[PDF] Football match prediction using deep learning | Semantic Scholar (2024)

The outcome of this hypothesis can be derived that deep learning may be used for successfully predicting the outcomes of football matches or any other sporting events.

The proposed model is the first DL-based model in predicting match winners from any professional football leagues in the Middle East and North Africa region and suggests that the last 15–30 minutes of match segments of the matches from QSL have a more significant impact on the match result than other match segments.

This project will attempt to design a prediction system powered by machine Recurrent Neural Networks and LSTMs.

An Artificial Neural Network model was developed following the Machine Learning (ML) Life Cycle and got an accuracy score of 97.92% with an MSE of 2.8644, implying that real-life games with unknown results can indeed be predicted with a high level of accuracy using machine learning.

One of the most extensive studies of its kind and applying a wide range of machine learning techniques to male and female professional singles matches of tennis shows that the average prediction accuracy cannot be increased to more than about 70%.

An Artificial Neural Network model was developed following the Machine Learning Life Cycle and got an accuracy score of 97.92% with MSE of 2.8644, implying that real life games with unknown results can indeed be predicted with a high level of accuracy using machine learning.

This paper analyzes if Deep Learning methods can outperform classical Machine Learning methods in predicting the winner of NFL-games.

It was shown that players’ performance metrics from the last five seasons would provide sufficient discriminative power to the proposed ML model to predict the match-winner in the upcoming season and the defenders’ role could not be ignored for match results.

This thesis explores the problem of predicting sports results and offers two approaches that utilize neural networks, a traditional artificial neural network with the embedding of the individual teams and a relatively new approach that employs Convolutional Graph Neural Networks for team representation.

A football match prediction model for Turkish Super League (TSL) using supervised machine learning techniques is proposed using classification techniques including logistics regression, linear and quadratic discriminant analyses, K-nearest neighbors, support vector machines, and random forests.

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The efficiency of black-box modeling capacity of neural networks in the case of forecasting soccer match results is noted, and two different input vector parameters have been tested via learning vector quantization networks in order to emphasize the importance of input parameter selection.

The statistical challenges in correctly predicting winners in the NCAA Men's Basketball Tournament are discussed and a machine learning strategy for predicting the games is presented.

This paper presents a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure, and finds that reversing the order of the words in all source sentences improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.

This paper proposes the convolutional LSTM (ConvLSTM) and uses it to build an end-to-end trainable model for the precipitation nowcasting problem and shows that it captures spatiotemporal correlations better and consistently outperforms FC-L STM and the state-of-the-art operational ROVER algorithm.

Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.

A deep learning approach is proposed which learns to extract a meaningful representation for each review in an unsupervised fashion and clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products.

This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.

It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.

This work identifies a weakness of LSTM networks processing continual input streams that are not a priori segmented into subsequences with explicitly marked ends at which the network's internal state could be reset, and proposes a novel, adaptive forget gate that enables an LSTm cell to learn to reset itself at appropriate times, thus releasing internal resources.

These advanced recurrent units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU), are found to be comparable to LSTM.

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