Sport-Specific or “Culture-Specific”?

Sport-Specific or “Culture-Specific”?

Recently a friend of mine and a fellow physical preparation coach, who was working with futsal and was preparing Olympic level Judokas, got an offer to take care of a pro basketball team. Since I was the one recommending him to the agent, I was questioned would he be a good fit, taking into account his lack of experience working in basketball. 

This is very common issue for physical preparation coaches because each sport is totally different and represents totally different needs and specifics. Right? Wrong!

Sport coaches believe that their sport is special and have special physical needs not shared with any other sport. That is because they were most likely never involved in working with other sports. Things are not black and white.

I believe that, when it comes to physical preparation, most sports are more similar than different. This might be a blasphemy to sport-specific movement/community out there, but I will take the risks and provide my rationale.

The shared commonalities are dynamic ~ they tend to be bigger or smaller between sports. I am NOT saying that all sports should approach physical preparation the same way, NOR I am saying that they should be approached in completely specific and different way. Physical preparation is multifaceted and involves different component that could be shared between sports in higher or lover degree (e.g. strength vs aerobic capacity). Truth is in the shades of grey.

Those who cannot understand this ‘complementary’ approach are better off reading some other blogs which are more black and white, dogmatic and ruled by beliefs and selling points and tricks. Here we (try to) use our brains.

Going back to aforementioned friend of mine ~ I reassured the agent, and he did the same with the head coach, that my friend is a great pick, but he will need some time to get into the basketball CULTURE along with getting into the specific needs of the basketball players (positions, physical demands & needs, injury tendencies, etc). I was pretty sure he was already versed in making HUMANS stronger, faster, more powerful, mobile, endurant and resilient and it will be matter of short time until he gets the feel of the basketball culture and specific needs. I hope one understands the message here: a lot of shared needs because we are training humans and humans need to run, jump and throw with some specifics of a given sport and culture.

Sometimes sport coaches (head coaches and managers) make the following mistake: since they believe that their sport is the same regardless of the country where it is being played, they fail miserably when they take the vacancy in abroad due completely different CULTURES.  Sport is the same, but the cultures are different. Cultures demand different approaches. One cannot put the square peg in the round hole even if the objects are built of the same color and material (i.e. same sport).

Sometimes I wonder whether the sports differ (in physical preparation aspect) based on the movement patterns involved and specific needs, or based on the CULTURE involved. Soccer coaches keep whining how their sport is being special flower and is demanding special treatment/approach (not far off from athletes involved, with the couple of exceptions of course) called soccer-specific training while keep hammering leg extensions, balance/bosu board, ab curls and partial bench presses. Strength and conditioning coaches coming into sport like soccer, most likely need to get a feel for a soccer culture rather than a soccer-specific demands. This is the thing that differ the most and the thing that one needs adapting to.

I am not saying here that sport physical preparation should resemble preparation of powerlifters, weightlifters, sprinters, marathoners, crossfitters, gymnasts, throwers and others. This is on the completely other extreme of the problem spectrum and it is also worth mentioning for the sake of having a full and clear picture of the issues.

In some sports, like (American) football, the physical preparation went to the completely other extreme ~ disregarding of the sport specifics and it’s needs, and pursuing strength numbers and basically making footballers a powerlifters.

Make sure to remember the goal of physical preparation for sports: TRANSFER. Transfer to the field performance and injury reduction and resilience (anti-fragility). Steve Maxwell wonderfully outlined in the recent article that the goal is not demonstrating strength (exercise as an end unto itself), but building strength (exercise as a mean to an end).

Powerlifters, weightlifters, gymnasts are strength specialists ~ they need feats of strength in specific movements. (Team) Sport athletes are strength generalists ~ they need general strength in movement patterns that build up general organism strength and resilience and provide performance transfer to the field and most notably to improve run, jump and throw (add maybe carry, tackle, throw down, kick, punch) – in other words also general movement patterns, and here comes the drums, which are common to most humans and hence sports. Nothing extremely special in the sprint, jump and throw (and other patterns) between sports that is not already being taken cared of by practicing one’s sport anyway.

Going back to strength specialists vs. generalists. Strength specialists approach strength training as either (1) skill training and skill acquisition, or as (2) ‘biomotor quality’ training or some combo solution between the two. The former train their lifts frequently and approach it as a ‘form’ (skill). The latter approach strength training as a ‘substance’ – these usually train specific lifts less frequently and try to increase strength as a ‘biomotor ability’ rather than as a specific skill. Think of this as Sheiko vs. Westside. This is what I call “The Root Problem: Substance vs. Form” and I actually did the whole presentation on it (click HERE and HERE).

