Update:ABSTRACTIntra-day merits- Median of intra-day merits over the period is 625; - Minimum and maximum of intra-day merits (full dataset) are 295, and 13018, on 03/8/2019 and 24/1/2018, respectively. Intra-week merits- Median of intra-week merits is 4478; - Minimum and maximum of intra-week merits are 3072 and 30960, in 2018w35, and 2018w4, respectively. Outliers- Intra-day merits: beyond 200 or 1068; and there are 34 outliers in total, only six of them occured in 2019. - Intra-week merits: beyond 2435 or 6483. 12 weeks are outliers, in total; and there is only one outlier week occured in 2019, in 2019w2 at 6632.
Time series plots:(1) Intra-day merits:Full dataset:Truncated dataset:(2) Merits over days of week:Outliers displayed as red circles. Outliers non-displayed. (3) Intra-week merits:(4) Time series plot of median and interquartile range(5) Weekly earned merits of top 100 merited profiles over 4 groups(6) One year anniversary of enhanced merit system
For more details, please get them there: Time Series Analysis on Distributed Merits in the forum (daily, weekly, monthly)Interquartile range of intra-day merits with time series plotOne year anniversary of merit systemWeekly earned merits (median) of top 100 merited users (newest)One year anniversary of enhanced merit system
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Update:ABSTRACT- Median of weekly earned merits for all 100 top merited profiles is 8. It means they usually earn around 1 merit per day.
- Median of weekly earned merits for the group of 1-25 is significantly higher than the figures of last 3 groups: 26-50 (9), 51-75 (7), and 76-100 (7).
- Min - max: 0 and 104, respectively.
- Period: 2019w26 - 2019w37
- Weekly earned merits: are not exacly 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 3 to 18. It means if someone can manage to earn more than 7 merits per week, over months, they might move to the top months later.
- The top 1-25: 13 merits per week, that is 30-percent higher than the the second group (top 26-50) at 9. 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 around 1 merit per day
Data for last week (2019w37) Source: https://bitcointalk.org/index.php?topic=5115154.msg52516331#msg52516331. list id username m37 c37, abb(30)
+-------------------------------------------+
| id username m37 c37 |
|-------------------------------------------|
1. | 1 theymos 5239 41 |
2. | 2 LoyceV 3638 70 |
3. | 3 suchmoon 2940 36 |
4. | 4 DdmrDdmr 2673 28 |
5. | 5 o_e_l_e_o 2644 52 |
|-------------------------------------------|
6. | 6 micgoossens 2476 16 |
7. | 7 The Pharmacist 2091 7 |
8. | 8 satoshi 1905 0 |
9. | 9 Last of the V8s 1810 25 |
10. | 10 achow101 1742 16 |
|-------------------------------------------|
11. | 11 hilariousetc 1694 2 |
12. | 12 gmaxwell 1416 6 |
13. | 13 1miau 1354 12 |
14. | 14 abhiseshakana 1336 8 |
15. | 15 HairyMaclairy 1313 17 |
|-------------------------------------------|
16. | 16 Vod 1291 1 |
17. | 17 HCP 1287 5 |
18. | 18 xhomerx10 1289 10 |
19. | 19 nutildah 1299 41 |
20. | 20 bob123 1268 12 |
|-------------------------------------------|
21. | 21 xtraelv 1254 5 |
22. | 22 Jet Cash 1246 10 |
23. | 23 krogothmanhattan 1215 3 |
24. | 24 Hhampuz 1210 0 |
25. | 25 iasenko 1210 7 |
|-------------------------------------------|
26. | 26 mikeywith 1197 33 |
27. | 27 qwk 1146 2 |
28. | 28 marlboroza 1129 6 |
29. | 29 Piggy 1126 7 |
30. | 30 Lauda 1118 0 |
|-------------------------------------------|
31. | 31 fillippone 1137 33 |
32. | 32 joniboni 1092 9 |
33. | 33 Steamtyme 1077 8 |
34. | 34 LFC_Bitcoin 1100 34 |
35. | 35 TMAN 1054 1 |
|-------------------------------------------|
36. | 36 coinlocket$ 1063 11 |
37. | 37 ETFbitcoin 1029 8 |
38. | 38 bitmover 1023 21 |
39. | 39 BitCryptex 1005 4 |
40. | 40 JayJuanGee 1020 20 |
|-------------------------------------------|
41. | 41 BobLawblaw 990 0 |
42. | 42 roycilik 983 1 |
43. | 43 Toxic2040 977 0 |
44. | 44 DarkStar_ 1006 31 |
45. | 45 TryNinja 969 3 |
|-------------------------------------------|
46. | 46 gentlemand 973 9 |
47. | 47 SaltySpitoon 960 12 |
48. | 48 mu_enrico 961 15 |
49. | 49 theyoungmillionaire 924 1 |
50. | 50 VB1001 950 45 |
|-------------------------------------------|
51. | 51 Alex_Sr 895 0 |
52. | 52 taikuri13 890 9 |
53. | 53 morillz7z 896 15 |
54. | 54 philipma1957 891 13 |
55. | 55 ICOEthics 877 3 |
|-------------------------------------------|
56. | 56 Carlton Banks 885 12 |
57. | 57 pooya87 880 17 |
58. | 58 Husna QA 860 9 |
59. | 59 minerjones 856 12 |
60. | 60 jojo69 849 8 |
|-------------------------------------------|
61. | 61 kenzawak 841 0 |
62. | 62 Veleor 841 23 |
63. | 63 pandukelana2712 823 6 |
64. | 64 Lutpin 815 1 |
65. | 65 Xal0lex 830 21 |
|-------------------------------------------|
66. | 66 PHI16168 793 0 |
67. | 67 Coolcryptovator 810 25 |
68. | 68 nullius 781 0 |
69. | 69 TheNewAnon135246 780 7 |
70. | 70 yogg 795 22 |
|-------------------------------------------|
71. | 71 infofront 772 0 |
72. | 72 Coding Enthusiast 781 13 |
73. | 73 CryptopreneurBrainboss 765 5 |
74. | 74 Quickseller 745 0 |
75. | 75 DireWolfM14 761 18 |
|-------------------------------------------|
76. | 76 loyvesmayfamilis 745 9 |
77. | 77 actmyame 739 8 |
78. | 78 mocacino 754 28 |
79. | 79 OgNasty 720 4 |
80. | 80 asche 715 4 |
|-------------------------------------------|
81. | 81 Goran_ 704 0 |
82. | 82 Flying Hellfish 697 1 |
83. | 83 witcher_sense 709 14 |
84. | 84 bones261 702 9 |
85. | 85 mole0815 694 5 |
|-------------------------------------------|
86. | 86 LeGaulois 681 10 |
87. | 87 Lafu 678 8 |
88. | 88 yahoo62278 669 15 |
89. | 89 hilariousandco 655 7 |
90. | 90 Pmalek 642 4 |
|-------------------------------------------|
91. | 91 tvplus006 655 27 |
92. | 92 HeRetiK 622 4 |
93. | 93 wwzsocki 634 21 |
94. | 94 stompix 632 20 |
95. | 95 Artemis3 614 6 |
|-------------------------------------------|
96. | 96 tranthidung 648 42 |
97. | 97 TheFuzzStone 603 3 |
98. | 98 chimk 602 5 |
99. | 99 Coin-1 610 19 |
100. | 100 mjglqw 586 15 |
+-------------------------------------------+
b]Statistics:[/b] - For all 100 users: - Mean +/- standard deviation: 12.6 +/- 14
- Median (interquartile range): 8 (3 - 18)
For each users (see details in below table). In median: the top 5 (just for last 13 weeks) are: - o_e_l_e_o: 50
- fillippone: 44
- nutildah: 41
- LoyceV: 40
- suchmoon: 36
There is no outliers found in the top 5. It means their weekly earned merits are very very stable.  For all top 100 merited users:. tabstat meritchange , s(n mean sd p50 p25 p75 min max)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
meritchange | 1400 12.57929 14.03073 8 3 18 0 104
----------------------------------------------------------------------------------------------
. 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 | 350 18.61429 18.26162 13 4 28 0 100
26-50 | 350 12.64 13.50078 9 2 19 0 76
51-75 | 350 8.814286 9.210516 7 2 13 0 79
76-100 | 350 10.24857 11.5045 7 3 13 0 104
-------+--------------------------------------------------------------------------------
Total | 1400 12.57929 14.03073 8 3 18 0 104
----------------------------------------------------------------------------------------
. 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 | 14 26.28571 24.93508 18 11 33 4 100
Alex_Sr | 14 6.071429 5.327206 5.5 1 8 0 18
Artemis3 | 14 7.071429 5.635933 6 3 10 0 19
BitCryptex | 14 17.85714 8.707708 18.5 11 27 4 30
BobLawblaw | 14 4.285714 3.688414 4 1 8 0 11
Carlton Banks | 14 14.57143 13.54033 11 8 14 7 60
Coding Enthusias | 14 11.07143 8.361673 9.5 5 14 2 31
Coin-1 | 14 8.857143 5.998168 8 4 11 2 23
Coolcryptovator | 14 6.142857 6.723748 4 1 9 0 25
CryptopreneurBra | 14 17.85714 18.50512 13 10 15 5 79
DarkStar_ | 14 13.5 12.15762 10 4 18 2 45
DdmrDdmr | 14 31.42857 17.59683 28 19 36 5 67
DireWolfM14 | 14 12.28571 12.1239 8.5 5 18 0 43
ETFbitcoin | 14 16.5 11.39332 13.5 10 18 3 47
Flying Hellfish | 14 6.428571 9.764254 1 0 10 0 26
Goran_ | 14 7.857143 4.943638 8 5 10 0 21
HCP | 14 12.5 8.318191 9.5 7 20 1 26
