Tuesday, March 10, 2009
Benchmarks and Forecasting
I woke up this morning and looked out the window to find a good accumulation of snow on the ground. I was a little surprised. I didn’t remember my local Fox meteorologist, Crystal Egger (pictured) saying anything about an upcoming snow storm. Usually, she gets it pretty right. In fact, if I think back a couple of decades to my childhood, it sure seems that weather forecasting has improved a whole lot. It seems that back in the day, it was a running joke that the weather that we would wind up with was basically the opposite of what the meteorologist would predict.
Of course, like most things, I am sure technology has played a part in the improved accuracy of weather forecasting. Scientists are now able to sample, minute by minute, a myriad of benchmark #’s, from barometric pressure to humidity to minor fluctuations in temperature and furthermore, they are able to summarize this data to create accurate computer models to predict future weather behavior.
If you’re reading this blog, chances are that you’re seeing where I’m gonna go with this.. there are parallels between weather forecasting and performance forecasting in the world of athletics. Similarly, there are those who are understanding and embracing these new technological tools to better forecast their athletes’ performances and there are those who still see this new science as ‘hit and miss’ at best. I want to chat a little, in this blog, about how to go about selecting appropriate benchmarks and how to use them to accurately forecast and plan improvements in performance.
Regular benchmarking is necessary and useful for a number of reasons on a number of levels….
1. Is the training program working?
Clearly, from peak to peak, we want and expect an improvement in performance from the preceding training program. It would be a foolish athlete who would commit to a multi-year training plan without some ‘checking in’ along the way to make sure the program is working. This ‘check in’ may take the form of racing or testing but it is clearly an important component of the feedback loop when it comes to training. You would probably be surprised by the number of athletes that I come across who don’t have regular effective ‘check ins’. It is common for Ironman athletes to race much less frequently than short course athletes, focusing more on work than testing out whether the work is working Additionally, athletes will often choose different events from year to year in the name of variety and miss the benefit of having a standard check-in at a local event at a given point in each training year. The problem is compounded by the fact that many athletes do not undergo regular field or lab testing.
Clearly, this represents the first and most important level of feedback for the athlete. Are they getting better? In my meteorological metaphor, this level of understanding as akin to having a general sense of what the weather is like in a given region of the Country. When I first moved from Florida to Colorado, I had a sense that the weather would be considerably colder than what I was used to, however, I had limited experience or information to understand how the differences may manifest over the course of a year or to predict the specific temperature on a given day. Which brings us to the second level of understanding…..
2. Is the athlete where they need to be in the context of the training cycle?
Better coaches will have a good general understanding of where they expect their athletes’ performance to be through the course of the training year. This is an important upgrade on the first level of understanding.
Experience has taught us that it is necessary to get ‘unfit’ at certain points in the season in order to get ‘more fit’ at other points. This ability represents a critical distinction between very good coaches and most self-coached athletes. On a lot of levels, many self-coached athletes expect continuous, steady improvement. They do not understand the performance ebb and flow that comes with the training seasons. Good coaches have a good general idea of how ‘out of shape’ they want their athletes to get and what level they expect their training sets to be at through the course of a training season.
It is really important that the athlete and coach establish seasonal benchmarks that are best correlated to their peak performance.
In weather terms, this level of understanding is akin to having local knowledge of the annual weather. For example, when I spent my first year in Colorado, I learned just how warm it would be in Summer, just how short the Spring and Fall seasons could be and just how varied the winter could be, from 60 degrees and Sunny one day to a 2 day snow storm the next. Even with this local knowledge, however, my ability to forecast the actual temperature on any one day is limited at best. Which brings us to the third level of understanding…
3. How can we ‘forecast ahead’ to determine the performance effect of different training strategies – different work:rest cycles, different peak training loads, different season structures.
If an “old school” coach wants to determine if an alternative training strategy is beneficial, they do so via long term trial and error. I have been fortunate to work with a number of truly world class coaches over the course of my career and, to a man, they are very structured, methodical individuals who rarely stray from the well tracked path. Occasionally, however, they will conduct an ‘experiment’ and will slightly modify their core training plan in accordance with the result. I remember during my coaching apprenticeship with Ian Thorpe’s coach, Doug Frost, Doug decided to change from Ian’s usual 3:1 work:rest cycles to a 2:1 structure. I asked him how he would determine whether this strategy was effective and he simply said – by Ian’s race results at the end of the season.
Simply put, the example above illustrates just how limited is the number of ‘experiments’ that the coach can conduct. Best case, an athlete may work with a given coach for 10 years. At most, this represents 10 or so alternative experiments that can be tailored to the individual. What if, however, we had a specific understanding of the fitness and fatigue dynamics for each individual athlete? What if we could predict relatively accurately how a given athlete will respond to a 2 week taper vs a 4 week taper without throwing away a whole season in the name of an experiment? What if we could determine whether an increase in load of 2hrs a week would lead to a new PR or overtraining for a given athlete, without compromising their health and athletic career in the process?
