Movielogr

movie poster

Rating: 4 stars

Tags

Netflix - DVD

Seen 2 times

Seen on: 08/10/2012, 04/09/2010

View on: IMDb | TMDb

Blood Creek (2009)

Directed by Joel Schumacher

Horror

Most recently watched by noahphex, noahphex

Overview

A man and his brother on a mission of revenge become trapped in a harrowing occult experiment dating back to the Third Reich.

Rated R | Length 90 minutes

Actors

Wentworth Miller | Dominic Purcell | Michael Fassbender | Lynn Collins | Gerard McSorley | Florin Piersic Jr. | Henry Cavill | Shea Whigham | Emma Booth | Rainer Winkelvoß | Tony Barger | Ana Popescu | Albert Gherasim

Viewing Notes

What’s scarier than nazis? Obviously it’s occult Nazis who are mostly undead and feeding off people and bringing animals and humans back to life!

A plot that could be expanded into a video game, this little seen movie from last year is quite effective, dark and full of some great gore.

Michael Fassbender, Dominic Purcell and Henry Cavill star in this Joel Schumacher horror flick that had a small theatrical run last year. To me that’s mind blowing. How did this movie, made by Joel “The Lost Boys” Schumacher starring 3 pretty great actors get the shaft? There’s certainly worse horror movies that get wider releases and make more money, but a competent flick from a well known director (who, don’t get me wrong has had some missteps along the way, but still…) gets released in a few unknown theaters and then hits DVD? Either way, I hope it gets some legs with the home market because this was a great watch.

Fassbender stars as a Nazi sent to a German family’s farm back in 1936 to study a runestone they have in their backyard. Cut to modern times and a young EMT (Cavill) is dealing with the loss of his brother (Purcell) who went missing from a camping trip 2 years earlier suddenly shows up and asks him to grab some guns, supplies and to head back into the swamp with him. He obliges and ends up embroiled in a fast-paced, gruesome and scary battle with an undead Nazi.

First thing I noticed was that Blood Creek never takes a lot of time with exposition, instead choosing to reveal story on the run. The movie goes from one crazy scene to the next, spilling gallons of blood along the way, telling an ever more interesting story and doing it with abandon that I found really charming. A few times the script does delve into some poorly written dialog but it never lasts long or is interrupted with a flaming, undead horse busting through a door, so really, who gives two shits? I loved all the performances and any ridiculousness in plot is made up for by it’s relentless continuance of action or by delivering more blood loss.

I really hope Blood Creek gets a following on the home market because while it’s not a perfect movie by any means it’s a lot of fun and deserves to be seen by anyone with a vague interest in Nazis, zombies, undead flaming horses, and the occult.

Comments

No comments yet. Log in and be the first!

  BENCHMARKS  
Loading Time: Base Classes  0.0016
Controller Execution Time ( Members / Movie Detail )  0.0238
Total Execution Time  0.0255
  GET DATA  
No GET data exists
  MEMORY USAGE  
540,080 bytes
  POST DATA  
No POST data exists
  URI STRING  
members/noahphex/movie_detail/4139
  CLASS/METHOD  
members/movie_detail
  DATABASE:  movielogr_dev (Members:$db)   QUERIES: 17 (0.0049 seconds)  (Hide)
0.0003   SELECT 1
FROM 
`ml_sessions`
WHERE `id` = '712a989892598dd5d8663258b8b072f4d3e2bbaa'
AND `ip_address` = '216.73.216.111' 
0.0002   SELECT GET_LOCK('8746df7f2341839e3aa085f2e1afac19'300) AS ci_session_lock 
0.0002   SELECT `data`
FROM `ml_sessions`
WHERE `id` = '712a989892598dd5d8663258b8b072f4d3e2bbaa'
AND `ip_address` = '216.73.216.111' 
0.0003   SELECT `username`, `user_id`
FROM `ml_users`
WHERE `username` = 'noahphex'
 
