It's a $2/year VPS  Up to you if you want the data Cool. If it only takes much time (one day) for the first scraping round (next Friday), but takes less time (that I guess) for the second round and later, please do it.  I already have the List of boards, subboards (some local subboards are not listed)For this analysis, I think I am going to make updates monthly or quarterly. |
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It works but honestly I don't think I should ask you to run your computer one day just to do this. It sounds crazy but now I understood why sometimes I asked your help and you rejected it.
For off-limited boards, I don't need them.
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To prepare for my another thread, I created the list. In the list, some sub-boards in some locals are not listed because I can not read and display them in results (maybe unicode issues), so I skipped them. +-----------------------------------------+
| boardid mainboard |
|-----------------------------------------|
1. | 159 ANNs( Alts) |
2. | 240 ANNs( Alts) |
|-----------------------------------------|
3. | 67 Altcoin Disc. |
|-----------------------------------------|
4. | 241 Arabic |
5. | 242 Arabic |
6. | 253 Arabic |
7. | 265 Arabic |
8. | 266 Arabic |
9. | 267 Arabic |
|-----------------------------------------|
10. | 17 Archival |
11. | 25 Archival |
12. | 26 Archival |
13. | 44 Archival |
14. | 59 Archival |
15. | 99 Archival |
|-----------------------------------------|
16. | 1 BTC Disc. |
17. | 74 BTC Disc. |
18. | 77 BTC Disc. |
19. | 86 BTC Disc. |
20. | 87 BTC Disc. |
|-----------------------------------------|
21. | 4 BTC Technical Support |
|-----------------------------------------|
22. | 39 Beginners & Help |
|-----------------------------------------|
23. | 30 Chinese |
24. | 117 Chinese |
25. | 118 Chinese |
26. | 119 Chinese |
27. | 146 Chinese |
28. | 196 Chinese |
|-----------------------------------------|
29. | 201 Croatian |
30. | 220 Croatian |
31. | 221 Croatian |
|-----------------------------------------|
32. | 6 Development & Technical Disc. |
33. | 37 Development & Technical Disc. |
34. | 97 Development & Technical Disc. |
35. | 98 Development & Technical Disc. |
36. | 100 Development & Technical Disc. |
37. | 138 Development & Technical Disc. |
38. | 231 Development & Technical Disc. |
39. | 261 Development & Technical Disc. |
|-----------------------------------------|
40. | 79 Dutch |
41. | 80 Dutch |
42. | 94 Dutch |
43. | 116 Dutch |
44. | 143 Dutch |
45. | 147 Dutch |
46. | 148 Dutch |
47. | 150 Dutch |
|-----------------------------------------|
48. | 7 Economics |
49. | 57 Economics |
|-----------------------------------------|
50. | 13 Français |
51. | 47 Français |
52. | 48 Français |
53. | 49 Français |
54. | 50 Français |
55. | 54 Français |
56. | 149 Français |
57. | 183 Français |
58. | 184 Français |
59. | 186 Français |
60. | 187 Français |
61. | 188 Français |
62. | 208 Français |
63. | 209 Français |
64. | 210 Français |
65. | 211 Français |
66. | 258 Français |
|-----------------------------------------|
67. | 16 German |
68. | 35 German |
69. | 36 German |
70. | 60 German |
71. | 61 German |
72. | 62 German |
73. | 63 German |
74. | 64 German |
75. | 139 German |
76. | 140 German |
77. | 141 German |
78. | 152 German |
|-----------------------------------------|
79. | 120 Greek |
80. | 136 Greek |
81. | 179 Greek |
82. | 195 Greek |
83. | 246 Greek |
84. | 247 Greek |
|-----------------------------------------|
85. | 95 Hebrew |
|-----------------------------------------|
86. | 89 India |
87. | 121 India |
88. | 122 India |
89. | 123 India |
90. | 124 India |
91. | 125 India |
92. | 126 India |
93. | 127 India |
|-----------------------------------------|
94. | 191 Indonesian |
95. | 192 Indonesian |
96. | 193 Indonesian |
97. | 194 Indonesian |
|-----------------------------------------|
98. | 28 Italian |
99. | 46 Italian |
100. | 107 Italian |
101. | 115 Italian |
102. | 132 Italian |
103. | 144 Italian |
104. | 145 Italian |
105. | 153 Italian |
106. | 162 Italian |
107. | 165 Italian |
108. | 169 Italian |
109. | 170 Italian |
110. | 171 Italian |
111. | 172 Italian |
112. | 173 Italian |
113. | 175 Italian |
114. | 176 Italian |
115. | 200 Italian |
116. | 205 Italian |
|-----------------------------------------|
117. | 252 Japanese |
118. | 255 Japanese |
|-----------------------------------------|
119. | 82 Korean |
120. | 182 Korean |
|-----------------------------------------|
121. | 5 Marketplace |
122. | 51 Marketplace |
123. | 52 Marketplace |
124. | 53 Marketplace |
125. | 56 Marketplace |
126. | 65 Marketplace |
127. | 71 Marketplace |
128. | 73 Marketplace |
129. | 75 Marketplace |
130. | 78 Marketplace |
131. | 84 Marketplace |
132. | 85 Marketplace |
133. | 88 Marketplace |
134. | 93 Marketplace |
135. | 207 Marketplace |
136. | 212 Marketplace |
137. | 217 Marketplace |
138. | 222 Marketplace |
139. | 223 Marketplace |
140. | 228 Marketplace |
141. | 234 Marketplace |
|-----------------------------------------|
142. | 161 Marketplace (Alts) |
143. | 197 Marketplace (Alts) |
144. | 198 Marketplace (Alts) |
145. | 238 Marketplace (Alts) |
|-----------------------------------------|
146. | 24 Meta |
147. | 167 Meta |
148. | 168 Meta |
|-----------------------------------------|
149. | 14 Mining |
150. | 40 Mining |
151. | 41 Mining |
152. | 42 Mining |
153. | 76 Mining |
154. | 81 Mining |
155. | 137 Mining |
|-----------------------------------------|
156. | 160 Mining (Atls) |
157. | 199 Mining (Atls) |
|-----------------------------------------|
158. | 9999 Off-limits |
|-----------------------------------------|
159. | 9 Off-topic |
|-----------------------------------------|
160. | 11 Other languages/locations |
|-----------------------------------------|
161. | 219 Pilipinas |
162. | 243 Pilipinas |
163. | 260 Pilipinas |
164. | 268 Pilipinas |
|-----------------------------------------|
165. | 34 Politics & Society |
|-----------------------------------------|
166. | 142 Polski |
167. | 163 Polski |
168. | 164 Polski |
169. | 263 Polski |
170. | 264 Polski |
|-----------------------------------------|
171. | 29 Portuguese |
172. | 69 Portuguese |
173. | 70 Portuguese |
174. | 131 Portuguese |
175. | 134 Portuguese |
176. | 135 Portuguese |
177. | 181 Portuguese |
178. | 206 Portuguese |
|-----------------------------------------|
179. | 12 Project Development |
|-----------------------------------------|
180. | 108 Romanian |
181. | 109 Romanian |
182. | 110 Romanian |
183. | 111 Romanian |
184. | 112 Romanian |
185. | 113 Romanian |
186. | 114 Romanian |
187. | 166 Romanian |
188. | 178 Romanian |
189. | 257 Romanian |
190. | 259 Romanian |
|-----------------------------------------|
191. | 10 Russian |
192. | 18 Russian |
193. | 19 Russian |
194. | 20 Russian |
195. | 21 Russian |
196. | 22 Russian |
197. | 23 Russian |
198. | 55 Russian |
199. | 66 Russian |
200. | 72 Russian |
201. | 90 Russian |
202. | 91 Russian |
203. | 92 Russian |
204. | 128 Russian |
205. | 185 Russian |
206. | 236 Russian |
207. | 237 Russian |
208. | 248 Russian |
209. | 256 Russian |
210. | 262 Russian |
|-----------------------------------------|
211. | 250 Serious Disc. |
212. | 251 Serious Disc. |
|-----------------------------------------|
213. | 45 Skandinavisk |
|-----------------------------------------|
214. | 27 Spanish |
215. | 31 Spanish |
216. | 32 Spanish |
217. | 33 Spanish |
218. | 101 Spanish |
219. | 102 Spanish |
220. | 103 Spanish |
221. | 104 Spanish |
222. | 105 Spanish |
223. | 130 Spanish |
224. | 151 Spanish |
225. | 177 Spanish |
226. | 202 Spanish |
227. | 203 Spanish |
228. | 204 Spanish |
229. | 254 Spanish |
|-----------------------------------------|
230. | 224 Speculation (Alts) |
|-----------------------------------------|
231. | 8 Trading Disc. |
232. | 83 Trading Disc. |
233. | 129 Trading Disc. |
|-----------------------------------------|
234. | 133 Turkish |
235. | 155 Turkish |
236. | 156 Turkish |
237. | 157 Turkish |
238. | 158 Turkish |
239. | 174 Turkish |
240. | 180 Turkish |
241. | 189 Turkish |
242. | 190 Turkish |
243. | 229 Turkish |
244. | 230 Turkish |
245. | 232 Turkish |
246. | 235 Turkish |
247. | 239 Turkish |
+-----------------------------------------+
- Dev. & Tech. Disc.: Development & Technical Discussion
- B Tech. Support: Bitcoin Technical Support
- Other: Other languages/locations
- BTC : Bitcoin.
