Exploring NCAA Data with BigQuery

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Exploring NCAA Data with BigQuery

45 minutes No cost


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BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without managing infrastructure or needing a database administrator. BigQuery uses SQL and takes advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.

We have a newly available dataset for NCAA Basketball games, teams, and players. The game data covers play-by-play and box scores back to 2009, as well as final scores back to 1996. Additional data about wins and losses goes back to the 1894-5 season in some teams' cases.

In this lab we will find and query the NCAA dataset using BigQuery.

What you'll learn

  • Using BigQuery
  • Query the NCAA Public Dataset
  • Writing and executing queries

What you'll need

Setup and requirements

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After a few moments, the Google Cloud console opens in this tab.

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Open the BigQuery console

  1. In the Google Cloud Console, select Navigation menu > BigQuery.

The Welcome to BigQuery in the Cloud Console message box opens. This message box provides a link to the quickstart guide and the release notes.

  1. Click Done.

The BigQuery console opens.

BigQuery opens, but there's nothing in here! Luckily, there are tons of Open Datasets available in BigQuery for you to query, and of course you can upload your own data, which you'll do in the next section.

Task 1. Find the NCAA public dataset in BigQuery

In this section, you pull in some public data so you can practice running SQL commands in BigQuery.

  1. Click on the + ADD then select Public Datasets:

Expanded Add Data dropdown menu with Explore public datasets option highlighted

  1. Type ncaa basketball in the search bar and press Enter.

  2. Click on the NCAA Basketball tile, then View Dataset.

Note: A new browser tab opens, you now have a new project called bigquery-public-data added to the Explorer panel, opened to ncaa_basketball


  1. If bigquery-public-data is not present in explorer panel, click on the + ADD then select Star a project by name.

  2. Type bigquery-public-data and click STAR .

  3. Click on the bigquery-public-data > ncaa basketball to view the tables you can explore.

Explorer panel with ncaa_basketball dataset highlighted and various tables listed below the dataset

  1. Click on mbb_games_sr (men's NCAA game results table) and then click the Preview tab to see sample rows of data. Click the Details tab to get metadata about the table.

  2. Click the Details tab to get metadata about the table.


Question: How many games does the dataset contain? How big is the table?

Answer: The table is about 50 MB and there are over 29k games for us to explore.

Question: But how many individual plays can we analyze?


  • Click on the mbb_pbp_sr (play-by-play) dataset.

ncaa dataset with mbb_pbp_sr table highlighted

  • Then click Details.


Answer: Over 4 million individual play basketball.

Let’s write some SQL to see what types of plays are there for us to explore.

Task 2. Writing queries

What types of basketball play events are there?

  1. Click "+" (Compose New Query) icon.

  2. Copy and paste the below query into the editor:

#standardSQL SELECT event_type, COUNT(*) AS event_count FROM `bigquery-public-data.ncaa_basketball.mbb_pbp_sr` GROUP BY 1 ORDER BY event_count DESC;
  1. Now click Run.

Looking at your results, how many historical shots were TWOPOINTMADE or FREETHROWMISS?

Query results table with columns for Row, event_type, and event_count. Data in rows 4 and 11 are highlighted

Click Check my progress to verify the objective.

Writing queries

Task 3. Fun queries to run

Which 5 games featured the most three point shots made? How accurate were all the attempts?

  1. Click "+" (Compose New Query) icon and add in the below query:
#standardSQL #most three points made SELECT scheduled_date, name, market, alias, three_points_att, three_points_made, three_points_pct, opp_name, opp_market, opp_alias, opp_three_points_att, opp_three_points_made, opp_three_points_pct, (three_points_made + opp_three_points_made) AS total_threes FROM `bigquery-public-data.ncaa_basketball.mbb_teams_games_sr` WHERE season > 2010 ORDER BY total_threes DESC LIMIT 5;
  1. Click Run.

Query results table

Wow! The Tigers made over 50% of their three point shots on 11-22-2016.

Click Check my progress to verify the objective.

Query 1

Which 5 basketball venues have the highest seating capacity?

  1. Click "+" (Compose New Query) icon and add the below query:
#standardSQL SELECT venue_name, venue_capacity, venue_city, venue_state FROM `bigquery-public-data.ncaa_basketball.mbb_teams_games_sr` GROUP BY 1,2,3,4 ORDER BY venue_capacity DESC LIMIT 5;
  1. Click Run.

Query results table

Imagine taking a shot with 80,000 people watching you!

Click Check my progress to verify the objective.

Query 2

Which teams played in the highest scoring game since 2010?

  1. Click "+" (Compose New Query) icon and add the below query:
#standardSQL #highest scoring game of all time SELECT scheduled_date, name, market, alias, points_game AS team_points, opp_name, opp_market, opp_alias, opp_points_game AS opposing_team_points, points_game + opp_points_game AS point_total FROM `bigquery-public-data.ncaa_basketball.mbb_teams_games_sr` WHERE season > 2010 ORDER BY point_total DESC LIMIT 5;
  1. Click Run.

Query results table

The Bulldogs and Terriers played in a game that scored 258 total points!

Click Check my progress to verify the objective.

Query 3

Since 2015, what was the biggest difference in final score for a National Championship?

  1. Click "+" (Compose New Query) icon and add the below query:
#standardSQL #biggest point difference in a championship game SELECT scheduled_date, name, market, alias, points_game AS team_points, opp_name, opp_market, opp_alias, opp_points_game AS opposing_team_points, ABS(points_game - opp_points_game) AS point_difference FROM `bigquery-public-data.ncaa_basketball.mbb_teams_games_sr` WHERE season > 2015 AND tournament_type = 'National Championship' ORDER BY point_difference DESC LIMIT 5;
  1. Click Run.

Query results table

The finals games are surprisingly close! The biggest difference was in 2018 with a delta of 17 points.

Click Check my progress to verify the objective.

Query 4


You've learned how to query the NCAA basketball dataset inside of BigQuery. We encourage you to modify the above queries and write your own to further your understanding. Looking for more NCAA query practice? Checkout the GitHub repo here.

Finish your quest

Continue your quest with Google Cloud Solutions ll: Data and Machine Learning or NCAA® March Madness®: Bracketology with Google Cloud. A quest is a series of related labs that form a learning path. Completing a quest earns you a badge to recognize your achievement. You can make your badge or badges public and link to them in your online resume or social media account. Enroll in any quest that contains this lab and get immediate completion credit. See the Google Cloud Skills Boost catalog to see all available quests.

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Manual Last Updated October 17, 2023

Lab Last Tested October 17, 2023

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