By assessing the performance dynamics of an athlete over the course of their training, we are able to get a firm handle on how quickly a given athlete acquires and loses fitness in response to a given training load.
I have presented the following figure a number of times now – Selye’s GAS curve.
Usually when this curve is referenced, it is done so in a general, theoretical sense of how a general athlete responds to training stress. However, like any curve, we can generate an actual, real world, mathematical expression for the curve and by using criterion performances, or Benchmark tests, we can change the constants of the formulae to create a curve that best fits the athlete’s specific response to training load.
When this is done, we can provide a relatively accurate performance forecast for a given athlete in response to different training protocols.
However, generating an accurate performance curve for the athlete demands that we have a good number of accurate criterion performances or ‘benchmarks’ for each athlete. We have a couple of options on this front (each with it’s own strengths and weaknesses):
a) “Flat Out” Criterion Performance Tests
In ‘pure’ terms, it is hard to beat regular races or time trials to answer the first of the questions listed above: Am I getting better?
However, there are a couple of problems with the use of time trials that compromise their effectiveness in answering the other questions. The first of these is assessing fitness at various points in the season. While theoretically possible to run time trials throughout the training year, many coaches prefer a progressive ramp up in the preparatory phase before including any ‘flat out’ work. It can be biomechanically and physiologically risky to throw a time trial in in the very early season. Therefore, we can miss out on assessing how fit the athlete is when ‘kicking things off’, or more importantly, how much fitness has been maintained and carried across from last season.
Additionally, the frequency that would be demanded to create sufficient samples for an accurate forecast model is prohibitive if the athlete wants to actually perform some training in addition to the testing trials. Or, put another way, flat out tests tire you out and wind up compromising total training load.
b) Critical Power/Pace monitoring.
Wko+ provides the weekly and monthly metrics of power and pace ‘bests’ for a given duration. IMHO, using these as a benchmark is a mistake because they do not take into context whether these were a 100% effort, an 80% effort etc.
Take for example, an athlete who performs a regular 2x20min session at a moderately-hard effort on a weekly basis. This is prescribed as a session at roughly the power than the athlete could sustain for 3-5hrs. Week after week, the athlete completes a pretty standard basic week, with a tolerable load and a similar (though slightly increasing) power level for this session. Then, 8 weeks out from their peak for the year, the coach decides to up the ante and rapidly increase the load, in addition to adding some races. With the addition of more load, the athlete’s fatigue #’s go through the roof (and their TSB sinks to -50) but, with the addition of a flat out race, they experience their highest 20min power for the season. A less than astute coach may attribute the highest power of the season to the increased training load, when in fact the 20min power best is actually the result of a change in training content rather than fitness.
Of course, the coach could include a critical power ‘best’ set each week however this brings with it the same issues as the ‘flat out’ time trial mentioned above.
c) Power or Pace vs. Heart Rate.
My preferred primary method of benchmarking is using power or pace vs heart rate for given aerobic sets throughout the training week.
The primary advantage to this method is the pure frequency of sampling. Several times per week I can look at the power:HR relationship for a given set and I can establish an improvement curve without compromising or affecting total training load. Additionally, I can assess performance throughout the training year, testing at times of fatigue as well as peak form to establish both fatigue and fitness components of performance with respect to a given training load.
Of course, this method has it’s shortfalls also. Heart rate is a physiological measure that is affected by many non-training related environmental factors. However, when it comes down to it, the pure number of data samples that I am able to accumulate with this method outweighs the standard error associated with the method.
A couple of key sessions that I like to use for the Bike and Run:
Run (Track Workout):
2mi Easy (@130bpm)
2mi Steady (@140bpm)
2mi Mod-Hard (@150bpm)
Record 400m splits for all.
Bike (Long Workout)
1hr on the trainer (30min easy w/up, 30min @ 135bpm) Record power
2-4hrs on the road
1 hr on the trainer (30min @ 135bpm, 30min cooldown) Record power
As mentioned, these workouts should be done regularly and during hard weeks as well as easy weeks so that the both the athlete’s fitness and fatigue responses to a given training load can be determined
Stay tuned in coming weeks for more on how you can use your training benchmarks to ‘calibrate’ your CTL and ATL constants in wko+
Posted by Alan Couzens at 9:41 AM
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great post Alan,
Bike (Long Workout)
1hr on the trainer (30min easy w/up, 30min @ 135bpm) Record power
2-4hrs on the road
1 hr on the trainer (30min @ 135bpm, 30min cooldown) Record power
I do similar tests as these. What
I do an 8km run test, 4km @ Aet and then 4km @ Aet +10bpm. I like to track the difference between these two intensities to assess progress.
For example, Sometimes I have made bigger gains at Aet pace then what I have at Aet +10bpm pace. I then structure my next training cycle around that result.
I focus primarly on imopoving my Aet pace and when it gets down to only 10-15sec/k difference to my Aet +10bpm I then do a cycle of training focusing on Aet +10bpm as I feel I have probably taken my Aet pace as far as I can get it. Efficiency and economy are good when the gap is narrow. Therefore I focus on strength endurance. When that improves the my Aet +10bpm paces improves faster than my Aet pace. I'll then return back to an endurance phase focus on efficiency and economy at my new faster Aet pace.