- ANNs : Announcements.
- Alts : Altcoins.
- Disc.: Discussion.
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Hi WO's citizens, The results from my analysis will make you shocked. Stats for total merits of topics and posts (since 24/1/2018 to 11/12/2018): ABSTRACT
< ... >Categorisations of merited-topics/ merited-posts:Topics: group | Freq. Percent Cum.
------------+-----------------------------------
<= 10 | 51,609 85.15 85.15
11 - 25 | 5,861 9.67 94.82
26 - 50 | 1,893 3.12 97.94
51 - 100 | 771 1.27 99.21
101 - 200 | 324 0.53 99.75
201 - 500 | 115 0.19 99.94
501 - 1000 | 27 0.04 99.98
1001+ | 12 0.02 100.00
------------+-----------------------------------
Total | 60,612 100.00
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Stats for total merits of topics and posts (since 24/1/2018 to 11/12/2018): ABSTRACT
Topics: variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
amount | 60612.0 8.9 171.2 2.0 1.0 6.0 1.0 34148.0
----------------------------------------------------------------------------------------------
+-------------------------+
| rank amount topic |
|-------------------------|
1. | 1 34148 178336 |
2. | 2 22047 5193860 |
3. | 3 7646 1006631 |
4. | 4 3397 2818350 |
5. | 5 2711 26136 |
|-------------------------|
6. | 6 1616 2827596 |
7. | 7 1516 5 |
8. | 8 1468 313900 |
9. | 9 1395 5105163 |
10. | 10 1234 2818398 |
|-------------------------|
11. | 11 1171 2840438 |
12. | 12 1122 5095156 |
13. | 13 928 2823701 |
14. | 14 915 4871955 |
15. | 15 903 2683530 |
|-------------------------|
16. | 16 865 2360806 |
17. | 17 839 1976285 |
18. | 18 831 4415262 |
19. | 19 796 753252 |
20. | 20 727 1608859 |
|-------------------------|
21. | 21 724 750446 |
22. | 22 718 0 |
23. | 23 711 2820637 |
24. | 24 690 1883902 |
25. | 25 659 155054 |
|-------------------------|
26. | 26 652 1458034 |
27. | 27 638 2818404 |
28. | 28 632 4742257 |
29. | 29 602 583449 |
30. | 30 566 5053833 |
|-------------------------|
31. | 31 550 569449 |
32. | 32 550 5157696 |
33. | 33 549 570886 |
34. | 34 544 375643 |
35. | 35 535 628413 |
|-------------------------|
36. | 36 528 421615 |
37. | 37 528 2040221 |
38. | 38 507 2544574 |
39. | 39 503 2897545 |
40. | 40 493 5114042 |
|-------------------------|
41. | 41 468 3918625 |
42. | 42 460 857670 |
43. | 43 457 5149062 |
44. | 44 446 2820262 |
45. | 45 445 5030366 |
|-------------------------|
46. | 46 441 615953 |
47. | 47 439 1840789 |
48. | 48 431 5203697 |
49. | 49 425 5023605 |
50. | 50 425 1161170 |
+-------------------------+
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
amount | 177718.0 3.0 9.0 1.0 1.0 3.0 1.0 1486.0
----------------------------------------------------------------------------------------------
+-------------------------------------+
| rank amount ntopic_initial |
|-------------------------------------|
1. | 1 1486 5.msg28 |
2. | 2 1435 2818350.msg28856522 |
3. | 3 717 0.msg0 |
4. | 4 648 155054.msg1643833 |
5. | 5 538 375643.msg4022997 |
|-------------------------------------|
6. | 6 395 5193860.msg52793373 |
7. | 7 393 5193860.msg53059246 |
8. | 8 377 2818398.msg28857585 |
9. | 9 354 5203697.msg53150887 |
10. | 10 346 5023605.msg45324722 |
|-------------------------------------|
11. | 11 327 2683530.msg27411367 |
12. | 12 325 1545652.msg15536651 |
13. | 13 323 1840789.msg18316056 |
14. | 14 305 137.msg1141 |
15. | 15 293 5114042.msg49909815 |
|-------------------------------------|
16. | 16 275 5193860.msg52800357 |
17. | 17 270 990345.msg10775516 |
18. | 18 252 5193860.msg53064307 |
19. | 19 244 1608859.msg16156239 |
20. | 20 244 2827596.msg28975211 |
|-------------------------------------|
21. | 21 243 615953.msg6815569 |
22. | 22 232 5123724.msg50275206 |
23. | 23 230 2895261.msg29766601 |
24. | 24 229 2228.msg29479 |
25. | 25 224 3232693.msg33646534 |
|-------------------------------------|
26. | 26 224 1161170.msg12233132 |
27. | 27 214 5193860.msg53115743 |
28. | 28 213 3315347.msg34617121 |
29. | 29 209 3920469.msg37611040 |
30. | 30 207 1883902.msg18714400 |
|-------------------------------------|
31. | 31 205 5193860.msg53137723 |
32. | 32 200 5193860.msg53095690 |
33. | 33 193 2022902.msg20155309 |
34. | 34 186 5193860.msg52803489 |
35. | 35 184 532.msg6306 |
|-------------------------------------|
36. | 36 180 2833350.msg29048068 |
37. | 37 178 996318.msg10820715 |
38. | 38 176 5194117.msg52803618 |
39. | 39 175 5193860.msg52846255 |
40. | 40 174 2820637.msg28883168 |
|-------------------------------------|
41. | 41 173 5030366.msg45810047 |
42. | 42 170 4519248.msg40698575 |
43. | 43 169 5124871.msg50326142 |
44. | 44 167 2840438.msg29137142 |
45. | 45 166 1976285.msg19673755 |
|-------------------------------------|
46. | 46 161 703657.msg7955645 |
47. | 47 159 2159012.msg21610436 |
48. | 48 159 2823221.msg28920985 |
49. | 49 159 2825523.msg28944688 |
50. | 50 156 5193860.msg53002575 |
+-------------------------------------+
Topics: group | Freq. Percent Cum.
------------+-----------------------------------
<= 10 | 51,609 85.15 85.15
11 - 25 | 5,861 9.67 94.82
26 - 50 | 1,893 3.12 97.94
51 - 100 | 771 1.27 99.21
101 - 200 | 324 0.53 99.75
201 - 500 | 115 0.19 99.94
501 - 1000 | 27 0.04 99.98
1001+ | 12 0.02 100.00
------------+-----------------------------------
Total | 60,612 100.00
Posts: group | Freq. Percent Cum.
------------+-----------------------------------
1 | 98,331 55.33 55.33
2 - 5 | 61,816 34.78 90.11
6 - 10 | 10,929 6.15 96.26
11 - 25 | 4,737 2.67 98.93
26 - 50 | 1,356 0.76 99.69
51+ | 549 0.31 100.00
------------+-----------------------------------
Total | 177,718 100.00
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Sorry for this big quote, but it is a visual display from boring data. I hope you like it. I dropped 16 transactions with negative merits before getting statistics below. - Total number of transactions : 274144
- 1-merit transactions: 190048 (69.3%)
- Sum merits of 1-merit transactions: 190048
- Sum merits of 2-to-5-merit transactions: 213021
- Sum of total merits: 540942
- Percent of 1-merit transactions (sum) per total merits: 35.1
- Percent of 2-to-5-merit transactions (sum) per total merits: 39.4
Bar charts & raw statistics:Number of merit transactions over groups:10 categories: group | Freq. Percent Cum.
------------+-----------------------------------
1 | 190,048 69.32 69.32
2 - 5 | 74,035 27.01 96.33
6 - 10 | 7,122 2.60 98.93
11 - 15 | 732 0.27 99.19
16 - 20 | 871 0.32 99.51
21 - 25 | 347 0.13 99.64
26 - 30 | 226 0.08 99.72
31 - 45 | 240 0.09 99.81
46 - 50 | 523 0.19 100.00
------------+-----------------------------------
Total | 274,144 100.00
5 categories: cat | Freq. Percent Cum.
------------+-----------------------------------
1 | 190,048 69.32 69.32
2 - 5 | 74,035 27.01 96.33
6 - 10 | 7,122 2.60 98.93
11 - 45 | 2,416 0.88 99.81
46 - 50 | 523 0.19 100.00
------------+-----------------------------------
Total | 274,144 100.00
Over 10 groups: +------------------------------------+
| group amount tmerit pmerit |
|------------------------------------|
1. | 1 190048 540942 35.1 |
2. | 2 - 5 213021 540942 39.4 |
3. | 6 - 10 61434 540942 11.4 |
4. | 11 - 15 9634 540942 1.8 |
5. | 16 - 20 16868 540942 3.1 |
|------------------------------------|
6. | 21 - 25 8275 540942 1.5 |
7. | 26 - 30 6539 540942 1.2 |
8. | 31 - 45 9042 540942 1.7 |
9. | 46 - 50 26081 540942 4.8 |
+------------------------------------+
Over 5 groups: +------------------------------------+
| cat amount tmerit pmerit |
|------------------------------------|
1. | 1 190048 540942 35.1 |
2. | 2 - 5 213021 540942 39.4 |
3. | 6 - 10 61434 540942 11.4 |
4. | 11 - 45 50358 540942 9.3 |
5. | 46 - 50 26081 540942 4.8 |
+------------------------------------+
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Those two lines have very different formats (different hierarchical formats, I meant). It's just taken from the HTML links on top of each page. Different child boards have different "depths", that's what makes it "messy". This one is what I need. time amount board/subboard_idnumber user_from user_to
1576204400 2 24 18321 307884
How about just the topicID (say: "5209104" taken from the msgID: "5209104.msg53329921") and the board? Is that enough to combine with my merit.all.txt ? Yes, I just need at least one variable in the two datasets to merge them together, but what did you mean by 'the board'? Is it the board's name or the board's id number. I can use both of them but it is more convenient for me if you have board's id number. If you don't have it available, I am going to do it, it's my turn. And if you only have board's name, please help me by moving it to the last column (last variable). It is helpful. This can literally be done in one (long) line of code For things you manage well, it is easy, but for the others who don't know how to do, it is a challenge.
