The rankings include the Integrated Postsecondary Education Data System’s ID numbers, making it possible to easily merge them with any of IPEDS’ datasets.

Since 1983, the magazine U.S. News and World Report has been ranking US universities and colleges. While I do not in anyway endorse the specific methodology used to construct these rankings, I know that many academic institutions and scholars use these rankings in their research and institutional assessment. I was in turn frustrated that historical rankings were not easily available anywhere online. The spreadsheets above contain all of the rankings from 1985-present for liberal arts colleges and from 1984-present for universities.

The data for liberal arts colleges were compiled from the actual articles releasing the data each year (any errors in entry are my own). I have included tiers for many years for the liberal arts college rankings, but most were only available online and not in print editions. The second tab lists the information that is still missing. If anyone has any of that information please contact me, I would like to fill in all of the gaps.

The data for universities is incomplete, with some years containing only the top 50 universities and others 150. They were compiled with the help of Akil Bello and Arjun Khandelwal from existing online sources. The second tab of each spreadsheet includes specific notes on the rankings for each year and sources. If anyone has any of the missing data, please contact me, I would like to fill in all of the gaps. Thanks to Melissa Whately for help with the IPEDS numbers.

Andrew G. Reiter, “U.S. News & World Report Historical Liberal Arts College and University Rankings,” available at: http://andyreiter.com/datasets/

Comprehensive dataset of college football teams ranked by all time record. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

Comprehensive dataset of college football teams ranked by all time wins. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

College Football Champions Dataset

College Football Champions: Season, Team, Conference, Poll Rankings

By Throwback Thursday [source]

About this dataset

This dataset provides comprehensive information on college football champions from the year 1936 to 2017, covering a wide range of seasons in the sport. It includes crucial details such as the season in which the college football season took place and the specific team that was crowned as the champion for each particular season. Notably, this dataset also offers insights into conference affiliations for these champion teams, shedding light on the conference to which they belong. Additionally, it reveals valuable data regarding poll rankings, providing an indication of the ranking achieved by each champion team in their respective final polls for a given season.

Furthermore, this dataset delves into other essential aspects associated with college football championships. One notable feature is the inclusion of revenue share information attributable to these championship teams. This revenue share provides insight into financial success and rewards earned by these outstanding football programs based on their championship achievements. Moreover, it features details on the system utilized to determine college football champions during various seasons.

By utilizing this dataset effectively, analysts and enthusiasts can discern patterns and trends within college football championships over several decades. These insights can be particularly insightful when unraveling historical developments or identifying dominant teams or conferences throughout different eras of college football. The extensive combination of accurate and informative variables provided within this dataset makes it a valuable resource for any study or analysis pertaining to collegiate sports history and success metrics.

Please note that there are two versions available: ThrowbackDataThursday Week 2 - College Football Champions encompasses detailed information about champions from 1936-2017 with variables including Champion Team Name (text), Season (numeric), Conference Affiliation (categorical), Poll Rankings (numeric), Conference Name (text), System Used For Determination (categorical) and Revenue Share Earnings (numeric). On other hand ThrowbackDataThursday Week 2 - College Football Bowl Revenue focuses specifically on bowl game revenue shares between 2013-2017 without explicitly mentioning champions

How to use the dataset

Introduction:

• Understanding the Columns: a) Conference: This column refers to the conference to which the champion team belongs. It provides insights into team affiliations and rivalries. b) Season: The season represents the year in which a specific college football season took place. c) System: This column indicates the system used to determine the college football champion for each season. d) Revenue Share: The revenue share column reveals how much of revenue was earned by the champion team during that particular season. e) Champion: The name of the college football team that was crowned as champion for a specific season. f) Champion Conference: This column details which conference does the champion team belong.

• Exploring Key Insights: a) Poll Rankings (Poll): Check out how each champion team ranked according to final polls for their respective seasons. It provides an understanding of public opinion and expectations surrounding different teams at that time. b) Revenue Comparison (Revenue Share): Analyzing revenue shares can give valuable insights into financial success and popularity of various teams.

• Analyzing Trends & Comparisons: a) Team Success by Season: Grouping data by seasons allows you to analyze patterns regarding winning teams' performance over time, including multiple championships or trends related to different conferences dominating certain periods or regions. b) Impact of Championship Systems (System): By examining different systems used throughout history, such as standings-based systems versus selection committee systems like BCS or Playoffs, you can uncover trends or debates surrounding the fairness and effectiveness of different championship systems.

