How to Combine Internal & External Training Load Monitoring
Summary
TLDRThe video discusses approaches to combine internal and external training load metrics to gain insights into player fitness and fatigue without additional testing. It covers using simple ratios of the loads and more complex regression models to predict expected internal load based on external load. The video highlights research on training efficiency indexes that scale the loads for comparison. It advocates for individualized analysis, noting machine learning techniques that find different external load metrics most predictive of internal load by player. A case study shows patterns of higher actual than predicted internal load, indicating potential deconditioning to flag for intervention.
Takeaways
- 😀 Combining internal and external training loads may provide insights into fitness and fatigue
- 📊 A simple approach is to calculate the ratio between internal and external loads
- 🔬 Ratios may correlate with fitness measures but become weaker under fatigue
- 🧪 Uncoupling of loads could indicate fatigue without direct testing
- ⚖️ Issues can arise from numbers being on different scales, the training efficiency index aims to address this
- 📈 Training efficiency index plots loads on a logarithmic scale to enable comparison
- 😕 Ratios may oversimplify the complexity of training sessions
- 🤖 Machine learning is being used to relate loads on an individualized basis
- 😃 Case study: Compared actual and predicted internal load based on external load
- 💡 Flagged potential issues through this "invisible monitoring" approach
Q & A
What are internal and external training loads?
-Internal training load refers to the physiological stress imposed on an athlete, often measured by heart rate. External training load refers to the work completed by the athlete, often measured by metrics like distance covered or accelerometry.
Why might combining internal and external loads be useful?
-Combining internal and external loads may provide more insight into an athlete's fitness and fatigue status. This 'invisible monitoring' allows coaches to assess fatigue without extensive additional testing.
What was the first study to investigate ratios of internal to external loads?
-A study by Ebi Acuba and his team first looked at ratios of individualized training impulse (iTRIM) to external load metrics like total distance. They found these ratios correlated with measures of fitness in soccer players.
What did Acuba's next study on internal/external load ratios find regarding fatigue?
-In their next study, Acuba's team found that the relationships between internal/external load ratios and measures of fitness became weaker under fatigue. This suggests uncoupling of loads could indicate fatigue.
What issue needs to be considered when creating ratios of internal to external loads?
-Internal and external loads use different scales - heart rate may be around 300, while distance could be thousands of meters. The numbers need to be scaled before taking a simple ratio.
How does the training efficiency index address the scaling issue?
-The training efficiency index plots internal and external loads on a logarithmic scale before taking the ratio. This scaling allows the comparison of different load metrics.
What does the case study example demonstrate regarding internal/external load analysis?
-The case study shows how regression equations can forecast athlete's internal load based on external load. Comparing predicted and actual internal load over time can indicate changes in fitness and fatigue.
What machine learning approach shows promise for relating internal and external loads?
-A machine learning model developed by John Bartlett demonstrated the need to relate loads on an individualized, athlete-by-athlete basis, with different metrics being most predictive for each athlete.
What is the overall message regarding approaches that combine internal and external training loads?
-There is no clear consensus, but analysis should be done on an individualized basis for each athlete to relate the right external metrics to internal load response.
What is the topic of the next video mentioned?
-The next video will continue the discussion of invisible monitoring - using existing data to monitor fatigue without additional testing.
Outlines
🏃♂️ Defining internal and external training loads
The paragraph introduces the concepts of internal and external training loads. It mentions a previous study by Impellizzeri et al. that found ratios between internal and external loads correlated with fitness measures in athletes. However, these relationships weakened under fatigue, suggesting potential use of internal/external load ratios to detect fatigue.
😕 Criticisms and limitations of using ratios
The paragraph discusses common criticisms and limitations of using simple ratios between internal and external loads, like oversimplification. It argues we likely need more complex, individualized approaches relating varied external load metrics to internal load. A case study is shared on forecasting internal load for each athlete from their external loads.