Another thing I don't know. I meant I use nearly same things for my statistical stuffs but with computer programming, I have to learn from scratch. Thanks. |
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Without much new work (only a lot of scraping), I can get OP a list like this one (but with the topic/msg-ID instead of a Merit count). @tranthidung: can you work with that? It is not perfect for me but looks cool and you have available data on it. That is a plus point, sure. But it looks a little bit messy and I can not use it. Example: 65 Merit earned in Bitcoin Forum > Other > Beginners & Help
1 Merit earned in Bitcoin Forum > Alternate cryptocurrencies > Marketplace (Altcoins) > Service Discussion (Altcoins)
Those two lines have very different formats (different hierarchical formats, I meant). In addition, I doubt that which types of data you have: - You directly scraped data with boards/ sub-boards names (Beginners & Help; Meta, ie.).
- You scraped data with boards' / sub-boards' id numbers, from which you define their labels.
If what you did is [1], you have to do more works to help me. I need only details on subboards (if posts released in subboards), from which I will trace back the main boards of posts. In this format: amount board/subboard_id
65 39
1 198
39 is for Beginners & help 198 is for Service Discussion (Altcoins) If what you did is [2], it is perfect because you can easily help me with your available data. I don't know which format you can dump if you are going to help me. That one is theymos's merit data dump: time amount msg user_from user_to
1576204400 2 5209104.msg53329921 18321 307884
This one is what I need. time amount board/subboard_idnumber user_from user_to
1576204400 2 24 18321 307884
Each variables separeted by : or , or tab or space. All are fine for me. If you have id numbers of boards/ subboards, please give me a dump too. According to https://bitcointalk.org/index.php?action=stats, there are 252 boards at the moment. If you don't have that list, I am going to get it myself. In short, it is better if you give me data dump with numbers, not texts because I can only directly import data from your club with numbers (takes me around 15 - 20 seconds only). With text, I have to copy and paste data and do some other steps, but I can not copy and paste it (not fully load on browser as I discussed with you previously.)
< ... >
Thanks for your informative explanations, but I have not yet had knowledge and skills to scrap data (in any methods). I will try to learn it. |
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Thanks, looks like a good guide but I have to learn about programming to do this. It surely takes time. |
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Working
OP, let's work to earn BTC, but don't spoil the forum to earn BTC. To work in the forum but don't spoil it, you have to learn first. It might take weeks or months, depend on your speed of learning, before you prepare enough prerequisites to work in the forum and earn BTC. The period of easily BTC-earning by spoiling the forum ended in late of Januray in 2018, with the appearance of the merit system. |
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Are you trying to figure out what board a particular thread is in from visiting the thread? Or do you want to know based on other information?
It is easy to visit one post to see which board / sub-board it was posted in, but it is a serious issue if you have hundreds or thousands of posts to check. I have to check it automatically with machine, not handy. I would like to check it with the available figures of topic or post numbers (as I described in OP). |
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Hi, I don't know how to get the details on boards or sub-boards of each posts or topics. I know from the numbers belong to each boards/ sub-boards, I can get their boards'/ sub-boards' names, but the problems for me is how to get those numbers. For example, in Meta, we have the board number at 24: https://bitcointalk.org/index.php?action=post;board=24.0I made that post in Meta board, in the topic "Merit & new rank requirements" https://bitcointalk.org/index.php?topic=2818350.msg53358425#msg53358425There are numbers for topic (2818350), and for that post (53358425), but can you guide me how to get the number of Meta board (24) if I only have that one: https://bitcointalk.org/index.php?topic=2818350.msg53358425#msg53358425I asked this because if it is easy to do, I want to get statistics on merit distributions over boards, sub-boards from raw merit data, dumped by theymos. If I can do this, I will make a thread like that one, but for the whole merit history. Daily merits over local boardsThank you. |
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Update:I dropped 16 transactions with negative merits before getting statistics below. - Total number of transactions : 274144
- 1-merit transactions: 190048 (69.3%)
- Sum merits of 1-merit transactions: 190048
- Sum merits of 2-to-5-merit transactions: 213021
- Sum of total merits: 540942
- Percent of 1-merit transactions (sum) per total merits: 35.1
- Percent of 2-to-5-merit transactions (sum) per total merits: 39.4
Bar charts & raw statistics:Number of merit transactions over groups:10 categories: group | Freq. Percent Cum.
------------+-----------------------------------
1 | 190,048 69.32 69.32
2 - 5 | 74,035 27.01 96.33
6 - 10 | 7,122 2.60 98.93
11 - 15 | 732 0.27 99.19
16 - 20 | 871 0.32 99.51
21 - 25 | 347 0.13 99.64
26 - 30 | 226 0.08 99.72
31 - 45 | 240 0.09 99.81
46 - 50 | 523 0.19 100.00
------------+-----------------------------------
Total | 274,144 100.00
5 categories: cat | Freq. Percent Cum.
------------+-----------------------------------
1 | 190,048 69.32 69.32
2 - 5 | 74,035 27.01 96.33
6 - 10 | 7,122 2.60 98.93
11 - 45 | 2,416 0.88 99.81
46 - 50 | 523 0.19 100.00
------------+-----------------------------------
Total | 274,144 100.00
Over 10 groups: +------------------------------------+
| group amount tmerit pmerit |
|------------------------------------|
1. | 1 190048 540942 35.1 |
2. | 2 - 5 213021 540942 39.4 |
3. | 6 - 10 61434 540942 11.4 |
4. | 11 - 15 9634 540942 1.8 |
5. | 16 - 20 16868 540942 3.1 |
|------------------------------------|
6. | 21 - 25 8275 540942 1.5 |
7. | 26 - 30 6539 540942 1.2 |
8. | 31 - 45 9042 540942 1.7 |
9. | 46 - 50 26081 540942 4.8 |
+------------------------------------+
Over 5 groups: +------------------------------------+
| cat amount tmerit pmerit |
|------------------------------------|
1. | 1 190048 540942 35.1 |
2. | 2 - 5 213021 540942 39.4 |
3. | 6 - 10 61434 540942 11.4 |
4. | 11 - 45 50358 540942 9.3 |
5. | 46 - 50 26081 540942 4.8 |
+------------------------------------+
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Update:- Total users: 11537
- Total earned merits: 433079
- Total sent merits: 472545
A summary table: | | | | | Earned merits | | | | | | Sent merits | | | | Groups | | | Freq. | Percent | Total earned | Percent | | | Freq. | Percent | Total sent | Percent | | __________________ | | | ___________ | _________ | _____________ | _______ | | | ___________ | _________ | _____________ | _______ | | 1-10 | | | 7299 | 63.3 | 33905 | 7.8 | | | 8265 | 71.6 | 29289 | 6.2 | | 11-100 | | | 3432 | 29.8 | 101606 | 23.5 | | | 2617 | 22.7 | 88836 | 18.8 | | 101-250 | | | 465 | 4.0 | 71340 | 16.5 | | | 423 | 3.7 | 66755 | 14.1 | | 251-500 | | | 173 | 1.5 | 59426 | 13.7 | | | 116 | 1.0 | 39815 | 8.4 | | 501-1000 | | | 107 | 0.9 | 72633 | 16.8 | | | 62 | 0.5 | 43391 | 9.2 | | 1001+ | | | 61 | 0.5 | 94169 | 21.7 | | | 54 | 0.5 | 204459 | 43.3 | | Total | | | 11537 | 100 | 433079 | 100 | | | 11537 | 100 | 472545 | 100 | | __________________ | | | ___________ | _________ | _____________ | _______ | | | ___________ | _________ | _____________ | _______ |
- Data on autobanned/ nuked users began at January 04, 2019, 08:03:06 AM *Counted at the start of this topic
| Merit status | Frequency | Percent | | _________________________ | ______________ | ______________ | | None | 1 | 0 | | Sent only | 7398 | 18.8 | | Earned only | 17922 | 45.6 | | Both | 14021 | 35.6 | | Total | 39342 | 100 | | _________________________ | ______________ | ______________ | | Account status | | | | _________________________ | ______________ | ______________ | | Legit | 11537 | 82.3 | | Autobanned | 2470 | 17.6 | | Nuked | 14 | 0.1 | | Total | 14021 | 100 | | _________________________ | ______________ | ______________ |
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For gamblers who recently visited the PlayBetr site and have not yet been familiar with the site, there is a page for updated news: https://www.playbetr.com/newsThere are bunches of news updated in the last 8 hours, but I would like to make a reminder on that one: https://www.playbetr.com/international-bitcoin-sportsbook-sportsbettingGamblers have different alternatives (13 for now) to Bitcoin. With Playbetr supporting deposits and withdrawals over a total of 14 blockchain networks, you can always find an alternative to Bitcoin. We have made it easy to sports bet on e-Sports using some of the largest cryptocurrencies in the world. Playbetr's The International 2020 crypto sportsbook & betting site runs on Ethereum (ETH), Ripple (XRP), TrueUSD (TUSD), Paxos Standard (PAX), Bitcoin Cash (BCH), Litecoin (LTC), Monero (XMR), Ethereum Classic (ETC), Dai (DAI), USD Coin (USDC), Bitcoin Satoshi Vision (BSV), Playbetr Coin (PBR), Dash (DASH), and Bitcoin (BTC).
