## 2022-02-27

### 2021 RBN Data

All the postings to the Reverse Beacon Network in 2021, along with the postings from prior years, are now available in this directory.

Some simple annual statistics for the period 2009 to 2021 follow (the 2009 numbers cover only part of that year, as the RBN was instantiated partway through that year).

Total posts:
2009:   5,007,040
2010:  25,116,810
2011:  49,705,539
2012:  71,584,195
2013:  92,875,152
2014:  108,862,505
2015:  116,385,762
2016:  111,027,068
2017:  117,973,111
2018:  131,930,432
2019:  135,558,461
2020:  173,655,453
2021:  180,965,313
Total posting stations:
2009: 151
2010: 265
2011: 320
2012: 420
2013: 473
2014: 515
2015: 511
2016: 590
2017: 625
2018: 550
2019: 583
2020: 616
2021: 659
Total posted distinct callsigns:
2009: 143,724
2010: 266,189
2011: 271,133
2012: 308,010
2013: 353,952
2014: 398,293
2015: 433,197
2016: 375,613
2017: 356,461
2018: 361,058
2019: 337,246
2020: 369,580
2021: 376,243
Obviously, statistics that are considerably more comprehensive may be derived rather easily from the files in the directory.

Note that if you intend to use the databaseߴs reported signal strengths in an analysis, you should be sure that you understand the ramifications of what the RBN means by SNR.

## 2022-02-26

### Reverse Beacon Network Actvity: 2009-2021

I here show various plots of the G(15, 100) grid-based scatter metric, G(15, 100), for the Reverse Beacon Network (RBN), using data from the inception of the RBN up to the end of 2020.

As in the past I note that a reasonable a priori case can be made on the basis of propagation characteristics that somewhat different metrics in the G(Δ, n) series might be better representations of RBN coverage on some of the bands. However, rather than make this into a full-scale research project, I shall here simply continue to use the G(15, 100) metric on the basis that it seems "good enough" on all bands.

#### RBN Posting Stations as a Function of Time

We begin by looking simply at how the number of per-band posters to the RBN has varied since the RBN's inception. (NB Throughout this post, we ignore posters for which the location is not recorded by the RBN; plots for which the abscissa is time show one datum per month.)

First, a plot of the total number of posters as a function of time:

This can be more compactly represented, along with similar per-band data for 160m through 10m (excluding 60m):

#### G(15, 100) as a Function of Time

Turning now to the geographical distribution of the posting stations, we can display the mensal values of G(15, 100) in a similar manner:

These figures seem to make rather clearly the point that, while the total number of stations posting to the RBN has increased slightly over the past few years (although in an uncertain manner), the underlying geographical distribution has barely changed, and is essentially static at 30% of the world's surface. One assumes (although I haven't checked) that this is because all the "easy" land-based locations now have at least one spotting station, and any expansion of the network, if such is desired, will probably have to concentrate on island states.

#### G(15, 100) as a Function of the Number of Posters

Finally, we can combine the mensal values of G(15, 100) and the number of posters. Firstly, including all bands:

The summary plot for these data is slightly different, as the ordinate is multi-valued for some values of the abscissa. So, in this summary plot, we take the mean value of G(15, 100) in bins of width equivalent to ten posters, and plot rectangles in the equivalent colours:

All in all, a picture emerges in which the RBN, after expanding and increasing coverage for the better part of a decade, became essentially static in early 2017 and is now more or less in statis.

## 2022-02-16

### Reverse NILs in CQ WW: 2021; 2012 to 2021

The basic notion of reverse NILs (rNILs; or, I suppose, RNILs) is described here, along with a description of a simple script for calculating rNILs for CQ WW contests and the result of applying that code to the contests for 2005. See also the comments at the end of that post. I would be delighted if anyone who spots any errors in the script would inform me.

We can also look at the results for periods of ten years at a time; the only difference in the way that these decadal tables are calculated is that the minimum number of rQSOs is raised from 50 to 250 for tables pertaining to all QSOs and from 25 to 125 for those pertaining to intra-W QSOs. (That is, the value of the variable MIN_QSOs is changed from 50 to 250.)