HairyMaclairy | 14 12.14286 5.096864 10.5 9 16 3 23
HeRetiK | 14 4.857143 5.096864 4 2 7 0 20
Hhampuz | 14 6.5 9.83518 3 2 6 0 36
Husna QA | 14 5.285714 5.044854 4.5 2 7 0 19
ICOEthics | 14 1.785714 2.577374 1 0 2 0 8
JayJuanGee | 14 16.64286 13.13016 14 11 20 3 57
Jet Cash | 14 6.285714 4.664573 5 3 9 1 16
LFC_Bitcoin | 14 24.5 9.685437 23 17 30 12 48
Lafu | 14 8.142857 5.842249 7.5 3 12 1 19
Last of the V8s | 14 8.357143 10.63092 3.5 0 17 0 28
Lauda | 14 3.5 4.536179 1 0 7 0 14
LeGaulois | 14 4.571429 4.552502 4 2 5 0 18
LoyceV | 14 41.92857 18.73074 40 24 60 16 70
Lutpin | 14 2.642857 2.924884 1 0 5 0 7
OgNasty | 14 2.142857 1.994498 1.5 1 3 0 7
PHI16168 | 14 3.642857 5.678666 1.5 0 4 0 17
Piggy | 14 2.071429 2.432608 1 0 4 0 7
Pmalek | 14 6.785714 5.220943 4.5 3 12 1 17
Quickseller | 14 5.428571 6.135825 4 0 8 0 18
SaltySpitoon | 14 9.428571 8.688757 5.5 2 17 0 27
Steamtyme | 14 9 7.081938 6.5 4 12 3 30
TMAN | 14 1.857143 1.875229 1.5 0 3 0 6
The Pharmacist | 14 9.214286 8.495959 7.5 5 10 0 29
TheFuzzStone | 14 14.5 11.98557 12 8 14 3 53
TheNewAnon135246 | 14 5.071429 4.232917 3.5 2 8 0 15
Toxic2040 | 14 8.428571 11.48051 2.5 0 16 0 36
TryNinja | 14 12.57143 8.006865 12 5 18 0 28
VB1001 | 14 26.21429 10.01674 25.5 22 35 6 45
Veleor | 14 14.92857 7.690797 15 9 22 2 24
Vod | 14 2.785714 3.067913 1.5 1 4 0 12
Xal0lex | 14 12.35714 9.644505 8.5 6 20 2 37
abhiseshakana | 14 10.35714 6.295385 9 8 16 0 20
achow101 | 14 20.5 13.04873 17 10 30 1 50
actmyame | 14 12.14286 9.461315 10 6 17 0 32
asche | 14 7.571429 8.006865 6 3 9 1 33
bitmover | 14 20.92857 15.07654 19.5 14 22 4 68
bob123 | 14 31.85714 13.01985 31 24 42 12 56
bones261 | 14 8 8.047933 6.5 3 9 0 27
chimk | 14 11.14286 7.134501 9.5 5 13 5 30
coinlocket$ | 14 1.928571 2.894671 1 0 2 0 11
fillippone | 14 46.21429 17.53317 44 32 63 23 76
gentlemand | 14 17.71429 7.300459 15 12 23 9 31
gmaxwell | 14 8.285714 9.659022 4.5 1 13 0 30
hilariousandco | 14 4.642857 5.5968 2 1 7 0 18
hilariousetc | 14 6.785714 8.630702 3.5 1 10 0 30
iasenko | 14 13.35714 12.30139 10.5 2 20 0 40
infofront | 14 4.285714 7.936562 1 0 5 0 30
jojo69 | 14 6.5 4.669871 5 3 8 1 18
joniboni | 14 3.714286 3.688414 3 0 6 0 10
kenzawak | 14 5 7.656068 1 0 7 0 23
krogothmanhattan | 14 7.785714 7.727857 5 3 9 0 28
loyvesmayfamilis | 14 19.57143 17.01002 21 0 29 0 49
marlboroza | 14 14.78571 14.60336 9 6 20 0 48
micgoossens | 14 30.21429 12.46027 28 22 33 16 62
mikeywith | 14 21.35714 8.992366 23.5 12 29 8 33
minerjones | 14 10.78571 6.623717 12 4 15 1 23
mjglqw | 14 11.14286 7.754474 12 4 18 0 24
mocacino | 14 10.5 7.582875 7.5 6 15 1 28
mole0815 | 14 5.142857 8.645649 2 1 6 0 34
morillz7z | 14 17.57143 8.993282 15.5 14 23 4 35
mu_enrico | 14 9.642857 5.227253 7.5 5 15 3 17
nullius | 14 .3571429 .8418974 0 0 0 0 3
nutildah | 14 40.42857 13.79361 41 29 44 21 66
o_e_l_e_o | 14 48.71429 16.69836 50 37 61 17 82
pandukelana2712 | 14 6.071429 7.173287 5 0 7 0 25
philipma1957 | 14 10.21429 6.459085 9 7 13 3 26
pooya87 | 14 14.07143 5.165928 12 11 18 7 25
qwk | 14 2.142857 2.070197 2 1 2 0 8
roycilik | 14 7.5 10.49359 2.5 1 9 0 35
satoshi | 14 9.142857 14.10323 2 0 17 0 44
stompix | 14 6.714286 6.05651 5 2 11 1 20
suchmoon | 14 35.35714 19.95999 35.5 18 45 10 84
taikuri13 | 14 15.28571 6.556601 15 10 19 8 31
theymos | 14 24.14286 22.82326 19.5 9 30 1 90
theyoungmilliona | 14 3.714286 6.280967 0 0 4 0 18
tranthidung | 14 22.64286 26.22567 13 8 30 5 104
tvplus006 | 14 14 12.17816 10 3 25 0 37
witcher_sense | 14 25.64286 18.18963 20 13 38 3 61
wwzsocki | 14 16.42857 14.16775 14 3 24 0 46
xhomerx10 | 14 8.428571 6.902779 6.5 3 11 0 22
xtraelv | 14 12.57143 9.982402 9.5 5 20 0 32
yahoo62278 | 14 9.714286 7.032108 9 4 12 0 26
yogg | 14 11.07143 8.677684 8 4 18 0 27
-----------------+--------------------------------------------------------------------------------
Total | 1400 12.57929 14.03073 8 3 18 0 104
--------------------------------------------------------------------------------------------------
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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Moreover now just need more promotions like before,
They alreadly reboot their campaign, there: [OPEN] WOLF.BET Signature Campaign - Sr/Hero/Legendary - Up to 0.008 BTC/week!Unfortunately, the new payment rates changed, and decreased a little bit, but they decided to expand their campaign to Senior Member rank. Let's see how the campaign makes its impacts on Wolf.bet platform (traffic, users, gambling orders, and income). |
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Being in development since 2017, it has already taken around 2 years to build a $1.5 million forum with top security and what not. I wish to know when are you guys planning to make us switch to it completely from here? I tried to visit the beta but I was unfortunate because it didn't open. Is it still in developing phase?
Here: The software is substantially complete. The main period of development was a while ago; the current work is mainly just maintenance & relatively minor improvements. Try running it yourself and you'll find that it's working, fast, and nearly feature-complete.
The things blocking a transition from the current software to the new software are:
- There hasn't been enough testing. I think that immediately after transition, a variety of small missed features, bugs, and performance issues would crop up. As a result, if the transition happened now (which is technically possible!), I'd expect the post-transition user experience to be poor for months while these things are fixed, which I don't want.
- I am the only bitcointalk.org sysadmin and on-demand programmer, and I'm used to the current software. Furthermore, I need to frequently make changes to the current software, but each change I make might require alterations to Epochtalk, which is problematic.
- The current PHP software, while ugly and sub-optimal in many ways, performs well, especially since I have extensively modified the backend to add features and improve performance. So I don't feel much urgency.
- The data-transition procedure still has a few known minor bugs.
We continue to work on these issues. I think that ultimately I may need to hire one or more full-time people, since a big problem is that the full transition is likely to create a ton of work which I won't be able to effectively handle alone.
The software is not vaporware (it's long existed in a runnable state, and is currently basically feature-complete), and is not abandoned (look at the git commit log). If anyone is unhappy with the progress, I invite them to take the Epochtalk code and create a competing forum with it; since they won't have to worry about the transition issues, they'd have a much easier time, and their testing will also end up helping us.
In short: If you want the software quicker, go run your own forum with it, and work to get any problems or missing features you find resolved via bug reports, etc. This would increase public interest, provide much-needed testing, and I might even hire you to work on bitcointalk.org when we're ready to do the final transition here.
Where can I find a list of the bugs (old and new) that are being reported? Can I be a beta tester like other members? Does it really take this long to complete a project that's already 2 years old and yet not finished?
There is at least one forum that use Epochtalk's source code, and theymos donated the one who set up and run that forum. You can join that forum, and test, report bugs, etc. Epochtalk is ready, theymos wants us to test it. C'mon...Cryptos-Currencies.Com : First forum using EpochtalkHere: www.cryptos-currenciesReminder: Anyone using this (or any other "unofficial" test site) please keep in mind privacy and safety implications:
- don't use your bitcointalk username or otherwise link yourself between sites if your don't want to expose your IP to third parties;
- don't use the same e-mail and/or password;
- don't assume other users have the same usernames.