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Update: (on 2019w49)
ABSTRACT- The effects from the Art contest have disappeared.
- Total merits distributed on 2019w49 is 4524.
- Compare to 4521 (the median of weekly merits on 2019w46 - 4 weeks ago), the percentage of last week merits increases only 0.7%.
- The last week ranked at 50th position over 98 weeks.
- Total distributed merits: 4524 (see)
- Median of weekly merits before the 2019w46 (when theymos began to dump massive merits): 4521
What does it means, guys? . di (4524-4521)/4521*100
.066357
It means that on the last week - 2019w49 - the forum community sent 3 more merits than they usually did in previous weeks (at 4521, the weekly median). In percentage, there is about 0.7 % increase compares to median of weekly merits, took at 2019w46 (4 weeks ago). In terms of rank for intra-week merits, the last week ranked at 50th position, that is not impressive. . list weeklyrank merit week, abb(30)
+------------------------------+
| weeklyrank merit week |
|------------------------------|
1. | 1 30960 2018w4 |
2. | 2 19979 2018w5 |
3. | 3 17685 2019w47 |
4. | 4 14251 2019w46 |
5. | 5 13313 2018w6 |
|------------------------------|
6. | 6 11745 2018w7 |
7. | 7 8833 2018w9 |
8. | 8 8767 2018w8 |
9. | 9 7837 2018w38 |
10. | 10 7317 2018w11 |
|------------------------------|
11. | 11 7261 2018w10 |
12. | 12 6952 2018w12 |
13. | 13 6744 2018w13 |
14. | 14 6632 2019w2 |
15. | 15 6423 2018w14 |
|------------------------------|
16. | 16 6130 2019w13 |
17. | 17 5907 2019w48 |
18. | 18 5644 2018w37 |
19. | 19 5542 2019w42 |
20. | 20 5494 2018w15 |
|------------------------------|
21. | 21 5454 2019w19 |
22. | 22 5354 2019w24 |
23. | 23 5317 2019w3 |
24. | 24 5271 2019w15 |
25. | 25 5214 2019w20 |
|------------------------------|
26. | 26 4975 2019w43 |
27. | 27 4965 2018w18 |
28. | 28 4929 2018w25 |
29. | 29 4913 2019w10 |
30. | 30 4829 2018w42 |
|------------------------------|
31. | 31 4803 2019w1 |
32. | 32 4766 2018w19 |
33. | 33 4764 2019w18 |
34. | 34 4742 2018w16 |
35. | 35 4735 2019w45 |
|------------------------------|
36. | 36 4730 2019w44 |
37. | 37 4726 2019w25 |
38. | 38 4688 2019w16 |
39. | 39 4687 2019w23 |
40. | 40 4667 2019w4 |
|------------------------------|
41. | 41 4638 2019w9 |
42. | 42 4612 2018w17 |
43. | 43 4609 2019w12 |
44. | 44 4580 2019w21 |
45. | 45 4575 2018w47 |
|------------------------------|
46. | 46 4565 2019w41 |
47. | 47 4538 2018w23 |
48. | 48 4526 2019w14 |
49. | 49 4525 2018w45 |
50. | 50 4524 2019w49 |
|------------------------------|
51. | 51 4521 2019w8 |
52. | 52 4520 2019w38 |
53. | 53 4491 2019w5 |
54. | 54 4465 2018w26 |
55. | 55 4448 2019w17 |
|------------------------------|
56. | 56 4445 2019w22 |
57. | 57 4395 2018w39 |
58. | 58 4367 2019w26 |
59. | 59 4357 2019w40 |
60. | 60 4353 2018w20 |
|------------------------------|
61. | 61 4332 2019w6 |
62. | 62 4326 2019w11 |
63. | 63 4318 2019w39 |
64. | 64 4310 2018w40 |
65. | 65 4278 2018w27 |
|------------------------------|
66. | 66 4277 2019w29 |
67. | 67 4247 2018w28 |
68. | 68 4236 2019w33 |
69. | 69 4225 2019w27 |
70. | 70 4221 2019w7 |
|------------------------------|
71. | 71 4194 2018w22 |
72. | 72 4176 2019w30 |
73. | 73 4167 2018w29 |
74. | 74 4119 2019w28 |
75. | 75 4043 2019w37 |
|------------------------------|
76. | 76 4011 2018w32 |
77. | 77 3953 2018w43 |
78. | 78 3864 2018w21 |
79. | 79 3863 2018w31 |
80. | 80 3839 2018w24 |
|------------------------------|
81. | 81 3816 2018w41 |
82. | 82 3809 2019w36 |
83. | 83 3805 2018w34 |
84. | 84 3805 2018w50 |
85. | 85 3769 2018w51 |
|------------------------------|
86. | 86 3765 2018w48 |
87. | 87 3747 2018w46 |
88. | 88 3661 2018w30 |
89. | 89 3631 2018w33 |
90. | 90 3622 2019w34 |
|------------------------------|
91. | 91 3590 2018w36 |
92. | 92 3571 2018w49 |
93. | 93 3549 2019w31 |
94. | 94 3540 2019w35 |
95. | 95 3347 2018w44 |
|------------------------------|
96. | 96 3338 2018w52 |
97. | 97 3207 2019w32 |
98. | 98 3072 2018w35 |
+------------------------------+
I believe that that is a solid proof of the disappearance of Art contest's effects (after 4 weeks). One of two purposes for the thread has been demonstrated.
The last purpose is observe the effects from reallocation of 30-days sMerits to merit sources. I am going to keep observing it next one month.
