Monday, February 23, 2015

So, I'm On Twitter Now...

Yeah, yeah, I know, I should have done this like 24 months ago... better late than never, right?  Follow me @CFGAnalysis to stay updated on what's going on in CFG Analysis world.

Tuesday, February 17, 2015

Fun with SWAGs: What Will 15.1 Be?

It's officially 2015 Open season, everyone, and that means one thing: making highly unlikely yet completely overconfident predictions about what the workouts will be.  This year we'll be unveiling the SWAG each week on the site, but the explanation of my thought process will be in the podcast for those who are interested (which should be all of you!).  My buddy and frequent commenter John Nail will join me each week on the podcast as we break down the prior week's workout and look ahead to what might be to come.

So without further ado, the official CFG Analysis SWAG for 15.1 (Rx'd division) is:

AMRAP 7 of burpee-box jumps (30"/24")

I encourage everyone to post your SWAGs to comments, since odds are you'll do better than me.

[NOTE 2/19/2015: I re-uploaded the podcast last night with the tracks mixed more evenly, so hopefully this fixes the issue that some listeners had with the original.]

Saturday, February 7, 2015

CFG Analysis Podcast - Episode 2 - Open Preview

In today's episode of the CFG Analysis Podcast, I'm joined by fellow CrossFit athlete John Nail to discuss what might be coming up in the 2015 Open.  John and I have worked out together for years and have gone HAM on many a WOD in that time.  John is best known on the site here for his excellent (though generally incorrect) SWAG's during the Open season.

The Pilot Podcast and all future episodes can be found at  or on iTunes.  I'll likely continue to post each new episode of the podcast directly on this site as well.

Also, as you may have noticed below, I'm not so great when it comes to creating artwork on a computer.  If anyone out there is so inclined to create a better image for the podcast, feel free to shoot it my way.  1,400x1,400 in .jpg format, please.

Saturday, January 24, 2015

CFG Analysis Podcast - Pilot Episode

I'm trying something new here at CFG Analysis.  In addition to the articles I'm posting, I'm adding a podcast to complement what I do with my written work.  These podcasts are going to be much more informal than what I typically do on the blog, and eventually I hope to chat with some athletes, coaches and everyday CrossFitters.

Today's Pilot Episode is just me, discussing the background on this site, how it came to be and who exactly I am.

Stay tuned for our next podcast here in a couple of weeks, and bear with me as I get the hang of this podcast thing.

The Pilot Podcast and all future episodes can be found here:  or below.  I'm currently working on getting it set up on iTunes.

Sunday, January 4, 2015

How Many Years Do CrossFit Athletes Last in the Open?

What you need to know from this post:
  • About 50% of athletes who compete in the Open on year will continue to the following season.
  • Athletes who have competed for multiple years have a higher likelihood of returning the next season than athletes who are competing in their first season.
  • Based on data from the past four years, approximately 20% of athletes who start competing in the Open will still be competing in their fourth season. I estimate that approximately 10% will still be competing in their seventh season.
  • The higher an athlete ranks in the Open, the higher the probability that they will return the next season.
As I mentioned a few weeks ago, I will be missing the Open for the first time this season.  I had arthroscopic hip surgery to repair a torn labrum.  Without getting into too much detail, the injury was a chronic wear-and-tear type of injury that wasn't a direct result of CrossFit, per se, but rather the fact that I had been so active for the past 10-15 years.  I had a hip impingement that I was born with that put me at risk for this type of injury.  I hope to return to CrossFit eventually, but I won't be ready to compete by late February.

So although my injury isn't directly attributable to CrossFit, it's forced me to face the fact that competing and training at the level I had been is not always easy to sustain.  For me, it was my hip.  For others, it is a shoulder or a knee.  This is true in any sport, and CrossFit is not immune.  This is not a commentary on whether CrossFit as a training methodology is dangerous.  That's a third rail I don't intend to touch.  My point is simply that injuries are inevitable when competing in any serious sport.

