Open Ranking Dropping Every Year Despite Getting Fitter? BeastScore Can Help

As CrossFit’s membership continues to grow each year, Open rankings become less meaningful for many athletes as their ranking drops year after year, even though their fitness isn’t getting worse. This is a result of more people joining the leaderboards – being top 10% one year might mean a ranking of 20,000, and the next year top 10% is 30,000. So even though you’re staying in the top 10%, your ranking plummeted 10,000 spots. If you find that frustrating, you may want to consider using BeastScore to track your fitness instead.

BeastScore is an absolute measurement system – just like pounds for back squat or seconds for a sprint. It has no dependence on the performance of other athletes like a ranking does. So if your fitness improves, then so does your BeastScore. It takes the guesswork out of trying to understand how your fitness is changing over time or with respect to confusing leaderboard rankings. (More on how BeastScore is calculated.)

BeastScore is also not biased towards gender, weight, height, age, or anything else. A higher BeastScore means more capacity to do work. It’s a direct measure of one’s ability without bias. So if you have a BeastScore higher than Sam Briggs, then more often than not, you would out perform her in a competition involving the “unknown and unknowable.” Your higher BeastScore indicates most prepared for whatever may come out of the hopper. And you know you are getting fitter as your BeastScore increases over time, regardless of what happens with your Open rank.

Before somebody kills themselves, let’s change how we prescribe weights in CrossFit

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.

Stats on CrossFit Affiliates

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