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Let's see how the ranks of the top 100 merited-users changed over the observed period (14 weeks - 36 to 49 - for idrankchange and 27 weeks - 23 to 49 - for rankmedian). Notes: - idrankchange: id of users according to descending rankchange
- rankchange: number of rank change between 2019w36 and 2019w49 (14 weeks)
- rankid2019w36: rank (according to total earned merits) in the week 2019w36
- rankid2019w49 rank (according to total earned merits) in the week 2019w49
- rankmedian: rank (according to median of weekly earned merits from 2019w23 to 2019w49)
The list of top rank-changed users. list rankchange rankid2019w49 rankid2019w36 username, abb(30)
+---------------------------------------------------------------------+
| rankchange rankid2019w49 rankid2019w36 username |
|---------------------------------------------------------------------|
1. | 32 56 88 yahoo62278 |
2. | 31 31 62 Veleor |
3. | 30 66 96 tranthidung |
4. | 25 50 75 DireWolfM14 |
5. | 20 30 50 VB1001 |
|---------------------------------------------------------------------|
6. | 19 12 31 fillippone |
7. | 18 65 83 witcher_sense |
8. | 18 52 70 yogg |
9. | 14 86 100 mjglqw |
10. | 12 14 26 mikeywith |
|---------------------------------------------------------------------|
11. | 12 64 76 lovesmayfamilis |
12. | 12 45 57 pooya87 |
13. | 12 44 56 Carlton Banks |
14. | 12 81 93 wwzsocki |
15. | 11 22 33 Steamtyme |
|---------------------------------------------------------------------|
16. | 11 80 91 tvplus006 |
17. | 9 58 67 Coolcryptovator |
18. | 9 36 45 TryNinja |
19. | 9 88 97 TheFuzzStone |
20. | 8 26 34 LFC_Bitcoin |
|---------------------------------------------------------------------|
21. | 7 74 81 Goran_ |
22. | 5 68 73 CryptopreneurBrainboss |
23. | 5 35 40 JayJuanGee |
24. | 5 55 60 jojo69 |
25. | 4 61 65 Xal0lex |
|---------------------------------------------------------------------|
26. | 3 95 98 chimk |
27. | 3 69 72 Coding Enthusiast |
28. | 2 75 77 actmyame |
29. | 2 92 94 stompix |
30. | 2 76 78 mocacino |
|---------------------------------------------------------------------|
31. | 2 16 18 xhomerx10 |
32. | 2 23 25 iasenko |
33. | 1 18 19 nutildah |
34. | 1 79 80 asche |
35. | 1 57 58 Husna QA |
|---------------------------------------------------------------------|
36. | 1 84 85 mole0815 |
37. | 1 11 12 gmaxwell |
38. | 0 8 8 satoshi |
39. | 0 17 17 HCP |
40. | 0 4 4 DdmrDdmr |
|---------------------------------------------------------------------|
41. | 0 9 9 Last of the V8s |
42. | 0 2 2 LoyceV |
43. | 0 7 7 The Pharmacist |
44. | 0 3 3 suchmoon |
45. | 0 41 41 BobLawblaw |
|---------------------------------------------------------------------|
46. | 0 10 10 achow101 |
47. | 0 1 1 theymos |
48. | 0 5 5 o_e_l_e_o |
49. | 0 6 6 micgoossens |
50. | 0 21 21 xtraelv |
|---------------------------------------------------------------------|
51. | -1 39 38 bitmover |
52. | -1 72 71 infofront |
53. | -1 54 53 morvillz7z |
54. | -1 53 52 taikuri13 |
55. | -1 100 99 Coin-1 |
|---------------------------------------------------------------------|
56. | -1 24 23 krogothmanhattan |
57. | -2 48 46 gentlemand |
58. | -2 32 30 Lauda |
59. | -2 13 11 hilariousetc |
60. | -2 89 87 Lafu |
|---------------------------------------------------------------------|
61. | -2 15 13 1miau |
62. | -3 51 48 mu_enrico |
63. | -3 38 35 TMAN |
64. | -4 99 95 Artemis3 |
65. | -4 46 42 roycilik |
|---------------------------------------------------------------------|
66. | -5 29 24 Hhampuz |
67. | -5 19 14 abhiseshakana |
68. | -5 59 54 philipma1957 |
69. | -5 20 15 HairyMaclairy |
70. | -5 91 86 LeGaulois |
|---------------------------------------------------------------------|
71. | -5 37 32 joniboni |
72. | -5 42 37 ETFbitcoin |
73. | -5 49 44 DarkStar_ |
74. | -5 97 92 HeRetiK |
75. | -6 28 22 Jet Cash |
|---------------------------------------------------------------------|
76. | -6 33 27 qwk |
77. | -6 34 28 marlboroza |
78. | -7 27 20 bob123 |
79. | -7 70 63 pandukelana2712 |
80. | -7 96 89 hilariousandco |
|---------------------------------------------------------------------|
81. | -7 43 36 coinlocket$ |
82. | -8 67 59 minerjones |
83. | -8 47 39 BitCryptex |
84. | -8 98 90 Pmalek |
85. | -9 25 16 Vod |
|---------------------------------------------------------------------|
86. | -10 94 84 bones261 |
87. | -11 90 79 OgNasty |
88. | -11 93 82 Flying Hellfish |
89. | -11 60 49 theyoungmillionaire |
90. | -11 40 29 Piggy |
|---------------------------------------------------------------------|
91. | -12 78 66 PHI16168 |
92. | -13 87 74 Quickseller |
93. | -14 83 69 TheNewAnon135246 |
94. | -15 62 47 SaltySpitoon |
95. | -16 77 61 kenzawak |
|---------------------------------------------------------------------|
96. | -17 85 68 nullius |
97. | -18 73 55 ICOEthics |
98. | -18 82 64 Lutpin |
99. | -20 63 43 Toxic2040 |
100. | -20 71 51 Alex_Sr |
+---------------------------------------------------------------------+
. list rankmedian username median p25 p75 min max if rankmedian <= 30, abb(30)
+----------------------------------------------------------------------+
| rankmedian username median p25 p75 min max |
|----------------------------------------------------------------------|
1. | 1 LoyceV 50.5 26 61 16 145 |
2. | 2 fillippone 46 33 64 23 113 |
3. | 3 o_e_l_e_o 40 21 52 12 82 |
4. | 4 suchmoon 36 21 45 10 87 |
5. | 5 micgoossens 29.5 24 40 16 65 |
|----------------------------------------------------------------------|
6. | 6 nutildah 27 15 41 2 66 |
7. | 7 mikeywith 27 21 33 8 118 |
8. | 8 theymos 26.5 13 54 1 501 |
9. | 9 DdmrDdmr 26 18 34 5 67 |
10. | 10 VB1001 24.5 16 34 0 88 |
|----------------------------------------------------------------------|
11. | 11 LFC_Bitcoin 21.5 15 27 10 48 |
12. | 12 1miau 21.5 11 33 4 100 |
13. | 13 witcher_sense 20.5 15 30 3 61 |
14. | 14 lovesmayfamilis 20 9 29 0 49 |
15. | 15 Veleor 19 10 24 2 81 |
|----------------------------------------------------------------------|
16. | 16 bob123 17.5 2 34 0 56 |
17. | 17 Last of the V8s 16.5 3 30 0 43 |
18. | 18 pooya87 16.5 11 20 6 30 |
19. | 19 achow101 16 8 28 1 60 |
20. | 20 tranthidung 15 12 33 1 104 |
|----------------------------------------------------------------------|
21. | 21 bitmover 15 9 21 4 68 |
22. | 22 morvillz7z 14.5 8 19 0 39 |
23. | 23 TryNinja 14 10 19 0 33 |
24. | 24 yogg 14 4 22 0 61 |
25. | 25 Carlton Banks 13.5 10 22 1 60 |
|----------------------------------------------------------------------|
26. | 26 CryptopreneurBrainboss 13.5 10 18 2 79 |
27. | 27 gentlemand 13.5 9 23 2 31 |
28. | 28 JayJuanGee 13 8 20 3 57 |
29. | 29 BitCryptex 12.5 4 20 0 30 |
30. | 30 iasenko 12.5 2 20 0 40 |
+----------------------------------------------------------------------+
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Update (2019w49):ABSTRACT- Observed weeks: 27
- Median (interquartile range) of weekly earned merits for all 100 top merited profiles is 8 (2 - 18). It means they usually earn around 1 merit per day.
- Median (interquartile range) of weekly earned merits for the group of 1-25 is 13 (4 - 28), that is significantly higher than the figures of last 3 groups: 26-50 ( 8 ), 51-75 (7), and 76-100 (7).
- Min - max: 0 and 501, respectively.
- Period: 2019w23 - 2019w49
- Weekly earned merits: are not exactly weekly in Calendar day; and come from theymos' merit data dumps and LoyceV's data update. See
- All users in the top 100: 8 merits per week, with interquartile range is 2 to 18. It means if someone can manage to earn more than 8 merits per week, over continuous months, they might move to the top months later.
- The top 1-25: 13 merits per week, that is nearly 63-percent higher than the the second group (top 26-50) at 8. The rest two groups (top 51-75, and top 76-100) have same median at 7. (see details below)