With an assist from my wife, who came up with the idea for this post, I decided to try to answer the the question: how long do CrossFit athletes tend to compete?  I'm not talking just about the Rich Fronings of the world, but the everyday athlete who signs up for the Open with no hope of even sniffing Regionals.  We see the Open growing in size each year, but that doesn't necessarily mean athletes are continuing on for multiple years; we could just be replacing the vast majority of athletes one year with an even bigger crop of newbies.

Of course there are multiple reasons an athlete won't compete: injury, lack of interest, disappointment from poor performance in a previous season, work commitments, etc.  With the data I have available, I can't identify which factors are most important, but I can get a pretty good idea of the rate at which athletes are dropping off.

Using the Open data from 2011-2014, I looked at how many athletes continued on each year and what attributes about the athlete (prior Open experience, prior Open finish, age, weight, height) may have an influence on their likelihood to continue.  Because I only have athlete names without any other identifier, this analysis is limited to "truly unique names," which are those that never appeared more than once in any year.  I also focused my analysis on men, because women's names are frequently changed due to marriage or divorce, making it hard to track which athletes truly dropped out and which simply changed name.*

The way I am defining it, an athlete successfully survives from one year to the next if he completes all the Open events in both years.  Failing to complete all the Open events in a given year is counted the same as not competing.  Keep that in mind, since typically about 30% of the Open field from week 1 is gone by the end of the Open.  For purposes of this analysis, those athletes never even competed.  I don't care that HQ still got their money.

Finally, in this study, once you're out, you're out.  If an athlete skips a year, I ignore whether or not they returned the following year.  This makes things much cleaner for the analysis.  In case you are wondering, only about 15% of athletes skip a year and return in a subsequent year (I hope to be one of those 15%!).

OK, so let's get to the results.  First, the simplest way to look at this is to evaluate the athletes that started in 2011 and see how many were left in each subsequent year.  Let's take a look at those results below.

You can see that nearly 60% of athletes survived to Year 2 and nearly 30% were after Year 4.  The issue with this analysis is that it ignores the athletes who started after 2011, which is a huge chunk of the current athlete pool.  The type of athlete who is competing today may be characteristically different than those who started in 2011, and we want to capture that.

The next chart estimates a survival curve for today's athlete population using only the most recent information.  To get the survival rate from Year 1 to Year 2, I looked at athletes who first competed in 2013 and see how many returned in 2014.  To get the survival rate from Year 2 to Year 3, I looked at athletes who competed in 2012 and 2013, then found out how many of those returned in 2014.  To get the survival rate from Year 3 to Year 4, I looked at athletes who competed in 2011-2013, then found out how many of those returned in 2014.

To get the cumulative survival rate for year 3, I multiplied the survival rates for years 1-2 and years 2-3.  To get the cumulative survival rate for year 4, I multiplied the survival rates for years 1-2, years 2-3 and years 3-4.  I then took it one step further, estimating survival rates in years 5-7 based on the rates for the first 4 years.  These are obviously estimates, as we don't have enough data to know the true likelihoods beyond year 4.

Here we see the survival rates are much lower than we previously observed.  About 50% of athletes remain after year 2, about 20% are left after year 4 and I'm estimating only about 10% will be left after year 7.  Clearly the majority of athletes don't make it too many years in this sport, but there is still a decent chunk of the population that sticks with it for years.

However, what's not obvious in the chart is that the chances that an athlete continues in the following year increases with each subsequent year of participation.  Below are the year-to-year survival rates:
  • Year 1-2: 47%
  • Year 2-3: 61%
  • Year 3-4: 72%
This is good news in my opinion.  We see that athletes who stick with it beyond the initial year are not likely to "burn out" the longer they compete.  Once an athlete is sufficiently invested, they are pretty likely to keep at it.**

In total, when you combine first-, second- and third-year competitors, about 52% of the field returns in the following season.  One offshoot of this is that if we consider how fast the Open has been expanding, we know that it must be largely made up of first-time competitors.  In order to maintain the size of the Open field from one year to the next, we need a lot of new athletes each season.  If there were 200,000 total athletes last season, we likely need about 100,000 new athletes to enter the field in 2015 simply to maintain the same size field as before.

