{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T05:27:05Z","timestamp":1764394025500,"version":"3.46.0"},"reference-count":22,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>This paper explores the replication of the DotA Plus prediction system using decision tree algorithms. The study implements and evaluates Extra Trees Classifier, Random Forest Classifier, and Hist Gradient Boosting Classifier, along with their combined average, for predicting the outcome of Defense of the Ancients (DotA) 2 matches. Data was collected using the OpenDotA API and the Steam API, and various features such as game duration, tower and barracks states, net-worth, assists, last hits, gold, level, gold per minute, and experience per minute were extracted for model training. Additionally, hero and item win rate features, derived from Dotabuff data, were incorporated to enhance the models\u2019 predictive accuracy. The models were trained on datasets with varying match durations, including segments for matches under 10 min, between 10 and 20 min, and over 20 min. The experimental results show that the Extra Trees Classifier consistently outperformed other individual models and performed comparably to the averaged models, achieving a peak performance of 98.6% test accuracy on matches longer than 20 min when using match duration segmentation and hero\/item embeddings. The study highlights the effectiveness of decision tree-based methods for real-time match outcome prediction in DotA 2 and offers insights into feature importance. The combined average of Extra Trees Classifier, Random Forest Classifier, and Hist Gradient Boosting Classifier models provides a robust and reliable prediction of DotA 2 match outcomes, thus showing potential as a real-time prediction system.<\/jats:p>","DOI":"10.3390\/bdcc9120302","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T16:31:52Z","timestamp":1764261112000},"page":"302","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DotA 2 Match Outcome Prediction System Using Decision Tree Ensemble Algorithms"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8651-2386","authenticated-orcid":false,"given":"Sukhrob","family":"Yangibaev","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Al-Khwarizmi University, Abulgazi Bakhodirkhan Street 195, Urgench 220100, Uzbekistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7614-118X","authenticated-orcid":false,"given":"Jamolbek","family":"Mattiev","sequence":"additional","affiliation":[{"name":"Computer Science and Artificial Intelligence Technologies Department, Urgench State University, Khamid Alimdjan 14, Urgench 220100, Uzbekistan"}]},{"given":"Sello","family":"Mokwena","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Limpopo, Polokwane 0727, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"ref_1","unstructured":"Akhmedov, K., and Phan, A.H. 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Inform."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/12\/302\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T05:22:58Z","timestamp":1764393778000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/12\/302"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,27]]},"references-count":22,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["bdcc9120302"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9120302","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2025,11,27]]}}}