- In median, top 26-100 merited users earn a more than 1 merit per day
Data for last week (2019w49) Source: https://bitcointalk.org/index.php?topic=5115154.msg53346084#msg53346084. list userid username rankid2019w36 m49 c49, abb(30)
+---------------------------------------------------------------+
| userid username rankid2019w36 m49 c49 |
|---------------------------------------------------------------|
1. | 35 theymos 1 6354 13 |
2. | 459836 LoyceV 2 4324 27 |
3. | 234771 suchmoon 3 3402 44 |
4. | 1582324 DdmrDdmr 4 2967 18 |
5. | 1188543 o_e_l_e_o 5 2955 12 |
|---------------------------------------------------------------|
6. | 1067333 micgoossens 6 2913 20 |
7. | 487418 The Pharmacist 7 2216 3 |
8. | 3 satoshi 8 2145 31 |
9. | 479624 Last of the V8s 9 2122 35 |
10. | 290195 achow101 10 1951 5 |
|---------------------------------------------------------------|
11. | 397737 hilariousetc 11 1750 0 |
12. | 11425 gmaxwell 12 1862 0 |
13. | 2143453 1miau 13 1633 23 |
14. | 1878246 abhiseshakana 14 1453 32 |
15. | 181806 HairyMaclairy 15 1434 12 |
|---------------------------------------------------------------|
16. | 30747 Vod 16 1343 4 |
17. | 867786 HCP 17 1491 27 |
18. | 120694 xhomerx10 18 1537 43 |
19. | 317618 nutildah 19 1490 2 |
20. | 579628 bob123 20 1330 0 |
|---------------------------------------------------------------|
21. | 897509 xtraelv 21 1429 11 |
22. | 698159 Jet Cash 22 1305 6 |
23. | 1000199 krogothmanhattan 23 1351 1 |
24. | 881377 Hhampuz 24 1278 1 |
25. | 1291828 iasenko 25 1371 1 |
|---------------------------------------------------------------|
26. | 2033515 mikeywith 26 1668 43 |
27. | 24140 qwk 27 1225 1 |
28. | 787736 marlboroza 28 1223 33 |
29. | 188198 Piggy 29 1152 3 |
30. | 101872 Lauda 30 1226 0 |
|---------------------------------------------------------------|
31. | 1852120 fillippone 31 1810 34 |
32. | 1275282 joniboni 32 1171 7 |
33. | 1112531 Steamtyme 33 1401 0 |
34. | 379487 LFC_Bitcoin 34 1342 22 |
35. | 98986 TMAN 35 1160 3 |
|---------------------------------------------------------------|
36. | 1339716 coinlocket$ 36 1119 1 |
37. | 359716 ETFbitcoin 37 1124 7 |
38. | 1554927 bitmover 38 1158 17 |
39. | 1169179 BitCryptex 39 1104 0 |
40. | 252510 JayJuanGee 40 1189 25 |
|---------------------------------------------------------------|
41. | 569455 BobLawblaw 41 1129 11 |
42. | 1051955 roycilik 42 1110 27 |
43. | 239406 Toxic2040 43 985 0 |
44. | 507936 DarkStar_ 44 1088 5 |
45. | 557798 TryNinja 45 1188 19 |
|---------------------------------------------------------------|
46. | 155345 gentlemand 46 1098 9 |
47. | 38894 SaltySpitoon 47 1001 3 |
48. | 1574226 mu_enrico 48 1080 17 |
49. | 1180530 theyoungmillionaire 49 1011 0 |
50. | 1138727 VB1001 50 1262 88 |
|---------------------------------------------------------------|
51. | 1762404 Alex_Sr 51 918 0 |
52. | 1855828 taikuri13 52 1064 6 |
53. | 1825672 morvillz7z 53 1044 11 |
54. | 64507 philipma1957 54 1013 7 |
55. | 2204241 ICOEthics 55 879 0 |
|---------------------------------------------------------------|
56. | 64205 Carlton Banks 56 1114 1 |
57. | 379147 pooya87 57 1110 14 |
58. | 1827294 Husna QA 58 1023 4 |
59. | 346731 minerjones 59 963 5 |
60. | 49008 jojo69 60 1038 10 |
|---------------------------------------------------------------|
61. | 1082600 kenzawak 61 841 0 |
62. | 1177936 Veleor 62 1255 4 |
63. | 1304130 pandukelana2712 63 946 7 |
64. | 520313 Lutpin 64 819 0 |
65. | 1068464 Xal0lex 65 1005 1 |
|---------------------------------------------------------------|
66. | 1071136 PHI16168 66 831 2 |
67. | 1980983 Coolcryptovator 67 1014 36 |
68. | 976210 nullius 68 783 0 |
69. | 153656 TheNewAnon135246 69 818 2 |
70. | 140827 yogg 70 1076 23 |
|---------------------------------------------------------------|
71. | 41175 infofront 71 906 4 |
72. | 879277 Coding Enthusiast 72 952 2 |
73. | 1052091 CryptopreneurBrainboss 73 959 2 |
74. | 358020 Quickseller 74 771 2 |
75. | 2003859 DireWolfM14 75 1081 47 |
|---------------------------------------------------------------|
76. | 1982152 lovesmayfamilis 76 974 22 |
77. | 465017 actmyame 77 862 2 |
78. | 405464 mocacino 78 854 4 |
79. | 18321 OgNasty 79 746 1 |
80. | 1580039 asche 80 825 12 |
|---------------------------------------------------------------|
81. | 1039323 Goran_ 81 866 9 |
82. | 79608 Flying Hellfish 82 710 0 |
83. | 1433865 witcher_sense 83 968 20 |
84. | 452769 bones261 84 706 0 |
85. | 1424178 mole0815 85 808 5 |
|---------------------------------------------------------------|
86. | 507856 LeGaulois 86 735 5 |
87. | 805820 Lafu 87 751 1 |
88. | 355846 yahoo62278 88 1032 38 |
89. | 164822 hilariousandco 89 702 0 |
90. | 112493 Pmalek 90 685 4 |
|---------------------------------------------------------------|
91. | 1311641 tvplus006 91 825 8 |
92. | 99837 HeRetiK 92 689 4 |
93. | 131333 wwzsocki 93 824 3 |
94. | 164749 stompix 94 720 5 |
95. | 980501 Artemis3 95 673 0 |
|---------------------------------------------------------------|
96. | 1292764 tranthidung 96 964 16 |
97. | 679341 TheFuzzStone 97 754 0 |
98. | 1202061 chimk 98 703 0 |
99. | 1133335 Coin-1 99 655 3 |
100. | 886521 mjglqw 100 772 7 |
+---------------------------------------------------------------+
- For all 100 users: - Mean +/- standard deviation: 13.4 +/- 18.8
- Median (interquartile range): 8 (2 - 18)
Details: . tabstat meritchange , s(n mean sd p50 p25 p75 min max)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
meritchange | 2600 13.38308 18.737 8 2 18 0 501
----------------------------------------------------------------------------------------------
. tabstat meritchange , s(n mean sd p50 p25 p75 min max) by(group)
Summary for variables: meritchange
by categories of: group
group | N mean sd p50 p25 p75 min max
-------+--------------------------------------------------------------------------------
1-25 | 650 20.12462 28.1251 13 4 28 0 501
26-50 | 650 12.98308 15.66713 8 2 18 0 129
51-75 | 650 10.14769 12.49282 7 1 14 0 107
76-100 | 650 10.27692 12.1469 7 2 14 0 112
-------+--------------------------------------------------------------------------------
Total | 2600 13.38308 18.737 8 2 18 0 501
----------------------------------------------------------------------------------------
(see details in below table). In median(interquartile range): the top 5 (just for last 27 weeks) are: - LoyceV: 51 (26 - 61)
- fillippone: 46 (33 - 64)
- o_e_l_e_o: 40 (21 - 52)
- suchmoon: 36 (21 - 45)
- nutildah: 30 (24 - 40)
List of top users in median of weekly earned meritsSorting them out descendingly by median of weekly earned merits (p50 - median) and list only the top 30. For the rest, please see the raw results below. . list rankmedian username median p25 p75 min max if rankmedian <= 30, abb(30)
+----------------------------------------------------------------------+
| rankmedian username median p25 p75 min max |
|----------------------------------------------------------------------|
1. | 1 LoyceV 50.5 26 61 16 145 |
2. | 2 fillippone 46 33 64 23 113 |
3. | 3 o_e_l_e_o 40 21 52 12 82 |
4. | 4 suchmoon 36 21 45 10 87 |
5. | 5 micgoossens 29.5 24 40 16 65 |
|----------------------------------------------------------------------|
6. | 6 nutildah 27 15 41 2 66 |
7. | 7 mikeywith 27 21 33 8 118 |
8. | 8 theymos 26.5 13 54 1 501 |
9. | 9 DdmrDdmr 26 18 34 5 67 |
10. | 10 VB1001 24.5 16 34 0 88 |
|----------------------------------------------------------------------|
11. | 11 1miau 21.5 11 33 4 100 |
12. | 12 LFC_Bitcoin 21.5 15 27 10 48 |
13. | 13 witcher_sense 20.5 15 30 3 61 |
14. | 14 lovesmayfamilis 20 9 29 0 49 |
15. | 15 Veleor 19 10 24 2 81 |
|----------------------------------------------------------------------|
16. | 16 bob123 17.5 2 34 0 56 |
17. | 17 pooya87 16.5 11 20 6 30 |
18. | 18 Last of the V8s 16.5 3 30 0 43 |
19. | 19 achow101 16 8 28 1 60 |
