The following statistics on CrossFit affiliates were compiled using data from games.crossfit.com. All stats are current as of 10/26/2014.
The accuracy of this data is unknown. In my opinion, it is probably very accurate, it just hasn’t been confirmed or verified by CrossFit. But I have no reason to believe that there are affiliates missing from their website, or that the data there is inaccurate.
Interesting Results
118 different countries have affiliates (if you include Antarctica as a country). Yes there is one, Deep Freeze CrossFit, in Antarctica at McMurdo Station! (I’m interviewing this box owner and coach in a later blog post.)
There are only 17 affiliates in China and Hong Kong for 1.4B people… talk about expansion opportunities…
The most competitive region in terms of number of boxes per spot at the Games is Latin America with 698 boxes and only 1 Games spot. Surprising how poorly Latin America has performed at the Games so far with such a large talent pool. The easiest region should be Canada West with only 192 boxes and 2 spots (96 boxes per individual competitor Games spot). Of course quality of the coaches and athletes, and popularity of the sport are bigger factors when it comes to region competitiveness rather than raw number of participants. The Central East region, home to 5 of the top 15 male world finishers in the 2014 regionals, is widely recognized as the most competitive region but but the size of the region’s talent pool is quite average (about 200 boxes per Games spot).
Iceland has the most boxes per capita with 11 affiliates for only 343,000 people (34.1 boxes per million people). I’m sure this has nothing to do with 2-time Games champ Annie Thorisdottir being from there. New Zealand and Australia are next with about 25 boxes per million people. The US ranks behind them at around 20 boxes per million people.
Pasadena, CA, where the author of this blog lives, has 3x as many boxes than the US average. That’s about 60 boxes per million people. (There are 9 affiliates for a population of 140,000.)
Total Number of Affiliates: 11,002
Affiliates by Region
Region |
Number of Affiliates |
Number of Spots at 2014 Games |
Boxes Per Spot |
Africa |
162 |
1 |
162 |
Asia |
312 |
1 |
312 |
Australia |
696 |
3 |
232 |
Canada East |
319 |
2 |
160 |
Canada West |
192 |
2 |
96 |
Central East |
617 |
3 |
206 |
Europe |
1714 |
3 |
571 |
Latin America |
698 |
1 |
698 |
Mid Atlantic |
784 |
3 |
261 |
North Central |
807 |
3 |
269 |
North East |
850 |
3 |
283 |
North West |
481 |
3 |
160 |
Northern California |
412 |
3 |
137 |
South Central |
847 |
3 |
282 |
South East |
946 |
3 |
315 |
South West |
561 |
3 |
187 |
Southern California |
544 |
3 |
181 |
Total Countries Represented: 118*
*If you include Antarctica.
