Relevant bibliographies by topics / Football Prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Football Prediction'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Football Prediction"

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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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Dissertations / Theses on the topic "Football Prediction"

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Ota, Karson L. "Football play type prediction and tendency analysis." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113120.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (page 33).<br>In any competition, it is an advantage to know the actions of the opponent in advance. Knowing the move of the opponent allows for optimization of strategy in response to their move. Likewise, in football, defenses mu
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Philp, Fraser Derek. "Validating models of injury risk prediction in football players." Thesis, Keele University, 2018. http://eprints.keele.ac.uk/4993/.

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Association football (soccer) is a popular sport and there is a high risk of injury for participants. Within the context of professional clubs, the risk of injury is also associated with the risk of financial costs. Therefore, injury reduction processes are considered important, and previous studies have sought to identify and model injury risk factors. Although formal screening tests e.g. The Functional Movement Screen (FMS) and monitoring procedures e.g. Union of European Football Associations (UEFA) have been developed for modelling and predicting injuries, the processes in current use, lac
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Rossi, A. "PREDICTIVE MODELS IN SPORT SCIENCE: MULTI-DIMENSIONAL ANALYSIS OF FOOTBALL TRAINING AND INJURY PREDICTION." Doctoral thesis, Università degli Studi di Milano, 2017. http://hdl.handle.net/2434/495229.

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Due to the fact that team sports such as football have a complex multidirectional and intermittent nature, an accurate planning of the training workload is needed in order to maximise the athletes’ performance during the matches and reduce their risk of injury. Despite the evaluation of external workloads during trainings and matches has become more and more easier thanks to the advent of the tracking system technologies such as Global Position System (GPS), the planning of the best training workloads aimed to obtain the higher performance during the matches and a lower risk of injury during s
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Anners, Carl, and Stefan Saarm. "The More the Merrier? : A Study Measuring Relative Efficiency of Two Prediction Markets." Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242132.

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Our aim of this paper was to create a method for comparing the overall relative efficiency of a prediction market for the English football league Premier League and a prediction market for the Swedish football league Allsvenskan. The purpose of this was to see how the overall turnover of a prediction market affects the efficiency of it. We conclude that while the implied probability of the two markets on average corresponds well to the win frequency, the Premier League prediction market has statistically significant lower variation than Allsvenskan. The method we created can also be used to te
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Falkstein, David Lawrence. "Prediction of Athletic Injury and Postinjury Emotional Response in Collegiate Athletes: A Prospective Study of an NCAA Division I Football Team." Thesis, University of North Texas, 1999. https://digital.library.unt.edu/ark:/67531/metadc278163/.

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Previous research has examined factors that predispose collegiate football players to injury (e.g., Petrie, 1993a, 1993b) as well as factors that influence athletes' psychological adjustment to being injured (e.g., Brewer, 1993; Leddy, Lambert, & Ogles, 1994). Despite the reports of the NCAA Injury Surveillance System that the greatest number of football injuries occur during the spring preseason (NCAA, 1997), studies have only examined injury during the regular season. Thus, the purpose of this study was to investigate the antecedents and consequences of injury in collegiate football players
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Long, Joseph. "Development of a Prediction Model for the NCAA Division-I Football Championship Subdivision." Thesis, North Dakota State University, 2013. https://hdl.handle.net/10365/27103.

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This thesis investigates which in-game team statistics are most significant in determining the outcome in a NCAA Division-I Football Championship Subdivision (FCS) game. The data was analyzed using logistic and ordinary least squares regression techniques to create models that explained the outcome of the past games. The models were then used to predict games where the actual in-game statistics were unknown. A random sample of games from the 2012 NCAA Division-I Football Championship Subdivision regular season was used to test the accuracy of the models when used to predict future games. Vario
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Carin, Yann. "La faillite des clubs français de football : un secteur spécifique." Thesis, Limoges, 2019. http://www.theses.fr/2019LIMO0040/document.

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Le football européen en général et le football français en particulier font état de difficultés financières et de faillites récurrentes de clubs professionnels. Sur la seule période de 1975 à 2018, 81 clubs français de football engagés dans les championnats des quatre premières divisions ont connu une faillite. Le sujet de la faillite d’entreprises a été largement traité pour les secteurs courants de l’économie. De nombreuses recherches se sont attachées à construire des modèles de prédiction, puis progressivement d’autres travaux se sont concentrés sur le processus et les différentes trajecto
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Mortensen, Bartley B. "Can a Preseason Screen Predict Injury or Performance over Three Years of College Football?" BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7339.