Here are the results for 2021 and for the period from 2012 to 2021. As usual, I refrain from comment.

2021 SSB:

Call Total rQSOs Total rNILs
F8DVD 2007 311
UT0RS 1078 233
HC1QRC 2970 208
PP5JN 444 195
PX2A 6670 182
A73A 9406 174
YT5A 10337 163
LZ9W 10751 156
EW5A 10389 150
VU3NPI 156 149

Call Total rQSOs Total rNILs % rNILs
N5FTY 56 54 96.4
VU3NPI 156 149 95.5
AB7HP 102 97 95.1
DJ5AM 117 97 82.9
HB9IIH 190 128 67.4
AG5MS 146 94 64.4
DK0WWB 56 32 57.1
WI9Z 89 49 55.1
UA0A 194 97 50.0
LV7H 86 43 50.0

Call Total rQSOs with Ws rNILs against Ws Total rQSOs with non-Ws rNILs against non-Ws
N7DD 53 42 585 14
NY6DX 49 31 1549 30
KV0Q 129 22 834 11
KM6Z 26 16 432 9
K3EST 341 15 2622 27
K9BGL 31 14 1668 31
WX3B 193 12 4654 68
K3LR 673 11 8301 87
K1TTT 353 8 4458 86
K4ZW 57 8 2882 42

Call Total rQSOs with Ws Total rNILs against Ws % rNILs against Ws Total rQSOs with non-Ws rNILs against non-Ws % rNILs against non-Ws
N7DD 53 42 79.2 585 14 2.4
NY6DX 49 31 63.3 1549 30 1.9
KM6Z 26 16 61.5 432 9 2.1
K9BGL 31 14 45.2 1668 31 1.9
AA4VT 42 8 19.0 2373 17 0.7
KV0Q 129 22 17.1 834 11 1.3
K4ZW 57 8 14.0 2882 42 1.5
NV9L 45 6 13.3 2651 22 0.8
KT7E 58 6 10.3 1254 13 1.0
K0MD 32 3 9.4 1310 15 1.1

2021 CW:

Call Total rQSOs Total rNILs
OK1AGE 324 290
N2FF 407 288
YS1MS 234 229
3B8M 8913 214
SV2AEL 687 198
PJ4K 10786 193
EA6FO 7668 192
CR3DX 11682 187
PJ2T 11000 177
9H6A 4694 159

Call Total rQSOs Total rNILs % rNILs
YS1MS 234 229 97.9
JM2LEI 163 154 94.5
OK1AGE 324 290 89.5
IS0ESG 97 76 78.4
JA9LNZ 65 47 72.3
N2FF 407 288 70.8
HL3AMO 114 78 68.4
K7XH 134 83 61.9
4M5K 189 114 60.3
JF2WXS 98 52 53.1

Call Total rQSOs with Ws rNILs against Ws Total rQSOs with non-Ws rNILs against non-Ws
NA3M 57 53 1784 23
K8LX 79 28 3378 34
N2IC 187 24 3256 23
N7DD 38 23 718 8
AK1MD 36 21 908 36
NX6T 169 19 2345 49
N6RO 418 18 3053 43
NN7CW 141 16 3500 46
K0RF 280 13 4006 43
KV2K 38 13 2393 52

Call Total rQSOs with Ws Total rNILs against Ws % rNILs against Ws Total rQSOs with non-Ws rNILs against non-Ws % rNILs against non-Ws
NA3M 57 53 93.0 1784 23 1.3
N7DD 38 23 60.5 718 8 1.1
AK1MD 36 21 58.3 908 36 4.0
K8LX 79 28 35.4 3378 34 1.0
KV2K 38 13 34.2 2393 52 2.2
WB9Z 39 8 20.5 964 7 0.7
W9VW 27 5 18.5 2264 46 2.0
N2SR 35 6 17.1 2670 74 2.8
WA3TD 27 4 14.8 38 2 5.3
N7XU 28 4 14.3 1106 13 1.2

As I write this, the results for the 2021 contests are yet to be announced. I shall, like last year, make a prediction based on the past many years of data that none of the people in the final table of each group whose rNIL rate against Ws exceeds 50% will be disqualified for unsportsmanlike conduct. In fact, I predict that none of them will even be investigated in order to determine the reason for what appears on its face to be an excessive rNIL rate against Ws. Year after year, the situation remains the same.