🤖 Machine learning approaches relating loads
The paragraph mentions recent studies using machine learning to relate internal and external loads. It highlights Bartlett et al.'s work showing the need to take an individualized approach. The conclusion is that there are various ways to combine loads for "invisible monitoring", detecting fatigue from normal data.
Mindmap
Keywords
💡internal load
💡external load
💡ratios
💡invisible monitoring
💡individualized
💡regression
💡machine learning
💡fitness
💡fatigue
💡conditioning
Highlights
Internal and external training loads can potentially be combined to gain insights into fitness or fatigue
When internal and external loads uncouple, it may indicate fatigue
Using load ratios to detect fatigue without additional testing is appealing and some studies show promise
Issues with load ratios include numbers being on different scales and oversimplifying complexity
The training efficiency index scales loads by plotting them on a logarithmic scale before calculating ratio
Criticisms of ratios - they may oversimplify, need more external metrics related to internal load
Case study: Compared predicted vs actual internal load based on regression of previous external load
In preseason, actual internal load was higher than predicted which matches deconditioning
During season actual internal load matched or was below predicted showing adaptation
Later in season internal load rose above predicted, flagging potential detraining
This analysis provided invisible monitoring to identify needs for individual players
Regressions simplify complexity, machine learning is now exploring internal/external relationships
Machine learning study showed need for individualized predictive modeling of loads
No consensus on best way to combine loads but should be individualized
Other ways to explore invisible monitoring by relating internal and external loads
Transcripts
so in my internal versus external load
video we Define these two different
types of training load and if you're not
comfortable with what they are then
check out that video first because now
what we're going to talk about is
potential ways that we can combine these
two measures to try and get some sort of
insight potentially about Fitness or
fatigue
we don't want to keep adding data
collection processes or testing to our
athletes so if we can combine these
measures to get more insight into our
athletes then that is a bonus for
everyone
so the simplest approach may be just to
look at a ratio between internal load
and external load and that is an
approach that ebi acuba and his team
previously looked into so this is their
first study on this topic and they took
individualized training impulse or eye
trim which is a heart rate so an
internal load measure from heart rate
and blood lactate profile and they
divided different measures of external
loads so total distance or high
intensity distance into this eye trim
for the different athletes to get their
ratios and they use this in this first
study with a soccer simulation
and then they found that these ratios
did significantly correlate with the
measures of Fitness that they also took
on the athletes
so they established those relationships
with measures of Fitness but what about
fatigue and in this the next study on
the topic they actually found that these
ratios the relationships became weaker
under fatigue now that is really
interesting because actually when
internal and external loads uncouple
then that may be a way that we can
measure fatigue and do some sort of
either fatigue testing without testing
which is often known as invisible
monitoring and this invisible monitoring
getting more out of the data that we are
already collecting without having to do
Fitness testing is really appealing to
practitioners and so we do see other
studies that are trying to use these
ratios such as this study by Conor
durbridge looking at the ratios during
standardized small gain to try to
determine if they can be used as a
detector of fatigue so there is perhaps
some promise in these ratios even if
they are very simplified and I'll come
back to that in a moment one of the
issues that we sometimes see with these
kinds of approaches is that actually the
numbers are on different scales so if we
think about a heart rate exertion number
maybe that is somewhere around 300 say
but if we're looking then at total
distances our measure of external load
that could be three thousand five
thousand eight thousand we want to be
able to scale for the different numbers
and there is a ratio again it is a
deemed an efficiency ratio that tries to
take this into account
so here in this article in science and
medicine in football Jace Delaney
presents his work comparing internal and
external loads and this is known as the
training efficiency index
though it is a ratio comparing internal
and external load because of this
scaling issue what Jace does is he plots
the internal and external loads on a
logarithmic scale and that then scales
these the numbers and then the slope of
the line gives us the scaling number
that we divide into the internal load
now this means whatever measure you are
using you will always be able to scale
it to compare against each other and
he's seen with some of the studies that
he's published some strong evidence in
support of this approach now Jace
actually used to on his blog provide a
