January 23rd, 2012
Mark Croonen
Based on my hugely popular UCI Pro Tour Stats post
I have now turned the data into an interactive set of graphs using some really cool software called Tableau. Once again this data was taken from the publically available UCI archives. The only thing I have done is to represent the raw point score as percentage of the yearly total to even out the differences between the total points scored each year.
With all these charts you can
- Select and highlight individual countries.
- Use the slider bar to select a specific year.
- Using the magnifying tools in the top left corner you can zoom into areas of interest or where the data is “crowded”
- Move the mouse over each data point reveal all the data for each point.
- if you get stuck with the filters you can click the “Revert all” button at the bottom of each infographic
Points vs. Riders
This is a comparison of how many riders each country has and how many points they have scored, the size of the circle represents the country’s population. So countries with a small circle above the trend line are considered to be the high performing countries.
Population vs. Riders
This is a comparison of how many riders each country has compared to its population, the size of the circle represents how many points the country has scored. So countries with a large circle below the trend line are considered to be the high performing countries.
The next two are more just to show the features of the Tableau software which I thought looked interesting.
Geo location of the riders.
This shows the data overlaid on a world map, showing where the riders come, the size of the circle represents how many riders come from the country. Remember you can use the magnifying tools to zoom in over Europe.
Geo location of points scored.
Similar to the previous one, this shows which country has scored points and how many.
Posted in General Bike News, UCI Pro tour
Tags: data visulisation, Statistics, UCI Pro tour
June 22nd, 2011
Mark Croonen
All the data for these stats came from the UCI Pro-Tour website http://www.uciprotour.com.
The term “Neo” is equivalent to a rookie, I guess it is a Euro thing.
The age format used below is year:days and the age is taken as of 1 Jan of each year
Year |
Total Riders |
Neo |
No: of teams |
Avg Age |
Avg age of Neo |
Biggest Team |
Smallest Team |
2006 |
553 |
61 |
20 |
27:255 |
21:262 |
30 |
24 |
2007 |
558 |
56 |
20 |
27:256 |
21:215 |
30 |
25 |
2008 |
499 |
48 |
18 |
27:204 |
21:295 |
30 |
24 |
2009 |
476 |
38 |
17 |
27:204 |
21:321 |
30 |
22 |
2010 |
471 |
38 |
18 |
27:248 |
21:120 |
28 |
24 |
2011 |
489 |
43 |
18 |
27:361 |
21:229 |
30 |
24 |
Oldest Rider |
Birthdate |
Age |
Year |
Youngest Rider |
Birth date |
Age |
EKIMOV Viatcheslaw – RUS |
04/02/1966 |
39:331 |
2006 |
CAPECCHI Eros – ITA |
13/06/1986 |
19:200 |
BROCHARD Laurent – FRA |
26/03/1968 |
38:278 |
2007 |
VINTHER Troels Ronning – DEN |
24/02/1987 |
19:311 |
BALDATO Fabio – ITA |
13/06/1968 |
39:200 |
2008 |
BARLA Luca – ITA |
29/09/1987 |
20:93 |
NOE Andrea – ITA |
15/01/1969 |
39:350 |
2009 |
BELLIS Jonathan – GBR |
16/08/1988 |
20:136 |
KNAVEN Servais – NED |
06/03/1971 |
38:299 |
2010 |
PINOT Thibaut – FRA |
29/05/1990 |
19:214 |
VOIGT Jens – GER |
17/09/1971 |
39:105 |
2011 |
VAN KEIRBULCK Guillaume – BEL |
14/02/1991 |
19:321 |
The table below shows the countries which are doing better than others based on their population, in other words, which nations are punching above the weight. It shows how many pro riders they have as a percentage of the population (The percentage is per 1,000,000 people of the population.). I only included countries that had at least three pro riders otherwise the percentages became a little too random.