(I do note that rNILs do occur naturally when one of the two operators is a bit sloppy, and a rate that is higher against Ws than against non-Ws could [possibly] be explained as a natural occurrence: for example, the running W operator hears two callers, one W and one DX, and the running station would normally go back to and work the DX station if he can, while the W caller might sloppily believe that he was being worked. What is concerning, though, and should reasonably be a cause for further investigation, is when the rNIL rate against Ws greatly exceeds the rate against non-Ws, or when the same calls appear in the tables year after year.)

2012 to 2021 SSB:

Call Total rQSOs Total rNILs
EA2DMH 5309 1645
CN3A 76412 1430
LZ9W 92452 1357
OT5A 64412 1250
A73A 65099 1243
JE5JHZ 1742 1186
PJ2T 70289 1150
JR4GPA 2788 1101
YL2014W 1106 1099
MI0M 3871 1085

Call Total rQSOs Total rNILs % rNILs
YL2014W 1106 1099 99.4
YY4HAH 493 489 99.2
CU4AT 274 270 98.5
KP4ROS 664 653 98.3
ZZ5Z 297 292 98.3
F8EMH 1176 1025 87.2
CO2SG 301 232 77.1
EA8DDS 503 387 76.9
OK1KZ 1404 975 69.4
JE5JHZ 1742 1186 68.1

Call Total rQSOs with Ws rNILs against Ws Total rQSOs with non-Ws rNILs against non-Ws
KV0Q 809 373 8113 127
N7DD 595 373 8980 176
N2IC 732 101 12913 101
K3LR 4801 97 63397 754
N9RV 662 89 10745 108
W3LPL 2605 73 47739 642
NY6DX 146 69 5028 73
K5TR 2731 67 21197 358
K3EST 1120 64 11620 219
W4QNW 148 60 958 23

Call Total rQSOs with Ws Total rNILs against Ws % rNILs against Ws Total rQSOs with non-Ws rNILs against non-Ws % rNILs against non-Ws
N7DD 595 373 62.7 8980 176 2.0
NY6DX 146 69 47.3 5028 73 1.5
KV0Q 809 373 46.1 8113 127 1.6
W4QNW 148 60 40.5 958 23 2.4
N2IC 732 101 13.8 12913 101 0.8
N9RV 662 89 13.4 10745 108 1.0
AB4B 173 23 13.3 7137 98 1.4
N3AD 169 21 12.4 10615 153 1.4
K3UL 181 19 10.5 9773 175 1.8
W8PR 248 26 10.5 11862 168 1.4

CW 2012 to 2021:

Call Total rQSOs Total rNILs
LZ9W 105536 1414
OK1KZ 1682 1400
PJ2T 90131 1361
P33W 81006 1123
D4C 53316 1086
9A1A 87400 1068
PJ4A 66230 1064
ZF1A 49933 1033
PI4DX 13356 1006
JR4GPA 5727 989

Call Total rQSOs Total rNILs % rNILs
OK1KZ 1682 1400 83.2
WB0CFF 432 313 72.5
RA9KY 683 461 67.5
RZ3QZ 293 195 66.6
AB3UM 276 153 55.4
XF1IM 702 385 54.8
TM0T 298 161 54.0
PP5BLU 305 148 48.5
W2ANQ 373 175 46.9
UT4AA 1072 489 45.6

Call Total rQSOs with Ws rNILs against Ws Total rQSOs with non-Ws rNILs against non-Ws
N7DD 328 198 7869 182
NR5M 1299 188 17027 167
K0RF 2270 126 33705 437
KV2K 353 108 19224 316
N9RV 733 84 18142 195
W3LPL 2835 72 70050 626
NA3M 184 70 11087 102
WJ9B 718 66 10735 96
N7AT 1561 65 24443 202
W1IE 140 63 3110 130