calculator I don't think it's currently
available but what I'll do is I'll link
to a video in which Jace is explaining
this concept in more detail so if it is
something that you want to calculate for
yourself
and explore in your own sport or your
own population then you'll be able to do
that along with Jace's instructions
now as I said invisible monitoring is
really appealing but let's pause for a
moment and think about these ratios we
are taking two very
overarching
numbers that are simplifying the
complexity of a training session and
then dividing them into some something
and often this can be a limitation with
a ratio that we are over simplifying the
process and there has been a lot of
discussion and criticism of the use of
ratios most notably of course the acute
chronic workload ratio and perhaps if
I'm feeling brave one day I will do a
video that addresses that topic but
these ratios have come under criticism
are they oversimplified
in reality what we probably want to do
is be able to take a whole mixture of
external lobe metrics for an individual
athlete to try and assess the
relationship with internal load and I
want to share a case study of one way
that I've approached this previously in
the applied setting we took a season's
worth of internal and external load data
internal load being heart rate exertion
and the external load coming from player
tracking metrics notably GPS and
accelerometry derived measures and for
each individual athlete we determined a
regression equation
that for that individual
predicted or forecast the internal load
based on their external load so
each individual athlete had a different
formula that was based on last season's
data and going forward in that season
every day based on the various
combinations of external load outputs
from the session we would get an
estimated value for heart rate exertion
that we could then compare to what they
actually had and let me show you an
example of that data from one athlete
so here we can see in the blue line for
this individual athlete over roughly a
six months period
they're predicted
heart rate exertion value in blue So
based on their external load output from
each session what was predicted as their
internal load and we've plotted that
then against the actual heart rate
exertion that was seen
and of course there aren't
um
set points where someone suddenly goes
from adapted to non-adapted but I think
there are some general Trends here which
I'm gonna highlight so it's absolutely
not this clear-cut but just for ease of
viewing what we see here we in this is
the pre-season training period so
particularly early on but quite
consistently across this period we're
seeing the actual heart rate exertion
for this individual is higher than what
is predicted
now that is totally what I would expect
to see because we based these
regressions on a season's worth of data
and in the pre-season period players are
deconditioned and therefore
expect to have a higher internal load
physiological cost to the same amount of
external load the same work done so
that's a pattern that I would expect to
see
and then we move into the season
and again we see very little times where
the actual is higher than the predicted
if anything is lower or quite frequently
around about the same amount but then we
can see this shift and this trend here
in October November December period
where it reverts back to consistently
having a higher actual heart rate
exertion than what was predicted
now in that case that athlete was
someone who was a uh a non-starter and
although they were getting Top-Up work
this an analysis was a red flag to us
that something in their Fitness fatigue
status was changing it was not ideal and
actually they weren't getting the Top-Up
conditioning that that athlete needed to
maintain their level of conditioning
that they had earlier in the season so
this analysis served to provide a red
flag to us for the need to explore
further with that individual and again
this was all done essentially through
this invisible monitoring process
whereby we were just trying to use the
data we were already collecting as a bit
of a fitness test
in reality your regression approach is
is essentially quite a simple
statistical approach and of course now
we are seeing more and more approaches
with machine learning and artificial
intelligence and there's been a couple
of studies now start to use machine
learning to assess the relationship
between internal and external load
now one being this paper again Bartlett
John Bartlett's paper we talked about in
the internal external load video and if
the most important message from this
paper is it highlights the need for an
individualized approach so again
session distance for instance was most
predictive of rpe in 36 or 41 players
whereas high speed running was more
predictive in three players and meters
per minute in two players so again
whatever approach you take it's
important that you're looking at players
on an individualized basis
so a few different approaches there in
terms of combining internal and external
load for you to play around with no real
clear consensus other than it should be
done on an individualized basis but
there are other ways that we can try and
encapsulate these this Theory or
concepts sorry of invisible monitoring
and that will be the topic of my next
video
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