2006 |
|
2007 |
|
2008 |
|
2009 |
|
2010 |
|
|
|
|
Country |
Riders |
% |
|
Country |
Riders |
% |
|
Country |
Riders |
% |
|
Country |
Riders |
% |
|
Country |
Riders |
% |
|
Country |
Riders |
% |
LUX |
4 |
8.431% |
ó |
LUX |
4 |
8.329% |
ó |
LUX |
4 |
8.329% |
ó |
LUX |
3 |
6.100% |
ó |
LUX |
5 |
10.167% |
ó |
LUX |
4 |
8.134 |
BEL |
43 |
4.143% |
ó |
BEL |
54 |
5.196% |
ó |
BEL |
44 |
4.234% |
ó |
BEL |
43 |
4.129% |
ó |
BEL |
46 |
4.417% |
ó |
BEL |
49 |
4.705 |
SUI |
24 |
3.190% |
ñ |
DEN |
14 |
2.560% |
² |
EST |
4 |
3.040% |
ñ |
DEN |
14 |
2.545% |
ñ |
SLO |
6 |
2.991% |
ó |
SLO |
7 |
3.490 |
ESP |
104 |
2.574% |
ò |
SUI |
19 |
2.515% |
ó |
SUI |
16 |
2.118% |
ò |
EST |
3 |
2.309% |
ò |
DEN |
16 |
2.909% |
ó |
DEN |
15 |
2.727 |
SLO |
5 |
2.487% |
ó |
SLO |
5 |
2.488% |
ó |
SLO |
4 |
1.991% |
ó |
SLO |
4 |
1.994% |
ñ |
ESP |
68 |
1.678% |
ñ |
NED |
34 |
2.034 |
DEN |
12 |
2.202% |
ò |
ESP |
85 |
2.101% |
ó |
ESP |
77 |
1.904% |
ñ |
NED |
30 |
1.795% |
ó |
NED |
27 |
1.615% |
ñ |
SUI |
13 |
1.710 |
NED |
28 |
1.698% |
ó |
NED |
32 |
1.931% |
ó |
NED |
28 |
1.690% |
ò |
ESP |
70 |
1.727% |
ñ |
NZL |
5 |
1.187% |
ò |
ESP |
60 |
1.481 |
FRA |
97 |
1.593% |
ó |
FRA |
98 |
1.538% |
ò |
DEN |
9 |
1.646% |
ò |
SUI |
11 |
1.447% |
ñ |
AUS |
24 |
1.129% |
ó |
AUS |
28 |
1.317 |
ITA |
84 |
1.445% |
ó |
ITA |
79 |
1.359% |
ò |
FRA |
103 |
1.616% |
ó |
FRA |
86 |
1.343% |
ò |
SUI |
8 |
1.052% |
ñ |
NOR |
6 |
1.287 |
AUS |
19 |
0.938% |
ó |
AUS |
24 |
1.175% |
ò |
ITA |
67 |
1.152% |
ó |
ITA |
54 |
0.929% |
ó |
ITA |
60 |
1.032% |
ó |
ITA |
69 |
1.187 |
AUT |
6 |
0.732% |
ñ |
KAZ |
11 |
0.720% |
ñ |
NOR |
4 |
0.864% |
ñ |
AUS |
17 |
0.800% |
ñ |
KAZ |
14 |
0.909% |
ò |
NZL |
5 |
1.187 |
IRL |
3 |
0.739% |
² |
NZL |
3 |
0.729% |
ò |
AUS |
17 |
0.832% |
² |
NZL |
3 |
0.712% |
ò |
FRA |
49 |
0.765% |
² |
IRL |
4 |
0.952 |
NOR |
3 |
0.651% |
ñ |
SWE |
6 |
0.664% |
ò |
KAZ |
10 |
0.654% |
ñ |
SWE |
6 |
0.662% |
² |
NOR |
3 |
0.644% |
ò |
KAZ |
14 |
0.909 |
FIN |
3 |
0.573% |
ò |
NOR |
3 |