I'm pretty sure there are many other pitfalls. Be smart please.
And, Cryptios team managed their forum too: Coinbistro.com - Epochtalk testing grounds |
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Bitcointalk username: tranthidung Bitcointalk profile URL: https://bitcointalk.org/index.php?action=profile;u=1292764Amount of earned merit in the last 120 days: ~ 300 - 350 BTC Address: 3KJta98PdkWnTpSanxSRURFRvYeGLAn2Ya
I am a participant of the last round of Wolf.bet campaign, without issues over weeks. |
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Update:Time series plot of median and interquartile range
Dataset for median, interquartile range of intraday merits
. list week median q1 q3 merit
+------------------------------------------+
| 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 |
+------------------------------------------+
List of median, q1, q3 of intra-day merits over weeks, in descending orders of medians.. list week median q1 q3 merit
+------------------------------------------+
| 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. | 2019w9 622 520 769 4638 |
9. | 2019w6 622 520 775 4332 |
10. | 2019w5 622 518 775 4491 |
|------------------------------------------|
11. | 2019w3 623 517 777 5317 |
12. | 2019w4 623.5 518.5 775 4667 |
13. | 2018w49 623.5 520 775 3571 |
14. | 2019w10 624 522 766 4913 |
15. | 2019w11 624 522 762 4326 |
|------------------------------------------|
16. | 2019w37 625 525 742 4043 |
17. | 2019w36 625.5 526.5 742 3809 |
18. | 2019w12 626.5 523 761 4609 |
19. | 2018w48 627 522 778 3765 |
20. | 2019w35 627 528 750 3540 |
|------------------------------------------|
21. | 2019w34 627 529 752 3622 |
22. | 2019w14 627.5 529 761 4526 |
23. | 2019w33 628 530 755 4236 |
24. | 2018w44 628 521 796 3347 |
25. | 2018w47 628 522.5 783.5 4575 |
|------------------------------------------|
26. | 2019w13 628 525 766 6130 |
27. | 2019w32 628 530 757 3207 |
28. | 2018w46 628 523 788 3747 |
29. | 2019w18 629 531 762 4764 |
30. | 2019w17 629 530 762 4448 |
|------------------------------------------|
31. | 2019w31 629 532 760 3549 |
32. | 2019w15 629 530 762 5271 |
33. | 2018w45 630 522 789 4525 |
34. | 2019w16 632.5 530.5 764 4688 |
35. | 2018w37 634 528 829 5644 |
|------------------------------------------|
36. | 2019w19 636 532 762 5454 |
37. | 2019w30 636.5 533 760 4176 |
38. | 2018w41 637 528 808 3816 |
39. | 2019w20 638.5 532.5 767.5 5214 |
40. | 2019w21 639 533 766 4580 |
|------------------------------------------|
41. | 2018w43 639 528 801 3953 |
42. | 2019w29 639 532 761 4277 |
43. | 2018w40 639 528 829 4310 |
44. | 2018w36 639 528 838 3590 |
45. | 2018w42 639 530 807 4829 |
|------------------------------------------|
46. | 2019w28 639 532.5 761 4119 |
47. | 2019w22 639 535 761 4445 |
48. | 2019w23 639 535 761 4687 |
49. | 2019w25 640 537 762 4726 |
50. | 2019w26 640 535 762 4367 |
|------------------------------------------|
51. | 2018w39 640 531 839 4395 |
52. | 2019w27 640 535 761 4225 |
53. | 2019w24 640 536 764 5354 |
54. | 2018w38 641 530 846 7837 |
55. | 2018w35 642 537 844 3072 |
|------------------------------------------|
56. | 2018w34 652 555 848 3805 |
57. | 2018w33 667 559 867 3631 |
58. | 2018w32 675 567 880 4011 |
59. | 2018w31 682 575 891 3863 |
60. | 2018w30 684 577 902 3661 |
|------------------------------------------|
61. | 2018w29 693 589 922 4167 |
62. | 2018w28 707 592 963 4247 |
63. | 2018w27 715 598 979 4278 |
64. | 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 2019w37, the dataset has: - 61 weeks in total. - Median of median of intraday merits over weeks is 629. - Interquartile range of median of median of intraday merits over weeks ranges from 629 to 639. . tabstat median, s(n mean sd p25 p50 p75 min max) format(%9.1f)
variable | N mean sd p25 p50 p75 min max
-------------+--------------------------------------------------------------------------------
median | 61.0 634.9 15.8 625.0 629.0 639.0 617.0 693.0
----------------------------------------------------------------------------------------------
- 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)
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ABSTRACT
Intra-day merits:
Notes:
- The part of the asbstract describes figures of intraday merits over the period from 19/2/2018 to 16/9/2019 (truncated dataset);
- Days from 24/1/2018 to 18/2/2018 truncated due to highly potential outliers; and days after 16/9/2019 truncated as well due to incomplete week (the 2019w38);
- Statistics presented in the post are for truncated dataset
(1) Potential outliers are days that have intraday total merits beyond 200 or 1068;
(2) Median of intraday merits over the period is 625;
(3) 50% of observed days have their intra-day merits range from 525 to 742 (the interquartile range);
(4) Friday [in GTM time] is the day over weeks has lowest intraday merits in terms of both median and mean, at 577, and 606, respectively.
(5) Monday [in GTM time] is the day over weeks has highest intraday merits in terms of median and mean, at 662, and 722.
(6) There are 34 potential outliers in total, and only six of them occured in 2019, on 04/01/2019 (1083) , 09/1/2019 (1162), 14/01/2019 (1128) , 27/3/2019 (1250), 13/5/2019 (1151), and 11/6/2019 (1188).
(7) Minimum and maximum of intraday merits (full dataset) are 295, and 13018, on 03/8/2019 and 24/1/2018, respectively.
Intra-week merits:
Notes:
The part of the abstract use full dataset, only dropped last two days due to incomple week (2019w38).
(1) The median of intra-week merits is 4478;
(2) 50% of observed weeks (86 weeeks in total), have total merits in the range from 3953 to 4965 (the interquaritle range of intra-week merits).
(3) Minimum and maximum of intra-week merits are 3072 and 30960, in 2018w35, and 2018w4, respectively;
(4) Twelve potential outliers [beyond 2435 or 6483], only one of them occurred in the year 2019, in 2019w2 at 6632.
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Intra-week merits (from 24/1/2018 to 16/9/2019)Last two days dropped due to incomplete weeks (2019w38)Converted dataset:. list merit week
+-----------------+
| merit week |
|-----------------|
1. | 30960 2018w4 |
2. | 19979 2018w5 |
3. | 13313 2018w6 |
4. | 11745 2018w7 |
5. | 8767 2018w8 |
|-----------------|
6. | 8833 2018w9 |
7. | 7261 2018w10 |
8. | 7317 2018w11 |
9. | 6952 2018w12 |
10. | 6744 2018w13 |
|-----------------|
11. | 6423 2018w14 |
12. | 5494 2018w15 |
13. | 4742 2018w16 |
14. | 4612 2018w17 |
15. | 4965 2018w18 |
|-----------------|
16. | 4766 2018w19 |
17. | 4353 2018w20 |
18. | 3864 2018w21 |
19. | 4194 2018w22 |
20. | 4538 2018w23 |
|-----------------|
21. | 3839 2018w24 |
22. | 4929 2018w25 |
23. | 4465 2018w26 |
24. | 4278 2018w27 |
25. | 4247 2018w28 |
|-----------------|
26. | 4167 2018w29 |
27. | 3661 2018w30 |
28. | 3863 2018w31 |
29. | 4011 2018w32 |
30. | 3631 2018w33 |
|-----------------|
31. | 3805 2018w34 |
32. | 3072 2018w35 |
33. | 3590 2018w36 |
34. | 5644 2018w37 |
35. | 7837 2018w38 |
|-----------------|
36. | 4395 2018w39 |
37. | 4310 2018w40 |
38. | 3816 2018w41 |
39. | 4829 2018w42 |
40. | 3953 2018w43 |
|-----------------|
41. | 3347 2018w44 |
42. | 4525 2018w45 |
43. | 3747 2018w46 |
44. | 4575 2018w47 |
45. | 3765 2018w48 |
|-----------------|
46. | 3571 2018w49 |
47. | 3805 2018w50 |
48. | 3769 2018w51 |
49. | 3338 2018w52 |
50. | 4803 2019w1 |
|-----------------|
51. | 6632 2019w2 |
52. | 5317 2019w3 |
53. | 4667 2019w4 |
54. | 4491 2019w5 |
55. | 4332 2019w6 |
|-----------------|
56. | 4221 2019w7 |
57. | 4521 2019w8 |
58. | 4638 2019w9 |
59. | 4913 2019w10 |
60. | 4326 2019w11 |
|-----------------|
61. | 4609 2019w12 |
62. | 6130 2019w13 |
63. | 4526 2019w14 |
64. | 5271 2019w15 |
65. | 4688 2019w16 |
|-----------------|
66. | 4448 2019w17 |
67. | 4764 2019w18 |
68. | 5454 2019w19 |
69. | 5214 2019w20 |
70. | 4580 2019w21 |
|-----------------|
71. | 4445 2019w22 |
72. | 4687 2019w23 |
73. | 5354 2019w24 |
74. | 4726 2019w25 |
75. | 4367 2019w26 |
|-----------------|
76. | 4225 2019w27 |
77. | 4119 2019w28 |
78. | 4277 2019w29 |
79. | 4176 2019w30 |
80. | 3549 2019w31 |
|-----------------|
81. | 3207 2019w32 |
82. | 4236 2019w33 |
83. | 3622 2019w34 |
84. | 3540 2019w35 |
85. | 3809 2019w36 |
|-----------------|
86. | 4043 2019w37 |
+-----------------+
- 50% of observed weeks ( 86 weeks) have total intra-week merits above 4478, whilst the rest 50% of them have total intra-week merits below 4478. 4478 is the median - p50. - 50% of observed weeks have total intra-week merits fluctuated in the range from 3953 to 4965 (the interquartile range, from p25 to p75, in raw statistics below). - Min - max: 3072 - 30960. . tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
merit | 86.0 5343.4 3629.8 4478.0 3953.0 4965.0 3072.0 30960.0
----------------------------------------------------------------------------------------------
. di 4965-3953
1012
. di 1012*1.5
1518
. di 4965+1518
6483
. di 3953-1518
2435
It means that potential outliers are weeks that have intra-week merits beyond 2435 or 6483. Results are as same as last week's data update. How many weeks are potential outliers? . count if (merit >= 6483 | merit < 2435) & merit != .
