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п»їNeural network sports betting.
Machine Learning for Sports Betting with Neural Network and custom loss function.
For our data we take a list of 200 games from the English Premier League, season 2018–2019, August to December 2018. It contains descriptive game data such as team names, bookmaker odds from BetFair, and our BetSentiment sentiment score (representing the percentage of positive tweets over the positive and negative tweets - 30 millions tweets analyzed so far!).
Custom loss function.
We set up our custom loss function with Keras on top of TensorFlow.
Using Neural Networks To Predict The Outcomes Of Sporting Events.
Predicting the outcomes of sports events using neural networks allows you to realize the vast possibilities of computers for analyzing and storing the flow of incoming information. It is an electronic version of the internal structure of the human brain.
Neural networks can solve analytical tasks much faster and more accurately of the following kind:
a) Classification of information - sorting it based on specific criteria.
b) Predict event, predict step on time series.
c) Pattern recognition using specific algorithms.
For this task, at the initial stages, only the first 2 points are needed.
Therefore, we consider them.
It must be divided into clusters according to the necessary characteristics - the sport, the features of players, matches, fights, and so on to perform the classification of information. It is required to take into account various factors that can be changed all possible outcomes, well, for example, the illness of one of the players of the team, a sudden "second wind" before the end of the match, or others. According to probability theory, the sum of all possible outcomes will be equal to one .
Forecasting can be built in the following stages:
1. Collect as many different kinds of statistics as it is possible.
2. Selection of the desired type of neural network.
3. Development or adaptation of the algorithm.
4. Creating a data sample for training.
5. Adjustment of the sampling algorithm.
6. Self-study program.
7. Formation of final forecasts based on sample analysis.
For everything to work correctly, you need a lot of information. This criterion is critical. Since forecasts are based on statistics and probabilities, it is essential to have quality data from quality sources. It is especially important if it is planned to predict not only a football sport but also others.
As for football, here, for example, you need to consider the following (relative pattern):
on which field is the team playing (“at home,” “away”)
• operational data obtained from public sources.
Only by maximizing all possible factors you can count on the most accurate forecast. Then the algorithm in the course of the action will be trained and produce more correct results.
To create your software for sports forecasting, you need to be highly qualified in the field of programming. If you approach the task professionally and responsibly, the results will be increasingly accurate and high-quality.
The most important thing here is the proper structuring of the available information. Each variable that matches the results of the game was used in the subsequent analysis.
Installing and configuring a neural network will be quite a long and challenging process. We need to adjust the size of the network and the number of neurons.
If everything is installed and configured correctly, the end-user will not have to make an effort to get the final result.
The neural network requires constant training during its use. Bookmakers use the capabilities of AI to get the final coefficients.
In the future, neural networks will learn how to predict the outcome of any sporting event, evaluate it, and open up new possibilities for forecasting.
A well-established AI will make a forecast, if not better than an experienced analyst, but certainly much faster. And with the development of technology, the quality level and the role of intelligence in the betting business will grow.
Sports Betting Forecasting with the Help of Neural Networks: Future of the Betting Business.
Such a scheme allows the implementation of analytical capacities into machines and an ability to store information. Thanks to the neural network it is possible to analyze incoming data. In fact, it is an Artificial Intelligence, a machinery version of the human brain, where millions of neurons transmit data in the form of electrical impulses.
Why Do We Need a Neural Network?
It allows you to quickly complete difficult tasks which require symbolic calculations.
Artificial Intelligence is represented in several varieties:
Neural network for classification – it marshals information that is based on settings. Neural network for prediction – we mean prediction of the outcome of different events, i.e. the next step. Neural network for detecting – it is the most common type of network. Google uses it to search by photos, cameras of mobile phones determine a position of faces, etc.
However, today we are going to consider the prediction function, and, in particular, sports forecasting.
Neural Networks Forecasting.
Sports forecasting is interesting mostly for owners of the betting business and betters. Clusterization makes it possible to do information categorization, which means to divide information into groups (clusters), based on common or similar characteristics. Sports betting forecasting should take into account an important factor: the same result is impossible in any case. Even we talk about a top English team and a low-grade Russian football club.
Players of the first team can suddenly be bowled over because of a disease, and guys from the second group may suddenly discover in themselves some hidden talents and win. It is all about the frequency of events. Neural networks forecasting allows us to determine a degree of possibility of all outcomes of an event. In sum, indicators are equal to 1.
Neural networks forecasting is carried out in one of its three categories:
Kohonen’s neural network; ART-2; radially-symmetrical.
Each of these neural networks can be a learning program on the basis of fetch. They can have a fixed structure, but sports forecasting needs the possibility of an independent organization. The learning process consists not only of customization of Wide Web weight but also of the structure formation. New clusters are created, and old (unused) clusters are deleted. Networks differ from each other in the update algorithm.
