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Journal articles on the topic 'Football Prediction'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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1

Madsen, Jens Koed. "Goal-line oracles: Exploring accuracy of wisdom of the crowd for football predictions." PLOS ONE 20, no. 1 (2025): e0312487. https://doi.org/10.1371/journal.pone.0312487.

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Football’s inherent volatility and low-scoring nature present unique challenges for predicting outcomes. This study investigates the efficacy of Wisdom of the Crowd in forecasting football match outcomes as well as expected goals (XG) across a Premier League season. Participants predicted team goal counts, which were then compared to actual expected goals (XG) and match results. Results across 760 team predictions reveal that while Wisdom of the Crowd accurately predicts XG on average, it overestimates ’big-6’ teams and underestimates others, hinting at inherent biases. Notably, however, colle

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Hu, Jiahao. "Research on predicting football matches based on handicap data and BPNN." Applied and Computational Engineering 31, no. 1 (2024): 29–35. http://dx.doi.org/10.54254/2755-2721/31/20230118.

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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 co

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An, Zhongqi. "Real-Time Football Match Prediction Platform." ITM Web of Conferences 70 (2025): 04003. https://doi.org/10.1051/itmconf/20257004003.

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The integration of real-time data into sports analytics has significantly enhanced the accuracy of football match predictions, which is vital for team management, tactical planning, and commercial applications *such as sports betting. This paper presents a Python-based platform for predicting football match outcomes by collecting and processing real-time data from the SofaScore website. The platform employs machine learning models, including Random Forest, Support Vector Machines (SVM), and Neural Networks, combined with feature engineering techniques, to generate accurate predictions. A user-

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Bhatia, Vedant, and Aditya More. "Implementing Football Prediction System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 392–96. http://dx.doi.org/10.22214/ijraset.2024.65056.

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Abstract: The "Football Match Prediction System using Machine Learning" aims to predict football match outcomes using machine learning techniques. The project involves data preprocessing, feature engineering, and training various machine learning models, including Naive Bayes, Random Forest, and XGBoost. The results show the model can predict match outcomes with reasonable accuracy, providing valuable insights into match performance. Future work aims to refine the model by incorporating additional features like player data and external factors like weather conditions to further enhance predict

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Mustafa Zebari, Gheyath, Subhi Zeebaree, Mohammed M.Sadeeq, and Rizgar Zebari. "Predicting Football Outcomes by Using Poisson Model: Applied to Spanish Primera División." Journal of Applied Science and Technology Trends 2, no. 04 (2021): 105–12. http://dx.doi.org/10.38094/jastt204112.

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During the past decades, sport, in general, has become one of the most powerful competitions and the most popular in the world. As well as, everyone is waiting for the winner, and who will be the champion in the end in different tournaments. Among these sports, football's popularity is more than all other sports. Football matches results predicting, as well as the champion in various competitions, has been seriously studied in recent years. Moreover, it has become an interesting field for many researchers. In this work, the Poisson model has been presented to predict the winner, draw, and lose

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Vaidya, Saurabh, Harshal Sanghavi, and Kushal Gevaria. "Football Match Winner Prediction." International Journal of Computer Applications 154, no. 3 (2016): 31–33. http://dx.doi.org/10.5120/ijca2016912066.

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Haitham Jawad Kadhim, Maab Fathi Hamzah, and Mohammed Lateif Hussain. "A Review of the Use of Artificial Intelligence Algorithms for Predicting Injuries and Performance in Football Players." Mustansiriyah Journal of Sports Science 7, no. 2 (2025): 148–61. https://doi.org/10.62540/mjss.2025.2.7.12.

