Hyperdrive

Summary: Tournament series where drivers from around the world compete on one of the largest automotive obstacle courses ever made.

Release Date: August 21, 2019

Genre: Reality-TV

Runtime: 0 h 45 min

MPAA: TV-PG

Production: Whalerock Industries, Denver and Delilah Productions, Brazil Production Services

Country: United States

Language: English

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50%13%

50% of the Stars & Main Cast are Female.

13% of the Stars & Main Cast are "diverse."

White87.5%Black12.5%

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CONTENT BREAKDOWN

Methodology

IMDb:

Methodology

Cast

Stars:

Supporting cast:

Other Cast, Production

Other cast:

Production:

Representation

Star

Supporting

Other

Production

Race	White	Black	Asian	Latino	Total	Visuals	Info
Forebears	4	1	1		6
Kairos	2	1			3		A third party facial recognition software company we used to maintain objectivity. Upload images to the facial recognition demo page to check the veracity of our data set. Or use their API and do a bulk peer review if you'd like. Source: https://www.kairos.com/demos
Ethnic	2				2		This is the most reliable data source for accurate ethnic breakdowns of famous people. Often the information is crowdsourced by users and they cite all their sources (family trees, obituaries, census archives, etc.) on the bottom of the page. The only issue is converting their ambiguous and inconsistent verdicts into quantifiable variables. Source: https://ethnicelebs.com/
Familysearch				1	1
Surname	1				1		A third party free program that uses US Census data to create an algorithm that analyzes surnames and guesses what ethnicity the person is. Obviously there are issues with this data set such as African Americans who have the last names of their ancestors' slave owner. However, when no other data set is available, this can give a rough estimate and expand the data pool for the less important cast members with no IMDb or TMDb image. Please consider downloading their source code via GitHub to verify the accuracy of our data set, and to peer review our conclusions. Source: https://pypi.org/project/ethnicolr/
Bettaface	1				1		A third party facial recognition software company we used to maintain objectivity. Upload images to the facial recognition demo page to check the veracity of our data set. Or use their API and do a bulk peer review if you'd like. Source: https://www.betafaceapi.com/demo_old.html
Total	10	2	1	1	14
Cast Percentages	71.43%	14.29%	7.14%	7.14%	100%	{"name":"Cast Percentages","series":[{"name":"White","y":71.43,"color":"#2b908f","sliced":true,"selected":true},{"name":"Black","y":14.29,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Asian","y":7.14,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Latino","y":7.14,"color":"#eeaaee","sliced":true,"selected":true}],"prefix":""}
U.S. Population (2020) Percentage	57.84%	12.4%	6%	18.73%		{"name":"U.S. Population (2020)","series":[{"name":"White","y":57.84,"color":"#2b908f"},{"name":"Arab","y":0.54,"color":"#DF5353"},{"name":"Asian","y":6,"color":"#90ee7e"},{"name":"Black","y":12.4,"color":"#f45b5b"},{"name":"Indian","y":1.35,"color":"#7798BF"},{"name":"Indigenous","y":1.12,"color":"#aaeeee"},{"name":"Latino","y":18.73,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":9.32,"color":"#55BF3B"},{"name":"Jewish","y":2.02,"color":"#ff0066"}],"prefix":""}	Source: US Census (Note: Racial and ethnic categories overlap so the sum will not equal 100%.)
U.S. Population (2020) Representation	+13.59	+1.89	+1.14	-11.59		{"name":"U.S. Population (2020)","series":[["White",57.84],["Black",12.4],["Asian",6],["Latino",18.73]],"cast":[{"name":"White","y":71.43,"color":"#2b908f"},{"name":"Black","y":14.29,"color":"#f45b5b"},{"name":"Asian","y":7.14,"color":"#90ee7e"},{"name":"Latino","y":7.14,"color":"#eeaaee"}]}
U.S. Buying Power (2018) Percentage	74.35%	7.96%	6.2%	9.42%		{"name":"U.S. Buying Power (2018)","series":[{"name":"White","y":12147700000000,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":1013300000000,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1300200000000,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":114600000000,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":1539000000000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":224700000000,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: Selig Center, University of Georgia
U.S. Buying Power (2018) Representation	-2.92	+6.33	+0.94	-2.28		{"name":"U.S. Buying Power (2018)","series":[["White",74.35],["Black",7.96],["Asian",6.2],["Latino",9.42]],"cast":[{"name":"White","y":71.43,"color":"#2b908f"},{"name":"Black","y":14.29,"color":"#f45b5b"},{"name":"Asian","y":7.14,"color":"#90ee7e"},{"name":"Latino","y":7.14,"color":"#eeaaee"}]}
U.S. Average Individual Income (2018) Percentage	65.34%	9.43%	7.52%	9.51%		{"name":"U.S. Average Individual Income (2018)","series":[{"name":"White","y":11825174998483,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":1360705388565,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1706765993728,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":164296821776,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":1721419354421,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":1318328353188,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: IPUMS-CPS, University of Minnesota
U.S. Average Individual Income (2018) Representation	+6.09	+4.86	-0.38	-2.37		{"name":"U.S. Average Individual Income (2018)","series":[["White",65.34],["Black",9.43],["Asian",7.52],["Latino",9.51]],"cast":[{"name":"White","y":71.43,"color":"#2b908f"},{"name":"Black","y":14.29,"color":"#f45b5b"},{"name":"Asian","y":7.14,"color":"#90ee7e"},{"name":"Latino","y":7.14,"color":"#eeaaee"}]}
World population (2019) Percentage	14.24%	14.47%	31.25%	5.62%		{"name":"World population (2019) ","series":[{"name":"Mixed \/ Other","y":14688882870,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Latino","y":42991259469,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Indigenous","y":10971786583,"color":"#aaeeee","sliced":true,"selected":true},{"name":"White","y":108889096410,"color":"#2b908f","sliced":true,"selected":true},{"name":"Black","y":110601821551,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Asian","y":238860161621,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Arab","y":67406299429,"color":"#DF5353","sliced":true,"selected":true},{"name":"Indian","y":167470202093,"color":"#7798BF","sliced":true,"selected":true},{"name":"Jewish","y":2568698093,"color":"#ff0066","sliced":true,"selected":true}],"prefix":""}	NOTE: ZR is the first organization to ever create a global buying power divided by race. Essentially we just took data from CIA.gov FactBook, UN Population Estimates, and The World Bank's Population Estimates / Purchasing Power Parity Per Capita.
World population (2019) Representation	+57.19	-0.18	-24.11	+1.52		{"name":"World population (2019) ","series":[["White",14.24],["Black",14.47],["Asian",31.25],["Latino",5.62]],"cast":[{"name":"White","y":71.43,"color":"#2b908f"},{"name":"Black","y":14.29,"color":"#f45b5b"},{"name":"Asian","y":7.14,"color":"#90ee7e"},{"name":"Latino","y":7.14,"color":"#eeaaee"}]}
World Buying Power Percentage	36.48%	6.38%	29.46%	9.35%		{"name":"World Buying Power","series":[{"name":"White","y":3010602822170000,"color":"#2b908f","sliced":true,"selected":true},{"name":"Latino","y":771525888400000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Jewish","y":91700962050000,"color":"#ff0066","sliced":true,"selected":true},{"name":"Black","y":526264226360000,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indigenous","y":174285727610000,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":148984454560000,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Arab","y":526400066100000,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":2431392083750000,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Indian","y":572687873900000,"color":"#7798BF","sliced":true,"selected":true}],"prefix":"$"}	NOTE: ZR is the first organization to ever create a global buying power divided by race. Essentially we just took data from CIA.gov FactBook, UN Population Estimates, and The World Bank's Population Estimates / Purchasing Power Parity Per Capita.
World Buying Power Representation	+34.95	+7.91	-22.32	-2.21		{"name":"World Buying Power","series":[["White",36.48],["Black",6.38],["Asian",29.46],["Latino",9.35]],"cast":[{"name":"White","y":71.43,"color":"#2b908f"},{"name":"Black","y":14.29,"color":"#f45b5b"},{"name":"Asian","y":7.14,"color":"#90ee7e"},{"name":"Latino","y":7.14,"color":"#eeaaee"}]}
Notes			The US Census considers Pakistani, Sri Lankan, Indian, and Bangladeshi as Asians. At ZR we group Pakistanis with the Arab world. And we group Sri Lankan and Bangladeshi with India because 1.) they are all genetically similar as "South Asians and 2.) there are only a handful of actors from those regions in our database. However, you are viewing the "Asian Alone" category from the US Census when comparing it with Domestic representation. Also note that the 2000 and 2010 estimates include MultiRacial Asians not just "Asian Alone" estimates, and in some categories they include Native Hawaiians as "Asian." So there is a margin of error less than 1% and some inconsistencies with the grouping.	Many Latinos check "White" or "Other" on the Census. So this overlaps with "Mixed" race and "White." Also, when there was a discrepancy with the percentage listed on the Wikipedia table and our calculated numbers, the Wiki table's numbers were used. Likewise, when the population total was missing from the Census pdf, the estimate was calculated using the percentage from the Wiki table. NOTE: It turns out that the reason America doesn't have a separate "Latino" race is because the League of Latin American Citizens protested against discrimination towards Mexicans and got themselves to legally be considered "White." Check the research links in our Google Doc for more info.