0.648% |
² |
SVK |
3 |
0.551% |
ò |
KAZ |
10 |
0.649% |
ñ |
BLR |
6 |
0.622% |
² |
LTU |
3 |
0.844 |
GER |
41 |
0.497% |
ó |
GER |
41 |
0.498% |
ñ |
AUT |
4 |
0.488% |
ñ |
BLR |
5 |
0.518% |
ñ |
AUT |
5 |
0.609% |
ñ |
SVK |
4 |
0.732 |
SWE |
4 |
0.444% |
ò |
AUT |
4 |
0.488% |
ò |
GER |
39 |
0.473% |
ò |
AUT |
4 |
0.487% |
² |
SVK |
3 |
0.549% |
ñ |
POR |
6 |
0.560 |
KAZ |
5 |
0.328% |
ñ |
CZE |
3 |
3.805% |
² |
BLR |
4 |
0.411% |
ò |
GER |
28 |
0.340% |
ò |
SWE |
4 |
0.442% |
ò |
FRA |
32 |
0.500 |
CZE |
3 |
3.825% |
ñ |
UKR |
8 |
0.173% |
ò |
SWE |
3 |
0.332% |
ñ |
RUS |
22 |
0.157% |
² |
POR |
4 |
0.374% |
ò |
BLR |
4 |
0.415 |
POR |
3 |
0.283% |
ñ |
COL |
6 |
0.135% |
ò |
CZE |
3 |
3.805% |
² |
CAN |
4 |
0.119% |
ò |
GER |
29 |
0.352% |
ò |
AUT |
3 |
0.365 |
UKR |
7 |
0.150% |
ñ |
GBR |
7 |
0.231% |
ò |
UKR |
7 |
0.151% |
ñ |
GBR |
6 |
0.098% |
ó |
GBR |
13 |
0.213% |
ò |
SWE |
3 |
0.331 |
COL |
5 |
0.115% |
ñ |
RUS |
12 |
0.085% |
ò |
COL |
4 |
0.090% |
ó |
COL |
4 |
0.088% |
ò |
RUS |
20 |
0.143% |
² |
CZE |
3 |
0.294 |
CAN |
3 |
0.091% |
ñ |
POL |
3 |
0.078% |
ò |
GBR |
5 |
0.165% |
ò |
UKR |
4 |
0.065% |
ñ |
POL |
5 |
0.130% |
ò |
GER |
22 |
0.267 |
POL |
3 |
0.078% |
ñ |
USA |
14 |
4.046% |
ò |
RUS |
11 |
0.078% |
² |
POL |
3 |
0.078% |
ò |
CAN |
4 |
0.119% |
ò |
GBR |
16 |
0.262 |
RUS |
11 |
0.077% |
|
|
|
|
ò |
USA |
9 |
2.601% |
ó |
USA |
19 |
5.437% |
ò |
UKR |
5 |
0.082% |
ò |
POL |
8 |
0.208 |
GBR |
4 |
0.142% |
|
|
|
|
|
|
|
|
|
|
|
|
ò |
COL |
3 |
0.069% |
ò |
UKR |
9 |
0.197 |
USA |
15 |
4.371% |
|
|
|
|
|
|
|
|
|
|
|
|
² |
RSA |
3 |
0.061% |
ò |
RUS |
20 |
0.143 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ò |
USA |
18 |
5.151% |
ó |
USA |
28 |
0.091 |
|
Details of the country codes can be found here http://en.wikipedia.org/wiki/List_of_IOC_country_codes
Sorry the traffic light trend arrows only work in a browser that supports wingding fonts. i.e. IE9 but you should bet the idea of the trend from the arrows
Here are the same stats but in alphabetical order.
