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The English Premier League (EPL) is a professional football league in England that is contested by 20 teams. It is considered one of the most popular and competitive football leagues in the world, and attracts top players and coaches from around the globe.
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The EPL season runs from August to May, with each team playing 38 games. The team with the most points at the end of the season is crowned the champion. Other teams may also qualify for European competitions based on their performance in the EPL.
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The EPL is known for its passionate fans, intense rivalries, and high-scoring matches. It is a major source of revenue for English football and is broadcast to millions of viewers around the world. Some of the most well-known clubs in the EPL include Manchester United, Liverpool, Chelsea, Manchester City and Arsenal.
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Sports analytics is the use of data and statistical analysis to improve the performance of sports teams and athletes. It involves collecting and analyzing data from a variety of sources such as player stats, team stats, and game footage, and using this data to identify trends and patterns, make predictions, and inform decision-making.
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Sports analytics can be used for a variety of purposes, including evaluating player performance, analyzing team strategy, and predicting game outcomes. It is increasingly being used by sports teams and organizations to gain a competitive advantage and improve their performance.
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Some common techniques used in sports analytics include statistical modeling, machine learning, and data visualization. These techniques can be applied to a variety of sports, including football, basketball, baseball, and soccer, to help teams and athletes improve their performance and achieve their goals.
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This repository contains a Python data analytics project that aims to provide valuable information on games or matches played in the English Premier League (EPL) from 1993 to 2022. The project is based on data from 50 teams, some of which have been present in the top flight since 1993 and others that have since been replaced.
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The project includes five notebooks containing Python codes and diagrams that showcase the findings and regression analysis used to identify the best determinants or predictors for estimating win percentages for teams. The project also includes cluster analysis to identify relationships between different groups of data. To complete these analyses, the project utilizes various libraries including pandas, numpy, matplotlib, seaborn, and the statsmodels library for data modeling. The KMeans algorithm is also used for the cluster analysis.
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Some of the notebooks contain data that has been split into two for better comparison and analysis, with some data covering the 1993 to 2000 season and others covering the 2001 to 2022 season. Other notebooks contain data on home statistics and away statistics for further analysis.
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Overall, this repository contains a comprehensive Python data analytics project that utilizes various tools and techniques to provide valuable insights on the EPL. These insights can be used to inform decision-making and improve performance in football.