Introduction

Reality TV Statistics: Many people guilty pleasure themselves with reality TV shows. Some watch them for the drama or to escape their issues, while others feel a real connection with the stars. No matter the reason, reality TV is very popular, and many know about famous figures like Kim Kardashian. Plus, reality TV has a strong influence. We’ve gathered key reality TV statistics, such as the percentage of reality TV shows and the money reality stars earn.

Surprisingly, reality TV has been around since 1948, even though it became popular in the 2000s. This will help you see how common reality TV is, what people think about it, and its impact on society. It took a lot of effort, but it's worthwhile.

The main reason given for watching reality TV by U.S. adults was that respondents liked the drama, with 28 percent of 18 to 29-year-olds citing this as the primary appeal of the genre. Other reasons for viewing reality television included it being ‘mindless’ or thought of as ‘good background noise’, and some survey participants agreed that reality TV helps them forget about real issues in their lives.

Attitudes to reality TV in the U.S.

Reality TV may make a lot of noise in the news and on social media, but a survey revealed that reality shows were the least popular in the U.S., with respondents preferring comedy, drama and even shows in the horror genre. It was also found that the majority of adults thought there were too many reality shows on air, which is unsurprising given the genre’s low favorability. However, quality of reality shows was considered to be an issue, with most adults saying that the reality TV genre has gotten worse in recent years.

Why does it matter who likes reality TV and who doesn’t?

On the whole, genre preferences are incredibly subjective, and even those who generally dislike reality TV often find themselves binge-watching shows like ‘RuPaul’s Drag Race’, ‘The Voice’ or ‘Big Brother’. However, demand for a genre is often reflected in the types of TV shows and movies available on streaming services. For companies like Netflix, demand and favorability of certain genres is a key factor to take into account when creating original content. Data shows that Netflix largely sidelined the reality genre in its original programming, instead focusing heavily on comedy which has long been one of the most favorable genres among U.S. audiences.

The statistic shows the perspectives on whether or not reality television shows are mostly based on a script according to adults in the United States as of November 2018. The findings reveal that 61 percent of surveyed adults in the U.S. believed that what actors say in reality TV shows is mostly based on a script, with just 18 percent saying that they thought most of the spoken content in such shows was largely unscripted.

This statistic illustrates the share of Americans who watched reality tv shows online or on TV. As of **************, ** percent of ******* year old consumers do so in the U.S. This is according to exclusive results from the Consumer Insights Global survey which shows that ** percent of ******* year old customers also fall into this category.Statista Consumer Insights offer you all results of our exclusive Statista surveys, based on more than ********* interviews.

The statistic presents information on the share of adults who think that the reality television genre has gotten worse in the last three years in the United States as of November 2018. The findings reveal that although 23 percent of responding adults in the U.S. said that they thought that the genre had not changed, 52 percent believed that the reality TV genre has gotten worse over the last three years.

The statistic presents information on the share of adults who think that there are too many reality shows on television in the United States as of November 2018. The findings reveal that 61 percent of surveyed U.S. adults felt that there were too many reality TV shows on television.

The statistic presents the perspectives on the reality television genre among adults in the United States as of November 2018. The findings reveal that 72 percent of surveyed U.S. adults stated that they thought that the reality TV genre was fake. Other terms respondents felt described the genre well included 'trashy', 'meaningless' and 'predictable'.

The statistic shows data on the popularity of reality TV genres in the United States as of September 2016, by gender. During the survey, eleven percent of female respondents stated they watched dating reality TV shows.

The legal deposit of the audiovisual web is the part of the legal deposit which the legislator has entrusted to the National Audiovisual Institute (Law No 2006-961 of 1 August 2006 on copyright and related rights in the information society, known as the “DADVSI Law”). It started in February 2009 with the collection of websites (audiovisual media services and online public communication services) and since 2014 includes social networks (including Twitter accounts) linked to French audiovisual. As part of the creation of audiovisual thematic corpuses by Ina documentalists, a study on the online presence of reality TV shows was conducted between November 2020 and January 2021. For each identified reality show as well as for its participants and production companies, social media accounts, Youtube channels, Wikipedia pages, websites and hashtags have been identified. The dataset contains the completeness of these web objects and when available, documentary information and metrics (number of subscribers, number of subscriptions, etc.) as of January 2021. All the publications, videos associated with these accounts and the websites can only be consulted in the hands of the INA, the custodian of this legal deposit. The dataset is divided into 4 data sub-games: — “Depot_legal_du_web-_telealité-_emissions.csv” contains all web objects by reality show — “Depot_legal_du_web-_teleality-_participant.e.s.csv” contains all the web objects per participant — “Depot_legal_du_web-_teleality-_production.csv” contains all web objects per production company — “Depot_legal_du_web-_teleality-_dones_documentaires.csv” contains all documentary data and metrics by web objects — “Depot_legal_du_web-_teleality-_dones_documentary_dictionnaire.csv” contains a dictionary of the previous file. The re-use of personal data present in the data sets published by Ina constitutes the processing of personal data as defined by Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 known as the General Data Protection Regulation (the “GDPR”) and Law No 78-17 of 6 January 1978 on computing, files and freedoms as amended, together ‘the Data Protection Regulation’. The re-user is therefore subject to compliance with the legal framework resulting from the Data Protection Regulation in order to ensure that such re-use of personal data is lawful. In any event, Ina disclaims any liability for non-compliance by a re-user with the above-mentioned rules.