A lot of sports ‘suffer’ from the similar problem: for example throwers in certain schools (or should I say CULTURES?) did ‘substance’ training to increase strength and only used actual throwing to ‘realize’ that substance into competitive form; others, with the prime example being Anatoly Bondarchuk did throws to improve ‘special strength’ and skills and put ‘substance’ training on hold after certain level is reached. I have also tried to explain my rationale for inclusion of running-based conditioning (‘substance’) alongside with play practices (‘form’) in team sports HERE.

It is important to realize that even strength specialists differ in their approach and philosophy (in how they solved the Root Problem). Anyway, strength generalists should always have transfer and injury resilience as a main objective and not pursuing strength feats number, although they do provide certain guidelines, possible thresholds and motivating goals.

Hence there is no need to split the hair whether front squats are better than back squats or trap bar deadlift/squat as long as we provide progressive overload and variety in double led squat pattern with our athletes without making them injured in the process. Some coaches differ on the dogmatic scale regarding how much they fall in love in certain exercises and how much they defend their “Precious” exercises. Their athletes buy in into those and hence we have a culture developed. And cultures differ, not the reality.

In team sports physical performance’ relationship to either game outcome or physical qualities of the players, is simply more complex, as Martin Buchheit would say. Things are not linear ~ they are complexly moderated and mediated between a lot of factors. Some coaches and researches would love us to believe that things are simple and linear: increase your aerobic power, which will increase your running/physical performance in a game (run more), which will make you dominate over the opponents, which will make you win. Unfortunately reality is far, far more complex than that. 

To summarize this before it becomes too long:

• Sometimes it is the culture that differs between sports the most, not physical needs. Culture specific vs. sport specific needs and differences.

• We are dealing with humans in most of the sports (if you didn’t realized this statement has some joke elements) ~ humans need to run, jump, throw, kick, punch, tackle, carry, throw down. They need to perform these tasks in their respectable sports. Improving these is the goal of physical preparation – there are some sport-specific differences, but things are more similar than they are different.

• The aim of physical preparation is not to make powerlifters of our athletes, nor to cuddle them with ‘sport-specific’ strength training (read: crap training involving some circus tricks while balancing on bosu ball, because, hey! sport  movements are done on a single leg in unstable environment). There is also no point in falling in love with certain exercises. Take care of movement patterns ~ create safe, progressive and variable training environment. Also make sure do to what NEEDS to be done, not only what CAN be done. This is often the problem, so we need to balance the two and find the best solution.

• There are no clear linear causal links between physical attributes, physical game performance and game outcome which make this more complex, but also more interesting. Some elements are more linked, some are not. Some links are moderated and mediated. Don’t be dogmatic – understand and appreciate the complexity

• Physical preparation in my opinion is 50% human specific (we need to improve the general movement patterns: run, jump, throw and others), 30% sport/culture specific (how are these movements performed in a sport and how much; how are they “modulated” taking into account skill related factors; positional demands and injury tendencies; what are cultural differences of the sport; what are sport view how these should be developed and approached) and 20% individual specific (individual player motivation and characteristics, preferences, injury history and tendencies)

(Not so) Random Thoughts - November 2013

(Not so) Random Thoughts - November 2013

End of the season – Good bye and thanks to Hammarby IF

The season 2013 ended with the last game on the home stadium Tele2 Arena on November 2^nd with a win against Östersunds FK.

It was a pretty rough ride this season with a coach change, new stadium and new training ground (currently in the making process). Unfortunately, we haven’t been able to qualify to play highest division in Sweden (Allsvenskan) this year, and I feel very sorry for this to all club supporters and players themselves. I wish all the best to the club and to the players, along with the great supporters in the upcoming seasons. Hammarby deserves to play in Allsvenskan, and Allsvenskan needs Hammarby and their supporters.

Unfortunately, it is time for me to move on. After the November, during which I will help the coaching staff with 4 week training block, I will head back to Serbia. I don’t have much in plan, but I am thinking about pursuing PhD and I have applied for two potential PhD positions in Australia and New Zealand. In the mean time I plan catching up with missed time with family and friends (everyone who works/lives outside of their homeland knows what I am talking about), training, reading, skiing and re-evaluating what could have been done differently and better.  