20. | 20 tranthidung 15 12 33 1 104 |
|----------------------------------------------------------------------|
21. | 21 bitmover 15 9 21 4 68 |
22. | 22 morvillz7z 14.5 8 19 0 39 |
23. | 23 TryNinja 14 10 19 0 33 |
24. | 24 yogg 14 4 22 0 61 |
25. | 25 CryptopreneurBrainboss 13.5 10 18 2 79 |
|----------------------------------------------------------------------|
26. | 26 Carlton Banks 13.5 10 22 1 60 |
27. | 27 gentlemand 13.5 9 23 2 31 |
28. | 28 JayJuanGee 13 8 20 3 57 |
29. | 29 mjglqw 12.5 7 18 0 45 |
30. | 30 BitCryptex 12.5 4 20 0 30 |
+----------------------------------------------------------------------+
. tabstat meritchange , s(n mean sd p50 p25 p75 min max) by(username)
Summary for variables: meritchange
by categories of: username (username)
username | N mean sd p50 p25 p75 min max
-----------------+--------------------------------------------------------------------------------
1miau | 26 24.88462 20.516 21.5 11 33 4 100
Alex_Sr | 26 4.153846 5.423595 1.5 0 7 0 18
Artemis3 | 26 6.076923 5.878252 3.5 2 9 0 21
BitCryptex | 26 13.42308 9.613212 12.5 4 20 0 30
BobLawblaw | 26 7.653846 6.973907 7 3 11 0 27
Carlton Banks | 26 16.65385 11.75055 13.5 10 22 1 60
Coding Enthusias | 26 12.53846 10.65544 10 5 17 0 45
Coin-1 | 26 6.5 5.323533 5 3 8 0 23
Coolcryptovator | 26 11.15385 9.128822 10.5 4 17 0 36
CryptopreneurBra | 26 17.07692 17.06675 13.5 10 18 2 79
DarkStar_ | 26 10.42308 9.733131 7.5 4 14 2 45
DdmrDdmr | 26 28.23077 15.44359 26 18 34 5 67
DireWolfM14 | 26 18.92308 22.78056 12.5 6 25 0 107
ETFbitcoin | 26 12.53846 9.916575 10 7 15 1 47
Flying Hellfish | 26 3.961538 7.639271 .5 0 4 0 26
Goran_ | 26 10.46154 6.969825 9 6 12 0 29
HCP | 26 14.57692 12.79429 10.5 5 20 1 51
HairyMaclairy | 26 11.19231 9.125872 10 4 16 0 42
HeRetiK | 26 5.192308 7.37574 3 2 5 0 35
Hhampuz | 26 6.115385 8.066359 3 1 7 0 36
Husna QA | 26 9.115385 11.60457 5 4 8 0 49
ICOEthics | 26 1.038462 2.068444 0 0 1 0 8
JayJuanGee | 26 15.46154 11.15789 13 8 20 3 57
Jet Cash | 26 5.653846 4.242097 5 2 9 0 16
LFC_Bitcoin | 26 22.5 8.81476 21.5 15 27 10 48
Lafu | 26 7.192308 6.053225 6 2 10 0 21
Last of the V8s | 26 16.5 14.26955 16.5 3 30 0 43
Lauda | 26 6.038462 6.206324 4.5 1 9 0 21
LeGaulois | 26 4.538462 4.159142 4 2 5 0 18
LoyceV | 26 48.96154 29.67084 50.5 26 61 16 145
Lutpin | 26 1.576923 2.435949 .5 0 1 0 7
OgNasty | 26 2.153846 2.148345 1.5 0 3 0 7
PHI16168 | 26 3.423077 5.307904 1.5 0 4 0 17
Piggy | 26 2.115385 2.902784 1 0 3 0 12
Pmalek | 26 5.307692 4.953942 3 2 9 0 17
Quickseller | 26 3.923077 5.268192 1.5 0 5 0 18
SaltySpitoon | 26 6.653846 7.525648 3.5 1 12 0 27
Steamtyme | 26 17.30769 26.19888 8 4 16 0 129
TMAN | 26 5.076923 12.24393 1.5 0 3 0 59
The Pharmacist | 26 9.769231 7.695753 8 4 11 0 29
TheFuzzStone | 26 13.61538 13.20629 12 7 17 0 53
TheNewAnon135246 | 26 4.192308 3.815958 3 1 7 0 15
Toxic2040 | 26 4.846154 9.246371 0 0 6 0 36
TryNinja | 26 15.19231 8.560464 14 10 19 0 33
VB1001 | 26 26.11538 18.16442 24.5 16 34 0 88
Veleor | 26 23.96154 21.62865 19 10 24 2 81
Vod | 26 3.5 3.754997 2 1 4 0 14
Xal0lex | 26 13.38462 11.24305 8.5 6 21 1 38
abhiseshakana | 26 10.07692 7.331701 8 5 15 0 32
achow101 | 26 19.07692 14.47736 16 8 28 1 60
actmyame | 26 11.26923 8.604918 8 4 17 0 32
asche | 26 8.307692 7.209822 7 4 10 1 33
bitmover | 26 16.46154 12.4233 15 9 21 4 68
bob123 | 26 19.53846 17.09323 17.5 2 34 0 56
bones261 | 26 4.461538 6.998461 1 0 8 0 27
chimk | 26 9.884615 7.168414 8.5 5 12 0 30
coinlocket$ | 26 3.192308 5.169288 2 1 3 0 25
fillippone | 26 50.76923 20.92521 46 33 64 23 113
gentlemand | 26 14.34615 8.089214 13.5 9 23 2 31
gmaxwell | 26 21.61538 37.16566 6.5 1 18 0 136
hilariousandco | 26 4.307692 4.864313 2 1 7 0 18
hilariousetc | 26 5.807692 8.625633 2 0 7 0 30
iasenko | 26 13.38462 11.55189 12.5 2 20 0 40
infofront | 26 7.461538 15.08173 3 0 5 0 72
jojo69 | 26 10.76923 10.55768 8 4 12 1 46
joniboni | 26 5.038462 4.218822 4.5 2 9 0 17
kenzawak | 26 2.692308 6.077955 0 0 1 0 23
krogothmanhattan | 26 9.423077 10.5458 6.5 3 10 0 48
lovesmayfamilis | 26 19.34615 14.33581 20 9 29 0 49
marlboroza | 26 11.57692 13.42586 6 2 15 0 48
micgoossens | 26 33.07692 13.57917 29.5 24 40 16 65
mikeywith | 26 29.61538 20.90565 27 21 33 8 118
minerjones | 26 9.923077 7.552076 10.5 3 15 0 23
mjglqw | 26 13.15385 10.19291 12.5 7 18 0 45
mocacino | 26 9.5 7.333485 7 5 15 0 28
mole0815 | 26 7.153846 9.714699 3.5 1 7 0 34
morvillz7z | 26 15.15385 9.954666 14.5 8 19 0 39
mu_enrico | 26 9.769231 7.976504 7 4 15 1 36
nullius | 26 .2692308 .6667949 0 0 0 0 3
nutildah | 26 29.11538 17.48903 27 15 41 2 66
o_e_l_e_o | 26 38.19231 19.24166 40 21 52 12 82
pandukelana2712 | 26 8 9.931767 6 2 10 0 47
philipma1957 | 26 10.19231 6.039995 10 7 13 2 26
pooya87 | 26 16.42308 6.859581 16.5 11 20 6 30
qwk | 26 4.192308 4.972076 2 1 5 0 22
roycilik | 26 8.923077 11.06318 4 1 15 0 37
satoshi | 26 14.15385 18.0304 4 1 23 0 71
stompix | 26 7 5.207687 5.5 2 11 1 20
suchmoon | 26 36.80769 19.37631 36 21 45 10 87
taikuri13 | 26 14.92308 8.615907 12 8 20 5 35
theymos | 26 55.88462 97.33995 26.5 13 54 1 501
theyoungmilliona | 26 5.346154 7.989705 1 0 10 0 25
tranthidung | 26 24.34615 21.85213 15 12 33 1 104
tvplus006 | 26 14.07692 10.92492 10.5 7 21 0 39
witcher_sense | 26 23.76923 14.1769 20.5 15 30 3 61
wwzsocki | 26 16.15385 17.07324 12 4 22 0 78
xhomerx10 | 26 14.07692 13.6614 10 5 19 0 55
xtraelv | 26 13.5 13.29737 10 3 20 0 47
yahoo62278 | 26 19.19231 23.46405 10.5 6 26 0 112
yogg | 26 16.76923 15.95069 14 4 22 0 61
-----------------+--------------------------------------------------------------------------------
Total | 2600 13.38308 18.737 8 2 18 0 501
--------------------------------------------------------------------------------------------------
(if their last week earned merits >=5). +------------------------------+
| username m49 c49 |
|------------------------------|
1. | VB1001 1262 88 |
2. | DireWolfM14 1081 47 |
3. | suchmoon 3402 44 |
4. | xhomerx10 1537 43 |
5. | mikeywith 1668 43 |
|------------------------------|
6. | yahoo62278 1032 38 |
7. | Coolcryptovator 1014 36 |
8. | Last of the V8s 2122 35 |
9. | fillippone 1810 34 |
10. | marlboroza 1223 33 |
|------------------------------|
11. | abhiseshakana 1453 32 |
12. | satoshi 2145 31 |
13. | LoyceV 4324 27 |
14. | HCP 1491 27 |
15. | roycilik 1110 27 |
|------------------------------|
16. | JayJuanGee 1189 25 |
17. | yogg 1076 23 |
18. | 1miau 1633 23 |
19. | lovesmayfamilis 974 22 |
20. | LFC_Bitcoin 1342 22 |
|------------------------------|
21. | witcher_sense 968 20 |
22. | micgoossens 2913 20 |
23. | TryNinja 1188 19 |
24. | DdmrDdmr 2967 18 |
25. | mu_enrico 1080 17 |
|------------------------------|
26. | bitmover 1158 17 |
27. | tranthidung 964 16 |
28. | pooya87 1110 14 |
29. | theymos 6354 13 |
30. | asche 825 12 |
|------------------------------|
31. | o_e_l_e_o 2955 12 |
32. | HairyMaclairy 1434 12 |
33. | morvillz7z 1044 11 |
34. | BobLawblaw 1129 11 |
35. | xtraelv 1429 11 |
|------------------------------|
36. | jojo69 1038 10 |
37. | Goran_ 866 9 |
38. | gentlemand 1098 9 |
39. | tvplus006 825 8 |
40. | mjglqw 772 7 |
|------------------------------|
41. | joniboni 1171 7 |
42. | philipma1957 1013 7 |
43. | pandukelana2712 946 7 |
44. | ETFbitcoin 1124 7 |
45. | taikuri13 1064 6 |
|------------------------------|
46. | Jet Cash 1305 6 |
47. | LeGaulois 735 5 |
48. | DarkStar_ 1088 5 |
49. | mole0815 808 5 |
50. | stompix 720 5 |
|------------------------------|
51. | minerjones 963 5 |
52. | achow101 1951 5 |
+------------------------------+
Box plots:For all top 100 merited users: For 4 groups of top 100 merited users: Over each users (outliers displayed with red circles):
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Update:Time series plot
Dataset for median, interquartile range of intraday merits +------------------------------------------+