The last piece of this analysis was to try to identify other factors (aside from number of years of prior experience) that made certain athletes more or less likely to continue in subsequent years.  For this, I limited the data again to only 2013 and 2014, then further limited the data to athletes who submitted a height and weight (I used the 2013 height/weight for all athletes).

Using a logistic regression model, I looked to see if 2013 percentile rank, age, height or weight had statistically significant impact on the likelihood of returning in 2014.  As it turned out, all but height had a statistically significant impact (p-value less than .01 for percentile rank, age and weight).  However, in my opinion, we can basically ignore weight because the predicted probability of returning did not vary a whole lot (only about 4% higher probability at 180 lbs. vs. 220 lbs.).  

The big key was the 2013 percentile rank.  As you might expect, athletes who finished near the top of the rankings had a much higher likelihood of returning.  Holding all other items at their mean, the predicted probability of returning was 74% for an athlete finishing in the 1st percentile, compared with 55% at the 50th percentile and 35% at the 99th percentile***.  If you aren't convinced by those somewhat opaque predictions from the logistic regression, the chart below shows the observed percentage of athletes who returned, by 2013 percentile rank.

As you can see, the pattern is very evident.  I'm not sure this will come as a surprise to anyone, but it's always nice to see your intuition confirmed in the data.

Age was an interesting factor.  There are two things going on here:
  1. Without controlling for the 2013 percentile rank, it appeared that age had basically no effect.  
  2. Older athletes tend to have worse rankings than younger athletes in general. As we just showed, athletes with lower rankings have lower persistency.
What happens here is that the logistic regression showed that all other things being equal, older athletes actually have a higher likelihood of returning than younger athletes. If we hold the other items at their mean, the predicted probability of returning was 51% for a 20-year-old and 65% for a 50-year-old.

Of course, the big question moving forward is how things will change with the changes made to the Open in 2015.  The addition of a scaled division is likely to siphon off some athletes who had previously competed in the Rx'd division, and it's possible that with only 20 Regional invitations in each region (as compared to 48 in 2013-2014 and 60 in 2011-2012), some additional athletes will drop out.  I also have to believe that the overall participation in the Open will not continue to expand the way it has since 2011, where it has roughly doubled each season.

There is no way to know the answers to these questions right now, but understanding what has happened in the past will certainly help us understand the impact of these changes in the future.

[Thanks a lot to Andrew Havko, Michael Girdley and Jeff King for pulling this data for me and/or making it publicly available]

*It appears that overall, the survival rate for women is very similar to that of men.  Initially, it appears about 4% lower, but using some very rough data for the marriage and divorce rates, that discrepancy could very easily be attributable entirely to name changes.

**For my estimates beyond year 4, I assumed this would continue to flatten out, so I used 77% for year 4-5, 79% for year 5-6 and 81% for year 6-7.

***These predicted probabilities are all probably about 4% too high.  That's because the subset of athletes used for the logistic regression was limited to those that submitted a legitimate height and weight.  In general, these athletes have slightly higher finishes and are slightly more likely to return than the average athlete.

Saturday, December 13, 2014

Understanding Year-to-Year Improvement in the Open

In this and most future posts, I'll start by giving you a very brief summary of the key takeaways from the post.

What you need to know from this post:

  • On average, athletes that compete in the Open in multiple years improve their rank percentile in the second year, but their absolute ranking declines.
  • The more years an athlete competes in the Open, the less they improve their percentile ranking in subsequent years, although the average improvement is still positive. 
  • There is pretty strong evidence that if the Open has a higher load-based emphasis on lifting (LBEL), this will favor taller and heavier athletes. If the Open has a lower LBEL, this will favor smaller and lighter athletes.
  • It is unclear how age is related to an athlete's percentile ranking improvement from year-to-year.

