TOP 140 effective Steemit curators in Stem-espanol category for the last week (2018.07.09-2018.07.15)

I want to share with you statistics about the most effective Steemit curators in Stem-espanol category.

This statistic is not about how much the authors or curators have earned. This statistics shows the list of curators, whose contribution to the reward pool distribution is the most significant.

image source

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Once I have published a post where I made a researsh what categories are most profitable on Steemit.

This time I even made a last week rating of the national and language communities included in the TOP-100 categories for total rewards.

№	Category	Authors	Posts	Reward (SBD)	Share %
1	kr	882	3207	9466	2,262
2	spanish	1385	2841	4682	1,119
3	deutsch	291	866	3083	0,737
4	tr	186	562	2498	0,597
5	cn	189	593	2066	0,494
6	ita	102	292	1865	0,446
7	japanese	111	334	1298	0,31
8	pt	97	291	1034	0,247
9	indonesia	335	741	874	0,209
10	stem-espanol	43	52	869	0,208
11	polish	164	378	816	0,195
12	fr	94	222	775	0,185

You can find an actual information about TOP 50 categories rewards in this post.

You can see here that Stem-espanol category took one of the largest part of the reward pool on that week. Looks like Stem-espanol community is one of the biggest and strongest on Steemit. So I decided to made a top of the most effective Steemit curators in Stem-espanol category.

The category is a first tag in the post

What does this mean?

You can say that there is nothing interesting in this statistics. That it is clear that the reward pool is distributed by whales. But!

The most interesting thing is how the curator distributes the reward pool. He can vote only for his posts and his comments. He can vote for a selected circle of authors and affiliated accounts. But also he can vote for a large number of authors who publish interesting content.

Thus, an effective curator is a curator who has sufficient Steem Power (he can be an investor or a popular author who has earned his Steem Power by publishing interesting content), devoting time and effort to the content curating at Steemit and distributing a reward pool in favor of the authors. And the efficiency level is estimated by how much SBD curator distributes among as many authors as possible.

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How it works?

Votes - Each curator distributes a certain part of the reward pool with his votes (upvote / flag).

Authors - This part of the pool he distributes between a certain number of authors.

VAS (vote average strength) - For general awareness, we can estimate the average vote strength in percents (%), with which the curator votes.

Contr. (SBD) (Contribution in SBD) - The distributed part of the pool is the contribution of the curator, it is calculated in SBD. In this case, the author's reward is taken into account. The curator's reward is not taken into account.

Share % - The percentage of the curator contribution among the contributions of all curators, we can also estimate for information purposes in %.

Eff. - The effectiveness of the curator is estimated in conventional units and is calculated as follows.

The total number of authors who published their posts for the period under study is determined.
Then the contribution of the curator is divided by the total number of authors, and then multiplied by the number of authors for whom the curator voted for the period.

Thus, we see that, having distributed even a smaller number of SBD between a larger number of authors, the curator can be more effective than other curators. At the same time, having a greater voice power, but not actively voting, supporting a small number of authors, the curator takes a lower position in the rating.

And now, statistics.

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43 authors published their posts during this week.

1926 accounts participated in the content curating according to information from the database.

9079 votes were made during this week.

1043,3 SBD - is the total author rewards for this week.

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TOP-140 effective curators for for the last week