Last two years were amazing and I am more than thankful to Hammarby, players, coaching staff and supporters for providing me opportunity and trust to work as head physical preparation coach. Will miss you all!   

New article on Velocity-based strength training

Together with a friend of mine and a co-author Eamonn Flanagan, strength and conditioning coach for the Irish Rugby Football Union, we have wrote the review paper entitled “Researched applications of velocity based strength training” and got accepted for publication in Journal of Australian Strength and Conditioning. 

The paper will cover topics such as load/velocity profile, minimal velocity threshold, novel velocity/exertion profile, daily 1RM estimates, and using velocity start and velocity stops to prescribe strength training. All this with how-to in Excel. In short it should be very applicable for coaches utilizing linear position transducers (LPTs) like GymAware and Tendo unit. Hopefully it will provide great reference and a starting point for velocity-based strength training.

Pragmatic or sport-specific approach in exercise testing and evaluation?

I urge everyone to read the recent opinion and point-counterpoint article by Alberto Mendez-Villanueva and Martin Buchheit in Journal of Sport Sciences: Football-specific fitness testing: adding value or confirming the evidence?, and great paper by Chris Carling (see the interview with Chris HERE) in Sports Medicine: Interpreting Physical Performance in Professional Soccer Match-Play: Should We be More Pragmatic in Our Approach?

I believe that the sport specific approach to testing and training has finally started to show its flaws, overseen by its supporters. It has also been a great selling point, for both books and job vacancies, pushed way too far. What about concepts such as human specific or training specific?

As a complementarist, I believe in importance of both sport specific and human specific approach, where one needs to understand details and nuances of the sport and its demands and culture, but also need not to forget how humans in general move, adapt, learn, behave. To be completely honest, I believe that 50% of physical training is human specific, 30% is sport specific and 20% is individual specific. Just don’t put the cart before the horse and remember the Big Rocks story.

Taking this discussion to testing and evaluation field, a lot of coaches and researchers ask what is the sport specific test for a certain factor of success in a given sport? Without going into the discussion what, in this case physical factor is related to success (and how do you describe and quantify success), for example is VO2max related to distance covered and is distance covered to game outcome and season outcome (see the paper by Carling on this), there is the issue are these tests getting anything new on the table to be pragmatically used in training?

I cannot agree more on this with Mendez-Villanueva and Martin Buchheit. What we see is a bunch of testing batteries that only describes (quantitatively) what coaches already know intuitively. Who is the fastest guy, who is the most endurant, who is strongest etc.

This is absolutely not an opinion against testing in general, but against testing (and monitoring) that doesn't have any pragmatic value and provide only descriptive quantification. For example, YoYo test is one of the most researched sport specific test in soccer, simulates the game, change of direction, short rests, and all that yada yada yada. But it gives you a distance that you cannot use in any prescriptive way at all. Ok, I know that one player increased from 2,400m to 2,600m in two months in YoYo test, but what type of actionable information does this gives me or any other coach except for comparing athletes (i.e. descriptive analysis)?

Again, this is not against YoYo testing overall, which can have great importance and value in descriptive roles (e.g. comparing teams, athletes or league levels), but against tests that don’t bring anything usable and actionable (pragmatic) on the table.

A lot of coaches ask me for advice what should be tested with their team. My first reaction is “How do you plan using that number”. My answer depends on their use of that number. I would say that if you don’t plan to use testing scores in any meaningful, actionable and pragmatic way, it might be just a waste of time, money and energy.

Taking this discussion one step further – we tend to focus on outcome or performance tests too much for both testing and monitoring. For example 40m time, vertical jump. Some of those can be used to prescribe training (MAS, 1RMs, etc), but we might miss the process underlying them that was responsible for a performance outcome. For example, different power output between legs in vertical jump, dip in vertical jump, etc.

Yes – we need specialized and expensive equipment for these, but they might tell us more about HOW certain performance is achieved. Human body is famous for system degeneracy. “Degeneracy is a property of complex systems in which structurally different components of the system interact to provide distinct ways to achieve the same performance outcome” -- from Sports Med. 2013 Jan;43(1):1-7.

In other words, especially for monitoring of training readiness, adaptation and overtraining, having an insight HOW are things achieved, can be more informative than only how much someone run, jumped, lifted or throw. Besides they can give PRAGMATICAL information about what should/can be done to improve performance (prescriptive vs. descriptive).