Tags / Keywords

driver non fiction

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75/100

83%63%

83% of the Stars & Main Cast are Female.

63% of the Stars & Main Cast are "diverse."

Black43.75%White37.5%Asian12.5%Latino6.25%

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The Real Housewives of Orange County (2006)

Summary: A look at five families living in a protected Southern California enclave, and the real-life housewives who reside in one of the wealthiest planned communities in the country.

Release Date: March 21, 2006

Genre: Reality-TV

Runtime: 0 h 43 min

MPAA: TV-14

Production: Dunlop Entertainment, Evolution Film & Tape, Kaufman Films

Country: United States

Language: English

The Metacritic audience rated this 1% higher than the critics.

Metacritic Meta:

61/100

Metacritic User:

62/100

89%13%

89% of the Stars & Main Cast are Female.

13% of the Stars & Main Cast are "diverse."

White87.5%Black6.25%Latino6.25%

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House Hunters International (2006)

Summary: Home hunters and their realtors check out all sorts of architectural styles and work through the idiosyncrasies of buying real estate in other countries.

Release Date: March 06, 2006

Genre: Reality-TV

Runtime: 0 h 21 min

MPAA: TV-G

Country: United States

Language: English

N/A

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33%6%

33% of the Stars & Main Cast are Female.

6% of the Stars & Main Cast are "diverse."

White93.75%Asian6.25%

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last updated: 2024-10-26Update data

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last updated: 2024-09-27Update data

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Neo-Marxism

Neo-Marxism, (sometimes referred to as "Cultural Marxism"), rates the amount of fanatic egalitarianism in a film. Consider this mainly a rating for anti-nationalist and anti-capitalist themes.

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Strong and multidimensional female characters are fine. This is mainly referring to girlboss crap, unrealistic Mary Sue content, and/or egregious scenes of manhood being disparaged.

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Gay Stuff rates the amount of non-tradional sexuality depicted.

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