№	Account	Votes	Authors	VAS(%)	Contr. (SBD)	Share %	Eff.
1	@utopian-io	30	25	8	647,26	62,04	376,31
2	@hendrikdegrote	26	22	3	85,95	8,24	43,97
3	@steemstem	31	25	26	55,71	5,34	32,39
4	@lafona-miner	32	26	26	47,6	4,56	28,78
5	@justtryme90	31	25	26	28,36	2,72	16,49
6	@abigail-dantes	31	25	26	24,66	2,36	14,34
7	@curie	26	22	3	7,42	0,71	3,8
8	@lemouth	31	25	25	6,05	0,58	3,52
9	@tombstone	14	14	1	9,38	0,9	3,05
10	@onthewayout	24	21	83	6,01	0,58	2,94
11	@fredrikaa	31	25	26	4,11	0,39	2,39
12	@kevinwong	14	14	4	6,6	0,63	2,15
13	@metmuseum	26	21	27	3,37	0,32	1,65
14	@wackou	23	20	1	3,52	0,34	1,64
15	@vact	26	22	3	3,19	0,31	1,63
16	@anarchyhasnogods	31	25	11	2,02	0,19	1,17
17	@howo	31	25	13	1,67	0,16	0,97
18	@anwenbaumeister	26	22	3	1,6	0,15	0,82
19	@steempress-io	2	2	8	13,44	1,29	0,63
20	@ydavgonzalez	27	22	100	1,22	0,12	0,62
21	@ausbitbank	5	4	10	6,47	0,62	0,6
22	@mahdiyari	31	25	13	1,02	0,1	0,59
23	@dber	31	25	26	0,86	0,08	0,5
24	@kryzsec	31	25	26	0,84	0,08	0,49
25	@followbtcnews	17	16	3	1,27	0,12	0,47
26	@thevenusproject	31	25	26	0,8	0,08	0,47
27	@bid.bot	2	2	99	9,67	0,93	0,45
28	@mobbs	8	8	60	2,35	0,23	0,44
29	@roelandp	15	13	2	1,32	0,13	0,4
30	@de-stem	31	25	24	0,59	0,06	0,34
31	@hr1	12	12	0	1,17	0,11	0,33
32	@mountainwashere	31	25	26	0,57	0,05	0,33
33	@ertwro	31	25	26	0,55	0,05	0,32
34	@carloserp-2000	32	25	32	0,44	0,04	0,26
35	@blodit	5	5	66	2,25	0,22	0,26
36	@gra	31	25	26	0,45	0,04	0,26
37	@kerriknox	31	25	26	0,43	0,04	0,25
38	@zest	31	25	18	0,4	0,04	0,23
39	@lamouthe	27	24	27	0,36	0,03	0,2
40	@laylahsophia	31	25	26	0,35	0,03	0,2
41	@nitesh9	31	25	26	0,34	0,03	0,2
42	@kenadis	31	25	26	0,35	0,03	0,2
43	@up-quark	30	24	27	0,32	0,03	0,18
44	@vjap55	23	20	100	0,38	0,04	0,18
45	@mountain.phil28	31	25	25	0,3	0,03	0,17
46	@fancybrothers	31	25	8	0,3	0,03	0,17
47	@mathowl	31	25	26	0,29	0,03	0,17
48	@lianaakobian	30	25	20	0,29	0,03	0,17
49	@chloroform	31	25	26	0,3	0,03	0,17
50	@haejin	1	1	30	6,3	0,6	0,15
51	@churchboy	31	25	26	0,26	0,02	0,15
52	@entropia	10	10	100	0,6	0,06	0,14
53	@terrylovejoy	29	23	10	0,25	0,02	0,13
54	@amavi	31	25	5	0,2	0,02	0,12
55	@elvigia	42	36	100	0,14	0,01	0,12
56	@liberosist	20	17	2	0,31	0,03	0,12
57	@eliaschess333	23	22	100	0,23	0,02	0,12
58	@dna-replication	26	22	19	0,22	0,02	0,11
59	@josedelacruz	36	29	100	0,16	0,02	0,11
60	@jaki01	1	1	100	4,35	0,42	0,1
61	@foundation	31	25	26	0,17	0,02	0,1
62	@canadian-coconut	1	1	16	4,01	0,38	0,09
63	@sco	31	25	6	0,16	0,02	0,09
64	@beanz	1	1	95	4,04	0,39	0,09
65	@djredimi2	26	23	100	0,16	0,02	0,09
66	@iamphysical	21	17	100	0,24	0,02	0,09
67	@alexzicky	31	25	7	0,16	0,02	0,09
68	@alexander.alexis	29	23	11	0,17	0,02	0,09
69	@tashidelek	1	1	100	3,83	0,37	0,09
70	@saunter	21	18	25	0,18	0,02	0,08
71	@provenezuela	4	3	50	0,95	0,09	0,07
72	@anaestrada12	19	15	100	0,19	0,02	0,07
73	@bue	8	8	100	0,38	0,04	0,07
74	@serylt	19	16	24	0,15	0,01	0,06
75	@borislavzlatanov	31	25	26	0,09	0,01	0,05
76	@dexterdev	28	23	27	0,09	0,01	0,05
77	@joe.nobel	29	24	14	0,09	0,01	0,05
78	@conficker	31	25	26	0,08	0,01	0,05
79	@whileponderin	31	25	26	0,09	0,01	0,05
80	@himal	31	25	26	0,09	0,01	0,05
81	@dysfunctional	31	25	13	0,08	0,01	0,05
82	@adetola	31	25	26	0,09	0,01	0,05
83	@schlunior	31	25	12	0,09	0,01	0,05
