Sports Analytics

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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.

• 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.

• 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.

• This GitHub repository contains notebooks of analysis for various sports including the National Hockey League (NHL), Indian Premier League (IPL), and National Basketball Association (NBA). The notebooks contain analysis on teams and players, utilizing statistical models to analyze winning percentages for teams using factors such as salaries, points or goals scored or conceded. The notebooks also compare the performance of two or more players.

• To complete these analyses, the repository makes use of various Python statistical libraries including pandas, numpy, seaborn, and matplotlib. The statsmodels library is also used for data modeling.

• Overall, this repository contains a range of sports analytics projects that utilize statistical models and data visualization techniques to analyze and compare the performance of teams and players. These projects provide valuable insights and can be used to inform decision-making and improve performance in sports.

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