Alone in the Dark

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Summary: A detective of the paranormal slowly unravels mysterious events with deadly results.

Release Date: January 28, 2005

Genres: Action, Horror, Sci-Fi

Director: Uwe Boll

Writers: Elan Mastai

, Michael Roesch

, Peter Scheerer

Runtime: 1 h 36 min

MPAA: R

Production: Boll Kino Beteiligungs GmbH & Co. KG, Herold Productions, Brightlight Pictures

Distributor: Lions Gate Films Home Entertainment, Lionsgate Home Entertainment

Countries: Canada, Germany, United States

Language: English

Movie budget: $ 20,000,000

Domestic: $ 5,178,569

International: $ 7,515,076

Box office:

Budget:$20MDomestic:$5.2MInternational:$7.5MWorldwide:$12.7M

Adaptation/remake:

based on video game

10%

The Rotten Tomatoes audience rated this 10% higher than the critics.

1%11%

The Metacritic audience rated this 6% higher than the critics.

Metacritic Meta:

9/100

Metacritic User:

15/100

17%19%

17% of the Stars & Main Cast are Female.

19% of the Stars & Main Cast are "diverse."

White75%Black6.25%Asian6.25%Latino6.25%Jewish6.25%

2.00

Possibly woke elements:

female soldier

Commonsensemedia

Violence5/5
Sex3/5
Language3/5
Drugs3/5

Dove

Sex3/5
Language4/5
Violence4/5
Nudity2/5

This film gets a 2.00/5 family friendly score

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CONTENT BREAKDOWN

Methodology

RottenTomatoes:

1%11%

Douban:

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IMDb:

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Metacritic User:

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OFDb:

Methodology

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Stars:

Supporting cast:

Other cast:

Production:

Star

Supporting

Other

Production

Race	White	Black	Latino	Jewish	Total	Visuals	Info
Kairos	4	4	1		9		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
Forebears	3		1		4
Surname	2				2		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/
Ethnic				1	1		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/
Crowd	1				1		This is the crowdsourced dataset created by users like you! We greatly appreciate your cooperation in making ZR the most accurate and massive database on the internet. Sometimes the verdicts are submitted by looks alone, sometimes users attach an image and a source link. Due to this inconsistency please take the results with a grain of salt and click each actor for more info.
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	11	4	2	1	18
Cast Percentages	61.11%	22.22%	11.11%	5.56%	100%	{"name":"Cast Percentages","series":[{"name":"White","y":61.11,"color":"#2b908f","sliced":true,"selected":true},{"name":"Black","y":22.22,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Latino","y":11.11,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Jewish","y":5.56,"color":"#ff0066","sliced":true,"selected":true}],"prefix":""}
U.S. Population (2020) Percentage	57.84%	12.4%	18.73%	2.02%		{"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	+3.27	+9.82	-7.62	+3.54		{"name":"U.S. Population (2020)","series":[["White",57.84],["Black",12.4],["Latino",18.73],["Jewish",2.02]],"cast":[{"name":"White","y":61.11,"color":"#2b908f"},{"name":"Black","y":22.22,"color":"#f45b5b"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Jewish","y":5.56,"color":"#ff0066"}]}
U.S. Buying Power (2023) Percentage	72.69%	8.02%	10.06%			{"name":"U.S. Buying Power (2023)","series":[{"name":"White","y":13897000000000,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":1333100000000,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1533100000000,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":136400000000,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":1924000000000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":295300000000,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: Selig Center, University of Georgia
U.S. Buying Power (2023) Representation	-11.58	+14.2	+1.05			{"name":"U.S. Buying Power (2023)","series":[["White",72.69],["Black",8.02],["Latino",10.06],["Jewish",0]],"cast":[{"name":"White","y":61.11,"color":"#2b908f"},{"name":"Black","y":22.22,"color":"#f45b5b"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Jewish","y":5.56,"color":"#ff0066"}]}
U.S. Average Individual Income (2018) Percentage	65.34%	9.43%	9.51%			{"name":"U.S. Average Individual Income (2018)","series":[{"name":"White","y":11825174998483,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":1360705388565,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1706765993728,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":164296821776,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":1721419354421,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":1318328353188,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: IPUMS-CPS, University of Minnesota
U.S. Average Individual Income (2018) Representation	-4.23	+12.79	+1.6			{"name":"U.S. Average Individual Income (2018)","series":[["White",65.34],["Black",9.43],["Latino",9.51],["Jewish",0]],"cast":[{"name":"White","y":61.11,"color":"#2b908f"},{"name":"Black","y":22.22,"color":"#f45b5b"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Jewish","y":5.56,"color":"#ff0066"}]}
World population (2019) Percentage	14.24%	14.47%	5.62%	0.34%		{"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	+46.87	+7.75	+5.49	+5.22		{"name":"World population (2019) ","series":[["White",14.24],["Black",14.47],["Latino",5.62],["Jewish",0.34]],"cast":[{"name":"White","y":61.11,"color":"#2b908f"},{"name":"Black","y":22.22,"color":"#f45b5b"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Jewish","y":5.56,"color":"#ff0066"}]}
World Buying Power Percentage	36.48%	6.38%	9.35%	1.11%		{"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	+24.63	+15.84	+1.76	+4.45		{"name":"World Buying Power","series":[["White",36.48],["Black",6.38],["Latino",9.35],["Jewish",1.11]],"cast":[{"name":"White","y":61.11,"color":"#2b908f"},{"name":"Black","y":22.22,"color":"#f45b5b"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Jewish","y":5.56,"color":"#ff0066"}]}
Notes			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.	If there were any inconsistencies with percentages calculated using our Census data, the Jewish Data Bank percentages were the ones used. Also please review the Jewish Data Bank pdf to understand the difference between the "Core" Jewish population and the "Extended" / "Law of Return" Jewish populations. We are displaying the "Core" Jewish populations since that is what the Jewish Data Bank and Wikipedia articles chose. Also, the 1942 population estimates were used for the 1940 row. For the 2000 estimates, the "Core" definition was not used, so we used the 5.2 mil broader estimate instead of the 4.3 mil estimate since the 5.2 broader estimate seemed too drastic of a drop and the definitions weren't congruent with Core and Law of Return.