12
weeks are outliers, in total. List of those twelve weeks: . list merit week if merit >= 6483 | merit <= 2435
+-----------------+
| merit week |
|-----------------|
1. | 30960 2018w4 |
2. | 19979 2018w5 |
3. | 13313 2018w6 |
4. | 11745 2018w7 |
5. | 8767 2018w8 |
|-----------------|
6. | 8833 2018w9 |
7. | 7261 2018w10 |
8. | 7317 2018w11 |
9. | 6952 2018w12 |
10. | 6744 2018w13 |
|-----------------|
35. | 7837 2018w38 |
51. | 6632 2019w2 |
+-----------------+
Most of them occured in the year 2018, and there is only one outlier week occured in 2019, in 2019w2 at 6632. 
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Medians and means of intra-day merits over days of weeks.Colors: - Green: highest.
- Red: Lowest.
- In median, the highest days are Monday, Wednesday & Thursday, and Tuesday at 662, 653 (both Wednesday & Thursday), and 639, respectively; whislt the lowest days are Friday, Saturday, and Sunday at 577, 588, and 608, respectively. - In means, the highest days are Monday, Wednesday, and Tuesday, at 722, 695, and 688, respectively; whilst the lowest days are Friday, Saturday, and Sunday, at 606, 610, and 668, respectively. - Monday has still been the highest day in terms of median and mean of intra-day merits over weeks, in contrast Friday is the lowest days in terms of median, and mean of intra-day merits over weeks. Calendar day is in GMT time.To take away all doubt: the first Merit was this one: 1516831941 1 2818066.msg28853325 35 877396 Use EpochConverter to convert 1516831941 (Unix Time) to GMT: Wednesday 24 January 2018 22:12:21. . tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f) by(dofw)
Summary for variables: merit
by categories of: dofw
dofw | N mean sd p50 p25 p75 min max
----------+--------------------------------------------------------------------------------
Sunday | 82.0 667.4 278.6 607.5 505.0 777.0 394.0 2464.0
Monday | 83.0 721.1 260.6 662.0 563.0 797.0 313.0 1863.0
Tuesday | 82.0 688.0 201.1 638.5 586.0 737.0 384.0 1327.0
Wednesday | 82.0 694.4 201.5 652.5 558.0 760.0 394.0 1271.0
Thursday | 82.0 682.5 192.2 653.0 541.0 768.0 348.0 1335.0
Friday | 82.0 605.3 190.4 576.5 497.0 651.0 349.0 1706.0
Saturday | 82.0 609.8 199.2 588.0 469.0 689.0 295.0 1410.0
----------+--------------------------------------------------------------------------------
Total | 575.0 667.0 222.9 625.0 525.0 742.0 295.0 2464.0
-------------------------------------------------------------------------------------------
Outliers displayed as red circles. Outliers non-displayed.
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During the period from 24/1/2018 to 18/9/2019, the minimum and maximum of intra-day merit are 295 and 13018, on 03/8/2019 and 24/1/2018, respectively. List of the top 50-highest day in terms of intra-day merits: . list merit id date dofw day month2 year week month
+-------------------------------------------------------------------------------+
| merit id date dofw day month2 year week month |
|-------------------------------------------------------------------------------|
1. | 13018 1 24jan2018 Wednesday 24 1 2018 2018w4 2018m1 |
2. | 6762 2 25jan2018 Thursday 25 1 2018 2018w4 2018m1 |
3. | 4500 3 26jan2018 Friday 26 1 2018 2018w4 2018m1 |
4. | 4193 7 30jan2018 Tuesday 30 1 2018 2018w5 2018m1 |
5. | 3800 6 29jan2018 Monday 29 1 2018 2018w5 2018m1 |
|-------------------------------------------------------------------------------|
6. | 3490 4 27jan2018 Saturday 27 1 2018 2018w4 2018m1 |
7. | 3190 5 28jan2018 Sunday 28 1 2018 2018w4 2018m1 |
8. | 2821 8 31jan2018 Wednesday 31 1 2018 2018w5 2018m1 |
9. | 2569 10 02feb2018 Friday 2 2 2018 2018w5 2018m2 |
10. | 2546 9 01feb2018 Thursday 1 2 2018 2018w5 2018m2 |
|-------------------------------------------------------------------------------|
11. | 2514 22 14feb2018 Wednesday 14 2 2018 2018w7 2018m2 |
12. | 2464 236 16sep2018 Sunday 16 9 2018 2018w37 2018m9 |
13. | 2310 14 06feb2018 Tuesday 6 2 2018 2018w6 2018m2 |
14. | 2182 12 04feb2018 Sunday 4 2 2018 2018w5 2018m2 |
15. | 2143 16 08feb2018 Thursday 8 2 2018 2018w6 2018m2 |
|-------------------------------------------------------------------------------|
16. | 2142 15 07feb2018 Wednesday 7 2 2018 2018w6 2018m2 |
17. | 2078 13 05feb2018 Monday 5 2 2018 2018w6 2018m2 |
18. | 1992 23 15feb2018 Thursday 15 2 2018 2018w7 2018m2 |
19. | 1868 11 03feb2018 Saturday 3 2 2018 2018w5 2018m2 |
20. | 1863 237 17sep2018 Monday 17 9 2018 2018w38 2018m9 |
|-------------------------------------------------------------------------------|
21. | 1748 18 10feb2018 Saturday 10 2 2018 2018w6 2018m2 |
22. | 1706 38 02mar2018 Friday 2 3 2018 2018w9 2018m3 |
23. | 1618 25 17feb2018 Saturday 17 2 2018 2018w7 2018m2 |
24. | 1580 21 13feb2018 Tuesday 13 2 2018 2018w7 2018m2 |
25. | 1449 17 09feb2018 Friday 9 2 2018 2018w6 2018m2 |
|-------------------------------------------------------------------------------|
26. | 1443 19 11feb2018 Sunday 11 2 2018 2018w6 2018m2 |
27. | 1416 24 16feb2018 Friday 16 2 2018 2018w7 2018m2 |
28. | 1410 32 24feb2018 Saturday 24 2 2018 2018w8 2018m2 |
29. | 1404 27 19feb2018 Monday 19 2 2018 2018w8 2018m2 |
30. | 1392 34 26feb2018 Monday 26 2 2018 2018w9 2018m2 |
|-------------------------------------------------------------------------------|
31. | 1355 48 12mar2018 Monday 12 3 2018 2018w11 2018m3 |
32. | 1335 37 01mar2018 Thursday 1 3 2018 2018w9 2018m3 |
33. | 1332 20 12feb2018 Monday 12 2 2018 2018w7 2018m2 |
34. | 1327 35 27feb2018 Tuesday 27 2 2018 2018w9 2018m2 |
35. | 1324 56 20mar2018 Tuesday 20 3 2018 2018w12 2018m3 |
|-------------------------------------------------------------------------------|
36. | 1295 238 18sep2018 Tuesday 18 9 2018 2018w38 2018m9 |
37. | 1293 26 18feb2018 Sunday 18 2 2018 2018w7 2018m2 |
38. | 1280 30 22feb2018 Thursday 22 2 2018 2018w8 2018m2 |
39. | 1271 239 19sep2018 Wednesday 19 9 2018 2018w38 2018m9 |
40. | 1268 29 21feb2018 Wednesday 21 2 2018 2018w8 2018m2 |
|-------------------------------------------------------------------------------|
41. | 1258 68 01apr2018 Sunday 1 4 2018 2018w13 2018m4 |
42. | 1250 428 27mar2019 Wednesday 27 3 2019 2019w13 2019m3 |
43. | 1246 41 05mar2018 Monday 5 3 2018 2018w10 2018m3 |
44. | 1229 57 21mar2018 Wednesday 21 3 2018 2018w12 2018m3 |
45. | 1188 504 11jun2019 Tuesday 11 6 2019 2019w24 2019m6 |
|-------------------------------------------------------------------------------|
46. | 1187 33 25feb2018 Sunday 25 2 2018 2018w8 2018m2 |
47. | 1170 28 20feb2018 Tuesday 20 2 2018 2018w8 2018m2 |
48. | 1162 351 09jan2019 Wednesday 9 1 2019 2019w2 2019m1 |
49. | 1160 50 14mar2018 Wednesday 14 3 2018 2018w11 2018m3 |
50. | 1151 475 13may2019 Monday 13 5 2019 2019w19 2019m5 |
List of the top 50-lowest days in terms of intra-day merits: . list merit id date dofw day month2 year week month
+-------------------------------------------------------------------------------+
| merit id date dofw day month2 year week month |
|-------------------------------------------------------------------------------|