Affiliates by Country
Country |
Number of Boxes |
Population (M) |
Boxes per million people |
Afghanistan |
30 |
30.55 |
0.98 |
Albania |
0 |
– |
– |
Algeria |
0 |
– |
– |
American Samoa |
0 |
– |
– |
Andorra |
1 |
0.08 |
12.50 |
Angola |
0 |
– |
– |
Anguilla |
0 |
– |
– |
Antarctica |
1 |
0.01 |
200.00 |
Antigua & Barbuda |
0 |
– |
– |
Argentina |
98 |
41.50 |
2.36 |
Armenia |
0 |
– |
– |
Aruba |
3 |
1.02 |
2.94 |
Australia |
578 |
23.13 |
24.99 |
Austria |
30 |
8.47 |
3.54 |
Azerbaijan |
0 |
– |
– |
Bahamas, The |
0 |
– |
– |
Bahrain |
6 |
1.33 |
4.50 |
Bangladesh |
0 |
– |
– |
Barbados |
2 |
2.84 |
0.70 |
Belarus |
0 |
– |
– |
Belgium |
34 |
11.20 |
3.04 |
Belize |
2 |
3.31 |
0.60 |
Benin |
0 |
– |
– |
Bermuda |
2 |
0.01 |
307.69 |
Bhutan |
0 |
– |
– |
Bolivia |
3 |
10.67 |
0.28 |
Bosnia and Herzegovina |
1 |
3.83 |
0.26 |
Botswana |
0 |
– |
– |
Brazil |
273 |
200.40 |
1.36 |
British Virgin Is. |
0 |
– |
– |
Brunei Darussalam |
4 |
4.17 |
0.96 |
Bulgaria |
1 |
7.27 |
0.14 |
Burkina Faso |
0 |
– |
– |
Burma |
0 |
– |
– |
Burundi |
0 |
– |
– |
Cambodia |
1 |
15.14 |
0.07 |
Cameroon |
0 |
– |
– |
Canada |
517 |
35.16 |
14.70 |
Cape Verde |
0 |
– |
– |
Cayman Islands |
3 |
0.58 |
5.17 |
Central African Rep. |
0 |
– |
– |
Chad |
0 |
– |
– |
Chile |
28 |
17.62 |
1.59 |
China |
8 |
1357.00 |
0.01 |
Colombia |
33 |
48.32 |
0.68 |
Comoros |
0 |
– |
– |
Congo, Dem. Rep. |
0 |
– |
– |
Congo, Repub. of the |
0 |
– |
– |
Cook Islands |
1 |
0.11 |
9.17 |
Costa Rica |
29 |
4.87 |
5.95 |
Cote d’Ivoire |
0 |
– |
– |
Croatia |
5 |
4.25 |
1.18 |
Croatia |
0 |
– |
– |
Cuba |
0 |
– |
– |
Cyprus |
10 |
1.14 |
8.76 |
Czech Republic |
10 |
10.52 |
0.95 |
Denmark |
48 |
5.61 |
8.55 |
Djibouti |
1 |
8.72 |
0.11 |
Dominica |
0 |
– |
– |
Dominican Republic |
7 |
10.40 |
0.67 |
East Timor |
0 |
– |
– |
Ecuador |
29 |
15.74 |
1.84 |
Egypt |
8 |
82.06 |
0.10 |
El Salvador |
1 |
6.34 |
0.16 |
Equatorial Guinea |
0 |
– |
– |
Eritrea |
0 |
– |
– |
Estonia |
2 |
1.33 |
1.51 |
Ethiopia |
0 |
– |
– |
Faroe Islands |
1 |
0.00 |
204.08 |
Fiji |
0 |
– |
– |
Finland |
45 |
5.44 |
8.27 |
France |
82 |
66.03 |
1.24 |
French Guiana |
0 |
– |
– |
French Polynesia |
0 |
– |
– |
Gabon |
0 |
– |
– |
Gambia, The |
0 |
– |
– |
Gaza Strip |
0 |
– |
– |
Georgia |
0 |
– |
– |
Germany |
155 |
80.62 |
1.92 |
Ghana |
0 |
– |
– |
Gibraltar |
0 |
– |
– |
Greece |
22 |
11.03 |
1.99 |
Greenland |
1 |
0.01 |
178.57 |
Grenada |
1 |
0.11 |
9.52 |
Guadeloupe |
0 |
– |
– |
Guam |
9 |
0.17 |
54.55 |
Guatemala |
13 |
15.47 |
0.84 |
Guernsey |
0 |
– |
– |
Guinea |
0 |
– |
– |
Guinea-Bissau |
0 |
– |
– |
Guyana |
0 |
– |
– |
Haiti |
0 |
– |
– |
Honduras |
4 |
8.10 |
0.49 |
Hong Kong |
9 |
7.19 |
1.25 |
Hungary |
9 |
9.90 |
0.91 |
Iceland |
11 |
0.32 |
34.1 |
India |
