It’s a great day in sports analytics! Today, we’re going to discuss the use of populations and samples, which are an integral part of probability and statistics. And we will examine populations and samples, as they are used in empirical research as a method of analyzing sports.
Establishing populations and samples through an empirical approach in sport analytics pave the way for sports analysts and coaches to represent the behavioral pattern of a team, or a sport in general, in a concise quantitative analysis.
This analysis helps analysts recognize the variability that appears in the team through measures of dispersion alongside measures of location. This measure is estimated by first describing the samples which are taking into consideration. Let’s see how samples are taken for probabilistic studies.
Before samples are selected in statistical analysis, population of category or topic under consideration is first drawn. Population refers to all the sets of a defined group that are collected for studying in order to make a statistical decision. For instance, a soccer analyst may decide to analyze the performance of players or teams in the league. Let’s take Premier League as a case study.
A soccer analyst who wants to study the Premier League will have to consider the twenty teams who play in the league, and then the total number of players in the league. In the case of the Premier League, the population of the statistics is 20. It is based on the performance of these twenty teams that a prediction or statistical decision is made.
A sample is a set of observations that are drawn from a population. Now, supposing the analyst wants to focus on the number of goals that are scored in the league each season, then the number of goals becomes the samples.
EMPIRICAL RESEARCH IN SPORT ANALYTICS
Sports analysts employ empirical research to find deep information regarding the number of goals scored in one season of the Premier League—more closely, the number of players that score the most goals, the average number of goals scored by a team or a player, the relationship between goal scorers and the top teams, and so on. Empirical studies help analysts quantify all of these variables by using populations and samples, and then making measures of variability using various statistical techniques.
The process of using populations and samples for empirical research in sport analytics can be grouped into five areas. They are:
- Abstraction: Abstraction represents the human behavior in numerical variables under the samples drawn from a population. This abstraction can help sport analysts quantify how often a player scores in a particular match relative to the strategy and strength of the opponents.
- Sampling: Sampling is the collection of data, which represents a sample of the population of interest. An example of this is the number of goals scored per team in a season of the Premier League.
- Summarizing: Summarizing determines the sample parameters, which describe the quantities of an average participant in the sample data. For instance, a soccer analyst may use summarizing techniques to determine the average number of goals scored by each team in a season.
- Analysis: Analysis is where the statistical procedures developed in abstraction, sampling and summarizing are used to determine relationships between the variables used in the statistics, such as the relationship between goal scorers and the top teams.
- Generalization: Having analyzed the statistical values, generalization is where the analysts make assumptions. For instance, a sports analyst may assume that the team that has the top scorers in the Premier League are the ones who end up in top four.
In conclusion, populations and samples in empirical studies are used in sport analytics to make certain deductions about a particular factor of a team or a set of players.
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