1. | 295 557 03aug2019 Saturday 3 8 2019 2019w31 2019m8 |
2. | 313 335 24dec2018 Monday 24 12 2018 2018w52 2018m12 |
3. | 317 333 22dec2018 Saturday 22 12 2018 2018w51 2018m12 |
4. | 328 564 10aug2019 Saturday 10 8 2019 2019w32 2019m8 |
5. | 344 340 29dec2018 Saturday 29 12 2018 2018w52 2018m12 |
|-------------------------------------------------------------------------------|
6. | 348 298 17nov2018 Saturday 17 11 2018 2018w46 2018m11 |
7. | 348 338 27dec2018 Thursday 27 12 2018 2018w52 2018m12 |
8. | 349 304 23nov2018 Friday 23 11 2018 2018w47 2018m11 |
9. | 368 566 12aug2019 Monday 12 8 2019 2019w32 2019m8 |
10. | 371 122 25may2018 Friday 25 5 2018 2018w21 2018m5 |
|-------------------------------------------------------------------------------|
11. | 377 191 02aug2018 Thursday 2 8 2018 2018w31 2018m8 |
12. | 377 342 31dec2018 Monday 31 12 2018 2018w52 2018m12 |
13. | 379 326 15dec2018 Saturday 15 12 2018 2018w50 2018m12 |
14. | 380 220 31aug2018 Friday 31 8 2018 2018w35 2018m8 |
15. | 382 599 14sep2019 Saturday 14 9 2019 2019w37 2019m9 |
|-------------------------------------------------------------------------------|
16. | 384 217 28aug2018 Tuesday 28 8 2018 2018w35 2018m8 |
17. | 386 214 25aug2018 Saturday 25 8 2018 2018w34 2018m8 |
18. | 387 339 28dec2018 Friday 28 12 2018 2018w52 2018m12 |
19. | 394 341 30dec2018 Sunday 30 12 2018 2018w52 2018m12 |
20. | 394 568 14aug2019 Wednesday 14 8 2019 2019w33 2019m8 |
|-------------------------------------------------------------------------------|
21. | 395 345 03jan2019 Thursday 3 1 2019 2019w1 2019m1 |
22. | 395 529 06jul2019 Saturday 6 7 2019 2019w27 2019m7 |
23. | 396 228 08sep2018 Saturday 8 9 2018 2018w36 2018m9 |
24. | 398 320 09dec2018 Sunday 9 12 2018 2018w49 2018m12 |
25. | 399 558 04aug2019 Sunday 4 8 2019 2019w31 2019m8 |
|-------------------------------------------------------------------------------|
26. | 400 262 12oct2018 Friday 12 10 2018 2018w41 2018m10 |
27. | 403 329 18dec2018 Tuesday 18 12 2018 2018w51 2018m12 |
28. | 406 287 06nov2018 Tuesday 6 11 2018 2018w45 2018m11 |
29. | 407 556 02aug2019 Friday 2 8 2019 2019w31 2019m8 |
30. | 411 565 11aug2019 Sunday 11 8 2019 2019w32 2019m8 |
|-------------------------------------------------------------------------------|
31. | 413 403 02mar2019 Saturday 2 3 2019 2019w9 2019m3 |
32. | 413 222 02sep2018 Sunday 2 9 2018 2018w35 2018m9 |
33. | 414 527 04jul2019 Thursday 4 7 2019 2019w27 2019m7 |
34. | 416 588 03sep2019 Tuesday 3 9 2019 2019w36 2019m9 |
35. | 416 278 28oct2018 Sunday 28 10 2018 2018w43 2018m10 |
|-------------------------------------------------------------------------------|
36. | 416 533 10jul2019 Wednesday 10 7 2019 2019w28 2019m7 |
37. | 417 587 02sep2019 Monday 2 9 2019 2019w35 2019m9 |
38. | 417 592 07sep2019 Saturday 7 9 2019 2019w36 2019m9 |
39. | 417 109 12may2018 Saturday 12 5 2018 2018w19 2018m5 |
40. | 419 186 28jul2018 Saturday 28 7 2018 2018w30 2018m7 |
|-------------------------------------------------------------------------------|
41. | 421 187 29jul2018 Sunday 29 7 2018 2018w30 2018m7 |
42. | 422 192 03aug2018 Friday 3 8 2018 2018w31 2018m8 |
43. | 425 276 26oct2018 Friday 26 10 2018 2018w43 2018m10 |
44. | 427 140 12jun2018 Tuesday 12 6 2018 2018w24 2018m6 |
45. | 427 277 27oct2018 Saturday 27 10 2018 2018w43 2018m10 |
|-------------------------------------------------------------------------------|
46. | 429 313 02dec2018 Sunday 2 12 2018 2018w48 2018m12 |
47. | 429 418 17mar2019 Sunday 17 3 2019 2019w11 2019m3 |
48. | 431 264 14oct2018 Sunday 14 10 2018 2018w41 2018m10 |
49. | 431 284 03nov2018 Saturday 3 11 2018 2018w44 2018m11 |
50. | 433 208 19aug2018 Sunday 19 8 2018 2018w33 2018m8 |
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Time-series plots:Full dataset:Truncated dataset:
Basic statistics:Full dataset (only dropped first three days):. tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
merit | 599.0 734.1 433.4 629.0 530.0 769.0 295.0 4500.0
----------------------------------------------------------------------------------------------
Applied formulas in previous weeks, potential outliers are days have intra-day merits beyond 172 or 1128. . di 769-530
239
. di 239*1.5
358.5
. di 769+358.5
1127.5
. di 530-358.5
171.5
There are 52 outliers in full dataset, in total. . count if (merit >= 1128 | merit <= 172) & merit != .
52
Those days are: . list id merit date if (merit >= 1128 | merit <= 172) & merit != .
+-------------------------+
| id merit date |
|-------------------------|
1. | 3 4500 26jan2018 |
2. | 4 3490 27jan2018 |
3. | 5 3190 28jan2018 |
4. | 6 3800 29jan2018 |
5. | 7 4193 30jan2018 |
|-------------------------|
6. | 8 2821 31jan2018 |
7. | 9 2546 01feb2018 |
8. | 10 2569 02feb2018 |
9. | 11 1868 03feb2018 |
10. | 12 2182 04feb2018 |
|-------------------------|
11. | 13 2078 05feb2018 |
12. | 14 2310 06feb2018 |
13. | 15 2142 07feb2018 |
14. | 16 2143 08feb2018 |
15. | 17 1449 09feb2018 |
|-------------------------|
16. | 18 1748 10feb2018 |
17. | 19 1443 11feb2018 |
18. | 20 1332 12feb2018 |
19. | 21 1580 13feb2018 |
20. | 22 2514 14feb2018 |
|-------------------------|
21. | 23 1992 15feb2018 |
22. | 24 1416 16feb2018 |
23. | 25 1618 17feb2018 |
24. | 26 1293 18feb2018 |
25. | 27 1404 19feb2018 |
|-------------------------|
26. | 28 1170 20feb2018 |
27. | 29 1268 21feb2018 |
28. | 30 1280 22feb2018 |
30. | 32 1410 24feb2018 |
31. | 33 1187 25feb2018 |
|-------------------------|
32. | 34 1392 26feb2018 |
33. | 35 1327 27feb2018 |
35. | 37 1335 01mar2018 |
36. | 38 1706 02mar2018 |
39. | 41 1246 05mar2018 |
|-------------------------|
46. | 48 1355 12mar2018 |
48. | 50 1160 14mar2018 |
49. | 51 1131 15mar2018 |
54. | 56 1324 20mar2018 |
55. | 57 1229 21mar2018 |
|-------------------------|
66. | 68 1258 01apr2018 |
67. | 69 1147 02apr2018 |
151. | 153 1139 25jun2018 |
234. | 236 2464 16sep2018 |
235. | 237 1863 17sep2018 |
|-------------------------|
236. | 238 1295 18sep2018 |
237. | 239 1271 19sep2018 |
349. | 351 1162 09jan2019 |
354. | 356 1128 14jan2019 |
426. | 428 1250 27mar2019 |
|-------------------------|
473. | 475 1151 13may2019 |
502. | 504 1188 11jun2019 |
+-------------------------+
Only five of them occured in 2019, on 09/1/2019 (1162), 14/1/2019 (1128), 27/3/2019 (1250), 13/5/2019 (1151), and 11/6/2019 (1188). Truncated dataset (first 25 days dropped):. tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
merit | 575.0 667.0 222.9 625.0 525.0 742.0 295.0 2464.0
----------------------------------------------------------------------------------------------
Applied same formulas I used in earlier analyses, potential outliers are days have intra-day merits beyond 200 or 1068. . di 742-525
217
. di 217*1.5
325.5
. di 742+325.5
1067.5
. di 525-325.5
199.5
There are 34 outliers in total, only six of them occured in 2019, on 04/01/2019 (1083) , 09/1/2019 (1162), 14/01/2019 (1128) , 27/3/2019 (1250), 13/5/2019 (1151), and 11/6/2019 (1188). . count if (merit >= 1068 | merit <= 200) & merit != .