16 |
1252.00 |
0.01 |
Indonesia |
4 |
249.90 |
0.02 |
Iran |
0 |
– |
– |
Iraq |
7 |
33.42 |
0.21 |
Ireland |
42 |
4.60 |
9.14 |
Isle of Man |
1 |
0.85 |
1.18 |
Israel |
34 |
8.06 |
4.22 |
Italy |
173 |
59.83 |
2.89 |
Jamaica |
0 |
– |
– |
Japan |
18 |
127.30 |
0.14 |
Jersey |
1 |
0.97 |
1.03 |
Jordan |
5 |
6.46 |
0.77 |
Kazakhstan |
3 |
17.04 |
0.18 |
Kenya |
0 |
– |
– |
Kiribati |
0 |
– |
– |
Korea |
28 |
50.22 |
0.56 |
Kuwait |
13 |
3.37 |
3.86 |
Kuwait |
0 |
– |
– |
Kyrgyzstan |
0 |
– |
– |
Laos |
0 |
– |
– |
Latvia |
4 |
2.01 |
1.99 |
Lebanon |
4 |
4.47 |
0.90 |
Lesotho |
0 |
– |
– |
Liberia |
1 |
4.29 |
0.23 |
Liberia |
0 |
– |
– |
Libya |
0 |
– |
– |
Liechtenstein |
0 |
– |
– |
Lithuania |
0 |
– |
– |
Luxembourg |
4 |
5.43 |
0.74 |
Macau |
0 |
5.63 |
0.36 |
Macedonia |
1 |
2.11 |
0.47 |
Madagascar |
0 |
– |
– |
Malawi |
0 |
– |
– |
Malaysia |
10 |
29.72 |
0.34 |
Maldives |
0 |
– |
– |
Mali |
0 |
– |
– |
Malta |
3 |
42.32 |
0.07 |
Marshall Islands |
0 |
– |
– |
Martinique |
1 |
38.64 |
0.03 |
Mauritania |
0 |
– |
– |
Mauritius |
0 |
– |
– |
Mayotte |
0 |
– |
– |
Mexico |
94 |
122.30 |
0.77 |
Micronesia, Fed. St. |
0 |
– |
– |
Moldova |
0 |
– |
– |
Monaco |
0 |
– |
– |
Mongolia |
0 |
– |
– |
Montserrat |
0 |
– |
– |
Morocco |
2 |
33.01 |
0.06 |
Mozambique |
0 |
– |
– |
N. Mariana Islands |
0 |
– |
– |
Namibia |
4 |
2.30 |
1.74 |
Nauru |
0 |
– |
– |
Nepal |
0 |
– |
– |
Netherlands |
89 |
16.80 |
5.30 |
Netherlands Antilles |
2 |
2.27 |
0.88 |
New Caledonia |
0 |
– |
– |
New Zealand |
117 |
4.47 |
26.17 |
Nicaragua |
2 |
6.08 |
0.33 |
Niger |
0 |
– |
– |
Nigeria |
0 |
– |
– |
Norway |
59 |
5.08 |
11.61 |
Oman |
2 |
3.63 |
0.55 |
Pakistan |
0 |
– |
– |
Palau |
0 |
– |
– |
Panama |
13 |
3.86 |
3.36 |
Papua New Guinea |
0 |
– |
– |
Paraguay |
6 |
6.80 |
0.88 |
Peru |
10 |
30.38 |
0.33 |
Philippines |
17 |
98.39 |
0.17 |
Poland |
31 |
38.53 |
0.80 |
Portugal |
42 |
10.46 |
4.02 |
Puerto Rico |
28 |
3.62 |
7.75 |
Qatar |
5 |
2.17 |
2.31 |
Reunion |
1 |
8.40 |
0.12 |
Romania |
3 |
19.96 |
0.15 |
Russia |
2 |
143.50 |
0.01 |
Russian Federation |
35 |
143.50 |
0.24 |
Rwanda |
0 |
– |
– |
Saint Helena |
0 |
– |
– |
Saint Kitts & Nevis |
0 |
– |
– |
Saint Lucia |
0 |
– |
– |
Saint Martin |
1 |
7.48 |
0.13 |
Saint Vincent and the Grenadines |
0 |
– |
– |
Samoa |
0 |
– |
– |
San Marino |
1 |
31.73 |
0.03 |
Sao Tome & Principe |
0 |
– |
– |
Saudi Arabia |
3 |
28.70 |
0.10 |
Senegal |
0 |
– |
– |
Serbia |
4 |
7.20 |
0.56 |
Seychelles |
0 |
– |
– |
Sierra Leone |
0 |
– |
– |
Singapore |
11 |
5.47 |
2.01 |
Slovakia |
7 |
5.40 |
1.30 |
Slovenia |
1 |
1.89 |
0.53 |
Solomon Islands |
0 |
– |
– |
Somalia |
0 |
– |
– |
South Africa |
142 |
51.80 |
2.74 |
South Korea |
1 |
50.00 |
0.02 |
Spain |
173 |
46.70 |
3.70 |
Sri Lanka |