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The purpose of this study is to investigate the research on artificial intelligence algorithms in football, specifically in relation to player performance prediction and injury prevention. To accomplish this goal, scholarly resources including Google Scholar, ResearchGate, Springer, and Scopus were used to provide a systematic examination of research done during the last ten years (2015–2025). Through a systematic procedure that included data collection, study selection based on predetermined criteria, categorisation based on AI applications in football, and assessment of major research proble

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Meng, Xiangkun. "Soccer match outcome prediction with random forest and gradient boosting models." Applied and Computational Engineering 40, no. 1 (2024): 99–107. http://dx.doi.org/10.54254/2755-2721/40/20230634.

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In order to accurately predict the results of soccer matches, this study introduces Machine Learning (ML) techniques in joint Random Forest (RF) and Gradient Boosting (GB) models. In order to forecast the results of the next World Cup, a model has been trained using past information from prior tournaments. The proposed model is evaluated using multiple performance criteria including precision and accuracy. The RF approach outperforms the GB approach in terms of both accuracy and precision, as concluded after the experiment. The most important features for predicting the outcome of football gam

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Jin, Yun. "Football Match Scoring Method Based on Adaptive Neural Network Algorithm." Security and Communication Networks 2022 (April 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/9502218.

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To study in more detail the impact of various indicators on the scoring system of a football match, the author suggests a football game scoring method based on an adaptive neural network algorithm. Firstly, the application background of football match prediction, the research and application status of the adaptive neural network algorithm, and the related research of football match prediction are described; Secondly, the factors affecting the outcome of football games are analyzed, and the applicability of the adaptive neural network algorithm in football match prediction are summarized. Throu

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M.NEPOLION, M. NEPOLION, and Dr USHA RANI. "Prediction of Football Playing Ability on Selected Physiological Variables of School Level Male Football Players." International Journal of Scientific Research 3, no. 5 (2012): 513–14. http://dx.doi.org/10.15373/22778179/may2014/165.

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Yu, Yue, and Zi Ye. "Healthcare Data-Based Prediction Algorithm for Potential Knee Joint Injury of Football Players." Journal of Healthcare Engineering 2021 (November 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/3461648.

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It is important to predict the potential harm to the knee joint in order to prevent football players from inflicting numerous injuries to the knee during activity. Numerous professionals have been drawn to this subject, and many viable prediction systems have been developed. Prediction of potential knee joint injury is critical to effectively avoid knee joint injury during exercise. The current prediction algorithms are mainly implemented through expert interviews, medical reports, and historical documents. The algorithms have problems with low prediction accuracy or precision values. There is

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Alim, Khairul, and Dewi Murni. "PREDIKSI HASIL PERTANDINGAN SEPAK BOLA LIGA PREMIER INGGRIS DENGAN ARTIFICIAL NEURAL NETWORK BACKPROPAGATION." Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 4, no. 3 (2023): 1523–31. http://dx.doi.org/10.46306/lb.v4i3.425.

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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

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Matthews, Tim, Sarvapali Ramchurn, and Georgios Chalkiadakis. "Competing with Humans at Fantasy Football: Team Formation in Large Partially-Observable Domains." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1394–400. http://dx.doi.org/10.1609/aaai.v26i1.8259.

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We present the first real-world benchmark for sequentially-optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker's beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the

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Wang, Chenrui. "Multiple Regression Models Were Used to Predict Match Outcome Through Football Team Match Data." International Journal of Computer Science and Information Technology 3, no. 2 (2024): 117–29. http://dx.doi.org/10.62051/ijcsit.v3n2.13.

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With the increasing prosperity of football, the result prediction of football matches has become a hot spot in the commercial operation of sports, and also an important issue studied by the academic circle. Research about the results of football prediction, most research scholars from the factors of the results, such as the team strength, the weather, the team ranking, team status, coach, team home and away combat ability, but a large number of historical game data collection is more difficult, and part of the political factors cannot be quantified. A former study found that gambling companies

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Fuadi, Muhammad Bahyul Anwar, and Alamsyah Alamsyah. "Optimization of the C4.5 Algorithm Using Particle Swarm Optimization and Discretization in Predicting the Results of English Premier League Football Matches." Journal of Advances in Information Systems and Technology 4, no. 2 (2023): 126–38. http://dx.doi.org/10.15294/jaist.v4i2.59531.