84	@mrbreeziewrites	31	25	26	0,09	0,01	0,05
85	@rjbauer85	31	25	26	0,08	0,01	0,05
86	@davidorcamuriel	1	1	100	2,23	0,21	0,05
87	@the-devil	31	25	26	0,09	0,01	0,05
88	@stem-espanol	31	24	28	0,09	0,01	0,05
89	@vanessahampton	31	25	13	0,08	0,01	0,05
90	@massivevibration	26	22	5	0,09	0,01	0,05
91	@drmake	26	22	2	0,09	0,01	0,05
92	@temitayo-pelumi	31	25	26	0,08	0,01	0,05
93	@jesusjacr	20	16	36	0,1	0,01	0,04
94	@derbesserwisser	13	12	25	0,13	0,01	0,04
95	@bearded-benjamin	8	8	50	0,21	0,02	0,04
96	@felixrodriguez	32	25	28	0,06	0,01	0,03
97	@cervantes	1	1	15	1,37	0,13	0,03
98	@alexdory	10	10	20	0,14	0,01	0,03
99	@florae	31	25	26	0,05	0	0,03
100	@flugschwein	12	12	22	0,12	0,01	0,03
101	@lorenzor	12	9	100	0,12	0,01	0,03
102	@lesshorrible	13	12	25	0,11	0,01	0,03
103	@kingabesh	31	25	13	0,03	0	0,02
104	@cryptoitaly	19	16	16	0,06	0,01	0,02
105	@ugonma	31	25	26	0,03	0	0,02
106	@osmy07	10	9	100	0,08	0,01	0,02
107	@talentclub	3	2	5	0,34	0,03	0,02
108	@joelagbo	30	24	27	0,03	0	0,02
109	@robotics101	31	25	21	0,03	0	0,02
110	@akeelsingh	31	25	26	0,03	0	0,02
111	@aboutcoolscience	5	3	26	0,3	0,03	0,02
112	@blessing97	27	22	27	0,03	0	0,02
113	@masterwriter	31	25	26	0,03	0	0,02
114	@cooperacion	8	8	15	0,09	0,01	0,02
115	@locikll	26	22	7	0,04	0	0,02
116	@rachelsmantra	31	25	26	0,03	0	0,02
117	@eniolw	25	21	100	0,05	0	0,02
118	@viannis	48	39	100	0,02	0	0,02
119	@planetenamek	15	13	4	0,05	0	0,02
120	@espoem	5	5	11	0,11	0,01	0,01
121	@tsoldovieri	7	5	100	0,08	0,01	0,01
122	@effofex	31	25	13	0,02	0	0,01
123	@tensor	4	2	100	0,2	0,02	0,01
124	@heiditravels	1	1	15	0,41	0,04	0,01
125	@marketstack	4	4	1	0,06	0,01	0,01
126	@tomastonyperez	8	7	100	0,08	0,01	0,01
127	@kurator-polski	2	2	100	0,17	0,02	0,01
128	@dick.sledge	4	4	1	0,06	0,01	0,01
129	@kobold-djawa	1	1	100	0,25	0,02	0,01
130	@abdulmath	13	12	100	0,03	0	0,01
131	@handmade	3	2	100	0,19	0,02	0,01
132	@pratik27	31	25	13	0,02	0	0,01
133	@star-vc	31	25	26	0,02	0	0,01
134	@eric-boucher	8	7	1	0,09	0,01	0,01
135	@michelios	25	22	1	0,01	0	0,01
136	@rafalski	3	3	67	0,11	0,01	0,01
137	@mayowadavid	23	20	14	0,02	0	0,01
138	@shoganaii	31	25	13	0,02	0	0,01
139	@benleemusic	24	22	0	0,02	0	0,01
140	@gargon	1	1	15	0,29	0,03	0,01

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I took all vote bots (most of them are working like pay for vote) in the separate table. Because we can't call them effective curators. Their work depends on authors payments and activity.
If you know another bots in the list above please tell me about it.

№	Account	Votes	Authors	VAS(%)	Contr. (SBD)	Share %	Eff.
1	@minnowsupport	7	6	1	0,54	0,05	0,08

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Now you know all these heroes. Their daily work gives hope to some authors, and some even a decent income. Their work attracts new authors in Steemit.

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Information sources

All information is prepared by querying data from a SteemSQL - SQL database containing information from Steem's blockchain, as well as by calculations based on this data.

Special thanks to @arcange for SteemSQL and for the advice provided to me during the development of this project.

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