Results Of Football Matches Research Articles

MODELOS PREDITIVOS BASEADOS EM MACHINE LEARNING PARA OTIMIZAÇÃO DE ESTRATÉGIAS EM APOSTAS ESPORTIVAS NO FUTEBOL

This study presents an investigation into the application of predictive models based on Machine Learning to optimize sports betting strategies in football. The research was conducted using a combination of quantitative and qualitative methods, including the analysis of historical football match data and the implementation of machine learning algorithms. The results indicate that predictive models can be effective in forecasting football match outcomes, which can be useful for optimizing sports betting strategies. The practical application of these models can provide bettors with a competitive advantage, allowing them to make more informed decisions and potentially improve their financial outcomes. It is concluded that while predictive models offer a valuable tool for enhancing betting strategies, a careful approach and ongoing evaluation of the models are essential to maximize their effectiveness. The research suggests that future investigations could explore the integration of additional data and the improvement of algorithms to increase prediction accuracy.

The association between physical outputs and match outcome across different playing styles for a professional second-tier football team across two complete seasons.

Elite-level football requires an array of physical, technical, psychological, and tactical skills. The aim of this study was to measure the association between physical outputs (distance, decelerations, accelerations) and the match outcome (win, draw, lose) in professional football. This research also examined whether the same association is influenced if a team adopts a possession or transition-based playing style. Thirty-six elite-outfield football players from an English Championship team participated in the study during the 2020/2021 and 2021/2022 seasons using physical and event data collected from an English Championship club over the 2020-2021 and 2021-2022 seasons, this study conducted a univariate analysis of variance (ANOVA) and Hedge g effect size (ES) to measure the research aims. The results showed no significant differences were found between match outcomes for each physical output metric calculated. There was a trivial ES shown for all conditions except decelerations, with win/lose having a moderate ES (g=0.53). When playing a possession-based playing style there was no significant difference or non-trivial ES found for any physical output and match outcomes. When playing a transition-based playing style there was a moderate ES found for win/draw (P=0.38, g=0.90) and win/loss (P=0.98, g=0.64). This research provides important evidence for utilizing intense deceleration actions as a physical KPI during match play for teams adopting a transitional playing style. Subsequently, training interventions should be adopted to physically prepare players to complete and sustain intense deceleration actions during match play.

Modelling dependence in football match outcomes: Traditional assumptions and an alternative proposal

The approaches routinely used to model the outcomes of football matches are characterized by strong assumptions about the dependence between the number of goals scored by the two competing teams and their marginal distribution. In this work, we argue that the assumptions traditionally made are not always based on solid arguments. Although most of these assumptions have been relaxed in the recent literature, the model introduced by Dixon and Coles in 1997 still represents a point of reference in the betting industry. While maintaining its conceptual simplicity, alternatives based on modelling the conditional distributions allow for the specification of more comprehensive dependence structures. In view of this, we propose a straightforward modification of the usual Poisson marginal models by means of thoroughly chosen marginal and conditional distributions. Careful model validation is provided, and a real data application involving five European leagues is conducted. The novel dependence structure allows to extract key insights on league dynamics and presents practical gains in several betting scenarios.

Factors associated with match outcomes in elite European football – insights from machine learning models

AIM To examine the factors affecting European Football match outcomes using machine learning models. METHODS Fixtures of 269 teams competing in the top seven European leagues were extracted (2001/02 to 2021/22, total >61,000 fixtures). We used eXtreme Gradient Boosting (XGBoost) to assess the relationship between result (win, draw, loss) and the explanatory variables. RESULTS The top contributors to match outcomes were travel distance, between-team differences in Elo (with a contribution magnitude to the model half of that of travel distance and match location), and recent domestic performance (with a contribution magnitude of a fourth to a third of that of travel distance and match location), irrespective of the dataset and context analyzed. Contextual factors such as rest days between matches, the number of matches since the managers have been in charge, and match-to-match player rotations were also shown to influence match outcomes; however, their contribution magnitude was consistently 4–8 times smaller than that of the three main contributors mentioned above. CONCLUSIONS Machine learning has proven to provide insightful results for coaches and supporting staff who may use their results to set expectations and adjust their practices in relation to the different contexts examined here.

