CrossFit Open 2019 Analysis - Benchmark Workouts
Posted on June 15, 2019 in CrossFit, Python
I've collected profile data for the 1,706,699 CrossFit athletes that have ever (presumably) participated in the CrossFit Open. Having all these statistics in one place is useful as a reference for comparison, and interesting to see how Open athletes fill their profile.
In this article, we'll take a final look at some benchmark CrossFit workouts included in the athlete self-reported statistics. These include the famous Fran and Grace, as well as some more obscure ones like Filthy Fifty.
It is important to remember that these statistics are all optional and self-reported. Right off the bat, the biggest problem is that people will typically not report the numbers they aren't proud of, or at least massage the truth a little. It might be as simple as rounding off a number here and there. So it's important to take this analysis with a grain of salt, and not treat it like data collected to write a scientific paper. We can still draw some very important conclusions from the analysis, keeping in mind that the numbers are probably rounded, excluded, outdated or outright false, all in order to look good. I'll do my best to include the sample size and trends in the analyses.
Goal¶
The reason behind this analysis is — you've guessed it, trying to optimize my training and working on my weaknesses. The sport of CrossFit is about being an all-round athlete, so if I see that I'm in the 15th percentile for one movement but 85th in another, I'll want to work on the first one instead of the second. Of course, any coach worth his/her salt would be able to tell you this, but it's another to be able to put a number on it. Put succintly:
To provide CrossFit athletes with a tool to see where their performance sits in regards to a small number of exercise standards and benchmarks. With this tool, athletes should be able to see:
- What their strengths and weaknesses are compared to other CrossFit Open participants
- Where they should focus their training for next season
Methodology¶
I scraped these 1.7M+ athlete profiles from the CrossFit Games profiles over the course of several days. A more thorough going into the actual methodology used to scrape, parse and load that data into a database will be the subject of another article.
Variables¶
We find a small number of athlete stats on their profile page:
- Athlete Stats
- Age
- Sex
- Height
- Weight
- Weightlifting
- Back Squat
- Clean and Jerk
- Snatch
- Deadlift
- Benchmark Workouts
- Fight Gone Bad
- Fran
- Grace
- Helen
- Filthy 50
- Bodyweight Exercices
- Max Pull-ups
- Sprint 400m
- Run 5k
As this list can be quite extensive, we'll be splitting this post into the four sections above. Another reason for this is quite simple: the interactive visualization package I'm using to make the graphs make for very large images, and I want to keep load times to a respectable minimum.
Inspiration¶
This post is largely inspired by Sam Swift's 2015 post called "What's normal (or top 5%) for a CrossFit athlete?" and a huge thank you to him for having created it in the first place. Be sure to check out his other posts on the sport, they're the best out there, bar none.
I won't be using his data directly nor do I have the time to make comparisons across the four years separating the posts, but it'd certainly be interesting to see how the sport has evolved with time.
Pre-processing¶
Even though a lot of the pre-processing was done in the steps leading to this analysis (see upcoming post on scraping methodology), we still have to deal with assigning sexes to participants, and with the all-important question of how to deal with missing values.
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly_express as px
from scipy import stats
import seaborn as sns
import sqlalchemy as sql
from IPython.display import display_html
Pull the data¶
In this case, our data was saved as a table in a PostgreSQL database running locally.
URI_DB = "postgres://leblancfg@localhost:5432/cf_analysis"
db = sql.create_engine(URI_DB)
df = pd.read_sql("cf_athletes", db, index_col="id")
As we can see, a lot of the athletes simply input their name, division and age; they either don't bother entering their stats, or don't know them well enough.
fran = bm.query("45 < fran < 800")
px.histogram(
fran,
"fran",
nbins=64,
color="sex",
barmode="group",
title="CrossFit Open 2019 Athlete Pages — Distribution % of Fran Workout Times, by Sex",
marginal="box",
color_discrete_sequence=COLOUR_SCHEME,
)
describe(fran, "fran")
We see here first a very big difference in overall numbers between men and women, as tracking — or at least entering — those numbers seem to be much more popular with the former.
For the men, we see a peak for the 175-179 second bin, where we see athletes pushing to get their times under 3 minutes. A lofty goal indeed! There are similar but less pronounced peaks at and before round numbers.
For the women, that elongated peak shape is more evenly distributed around five minutes, with many athletes under than all-important 5 minute mark.
Grace¶
Another classic, Grace is a deceptively simple benchmark, consisting of:
30 clean-and-jerks of 135/95 lbs, for time.