Call Total rQSOs with Ws Total rNILs against Ws % rNILs against Ws Total rQSOs with non-Ws rNILs against non-Ws % rNILs against non-Ws
N7DD 328 198 60.4 7869 182 2.3
W1IE 140 63 45.0 3110 130 4.2
NA3M 184 70 38.0 11087 102 0.9
KV2K 353 108 30.6 19224 316 1.6
NE3F 230 61 26.5 15428 261 1.7
KZ5D 217 54 24.9 9688 124 1.3
KV0Q 200 43 21.5 1926 63 3.3
W0QQG 193 29 15.0 1749 219 12.5
NR5M 1299 188 14.5 17027 167 1.0
W5ZN 142 18 12.7 3005 38 1.3

## 2022-02-14

### Busting Calls: CQ WW 2021

Prior posts in this series:
Throughout this post, I apply the procedures developed in the second post above.

For the purpose of this post, only verified QSOs are counted.

### Lowest Probability

I begin with an ordered list of the stations with the lowest probabilities of busting a call in 2021 CQ WW SSB.

2021 CQ WW SSB -- weighted mean values of $p_{bust}$ (all)
Position Call weighted mean $Q_v$ $B$
1 DF2RG 0.0010 998 0
2 DK1KC 0.0010 997 0
3 DL8RDL 0.0010 951 0
4 OH7GGX 0.0010 942 0
5 M3AWD 0.0010 923 0
6 CA3CLF 0.0010 912 0
7 G4NBS 0.0011 886 0
8 R7MM 0.0014 696 0
9 PG7M 0.0014 693 0
10 K2EP 0.0014 692 0

And for 2021 CQ WW CW:

2021 CQ WW CW -- weighted mean values of $p_{bust}$ (all)
Position Call weighted mean $Q_v$ $B$
1 UA3AP 0.0005 1,932 0
2 DL4FN 0.0005 1,925 0
3 DK8NT 0.0005 1,739 0
4 DP8M 0.0006 1,463 0
5 R2VM 0.0007 1,426 0
6 OH7GGX 0.0009 1,027 0
7 NA0N 0.0009 1,019 0
8 R5FQ 0.0010 949 0
9 N7IR 0.0010 944 0
10 YU1LM 0.0010 940 0

It is interesting to plot the aggregated probability function for $p_{bust}$ weighted by the verified number of QSOs, $Q_v$, for all stations:

In case it isn't clear, the location of the solid vertical lines represent the weighted means of the probability curves.

We can limit the analysis to calling stations (i.e., not the running station).

2021 CQ WW SSB -- weighted mean values of $p_{bust}$ (no-run)
Position Call weighted mean $Q_v$ $B$
1 DK1KC 0.0010 991 0
2 OH7GGX 0.0010 939 0
3 DF2RG 0.0010 920 0
4 G4NBS 0.0011 880 0
5 IK2LFF 0.0012 830 0
6 M3AWD 0.0012 809 0
7 9A2EU 0.0012 793 0
8 OG6N 0.0012 771 0
9 WA2CP 0.0013 729 0
10 WB1DX 0.0014 712 0

2021 CQ WW CW -- weighted mean values of $p_{bust}$ (no-run)
Position Call weighted mean $Q_v$ $B$
1 DL4FN 0.0006 1,572 0
2 RA3AN 0.0006 1,547 0
3 UA3AP 0.0007 1,411 0
4 DK8NT 0.0007 1,369 0
5 DP8M 0.0009 1,099 0
6 RL6M 0.0009 2,250 1
7 R7MM 0.0009 1,049 0
8 YO8DOH 0.0010 994 0
9 OL0W 0.0010 987 0
10 PJ4K 0.0010 981 0

And similarly for running stations.