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Football is one of the most popular sports. One of the most competitive football competitions is the English Premier League. This study aims to determine the prediction of the results of the football match in English Premier League. The prediction results in the form of home win, away win, and draw. This prediction uses data mining techniques, namely using the C4.5 algorithm as a classification algorithm with Particle Swarm Optimization as a feature selection method and Discretization as a preprocessing method. The dataset used was obtained from the football-data.co.uk website for four league

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Wang, Tianyou, Zheng Zhang, and Shengxin Zhu. "Machine learning-based football match prediction system." Applied and Computational Engineering 92, no. 1 (2024): 181–86. http://dx.doi.org/10.54254/2755-2721/92/20241749.

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This study develops a machine learning-based system to predict English Premier League (EPL) outcomes, employing models such as Principal Component Analysis (PCA), K-Nearest Neighbors (KNN), Random Forests, and Support Vector Machines (SVM). The analysis covered a large dataset of matches, with the data normalized to ensure consistency and accuracy across models. Among the methods used, Random Forests showed the most robust performance in predicting match outcomes, particularly in forecasting wins and losses. However, both Random Forests and SVM encountered difficulties in accurately predicting

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Penn, Matthew J., and Christl A. Donnelly. "Analysis of a double Poisson model for predicting football results in Euro 2020." PLOS ONE 17, no. 5 (2022): e0268511. http://dx.doi.org/10.1371/journal.pone.0268511.

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First developed in 1982, the double Poisson model, where goals scored by each team are assumed to be Poisson distributed with a mean depending on attacking and defensive strengths, remains a popular choice for predicting football scores, despite the multitude of newer methods that have been developed. This paper examines the pre-tournament predictions made using this model for the Euro 2020 football tournament. These predictions won the Royal Statistical Society’s prediction competition, demonstrating that even this simple model can produce high-quality results. Moreover, the paper also presen

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18

Empacher, Christina, Udo Kamps, and Grigoriy Volovskiy. "Statistical Prediction of Future Sports Records Based on Record Values." Stats 6, no. 1 (2023): 131–47. http://dx.doi.org/10.3390/stats6010008.

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Point prediction of future record values based on sequences of previous lower or upper records is considered by means of the method of maximum product of spacings, where the underlying distribution is assumed to be a power function distribution and a Pareto distribution, respectively. Moreover, exact and approximate prediction intervals are discussed and compared with regard to their expected lengths and their percentages of coverage. The focus is on deriving explicit expressions in the point and interval prediction procedures. Predictions and forecasts are of interest, e.g., in sports analyti

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19

Wang, Yihan. "Dealing with Probability Statistics Prediction on Sporting Events." Theoretical and Natural Science 100, no. 1 (2025): 239–46. https://doi.org/10.54254/2753-8818/2025.22285.

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Predicting and modeling sports events has always been one of the main applications of probability and statistics. From analyzing the likelihood of a team's victory based on historical performance data can be used to estimate the probability of a particular player scoring a goal. This paper focuses on the practical use of probability in football matches, summarizing several innovative prediction models based on classical probability distributions. It demonstrates the accuracy of the bivariate Poisson distribution model in predicting football scores, especially the number of draws through exampl

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Bendiaf, Messaoud, Hakima Khelifi, Djamila Mohdeb, Mouhoub Belazzoug, and Abdelhamid Saifi. "A Deep Learning Approach Based on Interpretable Feature Importance for Predicting Sports Results." International Journal of Computer Science in Sport 24, no. 1 (2025): 56–72. https://doi.org/10.2478/ijcss-2025-0004.

