The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review

• URL: http://arxiv.org/abs/1912.11762v1
• Date: Thu, 26 Dec 2019 03:12:21 GMT
• Title: The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review
• Authors: Rory Bunker (1), Teo Susnjak (2) ((1) Nagoya Institute of Technology, Japan, (2) Massey University, Auckland, New Zealand)
• Abstract summary: We provide a review of studies that have used Machine Learning for predicting results in team sport, covering studies from 1996 to 2019. This paper offers insights into which ML algorithms have tended to be used in this field, as well as those that are beginning to emerge with successful outcomes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the past two decades, Machine Learning (ML) techniques have been increasingly utilized for the purpose of predicting outcomes in sport. In this paper, we provide a review of studies that have used ML for predicting results in team sport, covering studies from 1996 to 2019. We sought to answer five key research questions while extensively surveying papers in this field. This paper offers insights into which ML algorithms have tended to be used in this field, as well as those that are beginning to emerge with successful outcomes. Our research highlights defining characteristics of successful studies and identifies robust strategies for evaluating accuracy results in this application domain. Our study considers accuracies that have been achieved across different sports and explores the notion that outcomes of some team sports could be inherently more difficult to predict than others. Finally, our study uncovers common themes of future research directions across all surveyed papers, looking for gaps and opportunities, while proposing recommendations for future researchers in this domain.

Related papers

• Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR Proceedings [1.3812010983144802]
  This study examines the alignment of emphConference on Computer Vision and Pattern Recognition (CVPR) research with the principles of the "bitter lesson" proposed by Rich Sutton. We analyze two decades of CVPR abstracts and titles using large language models (LLMs) to assess the field's embracement of these principles.
  arXiv  Detail & Related papers  (2024-10-12T21:06:13Z)
• Machine Learning for Missing Value Imputation [0.0]
  The main objective of this article is to conduct a comprehensive and rigorous review, as well as analysis, of the state-of-the-art machine learning applications in Missing Value Imputation. More than 100 articles published between 2014 and 2023 are critically reviewed, considering the methods and findings. The latest literature is examined to scrutinize the trends in MVI methods and their evaluation.
  arXiv  Detail & Related papers  (2024-10-10T18:56:49Z)
• What is the Role of Large Language Models in the Evolution of Astronomy Research? [0.0]
  ChatGPT and other state-of-the-art large language models (LLMs) are rapidly transforming multiple fields. These models, commonly trained on vast datasets, exhibit human-like text generation capabilities.
  arXiv  Detail & Related papers  (2024-09-30T12:42:25Z)
• MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows [58.56005277371235]
  We introduce MASSW, a comprehensive text dataset on Multi-Aspect Summarization of ScientificAspects. MASSW includes more than 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years. We demonstrate the utility of MASSW through multiple novel machine-learning tasks that can be benchmarked using this new dataset.
  arXiv  Detail & Related papers  (2024-06-10T15:19:09Z)
• The Evolution of Football Betting- A Machine Learning Approach to Match Outcome Forecasting and Bookmaker Odds Estimation [0.0]
  This paper explores the history of professional football and the betting industry, tracing its evolution from clandestine beginnings to a lucrative multi-million-pound enterprise. Initiated by the legalization of gambling in 1960 and complemented by advancements in football data gathering pioneered by Thorold Charles Reep, the symbiotic relationship between these sectors has propelled rapid growth and innovation. The primary aim of this study is to utilize Machine Learning (ML) algorithms to forecast premier league football match outcomes.
  arXiv  Detail & Related papers  (2024-03-24T20:08:16Z)
• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
  The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains. This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
  arXiv  Detail & Related papers  (2023-12-01T16:00:25Z)
• Why is the winner the best? [78.74409216961632]
  We performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE I SBI 2021 and MICCAI 2021. Winning solutions typically include the use of multi-task learning (63%), and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%) Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases.
  arXiv  Detail & Related papers  (2023-03-30T21:41:42Z)
• Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning [65.12226891589592]
  This paper proposes a new approach to automated game validation and testing. Our method leverages a data-driven imitation learning technique, which requires little effort and time and no knowledge of machine learning or programming.
  arXiv  Detail & Related papers  (2022-08-15T11:08:44Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)
• Measuring Domain Knowledge for Early Prediction of Student Performance: A Semantic Approach [5.721241882795979]
  The researchers have used various predictors in performance modelling studies. Association mining on nearly 0.35 million observations establishes that prior cognition impacts the student performance. The proposed approach of measuring domain knowledge can help the early performance modelling studies to use it as a predictor.
  arXiv  Detail & Related papers  (2021-07-15T23:46:27Z)
• A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
  The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
  arXiv  Detail & Related papers  (2020-08-02T00:09:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.