2021 CQ WW SSB -- weighted mean values of $p_{bust}$ (run)
Position Call weighted mean $Q_v$ $B$
1 CA3CLF 0.0013 745 0
2 VA7RR 0.0017 1,721 2
3 VE2IM 0.0018 4,564 7
4 OH8X 0.0021 2,339 4
5 PY2UD 0.0022 440 0
6 VC3X 0.0024 1,239 2
7 W6DVS 0.0024 403 0
8 DK5PD 0.0025 813 1
9 HA5JI 0.0025 391 0
10 RA0R 0.0025 389 0

2021 CQ WW CW -- weighted mean values of $p_{bust}$ (run)
Position Call weighted mean $Q_v$ $B$
1 R2VM 0.0009 1,040 0
2 JI1RXQ 0.0013 763 0
3 OM3TZZ 0.0013 721 0
4 IZ8VYU 0.0013 714 0
5 5Z4VJ 0.0014 2,137 2
6 JH1QDB 0.0015 634 0
7 LA8HGA 0.0016 619 0
8 DM2M 0.0016 612 0
9 W3FIZ 0.0017 583 0
10 K1ZZ 0.0017 2,979 4

We can also look at the changes over the period from 2005 to 2021.

First for all QSOs:

Then for calling stations:

And for running stations:

I think it's also interesting to see who appears to have the lowest probability of busting a call over an extended period. So, for the last ten years:

2012--2021 CQ WW SSB -- weighted mean values of $p_{bust}$ (all)
Position Call weighted mean $Q_v$ $B$
1 IK4OMU 0.0007 1,424 0
2 K0PC 0.0007 1,419 0
3 RU3SD 0.0007 1,313 0
4 ES2MC 0.0007 2,780 1
5 K4RUM 0.0007 1,267 0
6 JH8SLS 0.0008 1,163 0
7 JM1NKT 0.0008 2,477 1
8 K1ZZ 0.0008 4,885 3
9 OR2A 0.0008 1,140 0
10 IZ4JMA 0.0008 1,129 0

2012--2021 CQ WW CW -- weighted mean values of $p_{bust}$ (all)
Position Call weighted mean $Q_v$ $B$
1 HL1VAU 0.0002 4,324 0
2 NW0M 0.0002 3,532 0
3 SE6N 0.0004 5,304 1
4 WB4TDH 0.0004 5,218 1
6 K6WSC 0.0004 4,888 1
7 WW4XX 0.0004 2,005 0
8 JN3SAC 0.0005 6,571 2
9 DL4FN 0.0005 8,263 3
10 KM3T 0.0005 1,826 0

A good argument can be made that a better measure of copying ability is to consider only run QSOs:

2012--2021 CQ WW SSB -- weighted mean values of $p_{bust}$ (run)
Position Call weighted mean $Q_v$ $B$
1 R7MM 0.0008 1,223 0
2 F4FTA 0.0009 1,108 0
3 ES2MC 0.0012 1,682 1
4 CF7RR 0.0013 1,557 1
5 K0AP 0.0013 727 0
6 EE1A 0.0014 673 0
7 VA7RR 0.0015 5,494 7
8 K1ZZ 0.0015 1,367 1
9 YT9M 0.0017 1,211 1
10 DL8RDL 0.0017 1,206 1

2012--2021 CQ WW CW -- weighted mean values of $p_{bust}$ (run)
Position Call weighted mean $Q_v$ $B$
1 W3OA 0.0005 1,799 0
2 LY3CY 0.0005 1,713 0
3 RW5CW 0.0006 1,647 0
4 SE6N 0.0006 1,486 0
5 WB4TDH 0.0007 2,800 1
6 VX7SZ 0.0007 1,294 0
7 WQ5L 0.0008 1,220 0
8 YU1RA 0.0008 2,558 1
9 KM3T 0.0008 1,186 0
10 UA3QAM 0.0009 1,089 0

#### Highest Probability

We can also look at the calls associated with the highest probability of busting calls in either the forward or the reverse direction:

