March Madness Tournament Predictions Model: A Mathematical Modeling Approach
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910897307385856 |
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| author | McIver, Christian Avalos, Karla Nayak, Nikhil |
| author_facet | McIver, Christian Avalos, Karla Nayak, Nikhil |
| contents | This paper proposes a model to predict the outcome of the March Madness tournament based on historical NCAA basketball data since 2013. The framework of this project is a simplification of the FiveThrityEight NCAA March Madness prediction model, where the only four predictors of interest are Adjusted Offensive Efficiency (ADJOE), Adjusted Defensive Efficiency (ADJDE), Power Rating, and Two-Point Shooting Percentage Allowed. A logistic regression was utilized with the aforementioned metrics to generate a probability of a particular team winning each game. Then, a tournament simulation is developed and compared to real-world March Madness brackets to determine the accuracy of the model. Accuracies of performance were calculated using a naive approach and a Spearman rank correlation coefficient. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21790 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | March Madness Tournament Predictions Model: A Mathematical Modeling Approach McIver, Christian Avalos, Karla Nayak, Nikhil Applications Artificial Intelligence Machine Learning 68T01 I.2.0; G.3 This paper proposes a model to predict the outcome of the March Madness tournament based on historical NCAA basketball data since 2013. The framework of this project is a simplification of the FiveThrityEight NCAA March Madness prediction model, where the only four predictors of interest are Adjusted Offensive Efficiency (ADJOE), Adjusted Defensive Efficiency (ADJDE), Power Rating, and Two-Point Shooting Percentage Allowed. A logistic regression was utilized with the aforementioned metrics to generate a probability of a particular team winning each game. Then, a tournament simulation is developed and compared to real-world March Madness brackets to determine the accuracy of the model. Accuracies of performance were calculated using a naive approach and a Spearman rank correlation coefficient. |
| title | March Madness Tournament Predictions Model: A Mathematical Modeling Approach |
| topic | Applications Artificial Intelligence Machine Learning 68T01 I.2.0; G.3 |
| url | https://arxiv.org/abs/2503.21790 |