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Purpose: To investigate if the Functional Movement Screen (FMSâ„¢) total score, individual component test scores or number of asymmetries can predict noncontact injury risk or player performance over three consecutive seasons of NCAA Division I football. Methods: As football teams are comprised of individuals with vastly different physical characteristics and playing responsibilities, we divided the subjects into three homogeneous groups based on position (Big, Combo and Skill). Each FMSâ„¢ score was assessed with regard to the total team score as well as by individual position groups. For our
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Schroeder, Brian L. "Prediction equations as an alternative to 1-RM strength testing in division I college football players." Thesis, University of Hawaii at Manoa, 2003. http://hdl.handle.net/10125/7010.

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The purpose of this investigation was to develop a new prediction equation for 1RM bench press performance in Div. I college football players using both submaximal lifts and anthropometric variables. One repetition maximum (1-RM), 5-RM, reps at 225 lbs, and various anthropometric variables were collected on 85 Div. I college football players. Mean and SD were found for the following variables: height 182.3 cm ± 7.2; weight 102.0 kg ± 21.5; age 19.8 yrs ± 1.3; 1-RM 308.9 lbs ± 59.2; 5-RM 261.8 lbs ± 51.2; 225lb repetitions 14.1 reps ± 8.1; upper arm length 37.9 cm ± 2; CSA 125.5 cm2 ± 24.8; and
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Rudin, Pierre. "Football result prediction using simple classification algorithms, a comparison between k-Nearest Neighbor and Linear Regression." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187659.

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Ever since humans started competing with each other, people have tried to accurately predict the outcome of such events. Football is no exception to this and is extra interesting as subject for a project like this with the ever growing amount of data gathered from matches these days. Previously predictors had to make there predictions using there own knowledge and small amounts of data. This report will use this growing amount of data and find out if it is possible to accurately predict the outcome of a football match using the k-Nearest Neighbor algorithm and Linear regression. The algorithms
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Books on the topic "Football Prediction"

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Barber, John. Football results processing and predictions for the World Wide Web. University of Manchester, Department of Computer Science, 1996.

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Egeler, Daniel J. Indoor soccer: The Americanization of soccer. 1986.

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A comparison of Division IA football players' grades in season and out of season. 1994.

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A comparison of Division IA football players' grades in season and out of season. 1994.

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A comparison of Division IA football players' grades in season and out of season. 1994.

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Guestbooks, Trendy. Oh Baby! Guestbook: Soccer Football Sports Shower Signing Sign in Book, Welcome New Baby Girl with Gift Log Recorder, Address Lines, Prediction, Advice Wishes, Photo Milestones. Independently Published, 2019.

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Memories, Forever. Oh Baby! Guestbook: American Football Sports Shower Signing Sign in Book, Welcome New Baby Girl with Gift Log Recorder, Address Lines, Prediction, Advice Wishes, Photo Milestones. Independently Published, 2019.

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Books, Keepsake Guest. Welcome Baby Guestbook: Football Soccer Sports Fan Theme Shower Signing Sign in Book, Welcome New Baby Girl with Gift Log Recorder, Address Lines, Prediction, Advice Wishes, Photo Milestones. Independently Published, 2019.

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Occasions, Forever After. Welcome Baby Guestbook: American Football Sports Fan Player Theme Shower Signing Sign in Book, Welcome New Baby Girl with Gift Log Recorder, Address Lines, Prediction, Advice Wishes, Photo Milestones. Independently Published, 2019.

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Baby Shower Guest Book: American Football Sports Fan Player - Gender Reveal Boy Girl Signing Sign in Guestbook, Welcome New Baby with Gift Log Recorder, Address Lines, Prediction, Advice Wishes, Photo Milestones. Independently Published, 2019.

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Book chapters on the topic "Football Prediction"

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Fournier-Viger, Philippe, Tianbiao Liu, and Jerry Chun-Wei Lin. "Football Pass Prediction Using Player Locations." In Machine Learning and Data Mining for Sports Analytics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17274-9_13.

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Domingues, José, Bernardo Lopes, Petya Mihaylova, and Petia Georgieva. "Incremental Learning for Football Match Outcomes Prediction." In Pattern Recognition and Image Analysis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31321-0_19.

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Achkar, Roger, Ibraheem Mansour, Michel Owayjan, and Karim Hitti. "Prediction of Football Matches’ Results Using Neural Networks." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99010-1_15.