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Abstract Football match result prediction is a challenging task that has been the subject of much research. Traditionally, predictions have been made by team managers, fans, and analysts based on their knowledge and experience. However and recently there has been an increased interest in predicting match outcomes using statistical techniques and machine learning. These algorithms can learn from historical data to identify complex relationships between different variables, and then make predictions about the outcome of future matches. Accordingly, forecasting plays a pivotal role in assisting m

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21

Karlis, D., and I. Ntzoufras. "Robust fitting of football prediction models." IMA Journal of Management Mathematics 22, no. 2 (2010): 171–82. http://dx.doi.org/10.1093/imaman/dpq013.

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22

Wheatcroft, Edward. "Forecasting football matches by predicting match statistics." Journal of Sports Analytics 7, no. 2 (2021): 77–97. http://dx.doi.org/10.3233/jsa-200462.

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This paper considers the use of observed and predicted match statistics as inputs to forecasts for the outcomes of football matches. It is shown that, were it possible to know the match statistics in advance, highly informative forecasts of the match outcome could be made. Whilst, in practice, match statistics are clearly never available prior to the match, this leads to a simple philosophy. If match statistics can be predicted pre-match, and if those predictions are accurate enough, it follows that informative match forecasts can be made. Two approaches to the prediction of match statistics a

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Munđar, Dušan, and Diana Šimić. "Croatian First Football League: Teams' performance in the championship." Croatian Review of Economic, Business and Social Statistics 2, no. 1 (2016): 15–23. http://dx.doi.org/10.1515/crebss-2016-0006.

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Abstract The goal of our research was to use simulation modelling for prediction of the Croatian First Football League seasonal ranking and analyse variation in teams’ performance during a season. We have developed a model of the number of goals scored by a team in a match based on the Poisson distribution. Parameters of the model were estimated from the data on consecutive matches in a season. Variation in a team’s performance was modelled as a moving parameter estimate. The final rankings were predicted from 1000 simulation runs of the second part of the season based on parameter estimates f

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Pan, Nengchao. "Research on physical fitness training of football players based on improved LSTM neural network to improve physical energy saving and health." 3C Tecnología_Glosas de innovación aplicadas a la pyme 12, no. 01 (2023): 127–40. http://dx.doi.org/10.17993/3ctecno.2023.v12n1e43.127-140.

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In order to ensure that the physical function of football players adapts to the development of modern football level, and avoid the phenomenon of inability to adapt to the intensity of modern football games due to lack of physical fitness. Aiming at the physical training of football players, this paper proposes an improved long-short-term memory network (W-LSTM) model for the optimization and prediction of physical training. The model effectively combines the global feature extraction ability of LSTM for time series data and the preprocessing ability of the extracted data, which reduces the lo

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Qiao, Jinyu. "The Application of Artificial Intelligence in Football Risk Prediction." Computational Intelligence and Neuroscience 2022 (June 13, 2022): 1–9. http://dx.doi.org/10.1155/2022/6996134.

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Football is the most popular sports in the World, with an estimated global following of 4.0 billion fans worldwide. Football draws attention from people of various age groups. The result of the game only decides the performance of the team and individual players. The player has to train smarter to avoid a career-ending injury. Sports have also entered into the new era of artificial intelligence as any industry. Artificial intelligence (AI) in football acts like a teammate to the players and also plays the role of an assistant coach. The coach uses artificial intelligence and incorporates it in

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Feng, Qingkun, Yanying Liu, and Lijun Wang. "Wearable Device-Based Smart Football Athlete Health Prediction Algorithm Based on Recurrent Neural Networks." Journal of Healthcare Engineering 2021 (July 30, 2021): 1–7. http://dx.doi.org/10.1155/2021/2613300.

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For football players who participate in sports, the word “health” is extremely important. Athletes cannot create their own value in competitive competitions without a strong foundation. Scholars have paid a lot of attention to athlete health this year, and many analysis methods have been proposed, but there have been few studies using neural networks. As a result, this article proposes a novel wearable device-based smart football player health prediction algorithm based on recurrent neural networks. To begin, this article employs wearable sensors to collect health data from football players. T

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Liu, Huan, Jian Wang, and Jin Feng Li. "To Achieve Football Interception of Soccer Robots Based on Prediction." Applied Mechanics and Materials 220-223 (November 2012): 1095–98. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1095.