Nothing more, nothing less.
grace = bm.query("45 < grace < 600")
px.histogram(
grace,
"grace",
nbins=64,
color="sex",
barmode="group",
title="CrossFit Open 2019 Athlete Pages — Distribution % of Grace Workout Times, by Sex",
marginal="box",
color_discrete_sequence=COLOUR_SCHEME,
)
describe(grace, "grace")
Similar to Fran, we find skewed distributions with peaks at and before the minute marks.
For men, a good first milestone would be at the three minute mark, and a lump of elite athletes peaking under two minutes.
For the women, that ridge in the under-three-but-over-two minute numbers is smooshed out further, where the median is still just under the 3-minute mark.
Helen¶
An all-time favorite of mine, Helen consists of:
3 rounds for time:
- 400 m run
- 21 american kettlebell swings (55/36 lb)
- 12 pull-ups
helen = bm.query("250 < helen < 1200")
px.histogram(
helen,
"helen",
nbins=64,
color="sex",
barmode="group",
title="CrossFit Open 2019 Athlete Pages — Distribution % of Helen Workout Times, by Sex",
marginal="box",
color_discrete_sequence=COLOUR_SCHEME,
)
describe(helen, "helen")
Now, here it sure looks like there's more room to play than the previous two! We see a log-normal distribution here, but with extremely wider tails compared to the other two benchmark workouts. The fact that we see three modalities (run, KBS, PU) in here to me indicates that it's harder to optimize it to a science.
For men, the mean is at 10 m 11 s, and the median is at 9 m 47 s. We see the largest peak right under 9 minutes, and another big peak right under the 8 minute mark, at 479 seconds.
For women, the mean is at 11 m 28 s, and the median at 10 m 37 s. The leargest peak is this case is rght under the 10 minute mark.
CrossFit Benchmarks¶
Filthy50¶
This one was new to me as I had never heard of it before, but seems dreadful in a beautiful kind of way. I'm actually excited to try it soon! The Filthy Fifty is:
For Time:
- 50 Box Jumps (24/20 in)
- 50 Jumping Pull-Ups
- 50 Kettlebell Swings (1/.75 pood)
- 50 Walking Lunges
- 50 Knees-to-Elbows
- 50 Push Press (45/35 lb)
- 50 Back Extensions
- 50 Wall Balls (20/14 lb)
- 50 Burpees
- 50 Double-Unders
Obviously a good test of overall athletic ability, the F50 number seems like an excellent single number to use to determine both stamina and technical ability across a wide range of exercises. Even though it does not contain any "heavy" movements, it feels like the kind of benchmark you want to focus on coming into the Open, for example.
filt50 = bm.query("500 < filthy50 < 3000")
px.histogram(
filt50,
"filthy50",
nbins=128,
color="sex",
barmode="group",
title="CrossFit Open 2019 Athlete Pages — Distribution % of Filthy Fifty Workout Times, by Sex",
marginal="box",
color_discrete_sequence=COLOUR_SCHEME,
)
describe(filt50, "filthy50")
For once, we see similar time numbers for both men and women, at least compared to the other workouts. Again though, we see higher "peakedness" for the men, with the mode right under the 25-minute mark. For the men, under 22 minutes will put you in the 25% percentile, and under 23 minutes 30 seconds for women.
Fight Gone Bad¶
In what seems to be one of the hardest "old-school" benchmarks, lastly comes the Fight Gone Bad. A sprint-your-a$s-off-but-pace-yourself workout combining a shoulder-heavy mix of exercices that takes lots of strategy, planning, and mental fortitude, Fight Gone Bad represents a good number for competition potential and capacity to approach workouts with high numbers in mind.
3 Rounds For Total Reps in 17 minutes
- 1 minute Wall Balls (20/14 lb)
- 1 minute Sumo Deadlift High-Pulls (75/55 lb)
- 1 minute Box Jumps (20 in)
- 1 minute Push Press (75/55 lb)
- 1 minute Row (calories)
- 1 minute Rest
fgb = bm.query("100 < fgonebad < 600")
px.histogram(
fgb,
"fgonebad",
nbins=128,
color="sex",
barmode="group",
title="CrossFit Open 2019 Athlete Pages — Distribution % of Fight Gone Bad Workout Times, by Sex",
marginal="box",
color_discrete_sequence=COLOUR_SCHEME,
)
describe(fgb, "fgonebad")
We see here large peaks at 250, 300 and 400, and apparently some participants cranking numbers past 500. An interesting metric in itself, I have the impression that I would use this benchmark in a later blog post focusing on predicting factors for high placements in the Open.