2021 SSB -- Most Busts
Position Call QSOs Busts % Busts
1 LZ9W 10,561 197 1.9
2 HI3LT 5,081 173 3.4
3 CR6K 8,984 164 1.8
4 OT5A 7,469 160 2.1
5 YT5A 10,159 159 1.6
6 UA7K 6,289 149 2.4
7 RT6A 6,140 141 2.3
8 ED1R 7,892 125 1.6
9 A73A 9,221 120 1.3
10 K1TTT 4,693 118 2.5

2021 CW -- Most Busts
Position Call QSOs Busts % Busts
1 TK0C 14,766 237 1.6
2 PI4CC 7,777 213 2.7
3 UA7K 7,873 190 2.4
4 F6KOP 7,439 183 2.5
5 PT4A 4,073 181 4.4
6 3B8M 8,698 156 1.8
7 RW0A 11,039 148 1.3
8 PJ4K 10,577 142 1.3
9 RT6A 6,879 138 2.0
10 TO7A 5,934 135 2.3

2021 SSB -- Highest Percentage of Busts (≥100 QSOs)
Position Call QSOs Busts % Busts
1 IS0AGY 116 25 21.6
2 OM3ZAH 107 17 15.9
3 WA3RGH 102 16 15.7
4 I4PZP 182 27 14.8
5 OH1TS 163 24 14.7
6 R8CAX 103 15 14.6
7 R9MBY 202 29 14.4
8 R3PIQ 125 17 13.6
9 BD7OYA 263 34 12.9
10 WB7EUJ 102 13 12.7

2021 CW -- Highest Percentage of Busts (≥100 QSOs)
Position Call QSOs Busts % Busts
1 IS0ILP 107 25 23.4
2 DL7CO 117 26 22.2
3 DF6ON 107 22 20.6
4 EA3ERD 147 28 19.0
5 SP2EPV 116 22 19.0
6 K2HYD 111 20 18.0
7 UT5KL 151 27 17.9
8 CL3OR 245 42 17.1
9 VE3EKA 100 17 17.0
10 K9XR 165 28 17.0

2021 SSB -- Most Reverse Busts
Position Call QSOs Reverse Busts % Reverse Busts
1 CA3CLF 912 912 100.0
2 DF0HQ 9,555 310 3.2
3 EA8RM 6,712 199 3.0
4 YT5A 10,159 182 1.8
5 4X1DX 4,132 181 4.4
6 PX2A 6,492 161 2.5
7 Z60A 4,227 159 3.8
8 TM3R 4,702 158 3.4
9 EW5A 10,234 156 1.5
10 JA3YBK 3,915 137 3.5

2021 CW -- Most Reverse Busts
Position Call QSOs Reverse Busts % Reverse Busts
1 R2VM 1,426 1,426 100.0
2 ES9C 10,492 343 3.3
3 DF0HQ 9,565 268 2.8
4 UA4S 6,407 263 4.1
5 TK0C 14,766 250 1.7
6 OH3ZG 241 241 100.0
7 JF1NHD 2,801 240 8.6
8 RW0A 11,039 216 2.0
9 JS3CTQ 2,278 212 9.3
10 UA4M 8,992 206 2.3

2021 SSB -- Highest Percentage of Reverse Busts (≥100 QSOs)
Position Call QSOs % Reverse Busts
1 CA3CLF 912 100.0
2 BH7PCT 395 20.0
3 UA3R 125 16.0
4 DW2AFO 309 14.6
5 OM7AHJ 134 14.2
6 HB9HGI 110 13.6
7 N3UA 542 13.5
8 KO4HMB 102 12.7
9 VK2K 252 12.7
10 CA1FCS 174 12.6

2021 CW -- Highest Percentage of Reverse Busts (≥100 QSOs)
Position Call QSOs % Reverse Busts
1 OH3ZG 241 100.0
2 R2VM 1,426 100.0
3 OE120BKC 174 28.7
4 JS2GYN 114 19.3
5 BH4CAC 122 14.8
6 R7RBE 165 14.5
7 K3HW 526 13.7
8 V85T 239 13.0
9 HA7SQ 477 12.4
10 KB4MD 236 12.3

In tables of reverse busts, one sometimes finds what seems like an unreasonable number of reverse busts (as, in the last two tables, for CA3CLF, OH3ZG and R2VM). This is generally caused by a discrepancy between the call actually sent by the listed station and the one recorded as being sent in at least some QSOs in the station's log.