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Interception is a very important technological movement in robot football match. In this text, prediction of sport among the playing area, set football robot sport model, we have put forward the scheme of realizing interception movements fast，effectively ,and have verified the feasibility of the scheme in the real system.

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Kröske, Björn. "Prediction Model for Alcohol Consumption in Young Football Players in Germany." Zeitschrift für Gesundheitspsychologie 24, no. 4 (2016): 180–92. http://dx.doi.org/10.1026/0943-8149/a000168.

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Abstract. Alcohol consumption among adolescents is higher in athletes, especially in team sports such as football, compared with nonathletes. This study investigated factors influencing alcohol consumption in adolescent football players in Germany. Structural equation modeling was used to understand how the different predictors work together, thereby improving alcohol prevention in the field of football. The hypothesized model was largely confirmed and the most significant predictive factor of alcohol consumption was the drinking behavior of friends. Alcohol expectancies and drinking refusal s

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Takezaki, Yu, and Yasushi Nagata. "Statistical analysis and prediction in American football." Total Quality Science 7, no. 2 (2022): 51–59. http://dx.doi.org/10.17929/tqs.7.51.

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Doan, Le Minh Thao, Karl Hall, and Victor Chang. "Football Results Prediction and Machine Learning Techniques." International Journal of Business and Systems Research 17, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijbsr.2023.10051445.

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Bock, Joel. "Empirical Prediction of Turnovers in NFL Football." Sports 5, no. 1 (2016): 1. http://dx.doi.org/10.3390/sports5010001.

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Qin Cheng, Dimitris Agrafiotis, Alin M. Achim, and David R. Bull. "Gaze Location Prediction for Broadcast Football Video." IEEE Transactions on Image Processing 22, no. 12 (2013): 4918–29. http://dx.doi.org/10.1109/tip.2013.2279941.

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Chang, Victor, Karl Hall, and Le Minh Thao Doan. "Football results prediction and machine learning techniques." International Journal of Business and Systems Research 17, no. 5 (2023): 565–86. http://dx.doi.org/10.1504/ijbsr.2023.133178.

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S, Harish, Abishek Kevin A, Harsha Vardhan U, and Sharon Femi P. "Expected Goals Prediction in Football using XGBoost." ESP Journal of Engineering & Technology Advancements 2, no. 4 (2023): 22–27. http://dx.doi.org/10.56472/25832646/jeta-v3i1p104.

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Pan, Bomao. "Explore Machine Learning's Prediction of Football Games." ITM Web of Conferences 70 (2025): 04005. https://doi.org/10.1051/itmconf/20257004005.

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The aim of this study is to predict the outcome and score of football matches. To achieve this goal, this paper employs a variety of machine learning models, including Random Forest, support vector classifiers (SVC), and Logistic Regression, and conducts in-depth analysis of the data. The results show that home teams have a significantly higher win rate than away teams. In addition, the score changes show a high degree of randomness, reflecting that the game is affected by a variety of factors. The prediction performance of these models is different, and the prediction accuracy of the random f

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Hollaus, Bernhard, Christian Raschner, and Andreas Mehrle. "Development of release velocity and spin prediction models for passing machines in American football." Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology 233, no. 1 (2018): 27–33. http://dx.doi.org/10.1177/1754337118774448.

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This article focuses on the development of release velocity and spin prediction models for oval shaped footballs with a state-of-the-art passing machine. Since the trajectory of the ball can be predicted with aerodynamic models, the state of the ball at release time is of interest. At present, no prediction model for this initial state exists. This study measured release spin and velocity. A prediction model was developed based on various ball wear and measured release spin and velocity for different machine configurations. To sensor the motion, a high-speed camera with post image processing w

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Yallamati, Suresh, and Shaheda Akthar. "Entangling Quantum Adversarial Network with Football Optimization for Software Defect Prediction." Journal of Trends in Computer Science and Smart Technology 7, no. 2 (2025): 140–75. https://doi.org/10.36548/jtcsst.2025.2.003.