|
2006 |
|
|
2007 |
|
|
2008 |
|
|
2009 |
|
|
2010 |
|
|
2011 |
|
|
Country |
Riders |
% |
Rank |
Riders |
% |
Rank |
Riders |
% |
Rank |
Riders |
% |
Rank |
Riders |
% |
Rank |
Riders |
% |
Rank |
Argentina |
0 |
0.000 |
38 |
1 |
0.025 |
36 |
1 |
0.025 |
36 |
1 |
0.024 |
39 |
2 |
0.049 |
37 |
2 |
0.049 |
36 |
Australia |
19 |
0.938 |
11 |
24 |
1.175 |
11 |
17 |
0.832 |
12 |
17 |
0.800 |
11 |
24 |
1.129 |
8 |
28 |
1.317 |
8 |
Austria |
6 |
0.732 |
13 |
4 |
0.488 |
17 |
4 |
0.488 |
15 |
4 |
0.487 |
16 |
5 |
0.609 |
17 |
3 |
0.365 |
23 |
Belarus |
1 |
0.097 |
28 |
2 |
0.206 |
24 |
4 |
0.411 |
20 |
5 |
0.518 |
15 |
6 |
0.622 |
16 |
4 |
0.415 |
21 |
Belgium |
43 |
4.143 |
2 |
54 |
5.196 |
2 |
44 |
4.234 |
3 |
43 |
4.129 |
2 |
46 |
4.417 |
2 |
49 |
4.705 |
2 |
Brazil |
1 |
0.005 |
37 |
2 |
0.011 |
38 |
2 |
0.011 |
37 |
1 |
0.005 |
41 |
1 |
0.005 |
39 |
1 |
0.005 |
41 |
Canada |
3 |
0.091 |
29 |
1 |
0.030 |
35 |
1 |
0.030 |
34 |
4 |
0.119 |
31 |
4 |
0.119 |
33 |
2 |
0.060 |
35 |
China |
0 |
0.000 |
38 |
1 |
0.001 |
40 |
0 |
0.000 |
39 |
0 |
0.000 |
42 |
1 |
0.001 |
40 |
1 |
0.001 |
42 |
Columbia |
5 |
0.115 |
26 |
6 |
0.135 |
27 |
4 |
0.090 |
30 |
4 |
0.088 |
34 |
3 |
0.069 |
35 |
2 |
0.046 |
37 |
Costa Rica |
0 |
0.000 |
38 |
0 |
0.000 |
41 |
0 |
0.000 |
39 |
1 |
0.235 |
25 |
1 |
0.235 |
26 |
1 |
0.235 |
28 |
Croatia |
1 |
0.222 |
23 |
1 |
0.223 |
23 |
1 |
0.223 |
24 |
1 |
0.223 |
27 |
1 |
0.223 |
28 |
1 |
0.223 |
30 |
Cuba |
0 |
0.000 |
38 |
0 |
0.000 |
41 |
0 |
0.000 |
39 |
1 |
0.087 |
35 |
0 |
0.000 |
41 |
0 |
0.000 |
43 |
Czech Republic |
3 |
0.293 |
20 |
3 |
0.293 |
21 |
3 |
0.293 |
23 |
2 |
0.196 |
28 |
2 |
0.196 |
30 |
3 |
0.294 |
25 |
Denmark |
12 |
2.202 |
6 |
14 |
2.560 |
3 |
9 |
1.646 |
8 |
14 |
2.545 |
3 |
16 |
2.909 |
4 |
15 |
2.727 |
4 |
Estonia |
2 |
1.510 |
9 |
2 |
1.520 |
9 |
4 |
3.040 |
4 |
3 |
2.309 |
4 |
1 |
0.770 |
13 |
1 |
0.770 |
15 |
Finland |
3 |
0.573 |
15 |
2 |
0.382 |
20 |
2 |
0.382 |
21 |
2 |
0.381 |
20 |
2 |
0.381 |
21 |
2 |
0.381 |
22 |
France |
97 |
1.593 |
8 |
98 |
1.538 |
8 |
103 |
1.616 |
9 |
86 |
1.343 |
9 |
49 |
0.765 |
14 |
32 |
0.500 |
18 |
Germany |
41 |
0.497 |
17 |
41 |
0.498 |
16 |
39 |
0.473 |
17 |
28 |
0.340 |
22 |
29 |
0.352 |
23 |
22 |
0.267 |