2012--2021 SSB -- Most Busts
Position Call QSOs Busts % Busts
1 LZ9W 90,841 1,620 1.8
2 CN3A 74,906 1,359 1.8
3 A73A 63,716 1,230 1.9
4 OT5A 62,915 1,216 1.9
5 PJ2T 69,092 1,182 1.7
6 V26B 54,741 946 1.7
7 II2S 43,883 844 1.9
8 ED1R 55,290 841 1.5
9 HG7T 60,338 817 1.4
10 RT6A 47,522 808 1.7

2012--2021 CW -- Most Busts
Position Call QSOs Busts % Busts
1 LZ9W 103,757 1,190 1.1
2 PJ2T 88,714 1,177 1.3
3 PI4CC 51,789 1,057 2.0
4 TK0C 60,962 904 1.5
5 RW0A 54,499 886 1.6
6 ZW8T 12,253 881 7.2
8 JA3YBK 49,522 789 1.6
9 G3V 34,468 787 2.3
10 D4C 52,522 761 1.4

2012--2021 SSB -- Highest Percentage of Busts (≥500 QSOs)
Position Call QSOs Busts % Busts
1 K2JMY 1,632 262 16.1
2 EA7JQT 572 71 12.4
3 OH1TS 725 82 11.3
4 EA1HTF 1,470 160 10.9
5 EA4GWL 931 91 9.8
6 LU4DJB 672 64 9.5
7 YB9KA 706 67 9.5
8 PU2TRX 803 76 9.5
9 UY3CC 1,536 143 9.3
10 E20WXA 688 64 9.3

2012--2021 CW -- Highest Percentage of Busts (≥500 QSOs)
Position Call QSOs Busts % Busts
1 W2UDT 668 148 22.2
2 BD3MV 961 198 20.6
3 4Z5FW 527 108 20.5
4 DJ5UZ 723 136 18.8
5 WP3Y 570 93 16.3
6 LZ1BY 708 115 16.2
7 AE3D 1,016 161 15.8
8 DL7CO 810 127 15.7
9 IK0YUO 670 104 15.5
10 YO7LYM 1,697 254 15.0

2012--2021 SSB -- Most Reverse Busts
Position Call QSOs Reverse Busts % Reverse Busts
1 DF0HQ 84,060 2,426 2.9
2 JA3YBK 35,341 1,133 3.2
3 K3LR 67,184 1,068 1.6
4 TM3R 24,257 1,049 4.3
5 CA3CLF 982 915 93.2
6 IK2YCW 26,490 857 3.2
7 CN3A 74,906 811 1.1
8 W3LPL 49,498 799 1.6
9 HK1NA 40,363 788 2.0
10 CN2R 45,445 779 1.7

2012--2021 CW -- Most Reverse Busts
Position Call QSOs Reverse Busts % Reverse Busts
1 JS3CTQ 24,073 2,938 12.2
2 DF0HQ 80,064 2,694 3.4
3 ES9C 72,304 2,252 3.1
4 HG7T 67,691 1,444 2.1
5 R2VM 1,426 1,426 100.0
6 W2FU 52,558 1,412 2.7
7 RM9A 63,391 1,399 2.2
8 K3LR 70,734 1,364 1.9
9 DR1A 44,217 1,289 2.9
10 DR4A 52,236 1,236 2.4

2012--2021 SSB -- Highest Percentage of Reverse Busts (≥500 QSOs)
Position Call QSOs % Reverse Busts
1 CA3CLF 982 93.2
2 CW90A 1,370 30.9
3 CE6VMO 593 21.2
4 BA8AG 752 16.4
5 OG60F 4,258 11.4
6 ZP6DYA 1,225 11.3
7 BH7PCT 846 10.9
8 LU9DDJ 862 10.4
9 BV55D 919 10.2
10 BG6SNJ 802 10.1