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Jadon, Shailja, Aman Jain, Prathamesh Bagal, Kunal Bhatt, and Manish Rana. "Winner Prediction of Football Match Using Machine Learning." In Intelligent Computing and Networking. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0071-8_16.

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Javed, Danish, N. Z. Jhanjhi, and Navid Ali Khan. "Football Analytics for Goal Prediction to Assess Player Performance." In Lecture Notes in Bioengineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0297-2_20.

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Yu, Guo, Jingyong Yang, Xiongda Chen, Zhijian Qian, Bo Sun, and Qingyi Jin. "Prediction of Game Result in Chinese Football Super League." In Communications in Computer and Information Science. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-9195-0_49.

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Carloni, Luca, Andrea De Angelis, Giuseppe Sansonetti, and Alessandro Micarelli. "A Machine Learning Approach to Football Match Result Prediction." In HCI International 2021 - Posters. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78642-7_63.

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Jangir, Dipesh, Lopamudra Hota, Biraja Prasad Nayak, and Arun Kumar. "Football Match Result Prediction Using Twitter Statistical/Historical Data." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60935-0_20.

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Tüfekci, Pınar. "Prediction of Football Match Results in Turkish Super League Games." In Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29504-6_48.

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Datta, Manaswita, and Bhawana Rudra. "Prediction of Credibility of Football Player Rating Using Data Analytics." In Intelligent Systems Design and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96308-8_72.

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Conference papers on the topic "Football Prediction"

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Chanda, Rishav, and Adriz Chanda. "Total Football: A Machine Learning Approach to Utilize Holistic Football Data for Match Outcome Prediction." In 2024 IEEE Calcutta Conference (CALCON). IEEE, 2024. https://doi.org/10.1109/calcon63337.2024.10914181.

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Nivetha, S. K., M. Geetha, R. C. Suganthe, R. Manoj Prabakaran, S. Madhuvanan, and A. Mohamed Sameer. "Expression of Concern for: A Deep Learning Framework for Football Match Prediction." In 2022 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2022. http://dx.doi.org/10.1109/iccci54379.2022.10703356.

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Li, Zongqing, Guangyi Wang, Suhua Gao, Liqiang Xu, and Songzhi Su. "Research on Prediction of Football Match Results Based on XGBoost and LSTM." In 2024 14th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 2024. https://doi.org/10.1109/itme63426.2024.00155.

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Dyczkowski, Krzysztof, Bartłomiej Grzelak, Tomasz Górecki, et al. "Sensor-Based Injury Prediction in Football Using a New Interval-Valued Fuzzy Inference Library." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611885.

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Rosoł, Maciej, Jakub S. Gąsior, Kacper Korzeniewski, Jonasz Łaba, Robert Makuch, and Marcel Młyńczak. "Prediction of the duration of maximal exercise test in professional adolescent football players based on the cardiorespiratory signals – a pilot study." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782255.

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Hosein, Ismaeel, Kevin Baboolal, and Patrick Hosein. "Models for Predicting the Success of a Footballer." In 2024 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD). IEEE, 2024. https://doi.org/10.1109/ictmod63116.2024.10959128.

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Sharma, Tanish, Parv Bagga, Kinshuk Ahuja, and Seema Sharma. "Comparative Analysis of Machine Learning Models for Predicting Top Goal Scorer and Goalkeeper Performance in Football." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986354.

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Dip, Amartya Das, Nasimur Rahman, and Mohiuddin Ahmed. "Predicting Football Match Results: An Analysis of Feature Selection and Machine Learning Techniques Using a Curated Dataset." In 2024 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON). IEEE, 2024. https://doi.org/10.1109/peeiacon63629.2024.10800599.

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Razali, Nazim, Aida Mustapha, Nureize Arbaiy, and Pei-Chun Lin. "Deep learning for football outcomes prediction based on football rating system." In 10TH INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND TECHNOLOGY. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0104587.

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Wu, ason, Evan Gunnell, and Yu Sun. "PlayGuessr: Commercial Application of Machine Learning in Football Play Prediction." In 5th International Conference on Computer Science and Information Technology (COMIT 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111714.

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The offensive strategy in American football strives to be enigmatic. A strong offense has a well rounded offensive playbook, rotating offensive plays in attempts to disrupt any predictive patterns. Therefore, it has always been in theinterest of defensive coordinators to offer accurate predictions of the upcoming play to minimize offensive yardage gain. A well advised defense can change its positioning and coverage schemes, given solely whether the next play will be a run or a pass. Although coaches have developed traditional heuristics for tendency-based play prediction, they are limited to p
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