LIMIT 1 
0.0003   SELECT `MV`.`title_id`, `MT`.`tmdb_id`
FROM `ml_movie_titles` `MT`
LEFT JOIN `ml_movies` `MVON `MV`.`title_id` = `MT`.`title_id`
WHERE `MV`.`movie_id` = 4139
AND `MV`.`user_id` = 8
 LIMIT 1 
0.0002   SELECT `star_rating`
FROM `ml_users`
WHERE `ml_users`.`user_id` = 8
 LIMIT 1 
0.0001   SET SESSION group_concat_max_len 12288 
0.0008   SELECT `MV`.`title_id`, `MV`.`movie_id`, COUNT(DISTINCT MV.title_id) AS mv_count, `title`, `prefix`, `year`, `imdb_link`, `MT`.`imdb_id`, `MT`.`tmdb_id`, `overview`, `certification`, `runtime`, `genre_id_01`, `genre_id_02`, `genre_id_03`, MAX(MP.filename) AS filename, `G1`.`genre_nameAS `genre_name_01`, `G2`.`genre_nameAS `genre_name_02`, `G3`.`genre_nameAS `genre_name_03`, `date_viewed`, `notes`, `MV`.`format_id`, `FMT`.`format_name`, `MV`.`source_id`, `SRC`.`source_name`, `MV`.`device_id`, `DVC`.`device_name`, `MV`.`rating_id`, `STR`.`star_rating`, `rewatch`, GROUP_CONCAT(DISTINCT(DR.director_name) ORDER BY DR.tmdb_director_id ASC SEPARATOR "|") AS directorsGROUP_CONCAT(DISTINCT(DR.tmdb_director_id) ORDER BY DR.tmdb_director_id ASC SEPARATOR "|") AS director_idsGROUP_CONCAT(DISTINCT(ACT.actor_name) ORDER BY ACT.tmdb_actor_id ASC SEPARATOR "|") AS actorsGROUP_CONCAT(DISTINCT(ACT.tmdb_actor_id) ORDER BY ACT.tmdb_actor_id ASC SEPARATOR "|") AS actor_ids
FROM 
`ml_movie_titles` `MT`
LEFT JOIN `ml_movies` `MVON `MV`.`title_id` = `MT`.`title_id`
LEFT JOIN `ml_movie_posters` `MPON `MV`.`title_id` = `MP`.`title_id`
LEFT JOIN `ml_lookup_genres` `G1ON `MV`.`genre_id_01` = `G1`.`genre_id`
LEFT JOIN `ml_lookup_genres` `G2ON `MV`.`genre_id_02` = `G2`.`genre_id`
LEFT JOIN `ml_lookup_genres` `G3ON `MV`.`genre_id_03` = `G3`.`genre_id`
LEFT JOIN `ml_lookup_formats` `FMTON `MV`.`format_id` = `FMT`.`format_id`
LEFT JOIN `ml_lookup_sources` `SRCON `MV`.`source_id` = `SRC`.`source_id`
LEFT JOIN `ml_lookup_devices` `DVCON `MV`.`device_id` = `DVC`.`device_id`
LEFT JOIN `ml_movie_ratings_five_star` `STRON `MV`.`rating_id` = `STR`.`rating_id`
LEFT JOIN `ml_junction_movies_directors` `JMDON `MT`.`title_id` = `JMD`.`title_id`
LEFT JOIN `ml_directors_new` `DRON `JMD`.`tmdb_director_id` = `DR`.`tmdb_director_id`
LEFT JOIN `ml_junction_movies_actors` `JMAON `MT`.`title_id` = `JMA`.`title_id`
LEFT JOIN `ml_actors_new` `ACTON `JMA`.`tmdb_actor_id` = `ACT`.`tmdb_actor_id`
WHERE `MV`.`movie_id` = 4139
AND `MV`.`user_id` = 8
ORDER BY 
`date_viewedDESC 
0.0002   SET SESSION group_concat_max_len 1024 
0.0002   SELECT `event_title`, `JME`.`event_id`
FROM `ml_junction_movies_events` `JME`
LEFT JOIN `ml_movie_events` `EVON `JME`.`event_id` = `EV`.`event_id`
WHERE `JME`.`movie_id` = 4139 
0.0003   SELECT `tag`, `JMT`.`tag_id`
FROM `ml_junction_movies_tags` `JMT`
LEFT JOIN `ml_tags` `MTON `JMT`.`tag_id` = `MT`.`tag_id`
WHERE `JMT`.`movie_id` = 4139 
0.0003   SELECT `star_rating`
FROM `ml_users`
WHERE `ml_users`.`user_id` = 8
 LIMIT 1 
0.0002   SELECT `rating_id`
FROM `ml_movies` `MV`
WHERE `MV`.`user_id` = 8
AND `MV`.`movie_id` = 4139
 LIMIT 1 
0.0002   SELECT `star_rating`
FROM `ml_movie_ratings_five_star` `MR`
WHERE `MR`.`rating_id` = '18'
 
LIMIT 1 
0.0004   SELECT `MV2`.`date_viewed`, `MV2`.`movie_id`
FROM `ml_movies` `MV`
LEFT JOIN `ml_movies` `MV2ON `MV`.`title_id` = `MV2`.`title_idAND `MV`.`user_id` = `MV2`.`user_id`
WHERE `MV`.`user_id` = 8
AND `MV`.`movie_id` = 4139
GROUP BY 
`MV2`.`movie_id`
ORDER BY `MV2`.`date_viewedDESC 
0.0003   SELECT `comment_id`, `comment`, `commenter_id`, `timestamp`, `username`, `email_address`
FROM `ml_comments` `MC`
JOIN `ml_users` `MUON `MU`.`user_id` = `MC`.`commenter_id`
WHERE `movie_id` = 4139 
0.0004   SELECT `username`, `date_viewed`, `MV`.`movie_id`
FROM `ml_users` `MU`
JOIN `ml_movies` `MVON `MV`.`user_id` = `MU`.`user_id`
JOIN `ml_movie_titles` `MTON `MT`.`title_id` = `MV`.`title_id`
WHERE `MT`.`title_id` = 2638
ORDER BY 
`date_viewedDESC
 LIMIT 10 
  HTTP HEADERS  (Show)
  SESSION DATA  (Show)
  CONFIG VARIABLES  (Show)