As of December 2021, nearly six out of ten internet users surveyed in Brazil said they watched reality TV shows. One of the most popular attractions of this genre in the South American country is Big Brother Brasil, also known as BBB, broadcast by Globo.

This graph presents the results of a survey on the level of interest in reality television shows in Germany from 2015 to 2019. In 2019, **** million Germans were highly interested in watching reality television.

The reality show market has evolved into a multi-billion dollar industry over the past two decades, becoming a staple of television programming and a significant driver of audience engagement across various platforms. Defined by its unscripted narrative style, reality television showcases real-life situations, perso

The statistic shows the perspectives on the plots of reality television shows among adults in the United States as of November 2018. The findings reveal that 66 percent of surveyed adults in the U.S. believed that the plots of reality TV shows are mostly determined ahead of time, with just 13 percent saying they thought that the plots of such shows unfold in real time.

This dataset provides a detailed record of outcomes from over 300 episodes of "Beat Bobby Flay", a popular American television culinary competition. The show features two rounds: initially, two challenger chefs create a dish using a specific ingredient, with guest judges selecting one to advance. In the second round, the victorious challenger selects a signature dish for both themselves and renowned chef Bobby Flay to prepare, with an expert panel of judges deciding the winner through a blind taste test. A notable aspect of the show, highlighted by the data, is Bobby Flay's impressive approximately 63% win rate, despite the challenger having the advantage of choosing the dish for the final cook-off. This dataset offers unique opportunities to explore factors influencing culinary competition outcomes, investigate potential judge biases, and analyse the dynamics of high-stakes cooking challenges.

Columns

• num_overall: The overall episode number in the series.
• num_within_season: The episode number within its particular season.
• title: The title given to the episode.
• airdate: The original broadcast date of the episode.
• ingredients: The key ingredient(s) that must be incorporated into dishes during the first round.
• dish: The specific dish chosen by the winning challenger from round one for both themselves and Bobby Flay to prepare in the second round.
• winner: Indicates the winner of the episode, either Bobby Flay or the challenger.
• season: The season number to which the episode belongs.
• guest1, guest2, guest3: These columns list the guest judges who officiate the first round, deciding which challenger progresses. There can be up to three guest judges, and this round is not a blind taste test.
• judge1, judge2, judge3: These columns list the judges for the second and final round between the challenger and Bobby Flay. This round is a blind taste test, and all episodes feature three judges.
• contestant1: The name of the chef who is eliminated after the first round.
• contestant2: The name of the chef who successfully advances to challenge Bobby Flay in the second round.

Distribution

The dataset is structured with each row representing an individual episode of the "Beat Bobby Flay" television show. It contains 306 unique records, providing a substantial collection of episode outcomes. The format is typically tabular, suitable for common data analysis tools.

Usage

This dataset is ideal for: * Analysing dish effectiveness: Determine which types of dishes tend to offer the best or worst chance of beating Bobby Flay. * Investigating judge impartiality: Examine if any patterns suggest biases among the second-round judges, despite the blind taste test format. * Studying show trends: Observe changes in ingredients, popular dishes, or contestant performance over time. * Machine learning applications: Develop models for classification tasks, such as predicting episode winners, or for natural language processing (NLP) on episode titles or dish names. * Culinary insights: Gain understanding into competitive cooking strategies and popular food items.

Coverage

The dataset covers episodes of the "Beat Bobby Flay" television series aired from 24 August 2013 to 19 July 2020. While an American TV show, its analysis potential is global. It includes results for 306 episodes across various seasons.

