[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119865-en":3,"doc-seo-119865-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119865,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting Outcomes of Horse Racing using Machine Learning - research paper","Machine learning is increasingly applied across modern society, and horse racing prediction remains a high-value but complex betting domain with many interacting variables. This study trains multiple machine learning classifiers to predict winning horses using a Turf Club of India season dataset spanning 2017–2019, totaling over 14,700 races. The work addresses class imbalance with SMOTE and compares its effectiveness against other sampling strategies. The resulting framework achieves 97.6% accuracy, supporting further research and stakeholder decision-making.","Predicting Outcomes of Horse Racing using  \nMachine Learning  \nMeenakshi Gupta*, Latika Singh  \nSchool of Engineering & Technology,  \nSushant University, Gurgaon-122018, Haryana,  \nE-mail:* [meenakshi78gupta@gmail.com](meenakshi78gupta@gmail.com)  \nAbstract: Machine learning with its vast framework is making its way into every aspect of modern society. The segment of betting sports particularly horse racing calls for the attention from a large spectrum of research community owing to its value to the stakeholders and the amount of money involved. Horse racing prediction is a complex problem as there are a large number of influencing variables. The present study aims to contribute in this domain by training machine learning algorithms for predicting horse racing results or outcomes. For this, data for a whole racing season from 2017 to 2019 of races conducted by Turf Club of India was considered which amounts to over 14,700 races. Six algorithms namely Logistic Regression, Random Forest, Naive Bayes, and k-Nearest Neighbors) k-NN were used to predict the winning horse for each race. Synthetic Minority Oversampling Technique (SMOTE) technique was applied to the imbalanced horse racing data set and the attributes of the horse race repository were analyzed. The results were compared with other sampling methods to evaluate the relative effectiveness of this method. The proposed framework is able to give an accuracy of 97.6% which is substantially higher when compared to other similar studies. The research can be beneficial to the stakeholders as well as researchers in the same area to do further analysis and experiments.  \nKeywords: Machine learning, imbalanced data, SMOTE, prediction, classification model, sports betting, horse racing.  \nI. INTRODUCTION  \nDue to the betting aspect and the volatility of racing, horse racing has been one of the most exciting and entertaining sports. The 2022 Grand National (UK) recorded 21% increase in betting volume from 2019 to reach a total trading sum of £92.8 million with over 50 million bets placed through its online platform[1] .For the 2023 Grand National event, over £1,000,000 is allocated in prizes alone at United Kingdom with 600 million people watching in over 140 countries and more than 3.5 billion US dollars in the United States [2] . The global online gambling market size was valued at USD 57.54 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 11.7%% from 2022 to 2030 [3] . Horse racing is a business that is primarily supported by betting on horses. Prediction in horse racing has long been considered as one of the research problem. It is a challenging problem because of numerous qualitative and quantitative variables. In the present research study, it is proposed to use Machine Learning (ML) algorithms to forecast the outcome of the horse races. In their study Allinson and Merritt[4]discussed horse racing prediction using neural networks based on multilayer perceptrons. Their model considered 200 horses of two years of age only,so thereis a scope for more work in this direction by taking other age groups as well. In another study Hei et al[5] used two methods namely, Hope and Resheff[6]suggested a combined method of TensorFlow with Voting system to predict winner of the races accurately. The accuracy of their predictive model was 49%  \nwhich shows a scope for improvement in this area. Schumaker and Johnson[7] used Support Vector Regression using sequential minimal optimization function in Weka to assess accuracy, precision, and nature of betting in Grey Hound racing which is quiet similar to horse racing as both involve similar uncertainties. Their methodology was adapted for discrete numeric prediction instead of classification and included adataset of 1953 races that spanned 31 different race tracks. Another study by Williams and Li[8] applied four neural network algorithms and gave the best accuracy of 74% with Back-Propagation algorithm but it ne","cbCaib1T9sct63m8","https://ap.wps.com/l/cbCaib1T9sct63m8","pdf",529181,1,10,"English","en",105,"# Introduction\n# Related Work\n# Proposed Methodology\n## Data and Experimental Setup\n## Algorithms Used\n## Handling Imbalanced Data (SMOTE)\n# Results and Comparison\n# Conclusion","[{\"question\":\"What problem does the study address in horse racing?\",\"answer\":\"It addresses the challenge of predicting horse racing outcomes, particularly the winning horse, despite many qualitative and quantitative variables and market volatility.\"},{\"question\":\"What dataset and time period are used for training and testing?\",\"answer\":\"The study uses horse race data from the Turf Club of India covering the racing seasons from 2017 to 2019, totaling over 14,700 races.\"},{\"question\":\"Which machine learning approach is used to handle imbalanced data, and how is it evaluated?\",\"answer\":\"SMOTE is applied to the imbalanced dataset, and the results are compared with other sampling methods to assess relative effectiveness.\"}]","Predicting Outcomes of Horse Racing using Machine Learning - 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