**Part – 2**

__Data Analytics__

__Data Analytics__

Data Analytics is increasingly being used in deciding the price of a player in Sports Arena. Data analytics are increasingly being used in deciding the price of a player in the competitive professional sports auction.

Before going further let us appreciate the difference between classical statistical analysis and data analytics.

Statistical Analysis is focused on applying mathematical techniques to a sample of data to infer properties about the population. It’s a branch of applied mathematics that uses probability theory and other forms of statistical theorems to test hypotheses and make predictions. Statistical analysis is often more methodical, with a focus on understanding one aspect of the data at a time, such as mean, standard deviation, or confidence intervals.

Data Analytics, on the other hand, is a broad field that encompasses the entire process of analyzing raw data to find patterns, draw conclusions, and make informed decisions. It involves various steps like data cleaning, preparation, and analysis, often using advanced technologies like machine learning and artificial intelligence. Data analytics is typically used to process and analyze **large volumes of data**, and it’s not uncommon for data analysts to have skills in programming languages and frameworks such as Python, R, Hadoop, and Apache Spark

Data analytics is the process of collecting, analyzing, and interpreting data to gain insights and support decision-making. Data analytics can be used in various aspects of the competitive professional sports auction, such as:

– Evaluating the past performance of players based on objective metrics, such as runs scored, wickets taken, strike rate, economy rate, etc.

– Predicting the future performance of players based on factors such as age, fitness, form, injury history, etc.

– Assessing the value of players based on their expected contribution to the team, their role, their demand, their availability, etc.

– Comparing the players with similar skills, experience, and potential to find the best fit for the team.

– OPTIMIZING the budget allocation and bidding strategy to maximize the return on investment and build a balanced squad.

Some of the data analysis techniques that can be used in the competitive professional sports auction are:

– Descriptive statistics, such as mean, median, mode, standard deviation, etc., to summarize the data and identify the trends and patterns.

– Inferential statistics, such as hypothesis testing, confidence intervals, correlation, regression, etc., to draw conclusions and make predictions based on the data.

– Machine learning, such as classification, clustering, regression, etc., to build models that can learn from the data and make predictions or recommendations.

– Data visualization, such as charts, graphs, tables, etc., to present the data in a clear and effective way.

Data analytics can help the competitive professional sports teams to make informed and rational decisions in the auction, and AVOID relying on intuition, bias, or emotion. Data analytics can also help the teams gain a competitive edge over their rivals and improve their chances of winning the tournament.

To be Continued…