0 |
– |
– |
St Pierre & Miquelon |
0 |
– |
– |
St. Maaarten |
1 |
1.30 |
0.77 |
Sudan |
0 |
– |
– |
Suriname |
0 |
– |
– |
Swaziland |
0 |
– |
– |
Sweden |
124 |
9.42 |
13.16 |
Switzerland |
50 |
8.02 |
6.23 |
Syria |
0 |
– |
– |
Taiwan |
1 |
23.40 |
0.04 |
Tajikistan |
0 |
– |
– |
Tanzania |
0 |
– |
– |
Thailand |
8 |
61.50 |
0.13 |
Togo |
0 |
– |
– |
Tonga |
0 |
– |
– |
Trinidad & Tobago |
0 |
– |
– |
Trinidad and Tobago |
1 |
1.30 |
0.77 |
Tunisia |
0 |
– |
– |
Turkey |
14 |
76.60 |
0.18 |
Turkmenistan |
0 |
– |
– |
Turks & Caicos Is |
0 |
– |
– |
Tuvalu |
0 |
– |
– |
Uganda |
0 |
– |
– |
Ukraine |
4 |
45.40 |
0.09 |
United Arab Emirates |
24 |
9.20 |
2.61 |
United Kingdom |
395 |
64.00 |
6.17 |
United States |
6867 |
317.00 |
21.66 |
Uruguay |
3 |
3.30 |
0.91 |
Uzbekistan |
0 |
– |
– |
Vanuatu |
0 |
– |
– |
Venezuela |
0 |
– |
– |
Venezuela, Bolivarian Republic of |
2 |
27.00 |
0.07 |
Vietnam |
1 |
89.70 |
0.01 |
Virgin Islands, U.s. |
1 |
10.64 |
0.09 |
Wallis and Futuna |
0 |
– |
– |
West Bank |
0 |
– |
– |
Western Sahara |
0 |
– |
– |
Yemen |
0 |
– |
– |
Zambia |
1 |
14.58 |
0.07 |
Zimbabwe |
3 |
14.59 |
0.21 |
Affiliates by State
State |
Number of Boxes |
Population (M) |
Box per Million-People |
IN |
91 |
6.6 |
14 |
NY |
281 |
19.7 |
14 |
ND |
10 |
0.7 |
14 |
MI |
147 |
9.9 |
15 |
WI |
86 |
5.7 |
15 |
WV |
29 |
1.9 |
15 |
KY |
69 |
4.4 |
16 |
NE |
30 |
1.9 |
16 |
SD |
13 |
0.8 |
16 |
MO |
101 |
6.0 |
17 |
MS |
51 |
3.0 |
17 |
MN |
92 |
5.4 |
17 |
IL |
221 |
12.9 |
17 |
TN |
112 |
6.5 |
17 |
PA |
221 |
12.8 |
17 |
MD |
102 |
5.9 |
17 |
NM |
35 |
2.0 |
18 |
OH |
203 |
11.6 |
18 |
DE |
16 |
0.9 |
18 |
KS |
53 |
2.9 |
18 |
IA |
58 |
3.0 |
19 |
ME |
26 |
1.3 |
20 |
OK |
79 |
3.9 |
20 |
RI |
20 |
1.0 |
20 |
NJ |
191 |
8.9 |
21 |
AZ |
142 |
6.6 |
22 |
VA |
179 |
8.3 |
22 |
AL |
104 |
4.8 |
22 |
AR |
65 |
3.0 |
22 |
NC |
216 |
9.8 |
22 |
SC |
106 |
4.7 |
23 |
VT |
14 |
0.6 |
23 |
CA |
907 |
38.3 |
24 |
FL |
476 |
20.0 |
24 |
LA |
112 |
4.6 |
24 |
NV |
71 |
2.8 |
25 |
TX |
684 |
26.4 |
26 |
GA |
261 |
10.0 |
26 |
CT |
94 |
3.6 |
26 |
NH |
35 |
1.3 |
27 |
MA |
184 |
6.7 |
27 |
MT |
29 |
1.0 |
29 |
OR |
123 |
3.9 |
32 |
ID |
53 |
1.6 |
33 |
HI |
48 |
1.4 |
34 |
UT |
105 |
2.9 |
36 |
DC |
23 |
0.6 |
38 |
WA |
273 |
7.0 |
39 |
CO |
209 |
5.3 |
39 |
AK |
28 |
0.7 |
40 |
WY |
24 |
0.6 |
40 |
We see it all the time on the whiteboard – a WOD that includes a barbell movement with a specific weight prescribed like “3 power cleans (185/135).” But these weights would crush some athletes and bore others to death. Scaling is the answer, but how do we properly scale? Couldn’t we just define the weight as % of 1RM instead? Wouldn’t this be a better way for the coach to convey the intensity they are looking for, thus improving the effectiveness of the workout while also lowering the risk of injury?