34
List of those 34 outliers in truncated dataset . list id merit date if (merit >= 1068 | merit <= 200) & merit != .
+-------------------------+
| id merit date |
|-------------------------|
1. | 27 1404 19feb2018 |
2. | 28 1170 20feb2018 |
3. | 29 1268 21feb2018 |
4. | 30 1280 22feb2018 |
6. | 32 1410 24feb2018 |
|-------------------------|
7. | 33 1187 25feb2018 |
8. | 34 1392 26feb2018 |
9. | 35 1327 27feb2018 |
11. | 37 1335 01mar2018 |
12. | 38 1706 02mar2018 |
|-------------------------|
13. | 39 1090 03mar2018 |
15. | 41 1246 05mar2018 |
16. | 42 1075 06mar2018 |
17. | 43 1111 07mar2018 |
21. | 47 1092 11mar2018 |
|-------------------------|
22. | 48 1355 12mar2018 |
24. | 50 1160 14mar2018 |
25. | 51 1131 15mar2018 |
30. | 56 1324 20mar2018 |
31. | 57 1229 21mar2018 |
|-------------------------|
42. | 68 1258 01apr2018 |
43. | 69 1147 02apr2018 |
44. | 70 1081 03apr2018 |
127. | 153 1139 25jun2018 |
210. | 236 2464 16sep2018 |
|-------------------------|
211. | 237 1863 17sep2018 |
212. | 238 1295 18sep2018 |
213. | 239 1271 19sep2018 |
320. | 346 1083 04jan2019 |
325. | 351 1162 09jan2019 |
|-------------------------|
330. | 356 1128 14jan2019 |
402. | 428 1250 27mar2019 |
449. | 475 1151 13may2019 |
478. | 504 1188 11jun2019 |
+-------------------------+
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I think the value of being able to choose the best N of M is much better than the value of seeing progressive post quality increases, especially considering that most high-quality posters will inevitably end up applying here when the slots open.  The top quality posters inevitably join such highly competive contest in the top (highest) campaign. In my opinion, there are at least three causal components that motivate users to do their good quality posts and improve posting quality over time: (1) Their internal purpose to improve themselves by learning more about bitcoin, blockchain, and cryptocurrencies. (2) The merit system, that requires them to do quality posts to rank up. (3) The top campaigns that require them to compete each other (among top quality posters) to be accepted by managers. Personally, I will keep applying here whenever new open slots announced by manager. |
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Update:Converted intra-day merits for days in 2019. . list id merit date day month2 year week month dofw if year == 2019
+------------------------------------------------------------------------------+
| id merit date day month2 year week month dofw |
|------------------------------------------------------------------------------|
343. | 343 604 01jan2019 1 1 2019 2019w1 2019m1 Tuesday |
344. | 344 530 02jan2019 2 1 2019 2019w1 2019m1 Wednesday |
345. | 345 395 03jan2019 3 1 2019 2019w1 2019m1 Thursday |
346. | 346 1083 04jan2019 4 1 2019 2019w1 2019m1 Friday |
347. | 347 836 05jan2019 5 1 2019 2019w1 2019m1 Saturday |
|------------------------------------------------------------------------------|
348. | 348 784 06jan2019 6 1 2019 2019w1 2019m1 Sunday |
349. | 349 571 07jan2019 7 1 2019 2019w1 2019m1 Monday |
350. | 350 783 08jan2019 8 1 2019 2019w2 2019m1 Tuesday |
351. | 351 1162 09jan2019 9 1 2019 2019w2 2019m1 Wednesday |
352. | 352 988 10jan2019 10 1 2019 2019w2 2019m1 Thursday |
|------------------------------------------------------------------------------|
353. | 353 879 11jan2019 11 1 2019 2019w2 2019m1 Friday |
354. | 354 713 12jan2019 12 1 2019 2019w2 2019m1 Saturday |
355. | 355 979 13jan2019 13 1 2019 2019w2 2019m1 Sunday |
356. | 356 1128 14jan2019 14 1 2019 2019w2 2019m1 Monday |
357. | 357 818 15jan2019 15 1 2019 2019w3 2019m1 Tuesday |
|------------------------------------------------------------------------------|
358. | 358 881 16jan2019 16 1 2019 2019w3 2019m1 Wednesday |
359. | 359 1019 17jan2019 17 1 2019 2019w3 2019m1 Thursday |
360. | 360 612 18jan2019 18 1 2019 2019w3 2019m1 Friday |
361. | 361 644 19jan2019 19 1 2019 2019w3 2019m1 Saturday |
362. | 362 659 20jan2019 20 1 2019 2019w3 2019m1 Sunday |
|------------------------------------------------------------------------------|
363. | 363 684 21jan2019 21 1 2019 2019w3 2019m1 Monday |
364. | 364 619 22jan2019 22 1 2019 2019w4 2019m1 Tuesday |
365. | 365 737 23jan2019 23 1 2019 2019w4 2019m1 Wednesday |
366. | 366 716 24jan2019 24 1 2019 2019w4 2019m1 Thursday |
367. | 367 616 25jan2019 25 1 2019 2019w4 2019m1 Friday |
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368. | 368 588 26jan2019 26 1 2019 2019w4 2019m1 Saturday |
369. | 369 656 27jan2019 27 1 2019 2019w4 2019m1 Sunday |
370. | 370 735 28jan2019 28 1 2019 2019w4 2019m1 Monday |
371. | 371 613 29jan2019 29 1 2019 2019w5 2019m1 Tuesday |
372. | 372 511 30jan2019 30 1 2019 2019w5 2019m1 Wednesday |
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373. | 373 451 31jan2019 31 1 2019 2019w5 2019m1 Thursday |
374. | 374 596 01feb2019 1 2 2019 2019w5 2019m2 Friday |
375. | 375 942 02feb2019 2 2 2019 2019w5 2019m2 Saturday |
376. | 376 581 03feb2019 3 2 2019 2019w5 2019m2 Sunday |
377. | 377 797 04feb2019 4 2 2019 2019w5 2019m2 Monday |
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378. | 378 780 05feb2019 5 2 2019 2019w6 2019m2 Tuesday |
379. | 379 560 06feb2019 6 2 2019 2019w6 2019m2 Wednesday |
380. | 380 549 07feb2019 7 2 2019 2019w6 2019m2 Thursday |
381. | 381 612 08feb2019 8 2 2019 2019w6 2019m2 Friday |
382. | 382 624 09feb2019 9 2 2019 2019w6 2019m2 Saturday |
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383. | 383 560 10feb2019 10 2 2019 2019w6 2019m2 Sunday |
384. | 384 647 11feb2019 11 2 2019 2019w6 2019m2 Monday |
385. | 385 586 12feb2019 12 2 2019 2019w7 2019m2 Tuesday |
386. | 386 675 13feb2019 13 2 2019 2019w7 2019m2 Wednesday |
387. | 387 650 14feb2019 14 2 2019 2019w7 2019m2 Thursday |
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388. | 388 611 15feb2019 15 2 2019 2019w7 2019m2 Friday |
389. | 389 525 16feb2019 16 2 2019 2019w7 2019m2 Saturday |
390. | 390 608 17feb2019 17 2 2019 2019w7 2019m2 Sunday |
391. | 391 566 18feb2019 18 2 2019 2019w7 2019m2 Monday |
392. | 392 638 19feb2019 19 2 2019 2019w8 2019m2 Tuesday |
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393. | 393 698 20feb2019 20 2 2019 2019w8 2019m2 Wednesday |
394. | 394 505 21feb2019 21 2 2019 2019w8 2019m2 Thursday |
395. | 395 510 22feb2019 22 2 2019 2019w8 2019m2 Friday |
396. | 396 661 23feb2019 23 2 2019 2019w8 2019m2 Saturday |
397. | 397 609 24feb2019 24 2 2019 2019w8 2019m2 Sunday |
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398. | 398 900 25feb2019 25 2 2019 2019w8 2019m2 Monday |
399. | 399 737 26feb2019 26 2 2019 2019w9 2019m2 Tuesday |
400. | 400 554 27feb2019 27 2 2019 2019w9 2019m2 Wednesday |
401. | 401 708 28feb2019 28 2 2019 2019w9 2019m2 Thursday |
402. | 402 510 01mar2019 1 3 2019 2019w9 2019m3 Friday |
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403. | 403 413 02mar2019 2 3 2019 2019w9 2019m3 Saturday |
404. | 404 1003 03mar2019 3 3 2019 2019w9 2019m3 Sunday |
405. | 405 713 04mar2019 4 3 2019 2019w9 2019m3 Monday |
406. | 406 681 05mar2019 5 3 2019 2019w10 2019m3 Tuesday |
407. | 407 788 06mar2019 6 3 2019 2019w10 2019m3 Wednesday |
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408. | 408 714 07mar2019 7 3 2019 2019w10 2019m3 Thursday |
409. | 409 713 08mar2019 8 3 2019 2019w10 2019m3 Friday |
