Exploring and Selecting Features to Predict the Next Outcomes of MLB Games

Li, Shufen; Huang, Meiling; Li, Yunzhi

doi:10.3390/e24020288

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…To elaborate further, ACC is the ratio of correctly classified instances to the total number of instances. AUC on the other hand, assesses the model’s ability to differentiate between classes and quantifies its performance in separating the two classes [ 45 ]. Besides, recall measures the proportion of actual positive instances correctly predicted as positive, while PREC computes the ratio of the number of correct positive predictions to the total number of positive predictions [ 46 ].…”

Section: Methodsmentioning

confidence: 99%

Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis

Ab Rasid,

Muazu Musa,

Abdul Majeed

et al. 2024

PLoS ONE

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The identification and prediction of athletic talent are pivotal in the development of successful sporting careers. Traditional subjective assessment methods have proven unreliable due to their inherent subjectivity, prompting the rise of data-driven techniques favoured for their objectivity. This evolution in statistical analysis facilitates the extraction of pertinent athlete information, enabling the recognition of their potential for excellence in their respective sporting careers. In the current study, we applied a logistic regression-based machine learning pipeline (LR) to identify potential skateboarding athletes from a combination of fitness and motor skills performance variables. Forty-five skateboarders recruited from a variety of skateboarding parks were evaluated on various skateboarding tricks while their fitness and motor skills abilities that consist of stork stance test, dynamic balance, sit ups, plank test, standing broad jump, as well as vertical jump, were evaluated. The performances of the skateboarders were clustered and the LR model was developed to classify the classes of the skateboarders. The cluster analysis identified two groups of skateboarders: high and low potential skateboarders. The LR model achieved 90% of mean accuracy specifying excellent prediction of the skateboarder classes. Further sensitivity analysis revealed that static and dynamic balance, lower body strength, and endurance were the most important factors that contributed to the model’s performance. These factors are therefore essential for successful performance in skateboarding. The application of machine learning in talent prediction can greatly assist coaches and other relevant stakeholders in making informed decisions regarding athlete performance.

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Section: Methodsmentioning

confidence: 99%

Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis

Ab Rasid,

Muazu Musa,

Abdul Majeed

et al. 2024

PLoS ONE

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“…Studying football networks enhances our understanding of players' performance and the real-time game situation, which is helpful to estimate the outcome of the game. In recent years, deep learning methods such as convolutional neural networks have been used to predict the outcomes of different sports games [66][67][68]. Their models outperformed traditional approaches like Bayesian Networks and SVMs.…”

Section: Network-based Ranking Of Clubsmentioning

confidence: 99%

A survey on football network analysis

Li,

Xu,

Zhou

2023

EPL

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Being the world's most popular sport, football research has traditionally concentrated on empirical summaries or statistics, with only limited data available in the past. In recent years, social network analysis has been applied to a variety of fields, which also brings new perspectives to the study of football spots. In this paper, we survey the literature related to football networks and discuss the use of network measures to analyze the performance of footballers and teams in different types of football networks. We aim to find out how to construct appropriate football networks based on different perspectives on football research. Various studies on football network analysis, including team performance, player interactions, and club behavior, are reviewed. The findings provide insights into team performance, player roles, and social dynamics within football teams and clubs.

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“…Going deeper with the analysis, [12] applied multi-layer perceptrons, support vector machines, and decision trees to estimate the best playing positions of players in a basketball match. Machine learning has been applied in predicting the match outcome in the game of volleyball [13,14], baseball [15,16], rugby [17,18] etc., too.…”

Section: Introductionmentioning

confidence: 99%

SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners

AlMulla,

Islam,

Al-Absi

et al. 2023

PLoS ONE

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Winning football matches is the major goal of all football clubs in the world. Football being the most popular game in the world, many studies have been conducted to analyze and predict match winners based on players’ physical and technical performance. In this study, we analyzed the matches from the professional football league of Qatar Stars League (QSL) covering the matches held in the last ten seasons. We incorporated the highest number of professional matches from the last ten seasons covering from 2011 up to 2022 and proposed SoccerNet, a Gated Recurrent Unit (GRU)-based deep learning-based model to predict match winners with over 80% accuracy. We considered match- and player-related information captured by STATS platform in a time slot of 15 minutes. Then we analyzed players’ performance at different positions on the field at different stages of the match. Our results indicated that in QSL, the defenders’ role in matches is more dominant than midfielders and forwarders. Moreover, our analysis suggests that the last 15–30 minutes of match segments of the matches from QSL have a more significant impact on the match result than other match segments. To the best of our knowledge, the proposed model is the first DL-based model in predicting match winners from any professional football leagues in the Middle East and North Africa (MENA) region. We believe the results will support the coaching staff and team management for QSL in designing game strategies and improve the overall quality of performance of the players.

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Exploring and Selecting Features to Predict the Next Outcomes of MLB Games

Cited by 9 publications

References 6 publications

Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis

Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis

A survey on football network analysis

SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners

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