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2018 World Cup Predictions using decision trees

#artificialintelligence

In this study, we predict the outcome of the football matches in the FIFA World Cup 2018 to be held in Russia this summer. We do this using classification models over a dataset of historic football results that includes attributes from the playing teams by rating them in attack, midfield, defence, aggression, pressure, chance creation and building ability. This last training data was a result of merging international matches results with AE games ratings of the teams considering the timeline of the matches with their respective statistics. Final predictions show the four countries with the most chances of getting to the semifinals as France, Brazil, Spain and Germany while giving Spain as the winner. The objective of this study is to build a predictive model that will allow us to make good predictions for the coming World Cup 2018 so we looked for dataset with historic data for match results, for this purpose we chose a dataset from Kaggle with data of almost 40,000 international matches played between 1872 and 2018.


Norouzzadeh Ravari

AAAI Conferences

Competitive multi-player game play is a common feature in major commercial titles, and has formed the foundation for esports. In this paper, the question whether it is possible to predict match outcomes in First Person Shooter-type multi-player competitive games with mixed genres is addressed.The case employed is Destiny, which forms a hybrid title combining Massively Multi-player Online Role-Playing game features and First-Person Shooter games. Destiny provides the opportunity to investigate prediction of the match outcome, as well as the influence of performance metrics on the match results in a hybrid multi-player major commercial title. Two groups of models are presented for predicting match results: One group predicts match results for each individual game mode and the other group predicts match results in general, without considering specific game modes. Models achieve a performance between 63% and 99%in terms of average precision, with a higher performance recorded for the models trained on specific multi-player game modes, of which Destiny has several. We also analyzed performance metrics and their influence for each model. The results show that many key shooter performance metrics such as Kill/Death ratio are relevant across game modes, but also that some performance metrics are mainly important for specific competitive game modes. The results indicate that reliable match prediction is possible in FPS-type esports games.


Implementing Automated Machine Learning (AutoML) 7wData

#artificialintelligence

Automated machine learning (autoML) is the process of applying tools to data to apply the machine learning process to a real-world problem. Applying machine learning to a new dataset is a complicated process, and autoML systems provide tools and customization options. Machine learning is now deployed across many kinds of applications, and more often than not, we are seeing machine learning used to make recommendations and predictions across multiple use cases. Machine learning could potentially save time and money and help companies gain a competitive advantage in the marketplace through faster responses. AutoML-enabled tools can create models that are improved and customized.


Implementing Automated Machine Learning (AutoML)

#artificialintelligence

Automated machine learning (autoML) is the process of applying tools to data to apply the machine learning process to a real-world problem. Applying machine learning to a new dataset is a complicated process, and autoML systems provide tools and customization options. Machine learning is now deployed across many kinds of applications, and more often than not, we are seeing machine learning used to make recommendations and predictions across multiple use cases. Machine learning could potentially save time and money and help companies gain a competitive advantage in the marketplace through faster responses. AutoML-enabled tools can create models that are improved and customized.


Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model

arXiv.org Artificial Intelligence

In this paper, we present a new application-focused benchmark dataset and results from a set of baseline Natural Language Processing and Machine Learning models for prediction of match outcomes for games of football (soccer). By doing so we give a baseline for the prediction accuracy that can be achieved exploiting both statistical match data and contextual articles from human sports journalists. Our dataset is focuses on a representative time-period over 6 seasons of the English Premier League, and includes newspaper match previews from The Guardian. The models presented in this paper achieve an accuracy of 63.18% showing a 6.9% boost on the traditional statistical methods.