| week median q1 q3 merit |
|------------------------------------------|
1. | 2018w26 733 609 991 4465 |
2. | 2018w27 715 598 979 4278 |
3. | 2018w28 707 592 963 4247 |
4. | 2018w29 693 589 922 4167 |
5. | 2018w30 684 577 902 3661 |
|------------------------------------------|
6. | 2018w31 682 575 891 3863 |
7. | 2018w32 675 567 880 4011 |
8. | 2018w33 667 559 867 3631 |
9. | 2018w34 652 555 848 3805 |
10. | 2018w35 642 537 844 3072 |
|------------------------------------------|
11. | 2018w36 639 528 838 3590 |
12. | 2018w37 634 528 829 5644 |
13. | 2018w38 641 530 846 7837 |
14. | 2018w39 640 531 839 4395 |
15. | 2018w40 639 528 829 4310 |
|------------------------------------------|
16. | 2018w41 637 528 808 3816 |
17. | 2018w42 639 530 807 4829 |
18. | 2018w43 639 528 801 3953 |
19. | 2018w44 628 521 796 3347 |
20. | 2018w45 630 522 789 4525 |
|------------------------------------------|
21. | 2018w46 628 523 788 3747 |
22. | 2018w47 628 522.5 783.5 4575 |
23. | 2018w48 627 522 778 3765 |
24. | 2018w49 623.5 520 775 3571 |
25. | 2018w50 622 520 774 3805 |
|------------------------------------------|
26. | 2018w51 621.5 517.5 770 3769 |
27. | 2018w52 617.5 514 764 3338 |
28. | 2019w1 617 514 769 4803 |
29. | 2019w2 621.5 515 775 6632 |
30. | 2019w3 623 517 777 5317 |
|------------------------------------------|
31. | 2019w4 623.5 518.5 775 4667 |
32. | 2019w5 622 518 775 4491 |
33. | 2019w6 622 520 775 4332 |
34. | 2019w7 621 522 771 4221 |
35. | 2019w8 621.5 521 770 4521 |
|------------------------------------------|
36. | 2019w9 622 520 769 4638 |
37. | 2019w10 624 522 766 4913 |
38. | 2019w11 624 522 762 4326 |
39. | 2019w12 626.5 523 761 4609 |
40. | 2019w13 628 525 766 6130 |
|------------------------------------------|
41. | 2019w14 627.5 529 761 4526 |
42. | 2019w15 629 530 762 5271 |
43. | 2019w16 632.5 530.5 764 4688 |
44. | 2019w17 629 530 762 4448 |
45. | 2019w18 629 531 762 4764 |
|------------------------------------------|
46. | 2019w19 636 532 762 5454 |
47. | 2019w20 638.5 532.5 767.5 5214 |
48. | 2019w21 639 533 766 4580 |
49. | 2019w22 639 535 761 4445 |
50. | 2019w23 639 535 761 4687 |
|------------------------------------------|
51. | 2019w24 640 536 764 5354 |
52. | 2019w25 640 537 762 4726 |
53. | 2019w26 640 535 762 4367 |
54. | 2019w27 640 535 761 4225 |
55. | 2019w28 639 532.5 761 4119 |
|------------------------------------------|
56. | 2019w29 639 532 761 4277 |
57. | 2019w30 636.5 533 760 4176 |
58. | 2019w31 629 532 760 3549 |
59. | 2019w32 628 530 757 3207 |
60. | 2019w33 628 530 755 4236 |
|------------------------------------------|
61. | 2019w34 627 529 752 3622 |
62. | 2019w35 627 528 750 3540 |
63. | 2019w36 625.5 526.5 742 3809 |
64. | 2019w37 625 525 742 4043 |
65. | 2019w38 624 528 738 4520 |
|------------------------------------------|
66. | 2019w39 624 528 737 4318 |
67. | 2019w40 624 525 737 4357 |
68. | 2019w41 624 525 737 4565 |
69. | 2019w42 626 528 742 5542 |
70. | 2019w43 627 529 742 4975 |
|------------------------------------------|
71. | 2019w44 627 530 740 4730 |
72. | 2019w45 627 530 742 4735 |
73. | 2019w46 628 531 751 14251 |
74. | 2019w47 629 532 759 17685 |
75. | 2019w48 631.5 532.5 760 5907 |
|------------------------------------------|
76. | 2019w49 629 534 760 4524 |
List of median, q1, q3 of intra-day merits over weeks, in descending orders of medians. +------------------------------------------+
| week median q1 q3 merit |
|------------------------------------------|
1. | 2019w1 617 514 769 4803 |
2. | 2018w52 617.5 514 764 3338 |
3. | 2019w7 621 522 771 4221 |
4. | 2019w2 621.5 515 775 6632 |
5. | 2019w8 621.5 521 770 4521 |
|------------------------------------------|
6. | 2018w51 621.5 517.5 770 3769 |
7. | 2018w50 622 520 774 3805 |
8. | 2019w6 622 520 775 4332 |
9. | 2019w9 622 520 769 4638 |
10. | 2019w5 622 518 775 4491 |
|------------------------------------------|
11. | 2019w3 623 517 777 5317 |
12. | 2018w49 623.5 520 775 3571 |
13. | 2019w4 623.5 518.5 775 4667 |
14. | 2019w41 624 525 737 4565 |
15. | 2019w11 624 522 762 4326 |
|------------------------------------------|
16. | 2019w39 624 528 737 4318 |
17. | 2019w38 624 528 738 4520 |
18. | 2019w10 624 522 766 4913 |
19. | 2019w40 624 525 737 4357 |
20. | 2019w37 625 525 742 4043 |
|------------------------------------------|
21. | 2019w36 625.5 526.5 742 3809 |
22. | 2019w42 626 528 742 5542 |
23. | 2019w12 626.5 523 761 4609 |
24. | 2019w44 627 530 740 4730 |
25. | 2019w34 627 529 752 3622 |
|------------------------------------------|
26. | 2019w43 627 529 742 4975 |
27. | 2018w48 627 522 778 3765 |
28. | 2019w35 627 528 750 3540 |
29. | 2019w45 627 530 742 4735 |
30. | 2019w14 627.5 529 761 4526 |
|------------------------------------------|
31. | 2018w44 628 521 796 3347 |
32. | 2019w13 628 525 766 6130 |
33. | 2019w32 628 530 757 3207 |
34. | 2018w46 628 523 788 3747 |
35. | 2019w46 628 531 751 14251 |
|------------------------------------------|
36. | 2018w47 628 522.5 783.5 4575 |
37. | 2019w33 628 530 755 4236 |
38. | 2019w17 629 530 762 4448 |
39. | 2019w31 629 532 760 3549 |
40. | 2019w49 629 534 760 4524 |
|------------------------------------------|
41. | 2019w47 629 532 759 17685 |
42. | 2019w15 629 530 762 5271 |
43. | 2019w18 629 531 762 4764 |
44. | 2018w45 630 522 789 4525 |
45. | 2019w48 631.5 532.5 760 5907 |
|------------------------------------------|
46. | 2019w16 632.5 530.5 764 4688 |
47. | 2018w37 634 528 829 5644 |
48. | 2019w19 636 532 762 5454 |
49. | 2019w30 636.5 533 760 4176 |
50. | 2018w41 637 528 808 3816 |
|------------------------------------------|
51. | 2019w20 638.5 532.5 767.5 5214 |
52. | 2018w43 639 528 801 3953 |
53. | 2018w40 639 528 829 4310 |
54. | 2018w36 639 528 838 3590 |
55. | 2019w21 639 533 766 4580 |
|------------------------------------------|
56. | 2019w29 639 532 761 4277 |
57. | 2019w28 639 532.5 761 4119 |
58. | 2019w22 639 535 761 4445 |
59. | 2019w23 639 535 761 4687 |
60. | 2018w42 639 530 807 4829 |
|------------------------------------------|
61. | 2019w26 640 535 762 4367 |
62. | 2019w24 640 536 764 5354 |
63. | 2019w27 640 535 761 4225 |
64. | 2019w25 640 537 762 4726 |
65. | 2018w39 640 531 839 4395 |
|------------------------------------------|
66. | 2018w38 641 530 846 7837 |
67. | 2018w35 642 537 844 3072 |
68. | 2018w34 652 555 848 3805 |
69. | 2018w33 667 559 867 3631 |
70. | 2018w32 675 567 880 4011 |
|------------------------------------------|
71. | 2018w31 682 575 891 3863 |
72. | 2018w30 684 577 902 3661 |
73. | 2018w29 693 589 922 4167 |
74. | 2018w28 707 592 963 4247 |
75. | 2018w27 715 598 979 4278 |
|------------------------------------------|
76. | 2018w26 733 609 991 4465 |
Now, let's take a look at the variations of intra-day medians over weeks. Method: I took medians of intraday merits over weeks (since 2018w46, from id 293 - 299, here). The median of intraday merits at the end of 2018w46 will be calculated from intraday merits started from days with id #26 - # 299; days before id #26 truncated due to extremely outliers. For later weeks, just moving forwards with each 7-day-time-frame to calculate next medians of intradays over weeks. Since 2018w48 to 2019w49, the dataset has: - 73 weeks in total. - Median of median of intraday merits over weeks is 628. - Interquartile range of median of median of intraday merits over weeks ranges from 624 to 639. . tabstat median, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
median | 73.0 633.5 14.8 628.0 624.0 639.0 617.0 693.0
----------------------------------------------------------------------------------------------
- From LoyceV's weekly data dumps. - From my converted datasets in the topic: Time Series Analysis on Distributed Merits in the forum (daily, weekly, monthly)[/quote]
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