How cann soccer improve statistical learning? (original) (raw)
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How can soccer improve statistical learning?
This paper presents an active classroom exercise focusing on the interpretation of ordinary least squares regression coefficients. Methodologically, undergraduate students analyze Brazilian soccer data, formulate and test classical hypothesis regarding home team advantage. Technically, our framework is simply adapted for others sports and has no implementation cost. In addition, the exercise is easily conducted by the instructor and highly enjoyable for the students. The intuitive approach also facilitates the understanding of linear regression practical application.
Teaching statistics with soccer
Soccer matches are used to illustrate the interpretation of linear regression coefficients when the independent variable is dummy. By counting the number of goals in each game, creating a spreadsheet and estimating the model, students become actively engaged in learning process. Our framework is simply adapted for others sports and has no cost. In addition, the exercise is easily conducted by the instructor and highly enjoyable for the pupils. With this story we hope to help students not only enjoy learn statistics but also apply statistical reasoning in both academic and personal life.
Introducing Linear Regression: An Example Using Basketball Statistics
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The intuition behind linear regression can be difficult for students to grasp particularly without a readily accessible context. This paper uses basketball statistics to demonstrate the purpose of linear regression and to explain how to interpret its results. In particular, the student will quickly grasp the meaning of explanatory variables, r-squared, and the statistical significance of estimates of regression coefficients. Even if the student is not a sports fan the examples are easily understood and familiar. The student can easily replicate the procedures in this paper to reinforce learning.
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The Use of Match Statistics that Discriminate Between Successful and Unsuccessful Soccer Teams Three soccer World Cups were analysed with the aim of identifying the match statistics which best discriminated between winning, drawing and losing teams. The analysis was based on 177 matches played during the three most recent World Cup tournaments: Korea/Japan 2002 (59), Germany 2006 (59) and South Africa 2010 (59). Two categories of variables were studied: 1) those related to attacking play: goals scored, total shots, shots on target, shots off target, ball possession, number of off-sides committed, fouls received and corners; and 2) those related to defence: total shots received, shots on target received, shots off target received, off-sides received, fouls committed, corners against, yellow cards and red cards. Discriminant analysis of these matches revealed the following: (a) the variables related to attacking play that best differentiated between winning, drawing and losing teams w...
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This study shows how simple and multiple logistic regression can be used in observational methodology and more specifically, in the fields of physical activity and sport. We demonstrate this in a study designed to determine whether three-a-side futsal or five-a-side futsal is more suited to the needs and potential of children aged 6-to-8 years. We constructed a multiple logistic regression model to analyze use of space (depth of play) and three simple logistic regression models to determine which game format is more likely to potentiate effective technical and tactical performance.
Randwick International of Social Science Journal, 2022
This study applies the basic football learning model by using an application to improve the learning outcomes of football subjects. The research method used in this study is experimental, meaning that the research aims to find causality or causal relationships. The data obtained from the research results were analyzed quantitatively to answer the research problem formulation. The results of the study r arithmetic > r table the value of the r table is with a significance of 5% or 0.5, which is 0.138. The significance value (2-tailed) <0.05 indicates a significant increase between the initial variable and the final variable. This shows that there is a significant increase in the difference in treatment given to each variable. So it was concluded that the significance value of 0.000 < 0.05 was an increase in the application of the Basic Football Learning Model by Using Applications to Improve Learning
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: Physical education teaching and learning should be holistic, accommodating the physical, spiritual, and social aspects. Education through physical activities is expected to provide students with real learning experience. The aim of this research is to analyze the influences of problem-based learning on soccer skills, which are expected to contribute positively to the meeting of the objectives of Curriculum 2013 in Indonesia . The research adopted experimental method with non - equivalent group pre - test post - test design. It was conducted at the SMPN ( Public Junior High School) 2 Rangkasbitung, Lebak, Banten , Indonesia, with t he sample consisted of 60 students, in which 30 students for the experimental and control groups, respectively. The research was carried out for seven weeks, with each week consisting of two meetings. GPAI ( Game Performance Assessment Instrument ) was used as the instrument to measure soccer skills. Data were analyzed with t-test, aided by SPSS (Statist...
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This paper describes a graduate course in statistics in sport that was first offered in the summer semester of 2004 at Simon Fraser University. In this paper, we describe the topics that were covered in the course and we provide a rationale for introducing such a course. Specifically, we suggest that the course provides an immediate avenue to statistical modelling, an easy introduction to the reading of scientific papers and an introduction to an array of statistical methodology. It is further argued that the course provides an enjoyable experience in advanced statistical training.
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This study describes the process of modernizing the approach of the Southern Methodist University (SMU) Men's Soccer coaching staff through the use of location and tracking data from their matches in the 2019 season. This study utilizes a variety of modeling and analysis techniques to explore and categorize the data and use it to evaluate the types of plays that are most often correlated with victories. This study's contribution to college soccer analytics includes the implementation of a model to determine individual players' performance, the production of team-level metrics, and visualizations to increase the efficiency of the coaching staff's efforts. This research can serve as a blueprint for college soccer programs to utilize data science in their coaching.