Today I want to get into a topic that has interested me for some time.  Many readers of this site have been competing in the Open for several years and likely have used their performance each year to judge how much their fitness has improved from the past year.  There are two underlying assumptions that we use here:
  1. The Open is a pretty good test of overall fitness;
  2. Each year of the Open is a relatively similar test of fitness, compared to other years.
I'm going to leave the first assumption unchallenged today, although there could certainly be debate about that.  But let's assume that the Open is indeed a good test of fitness.  What I will try to do today, using data from 2011-2014, is try to test the second assumption and get a feel for how much impact, if any, variations in the programming might have from year-to-year.  Along the way, I'll also look for other interesting observations about how athletes are improving across multiple years in the Open.

Before we get into the results, here's a quick background on the data I'm using and my basic methodology:
  • For both men and women, I started with the Open results for athletes under the age of 55 (meaning no scaling in the Open).
  • I removed any athletes whose first/last name combination was not unique.  This is due to the fact that my data does not have any other identifiers for each athlete.  Since there are something like 9 or 10 Ben Smith's, I just threw them all out.
  • I removed all athletes that did not complete all five events.
Next, I split up the analysis into six cohorts: 2013-to-2014 male, 2013-to-2014 female, 2012-to-2013 male, 2012-to-2013 female, 2011-to-2012 male and 2011-2012 female (an athlete could be multiple cohorts).  For each section, I identified athletes that competed in both years.  For all athletes that submitted it, I also mapped on age, height and weight information from 2013 (except for the 2011-2012 cohorts, in which case I used the 2012 information).  I had to make the simplifying assumption that an athlete's weight did not change from year-to-year, which is probably not true for some athletes.

OK, with the background out of the way, let's move onto the findings.  The first thing I wanted to know was just how much improvement athletes were making from year-to-year.  The initial results might surprise you:

On average, athletes who continued from one year to the next actually finished lower in the second year than in the first.  In fact, between 60-80% of athletes had a lower rank in the subsequent year across all six cohorts.  

So what gives?  Well, the key here is that the field has been expanding, nearly doubling in size each year.  The easy way to account for that is to look at the change in an athlete's percentile rank from year-to-year.  If an athlete is 5,000th out of 50,000 in 2013 and 8,000th out of 100,000 in 2014, then the percentile rank actually shows an increase of 1% (5% to 4%), despite the 3,000-spot drop in absolute rank.

Now we see that in general, athletes are improving year-to-year, although it became a bit more difficult each year.  Approximately 89% of athletes improved their percentile rank from 2011-2012, compared to 80% from 2012-to-2013 and 71% from 2013-to-2014.  

Each year, we have more and more athletes who have been competing for several years.  Most of us who have been CrossFitting for a long time know that making incremental improvements becomes harder and harder (thought not impossible) as the years go on.  For evidence of this, I looked at the average percentile improvement from 2013-to-2014 of athletes who also competed in 2012 vs. those who did not.*

These numbers make it fairly clear that the amount of past Open experience is a factor in how much improvement athletes make from year-to-year in the Open.  But how about other variables, such as height, weight or age?  This is where things get a little tricky.

First, let's look at age.  The three charts below show the average improvement by age for males (orange) and females (blue) in each time period.

From these charts, we see that there is not a simple answer here.  In two cases (male 2012-2013 and female 2013-2014), improvements were generally higher at older ages, but in the other four cases, the reverse was true.  Across the four cohorts, the correlation between age and percentile rank improvement ranged from -12% to +14%.  Unfortunately, the results are not consistent by gender or by year, in which case we might be able to make some sort of generalization about what these results mean.  For now, I will simply conclude that there is no clear relationship between age and year-to-year improvement in the Open.

How about height and weight**?  Well, again, the results were mixed, but in this case, the mix of results might actually be able to provide some insight.  Let's focus on weight for now.  Four of the six cohorts did show a clear linear relationship between weight and percentile rank improvement, but the other two did not.  Here are charts showing the relationships for those cohorts.