26 |
Hungary |
1 |
0.100 |
27 |
1 |
0.100 |
29 |
1 |
0.100 |
28 |
1 |
0.101 |
32 |
0 |
0.000 |
41 |
1 |
0.101 |
34 |
Ireland |
3 |
0.739 |
12 |
2 |
0.487 |
18 |
2 |
0.487 |
16 |
2 |
0.476 |
17 |
2 |
0.476 |
19 |
4 |
0.952 |
12 |
Italy |
84 |
1.445 |
10 |
79 |
1.359 |
10 |
67 |
1.152 |
11 |
54 |
0.929 |
10 |
60 |
1.032 |
10 |
69 |
1.187 |
10 |
Japan |
1 |
0.008 |
36 |
1 |
0.008 |
39 |
0 |
0.000 |
39 |
1 |
0.008 |
40 |
1 |
0.008 |
38 |
1 |
0.008 |
40 |
Kazakhstan |
5 |
0.328 |
19 |
11 |
0.720 |
13 |
10 |
0.654 |
13 |
10 |
0.649 |
14 |
14 |
0.909 |
11 |
14 |
0.909 |
13 |
Latvia |
0 |
0.000 |
38 |
1 |
0.443 |
19 |
1 |
0.443 |
18 |
1 |
0.448 |
18 |
2 |
0.896 |
12 |
1 |
0.448 |
20 |
Lithuania |
2 |
0.558 |
16 |
1 |
0.280 |
22 |
2 |
0.559 |
14 |
1 |
0.281 |
24 |
1 |
0.281 |
25 |
3 |
0.844 |
14 |
Luxembourg |
4 |
8.431 |
1 |
4 |
8.329 |
1 |
4 |
8.329 |
1 |
3 |
6.100 |
1 |
5 |
10.167 |
1 |
4 |
8.134 |
1 |
Moldova |
0 |
0.000 |
38 |
0 |
0.000 |
41 |
0 |
0.000 |
39 |
1 |
0.231 |
26 |
1 |
0.231 |
27 |
1 |
0.231 |
29 |
Netherlands |
28 |
1.698 |
7 |
32 |
1.931 |
7 |
1 |
0.060 |
32 |
30 |
1.795 |
6 |
27 |
1.615 |
6 |
34 |
2.034 |
5 |
New Zealand |
1 |
0.245 |
22 |
3 |
0.729 |
12 |
28 |
6.803 |
2 |
3 |
0.712 |
12 |
5 |
1.187 |
7 |
5 |
1.187 |
11 |
Norway |
3 |
0.651 |
14 |
3 |
0.648 |
15 |
2 |
0.432 |
19 |
2 |
0.429 |
19 |
3 |
0.644 |
15 |
6 |
1.287 |
9 |
Poland |
3 |
0.078 |
30 |
3 |
0.078 |
32 |
4 |
0.104 |
27 |
3 |
0.078 |
36 |
5 |
0.130 |
32 |
8 |
0.208 |
31 |
Portugal |
3 |
0.283 |
21 |
1 |
0.094 |
30 |
2 |
0.188 |
25 |
2 |
0.187 |
29 |
4 |
0.374 |
22 |
6 |
0.560 |
17 |
Russia |
11 |
0.077 |
31 |
12 |
0.085 |
31 |
1 |
0.007 |
38 |
22 |
0.157 |
30 |
20 |
0.143 |
31 |
20 |
0.143 |
33 |
Slovakia |
1 |
0.184 |
24 |
1 |
0.184 |
25 |
11 |
2.019 |
6 |
2 |
0.366 |
21 |
3 |
0.549 |
18 |
4 |
0.732 |
16 |
Slovenia |
5 |
2.487 |
5 |
5 |
2.488 |
5 |
3 |
1.493 |
10 |
4 |
1.994 |
5 |
6 |
2.991 |
3 |
7 |
3.490 |
3 |
South Africa |
2 |
0.045 |
34 |
1 |
0.023 |
37 |
4 |
0.091 |
29 |
0 |
0.000 |
42 |
3 |
0.061 |
36 |
2 |
0.041 |
38 |
Spain |
104 |
2.574 |
4 |
85 |
2.101 |
6 |
77 |
1.904 |
7 |
70 |
1.727 |
7 |
68 |
1.678 |
5 |
60 |
1.481 |
7 |
Sweden |
4 |
0.444 |
18 |
6 |
0.664 |
14 |
3 |
0.332 |
22 |
6 |
0.662 |