At my box, most members can clean that weight, but many would certainly risk injury using it for medium to high rep counts. At the other end of the spectrum are folks like Dmitry Klokov, who would be bored out of his mind using 185 lbs. for anything other than a toothbrush.
So we scale. Everything in CrossFit is scaled so no one gets hurt. The typical way to scale is to pick a weight you find challenging but won’t kill you. The problem with this is that ‘challenging but not deadly’ is rarely how the WOD was actually designed.
Each WOD is designed with a specific level of intensity in mind. Are those cleans supposed to be done slowly and methodically with maybe some rest between reps, or are they supposed to be fast and speedy? When prescribing “185/135,” what ‘model’ man and woman did the WOD designer have in mind? You have to know one of these two things in order to understand what “185/135” really means.
The best way to prescribe a weight to ensure all athletes give the effort a coach is looking for is to prescribe it as a % of 1RM. If you want those power cleans quick and speedy, maybe it’s 50%. Or if you want them heavy and gut-wrenching, 90%. Now you have all athletes giving the exact amount of effort you were looking for when designing the WOD. And the athletes no longer have to fumble around trying to guess what the coach wants or who the ‘model’ athlete is that the coach had in mind when designing the WOD.
When writing “185 lbs” on the whiteboard, invariably coaches are asked by the class “is it supposed to be really heavy, moderate, or kind of light?” So why not just state exactly how you want it done by specifying the weight as % of 1RM instead. By using % of 1 RM, coaches can get the athletes to use the exact amount of intensity they were looking for.
Of course we can still score athletes based on the actual weight used. And if an athlete chooses to go heavy or light on their own, more power to them. But at least we’ll have an accurate starting point by using % of 1RM.
This isn’t to say we should change the way we define our benchmark WODs like Fran and Grace – having a specific weight defined is useful in this case for comparing and benchmarking athletes and performance. Nothing wrong with that.
But for regular training sessions, writing “185/135” on the whiteboard is meaningless. There is always a level of intensity in mind with the weight. The only way to convey that intention is by using % of 1RM. We don’t tell all athletes to squat precisely 315 lbs in the strength portion of class, so why do we do it during the metcon/WOD? There’s no advantage to doing it this way and every advantage to switch to using % of 1RM.