From 2012-to-2013 for females (third chart), it appears that heavier athletes tended to show more improvement than lighter female athletes.  In the other three cohorts shown, heavier athletes tended to show less improvement than lighter athletes.  

Another way to look at this is by examining the correlation between and percentile rank improvement  in each cohort.  A positive correlation means that higher weights tend to have higher percentile rank improvements; a negative correlation means that higher weights tend to have lower percentile rank improvements.  The correlations for the four cohorts shown above tell the same story: -11% correlation for 2011-2012 males, -15% correlation for 2011-2012 females, +8% correlation for 2012-2013 females and -6% correlation for 2013-2014 males.

What could be the reason for these results? 

For each of the Opens, I've evaluated the programming using a few metrics.  One that I reference quite frequently is the load-based emphasis on lifting (LBEL).  This metric attempts to quantify how "heavy" a CrossFit competition is based on the portion of the competition that was made up of lifts (as opposed to bodyweight movements), as well as how "heavy" those movements were.  After seeing the charts above, I went back and looked at the LBEL in each of the years for both males and females.  What I found was that in situations where there was a strong negative correlation between weight and improvement, the LBEL decreased significantly, and in situations where there was a strong positive correlation between weight and improvement, the LBEL increased significantly.

The chart below shows the following for each cohort:
  • Percentage change in LBEL;
  • Correlation between weight and improvement in percentile rank;
  • Correlation between height and improvement in percentile rank; and
  • Correlation between age and improvement in percentile rank.

While we still can't seem to tell much about the relationship between age and improvement, this chart above does seem to clearly indicate that a higher LBEL benefits larger athletes and a lower LBEL benefits smaller athletes.  Note that the color scales for the percent change in LBEL mirror the weight correlation almost exactly.

While this my seem intuitive, I think it is a very important result to lend credibility to the LBEL metric.  It is still my belief that LBEL is not a particularly useful metric when looking at individual events, but when used to evaluate a multi-event competition such as the Open or Regionals, I think it is very useful to help us understand the type of athletes that might benefit from the programming.  Keep in mind, of course, that these correlations are relatively small, so there are plenty of other factors that determine how much an athlete will improve from year-to-year.

Obviously, none of this is meant to de-emphasize the importance of training in determining how much an athlete will improve from year-to-year.  Rather, this can help us understand all the factors that might be impacting an athlete's improvement, which can in turn help evaluate how successful all that training really was.

[Thanks a lot to Andrew Havko, Michael Girdley and Jeff King for pulling this data for me and/or making it publicly available]

*I did also take a look briefly at the 2013-2014 improvement for athletes who competed back in 2011.  As expected, they were slightly lower than those who just competed in 2012.  However, they did still show a positive improvement in their percentile rank, on average.

**Many athletes did not submit their height or weight (or typed in something ridiculous, like 1,000 pounds).  Any time I looked at correlations between weight/height and percentile rank improvement, these are based only on the subset of athletes that reported a reasonable height and weight.  This ranged from about 50%-80% of the field (women generally reported less often).

Monday, November 24, 2014

What to Expect from the 2015 Open

So I'm a little late on my annual "What to Expect" post this year, but you've still got about 3 months left before 15.1 is announced.  That means there's still plenty of time to fine-tune your game, work on your weaknesses and get yourself as prepared as possible for this year's Open.  To help you do that, this post gives you an overview of the type of programming you should expect to face.

For those familiar with my past "What to Expect" posts, nothing here should come as a major shock to you.  But I have updated the numbers based on what happened last year, and I've also got some thoughts on what might change this year due to the changes mentioned by Dave Castro following the CrossFit Invitational a few weeks ago.  For those of you who are new, this should be a good primer on what the first stage of the CrossFit Games competition is all about.  To be sure, it is quite different from what you may have seen on ESPN (hint: you probably won't be pushing any sleds or doing 315-lb. squat cleans for time).

