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Jan 1, 2019 · This paper provides a critical analysis of the literature in ML, focusing on the application of Artificial Neural Network (ANN) to sport results prediction. In doing so, we identify the learning methodologies utilised, data sources, appropriate means of model evaluation, and specific challenges of predicting sport results.
- A Comparative Analysis of Data Mining Methods in Predicting NCAA Bowl Outcomes
The sample data for this study are collected from a variety...
- Tutorial and Critical Analysis of Phishing Websites Methods
5. Why fall prey to phishing? Phishing is an example of a...
- Nonlinear Neural Networks
Three sources of contemporary neural network research—the...
- Levenberg–Marquardt Methods With Strong Local Convergence Properties for Solving Nonlinear Equations With Convex Constraints
Table 1, Table 2 give the results for some square systems of...
- Predicting Football Results Using Bayesian Nets and Other Machine Learning Techniques
Predicting the outcome of a football match is an ideal...
- A Comparative Analysis of Data Mining Methods in Predicting NCAA Bowl Outcomes
networks to predict sports outcomes. We demonstrate our method’s efficacy over traditional vector-based representations for prediction tasks in American football and esports. Our contributions are (1) a sports-agnostic graph representation of game states to facilitate prediction in sports, (2) a graph-based
May 4, 2023 · One of the most common approaches to predicting sports outcomes is supervised learning, a type of machine learning algorithm that uses labeled data to train a model. In the case of sports...
Jun 30, 2020 · Sport predictions are usually treated as a classification problem with one class being predicted and rare cases being predicted as numerical values. Mostly used ML models are neural networks using data segmentation.
- Tomislav Horvat, Josip Job
- 54
- 2020
- 30 June 2020
Jul 9, 2020 · We trained a neural network on the last nine years of Major League Baseball games. It learned to weigh large amounts of data to predict the outcomes of plate appearances more accurately than previous techniques. The neural network also learned the strategies and rules of the game, and used this understanding to enhance prediction quality.
Oct 20, 2023 · In the realm of sports data mining, utilizing technologies like Naive Bayes, artificial neural networks, and decision trees within machine learning to construct sports prediction models through data FSS can heighten prediction accuracy and efficiency, enabling sports outcome predictions .
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Jul 28, 2022 · Graph Neural Networks to Predict Sports Outcomes. Predicting outcomes in sports is important for teams, leagues, bettors, media, and fans. Given the growing amount of player tracking data, sports analytics models are increasingly utilizing spatially-derived features built upon player tracking data.