Research on predicting football matches based on handicap data and BPNN

Football is one of the most influential sports in the world, and billions of people around the globe pay much attention to the football matches. With the growing popularity of football and the continuous development of the football betting industry, the prediction of the outcomes of football matches has become a hot topic in the commercial operations of sports especially footballs in recent years. It is also an important subject of academic research. In this paper, we develop a football match result prediction model based on the back propagation neural network. We take the German Bundesliga competitions as the research object in this paper. In addition to utilizing historical statistic data and team attributes from previous matches, we also incorporate a new dataset, known as handicap data, which refers to the odds data of the football matches, as the input layer of the BPNN (back propagation neural networks) for prediction. We also innovatively use varying numbers of hidden nodes, which greatly improves the prediction accuracy and stability of the model. Experimental results indicate that the average prediction accuracy of this football match prediction model is around 57.2%, with the highest prediction accuracy reaching 59.8% and the lowest prediction accuracy at 53.8%. The prediction model demonstrates relative stability, with no significant fluctuations in prediction accuracy.

PREDIKSI HASIL PERTANDINGAN SEPAK BOLA LIGA PREMIER INGGRIS DENGAN ARTIFICIAL NEURAL NETWORK BACKPROPAGATION

This study aims to enhance the accuracy of predicting English Premier League football match outcomes by utilizing a partially updated Artificial Neural Network (ANN) model based on match outcome data from the period 2017 to 2021. In this research, various statistical features such as the number of goals scored in the first half and the number of shots on target were incorporated as inputs to the ANN model. The match outcome data was normalized to improve the model's performance. The ANN model employed multiple hidden layers with ReLU (Rectified Linear Unit) activation functions and was trained using the Backpropagation algorithm. Throughout the training process, the model was periodically updated to reflect changes in match patterns over time. The research findings reveal that the ANN model with partial updates can predict football match outcomes with an accuracy of 77.89% in the final iteration, with a Mean Squared Error (MSE) of 0.769 and a Mean Absolute Error (MAE) of 0.689. Additionally, the prediction results are visualized in the form of a distribution graph comparing actual match outcomes with the predictions from the final iteration, providing a visual representation of the model's performance. This study makes a significant contribution to the development of modeling techniques for forecasting football match outcomes and underscores the importance of partial updates in adapting to changes in match patterns over time, offering potential for improvements in football match analysis and prediction in the future

Predicting Football Match Outcomes with Machine Learning Approaches

The increasing use of data-driven approaches has led to the development of models to predict football match outcomes. However, predicting match outcomes accurately remains a challenge due to the sport's inherent unpredictability. In this study, we have investigated the usage of different machine learning models in predicting the outcome of English Premier League matches. We assessed the performance of random forest, logistic regression, linear support vector classifier and extreme gradient boosting models for binary and multiclass classification. These models are trained with datasets obtained using different sampling techniques. The result showed that the models performed better when trained with dataset obtained using a balanced sampling technique for binary classification. Additionally, the models' predictions were evaluated by conducting simulation on football betting profits based on the 2022-2023 EPL season. The model achieved the highest accuracy is the binary class random forest, but the model provided the highest football betting profit is the binary class logistic regression.

Comparison of different ML models to Predict Football Match Results

Comparison of different ML models to Predict Football Match Results

Neural Networks in Sports Prediction

The following research paper is a literature review that I have pursued based on my interest in machine learning and the football. The paper explores the different types and variations of neural network systems used in the prediction of football matches and analyses the data available. Considering different factors such as input encoding, data types, training time and overall accuracy of the system, the paper tries to answer the question of ‘What neural network design factors influence the successful prediction of football match outcomes?’ The paper goes through and analyses specifically created data tables using a number of different papers in order to achieve this goal. The form of this paper is a literature review and provides an insight to what the future of predicting matches may be using machine learning and artificial neural networks.

How the 12th Man Influences Football Matches: The Role of Fans and Referees in Home Advantage Phenomenon.

Home advantage and referee bias are two sport factors that have been widely studied in the football (i.e., soccer) context. However, the mediating effect of spectators on these factors is not well understood, especially in the Turkish Super League. We examined (a) how variables of home advantage, referee bias, and spectator presence interacted to affect the outcome of past football matches; (b) how these variables have changed over time in the Turkish Super League; (c) and whether home advantage and referee bias differ in matches with spectators and ghost matches (without spectators). We analyzed regular season matches played in the Turkish Super League between the 2014-2015 and 2021-2022 seasons. We found that home teams had a significant advantage over away teams in matches with spectators (p < .001) and in ghost matches (p < .05 and p < .001), with small to medium effect sizes (d = .09 and .34, respectively). Regarding referee bias, away teams received more yellow cards and fewer penalties than home teams in matches with spectators (p < .001; d = -.10), and away teams received fewer yellow cards than home teams in ghost matches (p > .05; d = .05). There was no statistically significant difference between home and away teams on number of red cards in either type of match (p > .05). Thus, our data reveal that home teams have an edge over away teams with and without spectators, but this advantage is reduced during ghost matches (without spectators). We also revealed weak evidence of referee bias in favor of home teams, but only for some variables and only when spectators were present.

