What are some of the specific queries and analyses covered in this lab using BigQuery and the NCAA dataset?

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In the lab "Exploring NCAA data with BigQuery" on the Google Cloud Platform (GCP), several specific queries and analyses can be performed using BigQuery and the NCAA dataset. This lab provides a hands-on experience in leveraging the power of BigQuery to explore and analyze a large dataset related to the National Collegiate Athletic Association (NCAA). By utilizing BigQuery's capabilities, users can gain insights into various aspects of NCAA data.

One of the queries covered in this lab involves analyzing the number of games played by each team in a specific season. This can be done by querying the "games" table and filtering the data based on the desired season. For example, to find the number of games played by the Duke Blue Devils in the 2018 season, the following query can be used:

SELECT COUNT(*) AS num_games
FROM `bigquery-public-data.ncaa_basketball.mbb_games_sr`
WHERE season = 2018 AND (home_team_name = 'Duke' OR away_team_name = 'Duke')

This query counts the number of rows returned by filtering the games table for the specified season and either the home team or away team being Duke. The result provides the total number of games played by Duke in the 2018 season.

Another analysis covered in the lab involves finding the teams with the highest average scores in a given season. This can be achieved by querying the "games" table, grouping the data by team, and calculating the average score. For instance, to identify the top five teams with the highest average scores in the 2019 season, the following query can be used:

SELECT home_team_name AS team, AVG(home_team_score) AS avg_score
FROM `bigquery-public-data.ncaa_basketball.mbb_games_sr`
WHERE season = 2019
GROUP BY team
ORDER BY avg_score DESC
LIMIT 5

This query selects the home team name and calculates the average home team score for each team in the 2019 season. The results are then grouped by team, ordered in descending order by average score, and limited to the top five teams with the highest average scores.

Furthermore, the lab covers queries related to finding the teams with the most wins in a season, determining the distribution of scores, analyzing the performance of specific teams over multiple seasons, and more. These queries allow users to gain insights into various aspects of NCAA data, such as team performance, score trends, and historical analysis.

The lab "Exploring NCAA data with BigQuery" provides an opportunity to explore and analyze the NCAA dataset using BigQuery on the Google Cloud Platform. Users can perform queries and analyses to extract valuable insights related to team performance, score distributions, and historical trends. By leveraging the power of BigQuery, this lab enables users to gain a deeper understanding of the NCAA dataset and its applications in the field of sports analytics.

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