13 |
4 |
0.442 |
20 |
3 |
0.331 |
24 |
Switzerland |
24 |
3.190 |
3 |
19 |
2.515 |
4 |
16 |
2.118 |
5 |
11 |
1.447 |
8 |
8 |
1.052 |
9 |
13 |
1.710 |
6 |
Ukraine |
7 |
0.150 |
25 |
8 |
0.173 |
26 |
7 |
0.151 |
26 |
4 |
0.065 |
37 |
5 |
0.082 |
34 |
9 |
0.147 |
32 |
United Kingdom |
4 |
0.066 |
32 |
7 |
0.115 |
28 |
5 |
0.082 |
31 |
6 |
0.098 |
33 |
13 |
0.213 |
29 |
16 |
0.262 |
27 |
United States |
15 |
0.050 |
33 |
14 |
0.046 |
34 |
9 |
0.030 |
35 |
19 |
0.311 |
23 |
18 |
0.295 |
24 |
28 |
0.458 |
19 |
Uzbekistan |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
0.036 |
39 |
Venezuela |
1 |
0.039 |
35 |
2 |
0.077 |
33 |
1 |
0.038 |
33 |
1 |
0.037 |
38 |
0 |
0.000 |
41 |
0 |
0.000 |
43 |
Next will be something similar but focusing on the Tour de France riders.
Posted in General Bike News, UCI Pro tour
Tags: AG2R La Mondiale, BMC, EUSKALTEL-EUSKADI, GARMIN-CERVELO, HTC-HIGHROAD, KATUSHA, LAMPRE - ISD, LEOPARD TREK, LIQUIGAS-CANNONDALE, MOVISTAR, OMEGA PHARMA-LOTTO, PRO TEAM ASTANA, QUICKSTEP, RABOBANK, RADIOSHACK, Road Racing, SAXO BANK SUNGARD, SKY PROCYCLING, Statistics, UCI Pro tour, VACANSOLEIL-DCM PRO
August 30th, 2009
Mark Croonen
Due to some over developed love of Excel I’ve put together some stats for the cycling component for the upcoming World Masters Games in Sydney this October. I pulled these numbers from the world masters site from a PDF as such I make no claim that these numbers are completely accurate.
Firstly the big numbers. The overall number of competitors and the country they come from.
Country |
Comp |
Australia |
2596 |
Austria |
6 |
Canada |
167 |
Colombia |
20 |
Czech Republic |
14 |
Estonia |
2 |
France |
18 |
Germany |
28 |
Guatemala |
4 |
India |
9 |
Italy |
15 |
Japan |
2 |
Kazakhstan |
3 |
Mexico |
6 |
Netherlands |
2 |
New Zealand |
109 |
Papau New Guinea |
2 |
Poland |
7 |
Russian Federation |
28 |
Singapore |
2 |
Slovakia |
2 |
Slovenia |
7 |
South Africa |
14 |
Trinidad and Tobago |
7 |
Turkey |
4 |
UK |
131 |
Uruguay |
3 |
USA |
126 |
Venezuela |
3 |
Total |
3337 |
Drilling down to the Track event, this is the breakdown of age and gender in each event. (click on the image to enlarge)

Next, the break down of competitors from each country per event.