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Predicting software defects is a critical component of software quality control. It is essential to plan for early defect detection and mitigation to enhance performance and reliability. Traditional machine learning and deep learning models often face challenges in managing missing values, extracting meaningful features, and effectively distinguishing between defective and non-defective software modules due to their reliance on linear classifiers and limited feature representation capabilities. To address these challenges, this study proposes an Entangling Quantum Generative Adversarial Networ

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Stübinger, Johannes, Benedikt Mangold, and Julian Knoll. "Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics." Applied Sciences 10, no. 1 (2019): 46. http://dx.doi.org/10.3390/app10010046.

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In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such

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Peck, Bailey, Timothy Renzi, Hannah Peach, Jane Gaultney, and Joseph S. Marino. "Examination of Risk for Sleep-Disordered Breathing Among College Football Players." Journal of Sport Rehabilitation 28, no. 2 (2019): 126–32. http://dx.doi.org/10.1123/jsr.2017-0127.

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Context: Professional football linemen are at risk for sleep-disordered breathing (SDB) compared with other types of athletes. It is currently unknown whether college football linemen display a similar risk profile. Objective: (1) To determine for the first time whether college football linemen show risk for SDB and (2) test the hypothesis that SDB risk is higher in college football linemen compared with an athletic comparison group. Design: Descriptive laboratory study. Setting: The Health Risk Assessment Laboratory. Participants: Male football linemen (n = 21) and track (n = 19) Division I a

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N, Sathyanarayana, Anjani Lahoty, Anubhav ., Archana S, and Dhanush Rao H S. "PREDICTIVE ANALYSIS OF SPORTS DATA USING MACHINE LEARNING." International Research Journal of Computer Science 9, no. 8 (2022): 240–44. http://dx.doi.org/10.26562/irjcs.2022.v0908.17.

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There are numerous methods for making sports predictions, and data analysis is crucial to predicting. Previous attempts in sports data analysis have resulted in the prediction of sports such as football, tennis next shot location prediction, Olympic athlete performance, basketball slam dunk shot frequency, and many more. Cricket prediction is tough due to the numerous variables that might affect the result or outcome of a cricket match. Previously, simple cricket match prediction systems focused on the venue, ignoring aspects such as weather, stadium size, captaincy, etc. Factors such as the m

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Murali, Raghav, Shikhar Shrivastava, and Sornalakshmi Krishnan. "Analysis of Football Data on Twitter for Popularity Mapping and Transfer Predictions." International Journal of Engineering & Technology 7, no. 3.12 (2018): 452. http://dx.doi.org/10.14419/ijet.v7i3.12.16128.

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Twitter has gathered a reputation of being a reliable source for predictive modeling on various domains such as flu trends, sports data, political trends etc. Football is widely considered to be the most popular sport in the world. We introduce a novel approach of analyzing the tweets collected over a period of time which are related to football. We present a visualized world map with the high density areas indicating the parts of the world where football is most popular. Further, we incorporate the fan opinions of popular football players by analyzing their tweets individually. This ultimatel

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Bai, Yanyang, and Xuesheng Zhang. "Prediction Model of Football World Cup Championship Based on Machine Learning and Mobile Algorithm." Mobile Information Systems 2021 (September 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/1875060.

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With the technological development and change of the times in the current era, with the rapid development of science and technology and information technology, there is a gradual replacement in the traditional way of cognition. Effective data analysis is of great help to all societies, thereby drive the development of better interests. How to expand the development of the overall information resources in the process of utilization, establish a mathematical analysis–oriented evidence theory system model, improve the effective utilization of the machine, and achieve the goal of comprehensively p

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Alfredo, Yoel F., and Sani M. Isa. "Football Match Prediction with Tree Based Model Classification." International Journal of Intelligent Systems and Applications 11, no. 7 (2019): 20–28. http://dx.doi.org/10.5815/ijisa.2019.07.03.