Sports betting forecasting consist of the following stages:
Acquisition of information on sports events in the same sport and rank. Architecture selection – such architecture is going to complete the task. Selection or development of a program. Formation of teaching selection, which is set for the used software. Setting up a learning algorithm. The program is learning and carries out cluster analysis. Neural network does sports forecasting and calculates the probability of possible outcomes.
Neural Networks Forecasting: Source Data Capture.
You will need a huge array of information to make everything work properly. It is the main factor that influences the success of the analysis. Due to the fact that methods for football predictions work with the help of statistics and probabilities, both the amount of data and the quality of the source are important. Thus, any information on the Japanese championship will not be suitable, if you want to predict the outcome of the event within the framework of the Champions League.
Therefore, if the forecaster is going to do not only a football predictions analysis football match results prediction with the help of neural networks, but also to find out what will happen in other sports, it will be necessary for him to take this into account during the process of choosing training data.
Football match results prediction and prediction of outcomes of other events should be based on the following information:
the field factor (does the team play "at home" on neutral territory or “on a visit’); up-to-dateness (the strength of the team, a dynamic pattern in relation to the index. This number notation can be obtained from reputable updated ratings); results of previous games.
It is necessary to pay individual attention to factors that are difficult to determine: injuries, disqualifications of major players, change of coach, the current position in the league table. It is difficult to take them into account, but still it is possible. Only using the maximum number of factors, the sports forecasting program will become very close to reality. But due to the complexity of the presentation, the information should be taken into account in the teaching selection even before the program gives out the solution. The predictor will correct probabilities of the results relying on his own experience.
Neural Football Prediction: Architecture Selection andCreation of a Teaching Selection.
In order to create your own software, you need to have a skill level in the field of Artificial Intelligence and understand the subtleties of programming. With a professional approach, the sports forecasting program, created by your own forces, will do everything more accurately and qualitatively. In your software, you can identify every nuance in order to make predictions extremely exact.
The main thing is to properly structure the available information. In order for neural networks forecasting to be exact, it is necessary to ensure the correct usage of the database. But not all source data should be used in the clustering process. It is important that vars which correspond to the results of the game have a descriptive character, that is, they need to be involved in the subsequent cluster analysis. The inclusion of such information in the select is a fundamental point, which allows to carry out such an operation in the future.
Football Match Results Prediction: Setting up and Training.
It can take a long time to establish and train the neural network until the results are satisfactory. Setting up of an algorithm consists of correction of the size of the network and several additional factors.
If it is a learning program with fixed structure, then it will be about the number of clusters. Some groups are not used, others are overflowing. Non-operational cluster prevents learning ability because the algorithm, in this case, is slow. A non-operational cluster is dangerous as it clearly demonstrates the inability of a neural network to properly separate examples based on characteristics. Prediction accuracy of such a network will be questionable.
It is also necessary to strike a balance when you install the adaptation coefficient. If you set up a high learning speed, examples can migrate between the closest clusters. But if the program is well and properly organized, users will not need to participate in the learning process.
Sports Betting Forecasting: Practical Application in the Betting Business.
Neural network clustering provides training during the practical use. Constant expansion of the database increases the accuracy of subsequent calculations. A modern betting business takes the opportunity of Artificial Intelligence to form coefficients.
Big betting means big money. All profit of the betting office will depend on how competently calculated coefficients are. The advanced neural network is better than any specialist in a calculation of the probability of outcomes of events, and it does it instantly.
In the future, neural networks will learn how to independently analyze the behavior of players on the field, characterize them, explore the possession of the ball, make up the most effective models of attack, etc. Artificial Intelligence will break new grounds and create the most exact methods for football predictions analysis.
The betting business should do everything to win in any situation, regardless of the outcome. This is why it is necessary to correctly calculate coefficients for the outcome of the sports event. A well-trained Artificial Intelligence, taking into account a myriad of factors, will make a forecast, even if not better than a competent analyst, but certainly several-fold faster. And in the future, with the development of the described technology and the expansion of its capabilities, the role of neural networks in a betting industry will only enhance.
Neural network sports betting.
Machine learning (ML) is one of the intelligent methodologies that have shown promising results in the domains of classification and prediction. One of the expanding areas necessitating good predictive accuracy is sport prediction, due to the large monetary amounts involved in betting. In addition, club managers and owners are striving for classification models so that they can understand and formulate strategies needed to win matches. These models are based on numerous factors involved in the games, such as the results of historical matches, player performance indicators, and opposition information. 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. This then leads us to propose a novel sport prediction framework through which ML can be used as a learning strategy. Our research will hopefully be informative and of use to those performing future research in this application area.
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