Country |
Scratch |
Sprint |
Team Sprint |
Time Trial |
Pursuit |
Australia |
188 |
138 |
57 |
174 |
164 |
Canada |
11 |
9 |
4 |
12 |
11 |
Colombia |
4 |
|
2 |
1 |
3 |
Czech Republic |
1 |
1 |
|
1 |
1 |
Estonia |
|
1 |
|
1 |
|
France |
4 |
3 |
2 |
3 |
3 |
Germany |
4 |
4 |
|
4 |
4 |
India |
|
1 |
|
1 |
1 |
Italy |
3 |
3 |
3 |
3 |
3 |
Japan |
1 |
|
|
|
1 |
Mexico |
1 |
1 |
|
1 |
1 |
New Zealand |
5 |
5 |
1 |
6 |
7 |
Poland |
|
|
|
1 |
1 |
Russian Federation |
3 |
1 |
|
3 |
3 |
Slovenia |
1 |
|
1 |
1 |
1 |
South Africa |
3 |
3 |
|
2 |
2 |
Trinidad and Tobago |
1 |
1 |
|
1 |
1 |
UK |
19 |
15 |
7 |
18 |
15 |
USA |
13 |
15 |
8 |
17 |
11 |
|
262 |
201 |
85 |
250 |
233 |
Finally I was going to break it down to competitors based on age, gender and country per event but this was going to far and I think it’s a little too granular, also it takes up a lot of room. But here is a sample so you get the idea, if I get enough (any) feedback requesting more I may update article with the rest of the data
Age |
Gender |
Scratch |
Country |
No |
Sprint |
Country |
No |
30-34 |
Men |
5 |
Australia |
5 |
5 |
Australia |
5 |
|
Women |
2 |
Australia |
1 |
2 |
Australia |
1 |
|
|
|
Germany |
1 |
|
Germany |
1 |
35-39 |
Men |
36 |
Australia |
30 |
28 |
Australia |
23 |
|
|
|
Canada |
2 |
|
Italy |
1 |
|
|
|
Colombia |
1 |
|
South Africa |
2 |
|
|
|
Italy |
1 |
|
USA |
1 |
|
|
|
South Africa |
2 |
|
|
|
|
Women |
4 |
Australia |
3 |
4 |
Australia |
3 |
|
|
|
New Zealand |
1 |
|
New Zealand |
1 |
40-44 |
Men |
34 |
Australia |
28 |
21 |
Australia |
15 |
|
|
|
Italy |
2 |
|
Estonia |
1 |
|
|
|
Japan |
1 |
|
Italy |
2 |
|
|
|
Russian Federation |
1 |
|
USA |
3 |
|
|
|
UK |
1 |
|
|
|
|
|
|
USA |
1 |
|
|
|
|
Women |
4 |
Canada |
1 |
3 |
Canada |
1 |
|
|
|
Australia |
1 |
|
Australia |
1 |
|
|
|
USA |
2 |
|
USA |
1 |
|
|
|
|
|
|
|
|
Now turning to the Road (where the real cyclists are
)

The break down of competitors from each country per event.
Country |
Criterium |
Time Trial |
Road Race |
Australia |
577 |
537 |
761 |
Austria |
2 |
2 |
2 |
Canada |
28 |
45 |
46 |
Colombia |
4 |
2 |
4 |
Czech Republic |
3 |
4 |
3 |
France |
1 |
1 |
1 |
Germany |
3 |
5 |
4 |
Guatemala |
|
2 |
2 |
India |
2 |
2 |
2 |
Kazakhstan |
1 |
1 |
1 |
Mexico |
1 |
|
1 |
Netherlands |
1 |
|
1 |
New Zealand |
21 |
26 |
38 |
Papau New Guinea |
|
1 |
1 |
Poland |
2 |
1 |
2 |
Russian Federation |
6 |
6 |
6 |
Singapore |
|
1 |
1 |
Slovakia |
|
1 |
1 |
Slovenia |
1 |
1 |
1 |
South Africa |
2 |
1 |
1 |
Trinidad and Tobago |
1 |
1 |
1 |
Turkey |
2 |
2 |
2 |
UK |
19 |
18 |
20 |
Uruguay |
1 |
1 |
1 |
USA |
22 |
17 |
23 |
Venezuela |
1 |
1 |
1 |
Grand Total |
699 |
679 |
927 |
So there you go, know you know where the competition is coming from. To those going, see you there, should be a hoot.
Posted in General Bike News
Tags: Racing, Statistics, World Masters Games