Highest Rated Maps of 2017
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#351
4.16 / 5.00 from 7 votes
#352
3.53 / 5.00 from 6 votes
#353
2.65 / 5.00 from 21 votes
#354
KikuoHana - Nobore! Susume! Takai Tou
The Tragic Story of the Great Tower 5.56*
Nov 26th, 2017
Kuron-kun
The Tragic Story of the Great Tower 5.56*
Nov 26th, 2017
Kuron-kun
3.01 / 5.00 from 7 votes
#355
4.19 / 5.00 from 6 votes
#356
3.36 / 5.00 from 7 votes
#357
3.15 / 5.00 from 9 votes
#359
3.18 / 5.00 from 6 votes
#362
3.68 / 5.00 from 6 votes
#363
2.78 / 5.00 from 7 votes
#364
4.30 / 5.00 from 7 votes
#365
3.08 / 5.00 from 8 votes
#366
LONG SHOT PARTY - distance (NARUTO Ver.)
Hokage 6.19*
Oct 17th, 2017
Monstrata
simple, hexgrid, jump aim
Hokage 6.19*
Oct 17th, 2017
Monstrata
simple, hexgrid, jump aim
2.56 / 5.00 from 8 votes
#367
2.95 / 5.00 from 7 votes
#368
3.27 / 5.00 from 7 votes
#370
3.15 / 5.00 from 6 votes
#372
2.70 / 5.00 from 7 votes
#373
3.62 / 5.00 from 9 votes
#375
2.79 / 5.00 from 7 votes
#376
3.03 / 5.00 from 7 votes
#377
3.18 / 5.00 from 7 votes
#378
Hiroshi Iuchi - Butsutekkai
No Refuge 6.62*
Oct 10th, 2017
NOIN
simple, repetition, jump aim, bursts
No Refuge 6.62*
Oct 10th, 2017
NOIN
simple, repetition, jump aim, bursts
3.18 / 5.00 from 7 votes
#380
3.28 / 5.00 from 7 votes
#384
3.40 / 5.00 from 6 votes
#387
4.04 / 5.00 from 5 votes
#388
2.52 / 5.00 from 8 votes
#389
96neko - Aimai Elegy
Absence 6.20*
Jul 31st, 2017
Lasse
progression, wide aim, variable timing, slidershapes
Absence 6.20*
Jul 31st, 2017
Lasse
progression, wide aim, variable timing, slidershapes
3.07 / 5.00 from 6 votes
#390
3.33 / 5.00 from 6 votes
#392
2.49 / 5.00 from 9 votes
#393
3.06 / 5.00 from 7 votes
#395
3.40 / 5.00 from 5 votes
#396
3.40 / 5.00 from 5 votes
#398
3.22 / 5.00 from 6 votes
#399
3.68 / 5.00 from 5 votes
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The chart is based on an implementation of the Bayesian average method. It updates once every day.
The next update will happen in ---
Ratings are weighed based on user rating quality, one contributing factor being their rating distribution.
Info
The chart is based on an implementation of the Bayesian average method. It updates once every day.
The next update will happen in ---
Ratings are weighed based on user rating quality, one contributing factor being their rating distribution.
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