• Making Comparisons by Conference: a) Conference Dominance: Analyzing the number of champion teams from each conference can help identify which conferences have historically been more successful and influential in college football. b) Revenue & Conference: Identify whether there is a correlation between revenue share and conferences, indicating financial strength in certain regions or leagues.

Conclusion:

Research Ideas

• Analyzing the performance of different con...

This dataset tracks annual overall school rank from 2012 to 2023 for American History High School

Comprehensive dataset of college football teams ranked by weeks in poll. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

Context:

osu! is a music rhythm game that has 4 modes (check for more info). In this dataset, you can examine the rankings of the standard mode, taken on 30/01/2023 around 3 PM. The ranking is based on pp (performance points) awarded after every play, which are influenced by play accuracy and score; pps are then summed with weights: your top play will award you the whole pp points of the map, then the percentage is decreased (this can maintain balance between strong players and players who play too much). You can find here many other statistics.

Contents:

The dataset contains some columns (see below) reporting statistics for every player in the top 100 of the game in the standard mode. The ranking is ordered by pp. Some players seem to have the same points, but there are decimals that are not shown in the ranking chart on the site

Variables:

• rank: global rank (you can use this like an id too)
• player_name: in-game nickname
• country: country of origin
• accuracy: mean accuracy of your top plays
• play_count: lifetime plays
• level: level (not very influent on stats)
• hours: total hours played
• performance_points: pp which determine the rankings
• ss: number of ss plays (accuracy=100% and no miss)
• s: number of s plays (accuracy>=93% and no miss)
• a: number of a plays (accuracy>=93% but there are misses)
• watched_by: number of replays of the player watched by others

Acknowledgements:

I created this database to use it for my upcoming project in our Data Science.

I used the 2017 osu! rankings and description by Svidon as a reference in order to produce the 2023 osu! ranking in the top 100 as of January 30, 2023

This data will be public and can be accessible on this link https://osu.ppy.sh/rankings/osu/performance.

Here is his kaggle: https://www.kaggle.com/svidon

This dataset tracks annual overall school rank from 2012 to 2023 for Facing History School

Sugar Bowl College Football Data

Sugar Bowl game details - dates, teams, scores, and rankings

By Throwback Thursday [source]

About this dataset

The Sugar Bowl History Dataset provides a comprehensive collection of data on the Sugar Bowl college football games. This dataset offers valuable information about each game's details, including the college football season it took place in, various game notes and additional information, and precise locations where the games were held. Furthermore, it encompasses key details such as kickoff times, the number of people in attendance at each game, and the payout amount awarded for participating in the Sugar Bowl.

In addition to these general details, this dataset also includes specific information about both winning and losing teams. For each game recorded in this dataset, you can find data on the winning team's name and coach as well as their pre-game rank and record. Similarly, all corresponding information is provided for the losing team: its name, coach's name before the match-up being analyzed; pre-game rank; and record.

For those interested in analyzing individual match-ups further or comparing performance across seasons or teams over time, this dataset includes precise scores for both winning and losing teams across all recorded games. Additionally noteworthy are any TV networks that broadcasted these highly-anticipated games.

To unlock greater insights from this versatile dataset or to participate in data visualization challenges centered around it—such as #ThrowbackDataThursday—visit .

Please note that dates have been excluded from this description but are available within the actual dataset itself

How to use the dataset

Introduction:

• Familiarize Yourself with Columns: Understanding the columns in this dataset is essential to make better use of the data available. Here are some important columns you need to know:

  • College Football Season: The season in which the Sugar Bowl game took place.
  • Game Notes: Any additional notes or information about the game.
  • Location: The location where the Sugar Bowl game was held.
  • Kickoff: The time at which the Sugar Bowl game started.
  • Attendance: The number of people in attendance at the Sugar Bowl game.
  • Payout: The amount of money paid out for participating in the Sugar Bowl game.
  • TV Network: The television network that broadcasted the Sugar Bowl game.
  • Winning Team/Coach/Score/Record/Rank (Pre-game): Information related to winning teams such as name, coach's name, score, record before winning teams' ranking before Pre-game respectively.

• Analyzing Team Performances: One fascinating aspect of this dataset is analyzing team performances over different seasons throughout history. You can gain insights into how teams performed when ranked high or low pre-game by comparing their records and scores.

• Attendance Analysis: Understanding crowd engagement during each match can provide valuable insights into popularity and fan followings associated with different teams over time.

• Exploring Game Notes: The Game Notes column contains additional information about specific matches that may add context or interesting facts relevant to further analysis.

• Comparing Coaching Strategies: Analyzing the winning and losing team coaches' names can provide insight into how different coaching strategies affected game outcomes.

