College Football 2022 (Wins, Losses, Rankings)

Team Performance and Game Results

By [source]

About this dataset

This dataset contains comprehensive data about the 2022 season of college football in the United States, providing researchers and analysts alike with a powerful tool for studying the sport. The dataset includes rankings and games data that highlight individual teams, as well as game results, conference game data, and neutral-site games information. By examining this data through an in-depth analysis of team wins and losses across all levels of competition, users can gain deeper insights into how college football is played. This dataset offers a unique opportunity to explore collegiate sports in ways never before possible - uncovering new trends and helping to paint a picture of some of America's favorite pastimes!

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How to use the dataset

This dataset is an excellent resource for researchers and analysts interested in studying college football during the 2022 season. The compiled data includes team rankings, game results, conference game data, and neutral-site games data. The dataset is useful for both qualitative and quantitative analysis of college football teams’s performances throughout the season.

In order to get started with this dataset exploring it, users should first become familiar with the columns that make up the data set: - Season: This column indicates which year of college football (2022 in this case) it pertains to - Week: Indicates which week of the season a certain game was played
- Season Type: Regular or post season - Start Date: Date when a particular game was played - Neutral Site: Whether or not the featured game was on a “neutral site” (not home field for either team) such as a bowl or championship games - Conference Game Status/Type: Indicates if this particular matchup is an interconference matchup like divisional playoffs / championship title bout etc.
- Home Team/ Points / Level & Away Team/ Points / Level information will have some combination of these elements indicate which two teams competed against one another in any given instance and how they did was according to their number of points scored and their level(Division I, Division II etc.).

After becoming familiar with all columns included in this particular dataset, users can begin more detailed analysis by creating pivot tables that focus on different aspects that analyze wins & losses such as wins vs losses overall each team as well as by division within conferences (for example). The section below titled ‘Filtering For Desired Elements Of Analysis’ will provide instructions on how to do so within Microsoft Excel but similar concepts exist across other programs such as Tableau & Google Sheets too! Furthermore filtering could also be used across other fields such characteristics like start date / regular versus post season fixtures etc. To illustrate what types outcomes are possible via filtering let's say we wanted take closer look at all wins achieved by division one teams throughout course regular 2022 then we apply relevant filter conditions established within table would result overview containing only results related specification!.

After developing accurate Filters it possible extract only desired elements analysis produce visual displays reflect findings further gathered insights gain clearer understanding patterns behavior see here . Lastly aggregate statistics provided not just adequate help formulate thoughts hypothesis but also contribute towards various models predict future outcomes!

Research Ideas

• Analyzing the Season Winners – By analyzing the results, rating, volatility and other statistics of teams in this dataset, it is possible to predict which team(s) may take home their respective conference title(s) for the 2022 season and beyond.
• Receiver Performance Analysis – Several stats from this dataset can be compared to each other in order to analyze how receivers perform under varying levels of competition or for games on neutral sites, for example.
• Ranking the Best Seasons – This dataset can be used to rank the best college football seasons in terms of wins/losses over time by a specific school or within a given conference or division. This could provide insights into which teams have had long-term success across multiple years and which teams may need additional support going forward in order to compete with top-tier schools year after year

Acknowledgements

If you use this dataset in your research, please credit...

Youtuber, Doug DeMuro is popular for his car reviews and at the end of each review, he gives the car a score, a DougScore.

The data is gotten from his website. It contains description as well as the link to google sheets. The data is the same except for a little cleaning to convert it to csv.

So how does the DougScore work? There are 10 separate categories, and they’re each judged on a scale of 1 through 10 — with “1” being the worst, and “10” being the best, meaning the highest possible DougScore is 100. The ten categories are split into two separate groups: “Weekend” and “Daily.” The “Weekend” categories measure a car’s appeal to enthusiasts; in other words, how much fun it would be to drive on the weekend. The “Daily” categories, meanwhile, focus on a car’s livability and practicality.

The Weekend categories are Styling, Acceleration, Handling, Fun Factor, Cool Factor while the daily categories are Features, Comfort, Quality, Practicality, Value. Each category are summed as Weekend Total and Daily Total respectively.

Please upvote if you like this dataset, and don't forget Doug's channel. Thanks.

Quality of life varies significantly worldwide, influenced by factors like cost of living, healthcare, safety, and infrastructure. This case study analyzes Quality of Life Index rankings from 2022 and 2023 to identify trends, regional shifts, and key factors affecting global well-being.

🔍 What’s inside?

The 10 best and 10 worst ranked countries for both 2022 & 2023 Comparative analysis of ranking changes Visualizations: Bar charts, heatmaps, and Tableau maps for better insights Regional breakdowns to see if certain areas consistently rank high or low Key takeaways on what makes a country livable This project is designed to help analysts, travelers, and policymakers understand global quality-of-life trends.

📊 Tools Used: Excel, Google Sheets, Tableau, Python (optional for deeper analysis)

💡 Key Questions Explored:

Which countries improved or declined the most? Are there patterns in the best/worst-ranked regions? How do economic and social factors correlate with rankings?

The RS 500 was assembled by the editors of Rolling Stone, based on the results of two extensive polls. In 2003, Rolling Stone asked a panel of 271 artists, producers, industry executives and journalists to pick the greatest albums of all time. In 2009, we asked a similar group of 100 experts to pick the best albums of the 2000s. From those results, Rolling Stone created this new list of the greatest albums of all time.

Source: https://www.rollingstone.com/music/music-lists/500-greatest-albums-of-all-time-156826/outkast-aquemini-2-155441/

UPDATED with Rolling Stone Album Rankings

Added album rankings from Rolling Stone. h/t Data is plural. A visual essay from The Pudding looks at what makes an album the greatest of all time, and shares the data they put together for the essay.

A new visual essay from The Pudding compares Rolling Stone’s “500 Greatest Albums of All Time” lists from 2003, 2012, and 2020. A methodology note says the project began with a spreadsheet by Chris Eckert and eventually led the authors to develop a dataset of their own. Theirs lists every album in the rankings — its name, genre, release year, 2003/2012/2020 rank, the artist’s name, birth year, gender, and more — plus each year’s voters. [h/t Jason Kottke]

What are the characteristics of artists and genres popular at different times?

Data Dictionary

rolling_stone.csv

Welcome folks, and thanks for choosing this dataset. In this dataset, you will be finding a lot of useful stuff which enhances your excel skills and google sheets skills. In this dataset you will be having 2300 rows of data in which you will find all top 100 singers and their songs with their ranking every year Tasks using his data set all you need to do is obtain the top 5 % singers and make the bar graph using their scores to find the scores of each singer use the below formula 101-ranking of the singer and using this scores draw the bar graph Task2 In this, you need to draw the graph based on the frequency of the singers who appeared more than or equal to 15 times from 1992 to 2014 based on this frequency draw another graph and see the changes & feel free to post your queries in the discussion pace and try to post your answers too

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