A Deep Learning Approach to Predict Football Match Result

Rudrapal, Dwijen; Boro, Sasank; Srivastava, Jatin; Singh, Shyamu

doi:10.1007/978-981-13-8676-3_9

Cited by 20 publications

(11 citation statements)

References 9 publications

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“…Rudral et al [108] have used deep learning-based predictive modeling for football games to result in multilayer perception. Wakatsuki et al (2020) have used multi-player games for decision making.…”

Section: Application Of Game Theory In Deep Learning and Artificial I...mentioning

confidence: 99%

Applications of game theory in deep learning: a survey

Hazra

Anjaria

2022

Multimed Tools Appl

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This paper provides a comprehensive overview of the applications of game theory in deep learning. Today, deep learning is a fast-evolving area for research in the domain of artificial intelligence. Alternatively, game theory has been showing its multi-dimensional applications in the last few decades. The application of game theory to deep learning includes another dimension in research. Game theory helps to model or solve various deep learning-based problems. Existing research contributions demonstrate that game theory is a potential approach to improve results in deep learning models. The design of deep learning models often involves a game-theoretic approach. Most of the classification problems which popularly employ a deep learning approach can be seen as a Stackelberg game. Generative Adversarial Network (GAN) is a deep learning architecture that has gained popularity in solving complex computer vision problems. GANs have their roots in game theory. The training of the generators and discriminators in GANs is essentially a two-player zero-sum game that allows the model to learn complex functions. This paper will give researchers an extensive account of significant contributions which have taken place in deep learning using game-theoretic concepts thus, giving a clear insight, challenges, and future directions. The current study also details various real-time applications of existing literature, valuable datasets in the field, and the popularity of this research area in recent years of publications and citations.

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Section: Application Of Game Theory In Deep Learning and Artificial I...mentioning

confidence: 99%

Applications of game theory in deep learning: a survey

Hazra

Anjaria

2022

Multimed Tools Appl

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“…Our data collection and analysis efforts have many unique and novel properties. While there are already many datasets for sports analysis of rugby [22], soccer [11], [23], [25], [28] and basketball [24], [26], [27], they cannot be applied to volleyball analysis because of the completely different approaches to the sport. To the best of our knowledge, there are very few datasets for volleyball analysis, limited to a well-known dataset for indoor volleyball [13] and a dataset for beach volleyball [21].…”

Section: Introductionmentioning

confidence: 99%

VREN: Volleyball Rally Dataset with Expression Notation Language

Xia¹,

Tracy²,

Zhao³

et al. 2022

Preprint

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This research is intended to accomplish two goals: The first goal is to curate a large and information rich dataset that contains crucial and succinct summaries on the players' actions and positions and the back-and-forth travel patterns of the volleyball in professional and NCAA Div-I indoor volleyball games. While several prior studies have aimed to create similar datasets for other sports (e.g. badminton and soccer), creating such a dataset for indoor volleyball is not yet realized. The second goal is to introduce a volleyball descriptive language to fully describe the rally processes in the games and apply the language to our dataset. Based on the curated dataset and our descriptive sports language, we introduce three tasks for automated volleyball action and tactic analysis using our dataset:(1) Volleyball Rally Prediction, aimed at predicting the outcome of a rally and helping players and coaches improve decisionmaking in practice, (2) Setting Type and Hitting Type Prediction, to help coaches and players prepare more effectively for the game, and (3) Volleyball Tactics and Attacking Zone Statistics, to provide advanced volleyball statistics and help coaches understand the game and opponent's tactics better. We conducted case studies to show how experimental results can provide insights to the volleyball analysis community. Furthermore, experimental evaluation based on real-world data establishes a baseline for future studies and applications of our dataset and language. This study bridges the gap between the indoor volleyball field and computer science.

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“…The outcome prediction was made with the help of images taken during match with convolutional neural networks from deep learning methods [11]. Deep neural networks have been used to predict football match outcomes in another study [12]. By using different match results in different leagues, match results were also estimated with the help of various machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League

Taşpınar

Çinar

Köklü

2021

MANAS Journal of Engineering

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Football is one of the most popular sports in terms of number of fans in the world. This situation arises from the unpredictable nature of football. People are becoming more and more connected to this sport as it combines emotions such as excitement and joy that it creates in people. Match result prediction is a very challenging problem, and recently the solution to this problem has become very popular. With the result of this unpredictable game the events that occur during the match that affect this result are tried to be predicted by machine learning methods. This study demonstrates our work on finding the most effective features in match result prediction using match statistics from the Italian Serie A League's 2027 pieces match between the 2014-2015 and 2019-2020 seasons and with 54 features for each match. Feature selection testing was conducted to estimate the results of a football match and determine the most important factors. The selection of features was made using the ANOVA method and it was predicted that 28 of the 54 features would be effective in predicting match results. After this stage, fairly high rates classification success was achieved using the logistic regression method. 88.85% as a result of the prediction made with all features and 89.63% success was achieved as a result of the prediction made with 28 selected features. With these results, it is possible to say that process of feature selection increase success in match result prediction.

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A Deep Learning Approach to Predict Football Match Result

Cited by 20 publications

References 9 publications

Applications of game theory in deep learning: a survey

Applications of game theory in deep learning: a survey

VREN: Volleyball Rally Dataset with Expression Notation Language

Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League

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