On the swim a do a negative splitting set at increasing intensities and on hte bike it is 20k @ Aet and then 20k @ Aet +10bpm
Does that make sense?
Your thoughts on this?
I haven't been able to work out yet how I can account for fatigue through training load that could impact on these results.
I coach around 8-10 guys at different times of the year and these tests help show me and then the athletes I coach that in some cases they are training somewhere between Aet and Aet +10bpm when they think they are below of at Aet according to their results.
Thanks for your comments.
You've inspired me to delve into the HR dynamics as an indicator of strengths/weaknesses a little more deeply.
I typically use lactate & VO2 testing at the start of a training cycle to determine strengths/weaknesses and focus points for the coming cycle.
I have never taken the time to look at how HR/Pace corresponds with weaknesses on the lactate/FUEL curve.
When I get a chance I will definitely sift through some lab files. It makes good intuitive sense, however, the general conception within the sports science community is that there is at most one HR deflection point on a HR/Pace curve. Some would argue none. Now that we have sufficient lactate/HR files I'd be keen to take a look at this myself and see what we come up with.
Hope all is well.
You really know how to put this info into a more laymans terms to learn use data!
Are you maybe into writing a book called "How to use WKO+ for dummies" :D
Great idea. Those 'for Dummies' books sell! Cha-ching :-)
Amazes me how many folks are willing to concede dummy status. I'm sure it took a few chats with the initial publisher to convince them that they were on to a winner.
I think we're all kind of Dummies when it comes to wko. It's such a new tool that we're learning more as we use it.
LOL. You've just explained my desire for constant data and feedback from my training.
As a meteorologist in the Air Force for the past 22 years, this post really hit close to home.
On both counts.
Excellent post, as always, Alan.
"You've inspired me to delve into the HR dynamics as an indicator of strengths/weaknesses a little more deeply."
I think also it can give you indicators as to how hard people are training and a reality check for them too.
It's a real eye opener for novices and it takes time for them realize that Aet is very close to IM effort and they don't realize initially how easy they need to go. It takes time for people to then tune into the RPE and to make it accurate training tool.
Unfortunately, I don't have labs like you do, but I do use all of your theories as a way of showing people how to train, test, reflect, act etc with just a HR monitor or RPE.
Being able to transcribe the science of what you provide into something workable for an athlete has been a rewarding challenge for me as a coach.
"I typically use lactate & VO2 testing at the start of a training cycle to determine strengths/weaknesses and focus points for the coming cycle."
I look at the rates of improvement at Aet and Aet +10bpm, plus the narrowing or expanding of that gap in pace as a method of setting up their next training cycle. I'll generally need a lot more test results to indentify improvements, plateaus etc and to eliminate variables that can occur from test to test.
"I have never taken the time to look at how HR/Pace corresponds with weaknesses on the lactate/FUEL curve."
What I look for here is how much pace drops off in relation to HR over the 8km run test, for example.
An athlete may start at 140bpm and 5:00min/k but may end of at 140bpm and 5:30min/k. Compare that to someone who can start at 140bpm and 5:00min/k and end at 140bpm and 5:10min/k. So there is added information for me to digest within a test, not just HR and overall pace itself, but also what is happening to that pace over time. Inexperienced athletes seem to have a bigger drop in pace, which indicates to me a lack of efficiency and economy as a runner. If that is happening then, then possibly there technique is breaking down too soon in runs, because they are running to long. So I'll cut back on the distance an athlete runs but get them running more often to support and develop good technique. I wouldn't need to do this sort of thing with someone who pace does not drop off in the test. These guys will just continue to do what they are doing until the tests show something up that tells me their program needs to change.
I did all of this on myself and in 8months my Aet pace went from 5:40min/k and my Aet +10bpm was 4:50min/k down to an Aet pace of 4:32min/k and an Aet +10bpm pace of 4:19min/k. The gap between the two paces went from 40sec down to 13sec. I’m at a point where I don’t think I can narrow the gap any further, this indicates to me I may need a training cycle of Aet +10bpm in order to improve my strength endurance, as it appears to be more of a limiter than my efficiency and economy. However, at the start of my run program with a 40sec difference between the two paces, it just shows how poor more efficiency and economy was at Aet.
“When I get a chance I will definitely sift through some lab files. It makes good intuitive sense, however, the general conception within the sports science community is that there is at most one HR deflection point on a HR/Pace curve. Some would argue none. Now that we have sufficient lactate/HR files I'd be keen to take a look at this myself and see what we come up with.”
I will be eagerly waiting.
Thanks for listening.
With my own data obsession, I'm thinking I must have been a meteorologist in a former life :-)
Thanks for the kudos.
All my best,
Thanks for the additions.
I do concur with HR:pace decoupling over those tests as a good indicator of improving fitness.
Will be interesting to look at whether AeT-LT pace/power gaps also transfer to HR gaps in our sample. Does seem to make a lot of sense.
Thanks again for the food for thought.
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