• TV Network and Broadcasting: The TV Network column helps understand which networks aired the Sugar Bowl games over the years, highlighting broadcasting trends or changes in popularity.

• Visualization Opportunities: Utilize data visualization techniques to present and better understand trends, patterns, or anomalies hidden within the dataset. Visualizations like line plots, bar graphs, or heatmaps can help uncover interesting insights visually.

Conclusion:

Research Ideas

• Analysis of Winning Team Performance: The dataset can be used to analyze the performance of winning teams in the Sugar Bowl over different seasons. By examining their rankings, scores, and records, insights can be derived regarding the factors that contribute to a team's success in this particular bowl game.
• Comparison of Coaches' Records: The dataset provides information about both winning and losing team coaches. By comparing their records before the Sugar Bowl game, it is possible to identify patterns or trends in coaching success and how that translates into performance on a big stage like the Sugar Bowl.
• Examination of Attendance and Payouts: The dataset includes data on attendance num...

Comprehensive dataset of college football teams ranked by all-americans. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

This table provides the distribution of full-time academic teaching staff at Canadian universities by selected age groups (less than 40 years; 65 years and over) and academic rank since 1970.

Historical Dataset of College Gardens Elementary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1987-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1987-2023),Asian Student Percentage Comparison Over Years (1993-2023),Hispanic Student Percentage Comparison Over Years (1993-2023),Black Student Percentage Comparison Over Years (1993-2023),White Student Percentage Comparison Over Years (1993-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1993-2023),Free Lunch Eligibility Comparison Over Years (1991-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2001-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2011-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023)

Historical Dataset of American History High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2009-2023),Total Classroom Teachers Trends Over Years (2009-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2009-2023),Asian Student Percentage Comparison Over Years (2010-2023),Hispanic Student Percentage Comparison Over Years (2009-2023),Black Student Percentage Comparison Over Years (2009-2023),White Student Percentage Comparison Over Years (2008-2021),Diversity Score Comparison Over Years (2009-2023),Free Lunch Eligibility Comparison Over Years (2009-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2009-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023),Graduation Rate Comparison Over Years (2013-2023)

Comprehensive dataset of college football teams ranked by weeks at ap number one. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

Comprehensive dataset of college football teams ranked by national championships. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

Historical Dataset of Franklin County Early College is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2013-2023),Total Classroom Teachers Trends Over Years (2013-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2013-2023),American Indian Student Percentage Comparison Over Years (2011-2021),Asian Student Percentage Comparison Over Years (2012-2023),Hispanic Student Percentage Comparison Over Years (2013-2023),Black Student Percentage Comparison Over Years (2013-2023),White Student Percentage Comparison Over Years (2013-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (2013-2023),Free Lunch Eligibility Comparison Over Years (2013-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2013-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2011-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023),Graduation Rate Comparison Over Years (2014-2022)

Historical Dataset of Early College High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2007-2023),Total Classroom Teachers Trends Over Years (2008-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2008-2023),American Indian Student Percentage Comparison Over Years (2013-2020),Asian Student Percentage Comparison Over Years (2009-2023),Hispanic Student Percentage Comparison Over Years (2007-2023),Black Student Percentage Comparison Over Years (2008-2023),White Student Percentage Comparison Over Years (2007-2023),Native Hawaiian or Pacific Islander Student Percentage Comparison Over Years (2009-2012),Two or More Races Student Percentage Comparison Over Years (2021-2023),Diversity Score Comparison Over Years (2007-2023),Free Lunch Eligibility Comparison Over Years (2008-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2008-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2010-2023),Overall School Rank Trends Over Years (2010-2023),Graduation Rate Comparison Over Years (2013-2023)

Comprehensive dataset of college football teams ranked by heisman winners. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

Historical Dataset of Montgomery County Early College is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2019-2023),Total Classroom Teachers Trends Over Years (2019-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2019-2023),Asian Student Percentage Comparison Over Years (2019-2023),Hispanic Student Percentage Comparison Over Years (2019-2023),Black Student Percentage Comparison Over Years (2019-2023),White Student Percentage Comparison Over Years (2019-2023),Two or More Races Student Percentage Comparison Over Years (2019-2023),Diversity Score Comparison Over Years (2019-2023),Free Lunch Eligibility Comparison Over Years (2019-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2019-2023),Reading and Language Arts Proficiency Comparison Over Years (2018-2022),Math Proficiency Comparison Over Years (2019-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2019-2023),Graduation Rate Comparison Over Years (2021-2022)

Comprehensive dataset of college football teams ranked by first round nfl draft picks. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.