Decoding Argentina's path to World Cup glory: Unveiling the winning factors in match result analysis

The use of technology in evaluating the results of football matches has been used in various countries. This study aimed to identify and analyze the match results of the Argentina national team as the 2022 World Cup champion team. This research was a descriptive quantitative research that was analyzed in statistical form. The data sources were collected by the researcher by analyzing statistical data and video recordings of matches from the official FIFA website, Whoscored and Vidio. A total of 7 matches had been played by the Argentina national team in the 2022 World Cup competition, analyzed in terms of goals, ball possession, attempts at goal, direction of attack, distribution and defense. The results showed that most goals were scored in the 31-45th minute and most conceded in the 76-90 th minute, with the most ways being open play, most goals scored in area 3 with the striker being the top scorer. Argentina is quite dominant and controls the match in ball possession, attempt on target and total distribution (passing success, crossing success, and line breaks success) in every match with many attacks started from the right. When carrying out defensive pressure and tackles, Argentina is also more effective than its opponent. In this study, it could be concluded that the parameters to achieve victory were not only from ball possession, attempt success, and passing success but also seen from distribution parameters (crossing and line break success) and defensive parameters, especially defensive pressure which was also an indicator of team success in a match. It is hoped that in the future there will be further research discussing the successful journey of a team in several competitions followed by analysis parameters that are not much different, especially with a more detailed analysis of distribution and defense systems.

Real time prediction of match outcomes in Australian football

ABSTRACT This study aimed to determine whether machine learning models based on technical performance and not score margin could be used to predict end-of-match outcome of Australian football matches in real-time. If efficacious, these models could be used to generate insights about team performance and support the decision-making of coaches during matches. A database of 168 team technical performance indicators from 829 Australian Football League matches played between 2017 and 2021 was used. Two feature sets (data-driven and data-informed) were used to train and evaluate six models (generalised linear model, random forest and adaboost) on match outcome prediction (Win/Loss) over 120 epochs (a representation of normalised time during each match). All models performed well (mean classification accuracy = 73.5–75.8%) in comparison with a benchmark score-based model (mean classification accuracy = 77.4%). Data-informed feature sets performed better than data-driven in most cases. Classification accuracy was low at the start of a match (45.7–48.8%) but increased to a peak near the end of a match (87.2–92.7%). These findings suggest that any of the employed models can be used to formulate in-match decision support. The model which is best in practice will depend on factors such as time-cost trade-off, feasibility and the perceived value of its suggestions.

Forecasting football match results using a player rating based model

The paper presents a model for forecasting the results of football matches, which takes into account the abilities of the players on each team. The advantage of this approach is that the dynamic nature of team strengths is incorporated into the model directly. We test our model against the bookmaker’s predictions and in a Kelly-type betting strategy applied to the pre-match win/draw/loss market. The new model results in significant positive returns to betting.

A NOVEL APPROACH FOR PREDICTING FOOTBALL MATCH RESULTS: AN EVALUATION OF CLASSIFICATION ALGORITHMS

The task of predicting the outcomes of football matches is rendered increasingly complex by the intricate nature of the game and the variety of variables that could affect how things turn out. In the recent past, machine learning algorithms have been applied to this challenge, with varying degrees of success. In this particular research paper, we have meticulously evaluated the performance of several classification algorithms with the objective of predicting the outcomes of football matches in a tournament setting. The algorithms that were thoroughly tested encompassed a diverse range of classification models, including logistic regression, support vector machines and random forests. The study employed a dataset of historical match data drawn from the FIFA World Cup, historical team ranking data and team strength data from FIFA games. In order to accurately assess the efficacy of the algorithms tested, the evaluation metrics used were accuracy, precision and recall. The results of the study highlight the fact that machine learning algorithms can indeed prove to be effective tools for predicting the outcomes of football matches.