License

CC0

Who Can Use It

• Data Scientists and Analysts: For statistical analysis, predictive modelling, and exploring correlations within competitive culinary data.
• Fans of "Beat Bobby Flay": To gain a deeper, data-driven insight into the show, its outcomes, and character performance.
• Academics and Researchers: Studying media consumption, reality television formats, or human-computer interaction through analysis of show outcomes and judge behaviour.
• Developers: To build applications or visualisations based on television show data.

Dataset Name Suggestions

• Beat Bobby Flay Episode Archive
• Bobby Flay Show Results
• Culinary Challenge Outcomes
• TV Chef Battle Statistics

Attributes

Original Data Source: Beat Bobby Flay: Results of 300+ episodes

SharkTank dataset of Australia business reality television series.

Currently, the data set has information from SharkTank season 1 to Shark Tank Australia season 6. The dataset has 52 fields/columns and 275+ records.

Below are the features/fields in the dataset:

• Season Number - Season number
• Startup Name - Company name or product name or idea name
• Episode Number - Episode number within the season
• Pitch Number - Overall pitch number
• Season Start - Season first aired date
• Season End - Season last aired date
• Original Air Date - Episode original/first aired date, on OTT/TV
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Website of startup/company
• Pitchers Gender - Gender of pitchers
• Pitchers City - AU city of pitchers or place where company head office exists
• Pitchers State - AU state or country of pitchers, two letter shortcut of AU state where company head office exists
• Pitchers Average Age - Average age of all pitchers, <30 young, 30-50 middle, >50 old
• Entrepreneur Names - Pitcher names
• Multiple Entrepreneurs - Multiple entrepreneurs are present ? 1-yes, 0-no
• Viewers - Viewership in AU
• Original Ask Amount - Original Ask Amount, in AUD
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in AUD
• Received Offer - Received offer or not, 1-received, 0-not received
• Accepted Offer - Accepted offer or not, 1-accepted, 0-rejected
• Total Deal Amount - Total Deal Amount, in AUD, including debt/loan amount
• Total Deal Equity - Total Deal Equity, in percentages
• Deal Valuation - Deal Valuation, in AUD
• Number of Sharks in Deal - Number of sharks in deal
• Investment Amount Per Shark - Investment Amount Per Shark
• Equity Per Shark - Equity received by each Shark
• Royalty Deal - If Royalty deal, Royalty amount per item
• Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
• Loan - Loan/debt (line of credit) amount given by sharks, in AUD
• Deal Has Conditions - Deal has conditions or not? (yes or no)
• Mentorship - Shark names who has offered mentorship
• Steve Investment Amount - Amount Invested by Steve
• Steve Investment Equity - Equity received by Steve
• Janine Investment Amount - Amount Invested by Janine
• Janine Investment Equity - Equity received by Janine
• Andrew Investment Amount - Amount Invested by Andrew
• Andrew Investment Equity - Equity received by Andrew
• Naomi Investment Amount - Amount Invested by Naomi
• Naomi Investment Equity - Equity received by Naomi
• Glen Investment Amount - Amount Invested by Glen
• Glen Investment Equity - Equity received by Glen
• Guest Investment Amount - Amount Invested by Guests
• Guest Investment Equity - Equity received by Guests
• Guest Name - Name of Guest shark(s), if invested in deal
• Steve Present - Whether Steve present in episode or not
• Janine Present - Whether Janine present in episode or not
• Andrew Present - Whether Andrew present in episode or not
• Naomi Present - Whether Naomi present in episode or not
• Glen Present - Whether Glen present in episode or not
• Guest Present - Whether Guest(s) present in episode or not

As of December 2021, almost two-thirds – 64 percent – of female internet users surveyed in Brazil said they watched reality TV shows. Among men, the share stood at 54 percent.

The statistic presents data on the reasons for watching reality TV in the United States as of March 2017. During the survey, 18 percent of respondents stated that they watched reality television because they liked the drama.

This paper describes a mixed reality (MR) media browser that utilizes only the user’s gaze for interacting, opening, playing, and pausing 360-degree VR televisual films. The system enables hands-free interaction that activates the passive spectator by encouraging them to move about the physical world. As they shift their gaze, they engage with content in the MR environment and initiate VR films. This affords an immersive media browsing experience via a stylized MR scene with 3D-objects as representations of VR videos. The system is illustrative of a unique interaction design paradigm that augments the user’s gaze as a functional touch event. Its development aligns with current trends in interactive television in which screens are being replaced by goggles. It has been developed for Georgia Tech’s Argon AR Browser for the iPhone and can be viewed via a HUD such as Google Cardboard.

The statistic presents data on the reasons for watching reality TV in the United States as of March 2017, sorted by gender. During the survey, 20 percent of female respondents stated that they watched reality television because they liked the drama.