2000s abandoned mine action hero agent airplane airplane trip airport alien alternate dimension ambiguous ending animal attack anti hero archeologist army artifact autopsy axe babe scientist bad acting based on video game battle battlefield bazooka bitten on the arm blonde blood blood on camera lens blood splatter boat bomb booby trap brawl bridge brutality bullet time bulletproof vest captain car accident car chase car crash cave cell phone chase child in jeopardy combat commander commando commando mission commando raid commando unit computer corpse crap crashing through a window creature creature feature cult cult film curator dark hero dark past darkness death decapitation delivery boy demon detective disembowelment doctor eaten alive electro magnetic pulse escape euro trash evacuated city evil scientist ex soldier experiment exploding building exploitation film explosion falling from height fear female archeologist female fighter female soldier female warrior fight film starts with text final battle final showdown fistfight flash forward flashback foot chase former agent full moon gatling gun geiger counter german exploitation glock good versus evil gore gothic government agent grenade hand grenade hand to hand combat handgun haunted by the past held at gunpoint helicopter human experiment hypodermic needle impalement independent film injection invisibility jumping from height jumping through a window killing an animal knife knife in the chest knife throwing laboratory laptop computer lasersight love lovecraftian machine gun mad scientist martial arts massacre megalomaniac mercenary mercilessness microscope military mind control mind reading missing people mission mixed martial arts monster monster movie motorcycle murder museum mutation no opening credits nun old flame one man army opening action scene orphan orphanage outrunning explosion paranormal investigation paranormal phenomenon pistol possession private detective private investigator professor relic rescue revolver rifle rocket launcher scientist secret government organization security guard self sacrifice severed arm severed head sex sheriff ship shot in the arm shot in the back shot in the face shot in the forehead shot in the head shot in the knee shot in the leg shot in the shoulder shot to death showdown slow motion scene soldier special forces split head stabbed in the back stabbed in the chest stabbed in the head stabbed to death storm cellar subterranean super speed super strength supernatural power surprise ending surrealism symbiote tank taxi taxi driver taxi ride telepathy teleportation time bomb timeframe 2000s torso cut in half tough girl tough guy tracking device tragic hero tragic past violence voice over narration warrior woman wears eyeglasses woods worm x rayed skeleton z movie

Parental Guide: +add

This film gets a 2/5 family friendly score

MPAA Certification	No MPAA rating found yet. Add Family Friendly Rating?
IMDb Rating https://www.imdb.com/title/tt0369226/parentalguide last updated: 2024-10-25Update data
Commonsensemedia Rating https://www.commonsensemedia.org/movie-reviews/alone-in-the-dark last updated: 2024-10-25Update data
Violence 5/5	Intense and graphic violence.
Sex 3/5	Sexual situation, no nudity.
Language 3/5	Some strong language.
Drugs 3/5	Smoking.
Dove Rating https://dove.org/review/4764-alone-in-the-dark/
Faith 0/5	None
Sex 3/5	Sex scene (no nudity, however implied).
Language 4/5	S-10; F-9; OG-5; J-3; H-8; A-3; B-1.
Violence 4/5	Many creatures killed via gunfire; several people killed - some wounds shown; men fighting; man stabbed with pipe; man stabbed with knife.
Drugs 0/5	None
Nudity 2/5	Woman shown in bra.
Other 0/5	None

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