An introduction. MIT press."},{"key":"e_1_3_3_1_18_2","unstructured":"Richard\u00a0S Sutton David McAllester Satinder Singh and Yishay Mansour. 1999. Policy gradient methods for reinforcement learning with function approximation. Advances in neural information processing systems 12 (1999)."}],"event":{"name":"CODS-COMAD 2024: 8th International Conference on Data Science and Management of Data (12th ACM IKDD CODS and 30th COMAD)","location":"Jodhpur India","acronym":"CODS-COMAD Dec '24"},"container-title":["Proceedings of the 8th International Conference on Data Science and Management of Data (12th ACM IKDD CODS and 30th COMAD)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3703323.3703743","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T13:03:42Z","timestamp":1750856622000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3703323.3703743"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,18]]},"references-count":17,"alternative-id":["10.1145\/3703323.3703743","10.1145\/3703323"],"URL":"https:\/\/doi.org\/10.1145\/3703323.3703743","relation":{},"subject":[],"published":{"date-parts":[[2024,12,18]]},"assertion":[{"value":"2025-06-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}