410. | 410 724 09mar2019 9 3 2019 2019w10 2019m3 Saturday |
411. | 411 657 10mar2019 10 3 2019 2019w10 2019m3 Sunday |
412. | 412 636 11mar2019 11 3 2019 2019w10 2019m3 Monday |
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413. | 413 681 12mar2019 12 3 2019 2019w11 2019m3 Tuesday |
414. | 414 689 13mar2019 13 3 2019 2019w11 2019m3 Wednesday |
415. | 415 805 14mar2019 14 3 2019 2019w11 2019m3 Thursday |
416. | 416 581 15mar2019 15 3 2019 2019w11 2019m3 Friday |
417. | 417 483 16mar2019 16 3 2019 2019w11 2019m3 Saturday |
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418. | 418 429 17mar2019 17 3 2019 2019w11 2019m3 Sunday |
419. | 419 658 18mar2019 18 3 2019 2019w11 2019m3 Monday |
420. | 420 759 19mar2019 19 3 2019 2019w12 2019m3 Tuesday |
421. | 421 652 20mar2019 20 3 2019 2019w12 2019m3 Wednesday |
422. | 422 721 21mar2019 21 3 2019 2019w12 2019m3 Thursday |
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423. | 423 676 22mar2019 22 3 2019 2019w12 2019m3 Friday |
424. | 424 626 23mar2019 23 3 2019 2019w12 2019m3 Saturday |
425. | 425 596 24mar2019 24 3 2019 2019w12 2019m3 Sunday |
426. | 426 579 25mar2019 25 3 2019 2019w12 2019m3 Monday |
427. | 427 727 26mar2019 26 3 2019 2019w13 2019m3 Tuesday |
|------------------------------------------------------------------------------|
428. | 428 1250 27mar2019 27 3 2019 2019w13 2019m3 Wednesday |
429. | 429 928 28mar2019 28 3 2019 2019w13 2019m3 Thursday |
430. | 430 729 29mar2019 29 3 2019 2019w13 2019m3 Friday |
431. | 431 656 30mar2019 30 3 2019 2019w13 2019m3 Saturday |
432. | 432 852 31mar2019 31 3 2019 2019w13 2019m3 Sunday |
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433. | 433 988 01apr2019 1 4 2019 2019w13 2019m4 Monday |
434. | 434 701 02apr2019 2 4 2019 2019w14 2019m4 Tuesday |
435. | 435 617 03apr2019 3 4 2019 2019w14 2019m4 Wednesday |
436. | 436 533 04apr2019 4 4 2019 2019w14 2019m4 Thursday |
437. | 437 617 05apr2019 5 4 2019 2019w14 2019m4 Friday |
|------------------------------------------------------------------------------|
438. | 438 620 06apr2019 6 4 2019 2019w14 2019m4 Saturday |
439. | 439 729 07apr2019 7 4 2019 2019w14 2019m4 Sunday |
440. | 440 709 08apr2019 8 4 2019 2019w14 2019m4 Monday |
441. | 441 708 09apr2019 9 4 2019 2019w15 2019m4 Tuesday |
442. | 442 742 10apr2019 10 4 2019 2019w15 2019m4 Wednesday |
|------------------------------------------------------------------------------|
443. | 443 909 11apr2019 11 4 2019 2019w15 2019m4 Thursday |
444. | 444 613 12apr2019 12 4 2019 2019w15 2019m4 Friday |
445. | 445 791 13apr2019 13 4 2019 2019w15 2019m4 Saturday |
446. | 446 770 14apr2019 14 4 2019 2019w15 2019m4 Sunday |
447. | 447 738 15apr2019 15 4 2019 2019w15 2019m4 Monday |
|------------------------------------------------------------------------------|
448. | 448 678 16apr2019 16 4 2019 2019w16 2019m4 Tuesday |
449. | 449 629 17apr2019 17 4 2019 2019w16 2019m4 Wednesday |
450. | 450 785 18apr2019 18 4 2019 2019w16 2019m4 Thursday |
451. | 451 609 19apr2019 19 4 2019 2019w16 2019m4 Friday |
452. | 452 663 20apr2019 20 4 2019 2019w16 2019m4 Saturday |
|------------------------------------------------------------------------------|
453. | 453 777 21apr2019 21 4 2019 2019w16 2019m4 Sunday |
454. | 454 547 22apr2019 22 4 2019 2019w16 2019m4 Monday |
455. | 455 525 23apr2019 23 4 2019 2019w17 2019m4 Tuesday |
456. | 456 535 24apr2019 24 4 2019 2019w17 2019m4 Wednesday |
457. | 457 930 25apr2019 25 4 2019 2019w17 2019m4 Thursday |
|------------------------------------------------------------------------------|
458. | 458 651 26apr2019 26 4 2019 2019w17 2019m4 Friday |
459. | 459 478 27apr2019 27 4 2019 2019w17 2019m4 Saturday |
460. | 460 598 28apr2019 28 4 2019 2019w17 2019m4 Sunday |
461. | 461 731 29apr2019 29 4 2019 2019w17 2019m4 Monday |
462. | 462 624 30apr2019 30 4 2019 2019w18 2019m4 Tuesday |
|------------------------------------------------------------------------------|
463. | 463 589 01may2019 1 5 2019 2019w18 2019m5 Wednesday |
464. | 464 550 02may2019 2 5 2019 2019w18 2019m5 Thursday |
465. | 465 523 03may2019 3 5 2019 2019w18 2019m5 Friday |
466. | 466 919 04may2019 4 5 2019 2019w18 2019m5 Saturday |
467. | 467 864 05may2019 5 5 2019 2019w18 2019m5 Sunday |
|------------------------------------------------------------------------------|
468. | 468 695 06may2019 6 5 2019 2019w18 2019m5 Monday |
469. | 469 734 07may2019 7 5 2019 2019w19 2019m5 Tuesday |
470. | 470 755 08may2019 8 5 2019 2019w19 2019m5 Wednesday |
471. | 471 892 09may2019 9 5 2019 2019w19 2019m5 Thursday |
472. | 472 702 10may2019 10 5 2019 2019w19 2019m5 Friday |
|------------------------------------------------------------------------------|
473. | 473 593 11may2019 11 5 2019 2019w19 2019m5 Saturday |
474. | 474 627 12may2019 12 5 2019 2019w19 2019m5 Sunday |
475. | 475 1151 13may2019 13 5 2019 2019w19 2019m5 Monday |
476. | 476 913 14may2019 14 5 2019 2019w20 2019m5 Tuesday |
477. | 477 845 15may2019 15 5 2019 2019w20 2019m5 Wednesday |
|------------------------------------------------------------------------------|
478. | 478 752 16may2019 16 5 2019 2019w20 2019m5 Thursday |
479. | 479 643 17may2019 17 5 2019 2019w20 2019m5 Friday |
480. | 480 612 18may2019 18 5 2019 2019w20 2019m5 Saturday |
481. | 481 647 19may2019 19 5 2019 2019w20 2019m5 Sunday |
482. | 482 802 20may2019 20 5 2019 2019w20 2019m5 Monday |
|------------------------------------------------------------------------------|
483. | 483 730 21may2019 21 5 2019 2019w21 2019m5 Tuesday |
484. | 484 823 22may2019 22 5 2019 2019w21 2019m5 Wednesday |
485. | 485 673 23may2019 23 5 2019 2019w21 2019m5 Thursday |
486. | 486 627 24may2019 24 5 2019 2019w21 2019m5 Friday |
487. | 487 513 25may2019 25 5 2019 2019w21 2019m5 Saturday |
|------------------------------------------------------------------------------|
488. | 488 552 26may2019 26 5 2019 2019w21 2019m5 Sunday |
489. | 489 662 27may2019 27 5 2019 2019w21 2019m5 Monday |
490. | 490 592 28may2019 28 5 2019 2019w22 2019m5 Tuesday |
491. | 491 729 29may2019 29 5 2019 2019w22 2019m5 Wednesday |
492. | 492 733 30may2019 30 5 2019 2019w22 2019m5 Thursday |
|------------------------------------------------------------------------------|
493. | 493 626 31may2019 31 5 2019 2019w22 2019m5 Friday |
494. | 494 639 01jun2019 1 6 2019 2019w22 2019m6 Saturday |
495. | 495 475 02jun2019 2 6 2019 2019w22 2019m6 Sunday |
496. | 496 651 03jun2019 3 6 2019 2019w22 2019m6 Monday |
497. | 497 675 04jun2019 4 6 2019 2019w23 2019m6 Tuesday |
|------------------------------------------------------------------------------|
498. | 498 489 05jun2019 5 6 2019 2019w23 2019m6 Wednesday |
499. | 499 634 06jun2019 6 6 2019 2019w23 2019m6 Thursday |
500. | 500 587 07jun2019 7 6 2019 2019w23 2019m6 Friday |
501. | 501 994 08jun2019 8 6 2019 2019w23 2019m6 Saturday |
502. | 502 517 09jun2019 9 6 2019 2019w23 2019m6 Sunday |
|------------------------------------------------------------------------------|
503. | 503 791 10jun2019 10 6 2019 2019w23 2019m6 Monday |
504. | 504 1188 11jun2019 11 6 2019 2019w24 2019m6 Tuesday |
505. | 505 792 12jun2019 12 6 2019 2019w24 2019m6 Wednesday |
506. | 506 654 13jun2019 13 6 2019 2019w24 2019m6 Thursday |
507. | 507 538 14jun2019 14 6 2019 2019w24 2019m6 Friday |
|------------------------------------------------------------------------------|
508. | 508 778 15jun2019 15 6 2019 2019w24 2019m6 Saturday |
