Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Tanni, Nikita; George, Tessa; Gokulapriya, R.; Ravindranath Chowdary, Cherukuri Title A name given to the resource Stride Insights: AI-Powered Field Position Forecasting System Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1354 LNNS;pp.501-514 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4880-1_40" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4880-1_40</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019313419?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019313419?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Tanni N., Christ University, Bengaluru, India; George T., Christ University, Bengaluru, India; Gokulapriya R., Christ University, Bengaluru, India; Ravindranath Chowdary C., Christ University, Bengaluru, India Description An account of the resource This research discovers an AI-predictive model that uses a variety of machine learning algorithms to predict the top five finishers in a race. Horse racing is one of those paradigms that presents a challenging dataset against which the race outcomes can be predicted. Horse racing involves numerous variables: horse performance metrics, race conditions, and the jockey, together, decide the outcome of a race. To tackle such complexity, we test several algorithms, including CatBoost, Random Forest, k-Nearest Neighbors, Logistic Regression, Decision Trees, Support Vector Machines, Linear Regression, Naive Bayes, and Gradient Boosting, relating to the incorporation of categorical and continuous data. Our experiments demonstrate that the highest accuracy was achieved with CatBoost, which allows the model to handle categorical features well and is resistant to overfitting. The game theory component supplies useful elements in the strategic interaction between competing horses, thereby further increasing predictive accuracy. Performance metricsaccuracy, precision, and recall were used to estimate each model. The accuracy of CatBoost was found to be 74.1, while others were less accurate. This research provides an important resource for racing stakeholders, from trainers to punters. The research will be valuable in delineating race strategy and which horses are likely to win. This is an advancement in horse racing analytics and lays the foundation for predictive modeling to be explored in similar competitive environments in the future. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource CatBoost; Decision trees; Gradient boosting; Historical data; k-nearest neighbors; Linear regression; Logistic regression; Machine learning accuracy; Naive Bayes; Race conditions; Random forest; Support vector machines Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964879-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online