The way this post will be laid out is the same as last year.  I'll pose some questions that many newbies might have, and I'll answer them as best I can based on the information we have from the 2011-2014 Opens.  As I have noted in the past, I won't be covering how you might want to train for the Open in this post, but rather what you should be training for.

Let's get started.

What movements will I need to do?

Fantastic question.  I like where your head is at.  Although the CrossFit Games is all about preparing for the "unknown and unknowable" (do they still use that tagline?), you can expect to face a small subset of movements in the Open.  By my count, there have been 52 different movements tested at the CrossFit Games finals, there have only been 15 tested in the Open.  The chart below shows how much value each of these movements has been given in the past four years, along with the best estimate of what might be to come this year.  All of the "best estimates" in this post give more weight to recent years, but they are influenced to some extent by all years dating back to 2011.

Last year's open had a little wider spread of movements than in the past, but still, you can see that historically, there are a few movements that are tested heavily again and again.  This year, you can expect about 60% of the points to come from six movements: snatch, burpee, thruster, pull-up, double-under and box jump.  Just in those six alone, you can see that there is a jumping theme.  All but the pull-up and thruster involve some sort of jump or explosive hip movement (and you could argue the thruster does, too, especially at higher weights).

It's also worth noting (for all you home-gym folks) that you may need a Concept 2 rower available to be able to compete in the Open.  From 2011-2013, nothing was required other than a barbell, weights, rings, a box and a pull-up bar, but in 2014 there was a workout that involved rowing.  This came as a surprise to many, but now that it's been done once, don't be caught off-guard if it happens again.

In the previous chart, there was also a "Subcategory" listed for each lift (I know a wall ball isn't actually KB or DB, but I put it in there because it's a lift that doesn't use a barbell). Grouping movements by the subcategory can be useful, because it reduces some of the noise and gives us an idea of the type of movements that will be used. For instance, cleans haven't actually been used that much in the Open the past three years, but I wouldn't recommend skipping them in training - the movement pattern is similar to that of a snatch, which is highly valued. Below is a table similar to the one above, but looking at subcategories instead of specific movements.

The big theme here has not changed since 2011.  The focus of the Open is on two things: Olympic-style lifts and basic bodyweight gymnastics. This is partly due to equipment restrictions in the Open, but partly due to the fact that HQ seems to really value those two types of movements when making the first cut of athletes.

For those wondering why I have a category for "Uncommon CrossFit Movements" that has 0% value each year, it's because that category contains things like sled pushes, sandbag carries and swimming that have appeared at the Games but are rarely seen at other levels of competition.

How heavy will the lifts be?

Two years ago, I came up with the concept of average relative weights last year as a way to understand this topic a bit better. There is more background in this post from 2013, but here's the concept: depending on the movement, a certain weight may be heavy, medium or light, so I have normalized the weights prescribed on each workout so that we can get a fairer indication of how "heavy" the lift was. After looking at the normalized loads that were prescribed in the past three years, I applied the average relative weight we've seen to the various lifts to show the average expected weights in this year's Open.

The above graph is useful, particularly for new competitors who'd like to get a feel for what they will have to handle in the Open workouts.  However, there are some other factors to consider:
  • These are only averages. The "heaviest" load required in the Open was a 165-lb. clean and jerk (squat clean and jerk technically), which is roughly equivalent to a 130-lb. snatch, a 290-lb. deadlift or a 145-lb. overhead squat. Last year, I said that HQ would likely never require something that heavy again, but with the scaled division being added this year, I actually think it is possible (but unlikely) to see something that heavy required in an Open workout this year.
  • Once in the each of the past three years, HQ has programmed workouts where the weight starts light (to allow everyone to participate) but gets progressively heavier.  Expect this to happen again in 2015.  For those looking to make the Regionals, you'll likely need to be able to comfortably move weights that are about 75% heavier than the loads shown above (for instance, 165-lb. snatch for men).
  • From 2011-2013, HQ never really went heavy on a "heavy" lift (like the deadlift), rather going with heavier loads on the Olympic lifts.  Last year in 13.3 (deadlift/box jump), they showed that you need to have plenty of weights on hand.  Anyone qualifying for Regionals would have needed to handle plenty of deadlifts at 315 for men or 225 for women.
The chart below illustrates the distribution of weights that have been used in the past.  For workouts where the weights got progressively heavier, the weight used for this chart was the average that would be lifted for a typical Regional-level athlete.  Above each bar on the chart, I've given some examples of the type of movement/loading combinations that would fall into that range.