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Rodrigues, Fátima, and Ângelo Pinto. "Prediction of football match results with Machine Learning." Procedia Computer Science 204 (2022): 463–70. http://dx.doi.org/10.1016/j.procs.2022.08.057.

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Hulwan, Mr Nishant G. "FOOTBALL MATCH WINNING TEAM PREDICTION USING MACHINE LEARNING." International Journal of Advanced Research in Computer Science 9, no. 6 (2018): 12–17. http://dx.doi.org/10.26483/ijarcs.v9i6.6337.

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E.Jones. "Artificial Neural Network Approach for Football Scores Prediction." Journal of Networking and Communication Systems (JNACS) 6, no. 3 (2023): 13–21. http://dx.doi.org/10.46253/jnacs.v6i3.a2.

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Wang, Shuo, Zhiqiang Li, and Hang Chen. "An Intelligent Physical Training System in Football Education." Lecture Notes in Education, Arts, Management and Social Science 3, no. 3 (2025): 35–40. https://doi.org/10.18063/lne.v3i3.816.

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With the advancement of intelligent technology, data-driven evaluation methods have gained increasing attention in physical education, particularly in the application of intelligent physical training systems in football education. These systems enable precise assessment of athletes’ training status and provide scientific support for personalized training, thereby enhancing training efficiency and game performance. This study employs Random Forest and Neural Network models to construct an intelligent evaluation system for predicting students’ overall performance in football training. Key perfor

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Lee Dow, Connor, Ryan G. Timmins, Joshua D. Ruddy, et al. "Prediction of Hamstring Injuries in Australian Football Using Biceps Femoris Architectural Risk Factors Derived From Soccer." American Journal of Sports Medicine 49, no. 13 (2021): 3687–95. http://dx.doi.org/10.1177/03635465211041686.

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Background: Hamstring strain injuries are the most common injuries in team sports. Biceps femoris long head architecture is associated with the risk of hamstring injury in soccer. To assess the overall predictive ability of architectural variables, risk factors need to be applied to and validated across different cohorts. Purpose: To assess the generalizability of previously established risk factors for a hamstring strain injury (HSI), including demographics, injury history, and biceps femoris long head (BFlh) architecture to predict HSIs in a cohort of elite Australian football players. Study

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Piepiora, Paweł, Damian Kwiatkowski, Justyna Bagińska, and Dimitris Agouridas. "Sports Level and the Personality of American Football Players in Poland." International Journal of Environmental Research and Public Health 18, no. 24 (2021): 13026. http://dx.doi.org/10.3390/ijerph182413026.

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Research on personality in sport is very popular as it allows prediction of the behavior of players in the starting situation. Hence, verifications of players due to their sports level may turn out to be crucial. Due to the dynamic development of American football in Poland, we undertook research to verify the relationship between the sports level and the personality of these players. The Big Five personality study that we carried out involved players aged from 20 to 29—the representatives of American football clubs in Poland (N = 140) from three league games levels: LFA 1 (n = 75), LFA 2 (n =

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Lee, Jaemin, Juhuhn Kim, Hyunho Kim, and Jong-Seok Lee. "A Bayesian Approach to Predict Football Matches with Changed Home Advantage in Spectator-Free Matches after the COVID-19 Break." Entropy 24, no. 3 (2022): 366. http://dx.doi.org/10.3390/e24030366.

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Since the coronavirus disease 2019 (COVID-19) pandemic, most professional sports events have been held without spectators. It is generally believed that home teams deprived of enthusiastic support from their home fans experience reduced benefits of playing on their home fields, thus becoming less likely to win. This study attempts to confirm if this belief is true in four major European football leagues through statistical analysis. This study proposes a Bayesian hierarchical Poisson model to estimate parameters reflecting the home advantage and the change in such advantage. These parameters a

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