2012--2021 CW -- Highest Percentage of Reverse Busts (≥500 QSOs)
Position Call QSOs % Reverse Busts
1 R2VM 1,426 100.0
2 G3RWF 1,459 64.8
3 YT65A 1,149 37.2
4 5K0A 1,853 32.1
5 PE75W 1,408 29.3
6 OG55W 3,265 28.6
7 TA1C/2 1,513 18.1
8 DP65HSC 516 16.9
9 5J1E 1,523 15.6
10 SB0A 1,102 15.5

## 2022-02-06

### Continent-Based Analyses from 2021 CQ WW SSB and CQ WW CW logs

In addition to zone-based analyses, we can perform similar analyses based on continent rather than zone using the various public CQ WW logs (cq-ww-2005--2021-augmented.xz; see here for details of the augmented format) for the period from 2005 to 2021.

### Continent Pairs

We start by looking at the number of QSOs for pairs of continents from the contests for 2021.

The procedure is simple. We consider only QSOs that meet the following criteria:
1. marked as "two-way" QSOs (i.e., both parties submitted a log containing the QSO);
2. no callsign or zone is bust by either party.

A counter is maintained for every possible pair of continents and the pertinent counter is incremented once for each distinct QSO between stations in those continents.

Separate figures are provided below for each band, led by a figure integrating QSOs on all bands. The figures are constructed in such a way as to show the results for both the SSB and CW contests on a single figure. (Any pair of continents with no QSOs that meet the above criteria appears in black on the figures.)

### Continents and Distance

Below is a series of figures showing the distribution of distance for QSOs as a function of continent.

Each plot shows a colour-coded distribution of the distance of QSOs for each continent, with the data for SSB appearing above the data for CW within each continent.

For every half-QSO in a given continent, the distance of the QSO is calculated; in this way, the total  number of half-QSOs in bins of width 500 km is accumulated. Once all the QSOs for a particular mode have been binned in this manner, the distribution for each continent is normalised to total 100% and the result coded by colour and plotted. The mean distance for each continent and mode is denoted by a small white rectangle added to the underlying distance distribution. The 99% confidence range of the value of mean is marked by a small blue rectangle (typically entirely subsumed by the white rectangle). The median is marked with a vertical brown rectangle.

As usual, only QSOs for which logs have been provided by both parties, and which show no bust of either callsign or zone number are included. Bins coloured black are those for which no QSOs are present at the relevant distance.

The resulting plots are reproduced below.

### Half-QSOs Per Continent, 2005 to 2021

A simple way to display the activity in the CQ WW contests is to count the number of half-QSOs in each continent (a single QSO contains two half-QSOs, so a single QSO may contain two different continents or the same continent twice). We count half QSOs, making sure to include each valid QSO only once (that is, if the same QSO appears in two submitted logs, it is counted only once).

If we do this for the entire contest without taking the individual bands into account, we obtain this figure:

The plot shows data for both SSB and CW contests over the period from 2005 to 2021. I include only QSOs for which both parties submitted a log and neither party bust either the zone or the call of the other party. The black triangles represent contests in which no half-QSOs were made from (or to) a particular continent. Perhaps more than any other plot, this makes unmistakable the dominance of EU in the CQ WW contests.

We can, of course, generate equivalent plots on a band-by-band basis:

As in prior years, the activity from EU so overwhelms these figures that in order to get a feel for the activity elsewhere, we need to move to a logarithmic scale:

### Intra-Continental QSOs

We can also easily look at the percentage of QSOs that are between two stations on the same continent, and in particular between two EU stations:

So, for example, in CQ WW CW in 2021, 30% of all QSOs were within the same continent; about a quarter of all QSOs were between two European stations.

Flogging a dead horse, on 160m about two thirds of QSOs in this "world wide DX" contest were between two European entrants, even in the more DX-friendly mode. On SSB, more than three quarters of all QSOs were between two European entrants.