509. | 509 692 16jun2019 16 6 2019 2019w24 2019m6 Sunday |
510. | 510 712 17jun2019 17 6 2019 2019w24 2019m6 Monday |
511. | 511 660 18jun2019 18 6 2019 2019w25 2019m6 Tuesday |
512. | 512 673 19jun2019 19 6 2019 2019w25 2019m6 Wednesday |
|------------------------------------------------------------------------------|
513. | 513 761 20jun2019 20 6 2019 2019w25 2019m6 Thursday |
514. | 514 618 21jun2019 21 6 2019 2019w25 2019m6 Friday |
515. | 515 545 22jun2019 22 6 2019 2019w25 2019m6 Saturday |
516. | 516 490 23jun2019 23 6 2019 2019w25 2019m6 Sunday |
517. | 517 979 24jun2019 24 6 2019 2019w25 2019m6 Monday |
|------------------------------------------------------------------------------|
518. | 518 844 25jun2019 25 6 2019 2019w26 2019m6 Tuesday |
519. | 519 769 26jun2019 26 6 2019 2019w26 2019m6 Wednesday |
520. | 520 755 27jun2019 27 6 2019 2019w26 2019m6 Thursday |
521. | 521 442 28jun2019 28 6 2019 2019w26 2019m6 Friday |
522. | 522 486 29jun2019 29 6 2019 2019w26 2019m6 Saturday |
|------------------------------------------------------------------------------|
523. | 523 580 30jun2019 30 6 2019 2019w26 2019m6 Sunday |
524. | 524 491 01jul2019 1 7 2019 2019w26 2019m7 Monday |
525. | 525 723 02jul2019 2 7 2019 2019w27 2019m7 Tuesday |
526. | 526 617 03jul2019 3 7 2019 2019w27 2019m7 Wednesday |
527. | 527 414 04jul2019 4 7 2019 2019w27 2019m7 Thursday |
|------------------------------------------------------------------------------|
528. | 528 522 05jul2019 5 7 2019 2019w27 2019m7 Friday |
529. | 529 395 06jul2019 6 7 2019 2019w27 2019m7 Saturday |
530. | 530 689 07jul2019 7 7 2019 2019w27 2019m7 Sunday |
531. | 531 865 08jul2019 8 7 2019 2019w27 2019m7 Monday |
532. | 532 688 09jul2019 9 7 2019 2019w28 2019m7 Tuesday |
|------------------------------------------------------------------------------|
533. | 533 416 10jul2019 10 7 2019 2019w28 2019m7 Wednesday |
534. | 534 811 11jul2019 11 7 2019 2019w28 2019m7 Thursday |
535. | 535 528 12jul2019 12 7 2019 2019w28 2019m7 Friday |
536. | 536 604 13jul2019 13 7 2019 2019w28 2019m7 Saturday |
537. | 537 559 14jul2019 14 7 2019 2019w28 2019m7 Sunday |
|------------------------------------------------------------------------------|
538. | 538 513 15jul2019 15 7 2019 2019w28 2019m7 Monday |
539. | 539 622 16jul2019 16 7 2019 2019w29 2019m7 Tuesday |
540. | 540 666 17jul2019 17 7 2019 2019w29 2019m7 Wednesday |
541. | 541 696 18jul2019 18 7 2019 2019w29 2019m7 Thursday |
542. | 542 487 19jul2019 19 7 2019 2019w29 2019m7 Friday |
|------------------------------------------------------------------------------|
543. | 543 538 20jul2019 20 7 2019 2019w29 2019m7 Saturday |
544. | 544 487 21jul2019 21 7 2019 2019w29 2019m7 Sunday |
545. | 545 781 22jul2019 22 7 2019 2019w29 2019m7 Monday |
546. | 546 495 23jul2019 23 7 2019 2019w30 2019m7 Tuesday |
547. | 547 670 24jul2019 24 7 2019 2019w30 2019m7 Wednesday |
|------------------------------------------------------------------------------|
548. | 548 599 25jul2019 25 7 2019 2019w30 2019m7 Thursday |
549. | 549 629 26jul2019 26 7 2019 2019w30 2019m7 Friday |
550. | 550 571 27jul2019 27 7 2019 2019w30 2019m7 Saturday |
551. | 551 625 28jul2019 28 7 2019 2019w30 2019m7 Sunday |
552. | 552 587 29jul2019 29 7 2019 2019w30 2019m7 Monday |
|------------------------------------------------------------------------------|
553. | 553 623 30jul2019 30 7 2019 2019w31 2019m7 Tuesday |
554. | 554 502 31jul2019 31 7 2019 2019w31 2019m7 Wednesday |
555. | 555 760 01aug2019 1 8 2019 2019w31 2019m8 Thursday |
556. | 556 407 02aug2019 2 8 2019 2019w31 2019m8 Friday |
557. | 557 295 03aug2019 3 8 2019 2019w31 2019m8 Saturday |
|------------------------------------------------------------------------------|
558. | 558 399 04aug2019 4 8 2019 2019w31 2019m8 Sunday |
559. | 559 563 05aug2019 5 8 2019 2019w31 2019m8 Monday |
560. | 560 459 06aug2019 6 8 2019 2019w32 2019m8 Tuesday |
561. | 561 547 07aug2019 7 8 2019 2019w32 2019m8 Wednesday |
562. | 562 594 08aug2019 8 8 2019 2019w32 2019m8 Thursday |
|------------------------------------------------------------------------------|
563. | 563 500 09aug2019 9 8 2019 2019w32 2019m8 Friday |
564. | 564 328 10aug2019 10 8 2019 2019w32 2019m8 Saturday |
565. | 565 411 11aug2019 11 8 2019 2019w32 2019m8 Sunday |
566. | 566 368 12aug2019 12 8 2019 2019w32 2019m8 Monday |
567. | 567 620 13aug2019 13 8 2019 2019w33 2019m8 Tuesday |
|------------------------------------------------------------------------------|
568. | 568 394 14aug2019 14 8 2019 2019w33 2019m8 Wednesday |
569. | 569 652 15aug2019 15 8 2019 2019w33 2019m8 Thursday |
570. | 570 763 16aug2019 16 8 2019 2019w33 2019m8 Friday |
571. | 571 535 17aug2019 17 8 2019 2019w33 2019m8 Saturday |
572. | 572 627 18aug2019 18 8 2019 2019w33 2019m8 Sunday |
|------------------------------------------------------------------------------|
573. | 573 645 19aug2019 19 8 2019 2019w33 2019m8 Monday |
574. | 574 493 20aug2019 20 8 2019 2019w34 2019m8 Tuesday |
575. | 575 607 21aug2019 21 8 2019 2019w34 2019m8 Wednesday |
576. | 576 602 22aug2019 22 8 2019 2019w34 2019m8 Thursday |
577. | 577 434 23aug2019 23 8 2019 2019w34 2019m8 Friday |
|------------------------------------------------------------------------------|
578. | 578 516 24aug2019 24 8 2019 2019w34 2019m8 Saturday |
579. | 579 515 25aug2019 25 8 2019 2019w34 2019m8 Sunday |
580. | 580 455 26aug2019 26 8 2019 2019w34 2019m8 Monday |
581. | 581 629 27aug2019 27 8 2019 2019w35 2019m8 Tuesday |
582. | 582 470 28aug2019 28 8 2019 2019w35 2019m8 Wednesday |
|------------------------------------------------------------------------------|
583. | 583 584 29aug2019 29 8 2019 2019w35 2019m8 Thursday |
584. | 584 453 30aug2019 30 8 2019 2019w35 2019m8 Friday |
585. | 585 538 31aug2019 31 8 2019 2019w35 2019m8 Saturday |
586. | 586 449 01sep2019 1 9 2019 2019w35 2019m9 Sunday |
587. | 587 417 02sep2019 2 9 2019 2019w35 2019m9 Monday |
|------------------------------------------------------------------------------|
588. | 588 416 03sep2019 3 9 2019 2019w36 2019m9 Tuesday |
589. | 589 553 04sep2019 4 9 2019 2019w36 2019m9 Wednesday |
590. | 590 613 05sep2019 5 9 2019 2019w36 2019m9 Thursday |
591. | 591 717 06sep2019 6 9 2019 2019w36 2019m9 Friday |
592. | 592 417 07sep2019 7 9 2019 2019w36 2019m9 Saturday |
|------------------------------------------------------------------------------|
593. | 593 534 08sep2019 8 9 2019 2019w36 2019m9 Sunday |
594. | 594 559 09sep2019 9 9 2019 2019w36 2019m9 Monday |
595. | 595 525 10sep2019 10 9 2019 2019w37 2019m9 Tuesday |
596. | 596 646 11sep2019 11 9 2019 2019w37 2019m9 Wednesday |
597. | 597 768 12sep2019 12 9 2019 2019w37 2019m9 Thursday |
|------------------------------------------------------------------------------|
598. | 598 482 13sep2019 13 9 2019 2019w37 2019m9 Friday |
599. | 599 382 14sep2019 14 9 2019 2019w37 2019m9 Saturday |
600. | 600 666 15sep2019 15 9 2019 2019w37 2019m9 Sunday |
601. | 601 574 16sep2019 16 9 2019 2019w37 2019m9 Monday |
602. | 602 576 17sep2019 17 9 2019 2019w38 2019m9 Tuesday |
|------------------------------------------------------------------------------|
603. | 603 788 18sep2019 18 9 2019 2019w38 2019m9 Wednesday |
+------------------------------------------------------------------------------+
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