What types of WODs will be programmed? Will there be any ridiculous chippers like 121201?

First of all, there will not be anything like that workout.  There should never be anything like that.  If you are a gym owner, do not program 20 different movements into one workout.  Please.

Anyway, HQ has made it clear that they believe couplets and triplets are the bread-and-butter, so I'm sorry if you're really awesome at "Filthy Fifty," because it's probably not getting programmed.  We did see a little more variety last year with 14.4 and 14.5, but still, it's going to be pretty straightforward.

In the past four years, we've seen 21 workouts:
  • 20 of them included either one, two or three movements.  One involved five movements.
  • All were metcons (no max-effort lifts)
  • 18 had a fixed time limit, and 2 had a time limit that could be extended if you completed a certain amount of work in the first time limit.  Only one has had a required amount of work.
  • All fixed-time workouts have been between 4 and 20 minutes.  Anyone competing at Regionals would have finished the "for time" workout in less than 15 minutes.
Is it impossible that a max-effort workout will be used this year?  No, not after seeing a few of those in the team series.  But I still wouldn't count on it.  Work on your metcons if you want to qualify for regionals.

The chart below helps to get a sense visually of what combinations of loading, duration and number of movements we have seen in the Open.  Each ball represents a workout, and the size of the ball indicates the load-based emphasis on lifting (LBEL) of that workout. The LBEL is a metric that tells us not only how heavy the loads were, but what percentage of the workout was based on lifting. So if the workout had 135-lb. cleans and burpees, the average relative weight is 1.00 but the LBEL is 1.00x50% = 0.50.  The left-right arrows indicate the time domain was variable; the plus-symbol indicates the weight was variable.  In those cases, I've used the average time/load for Regional competitors.

You'll notice that as the workouts get longer, the weights tend to decrease and the number of movements tends to increase. This is also typically what we see in CrossFit programming on the main site and in most gyms. For instance, programming 7 minutes of burpees (Open 12.1) is torturous, but not unreasonable; programming 20 minutes of burpees is a recipe for Rhabdo.

Are past years really a good predictor of what's to come this year?

In many ways, they are.  We have not seen big shifts in the types of movements tested, and the loading has not drastically changed in the past four years of the Open (although it has been declining slowly each year).  There were a couple of curveballs last year with the 5-movement chipper (14.4) and the for-time workout (14.5), but those workouts were still not wildly different than what we had seen in 2011-2013.

That being said, the introduction of the scaled division this year could change things.  It's all speculation at this point, but as I said a few weeks ago, HQ may be more willing to push the required loading up a bit in the Rx division.  I'd also say that we could see some slightly more challenging movements (such as handstand push-ups).  That being said, I doubt that HQ is going to change the over ally feel of the Open dramatically.  The Open is likely still going to be a test of conditioning primarily, with a modest amount of strength required in order to move on.  The big weights will probably come into play more at the Regional level, and the truly unexpected movements and workouts will be reserved for the Games.

I hope that new and old athletes alike can get some value out of this.  As I've said before, I'm not going to tell you how to train, but I'll try to help you understand what you should be training for.

*Note that for this chart, I considered Open 11.1 a single-modality despite technically being a clean and jerk. Also, in calculating the LBEL for the snatch workouts with varying weights, I took the average weight lifted for someone who reached regionals. This reflects the fact that, while only 75 lbs. is required, for a regional-level athlete, they'll be moving somewhere around 130 lbs. on average throughout the workout.