Prediction of Football Match Results Based on Edge Computing and Machine Learning Technology

With the rapid development of artificial intelligence, various machine learning algorithms have been widely used in the task of football match result prediction and have achieved certain results. However, traditional machine learning methods usually upload the results of previous competitions to the cloud server in a centralized manner, which brings problems such as network congestion, server computing pressure and computing delay. This paper proposes a football match result prediction method based on edge computing and machine learning technology. Specifically, we first extract some game data from the results of the previous games to construct the common features and characteristic features, respectively. Then, the feature extraction and classification task are deployed to multiple edge nodes.Finally, the results in all the edge nodes are uploaded to the cloud server and fused to make a decision. Experimental results have demonstrated the effectiveness of the proposed method.

Prediction of football match results with Machine Learning

Football is one of the most popular sports in the world, so the perception of the game and the prediction of results is of general interest to fans, coaches, media and gamblers. Although predicting football results is a very complex task, the football betting business has grown over time. The unpredictability of football results and the growing betting business justify the development of prediction models to support gamblers. In this article, we develop machine learning methods that take multiple statistics of previous matches and attributes of players from both teams as inputs to predict the outcome of football matches. Several prediction models were tested, with the experimental results showing encouraging performance in terms of the profit margin of football bets.

Eliminating supportive crowds reduces referee bias

AbstractWe use a series of historical natural experiments in association football to test whether social pressure from a home stadium crowd affected behavior and outcomes. The standout effect of an empty stadium was that referees cautioned visiting players less often, by over a third of a yellow card per match or once for every 22 fouls committed. Stadium crowds caused referees to favor the home team in their decision‐making. Empty stadiums appear to have reduced the overall home advantage in the final outcomes of football matches, but we cannot statistically reject no effect.

Research and application of constructing football training linear programming based on multiple linear regression equation

Abstract Based on the factors affecting sports performance, from a more comprehensive and broad perspective, after consulting the literature, 52 factors that affect the outcome of football matches are selected, including technology, tactics, physical fitness and referees’ penalties. By watching the video of the game, 52 influencing factors of 200 games and 400 teams were counted. The original data was statistically processed with correlation analysis and multiple linear regression analysis, and the statistics of the 26 European Cup games were substituted into the winning formula. To verify the scientific nature and objectivity of the formula, we aim to ascertain the core factors in the winning factors of a football game and the quantitative relationship between these factors and the result of the game, so as to provide a certain reference for football training, game analysis and scientific research. The technical and tactical ability of individuals and teams is the core competitive ability factor that affects the result of the game; from a single factor, 15 factor indicators have a significant impact on the result of a football match; on the whole, 10 factor indicators have a significant effect on the result of a football match. In addition, there is a certain quantitative relationship between these influencing factors and the results of the game; empirical evidence shows that the football game winning formula has a certain degree of science and objectivity.

On-Match Impact and Outcomes of Scoring First in Professional European Female Football.

Background: Scoring first seems to be a determinant in professional football playing; several factors could influence the development of the match and the outcome. This study aimed to identify which factors could influence scoring first and impact match outcomes in professional European female football. Methods: There were 504 official matches held on 74 match days during the 2018–2019 professional female European football seasons (Primera Iberdrola, D1 Féminine, and Frauen-Bundesliga), analysed using a notational and inferential assessment. Results: There was a direct positive relationship (p < 0.05) between scoring first and winning the match; 75.9% of the winning teams scored first. Moreover, those teams that usually scored first had a better final league classification (p < 0.05). These relationships were not influenced by home or away conditions. Conclusions: Scoring first is a determinant in the outcomes of professional European female football matches. Physical and tactical training and programming should focus on those variables, leading female teams to score first.

Forecasting binary outcomes in soccer

Several studies deal with the development of advanced statistical methods for predicting football match results. These predictions are then used to construct profitable betting strategies. Even if the most popular bets are based on whether one expects that a team will win, lose, or draw in the next game, nowadays a variety of other outcomes are available for betting purposes. While some of these events are binary in nature (e.g. the red cards occurrence), others can be seen as binary outcomes. In this paper we propose a simple framework, based on score-driven models, able to obtain accurate forecasts for binary outcomes in soccer matches. To show the usefulness of the proposed statistical approach, two experiments to the English Premier League and to the Italian Serie A are provided for predicting red cards occurrence, Under/Over and Goal/No Goal events.