In terms of monitoring for neuromuscular fatigue (NMF), a lot of coaches use jump assessment. They track jump height over period of time to see any meaningful drops in performance. What might happen and it might be very meaningful, is that even without change in performance (vertical height) athletes might use different process to achieve same score – using smaller dip, longer contraction time, etc that could be very meaningful in NMF assessment and even injury prevention.

I hope that this random though raised some questions – and that was the whole point of it. One more time I am not bashing testing in general, but testing without purpose and pragmatic value. Sometimes this pragmatic value is only descriptive, sometimes, and we should aim toward this, is more prescriptive. It should also give us some information that we don’t already know about the athletes and something that we can use to break the performance plateaus and prevent injuries.

Inferential statistics for coaches? Naaaah!

 There is HUGE discrepancy between analysis and visualization for coaches and clubs and researchers and journals. What researchers and journals are interested about are is that “is there effect in the population (usually on average)”. Since researchers can’t measure the whole population (this is no Earth population, but certain population involved in research question, like elite rugby player, elder lifters, etc) they pick up smaller samples. Using inferential (inferential means providing estimates to population based on samples) statistics, researcher look if the effect in the sample have any statistical significance to a population. This is usually expressed with P<0.05 or confidence intervals using Null hypothesis testing (null hypothesis is that there is no effect in the population).

One example might be if cold baths improve recovery in soccer players. Since the question involves all soccer players, and researchers cannot test all of them, the take a random sample and create experiment research. One group (experimental group) get the cold bath treatment and other group (control group) don’t do it. Then they measure some performance estimate (e.g. vertical jump) or subjective feeling (rating of how sore or tire you are) and compare between groups. For an example, both groups had mean vertical jump of 44cm before treatment (cold bath). After treatment, experimental group improved to 50cm while control group to 46cm. 

Most of the researchers are not interested in practical meaningfulness or significance of such effect (luckily with Will Hopkins and magnitude based inferences this is changing), but rather into something that is called statistical significance. In the case of cold bath, researchers want to see if the effect is significant in the population. 

Using null hypothesis testing (null hypothesis being no effect of cold batch treatment) they estimate how probable occurrence of the effect is if the null hypothesis is true. This is called P Value (for more info check statistics books). Everything under P<0.05 has effect and usually gets published. There is no talk of magnitude of this effect – only if it is statistically significant (and that is VERY influenced by number of subjects – more money, more subjects, higher chance of seeing an effect and getting published and getting your research score).

All of this inferential statistic for researchers is interested whether there is an effect (on average) in the population. Even if there is an effect, the range of that effect might differ a lot. Remember the story behind averages? “Having my feet in the oven, head in the freezer, on average I am just fine”.

Coaches on the other hand are not interested into making inferences to a population. They are interested in the SINGLE athletes, not averages. No wonder they don’t understand inferential data analysis, because they don’t need it.

It is beyond me, why the sport scientists still present analysis and figures to the coaches using inferential statistics and figures (averages), along with using normality assumptions that are usually violated. Besides, inferential statistics is afraid of outliers, while in sport outliers and their discovery are of utmost importance.

Coaches are not interested does altitude training have an effect on average in the population of elite athletes, but rather will it work for John, Mickey and Sarah. Single case studies. Ranges in single case studies.

What I had in mind is to write a practical paper outlining the best methods for simple descriptive analysis and visualization techniques that coaches can use from day one. Using Smallest Worthwhile Effect (SWE), Typical Error (TE) and how to visualize them to get the idea of practical significance of the effect. I am currently in the process of collecting good visualization practices for this purpose and trying to put them to either Excel or R/ggplot2 (I believe R is going to be the choice) and write a review paper.

Anyone who might provide any help, or is interested in contributing is welcome to contact me.

Setting goals and stoicism

Take any psychology book or training book and it will talk about setting goals and goals classification to (1) outcome, (2) performance and (3) process goals. 

Outcome goals are related to competition results, like "I want to be first in competition", "We want to get over 50 point in the league", etc.

Performance goals are related to, well performance improvements that could increase chances of acquiring outcome goals - "I want to improve my shooting percentage", "I want to improve my 1RM for 2,5%", "I want to improve my minutes per mile for 10 seconds", etc.

Process goals are related to the journey and training. "I want to give my best effort, sleep well, and get that training done", "I want to accumulate over 100 TSS units daily on a bike", "I want to get to the gym 5 times a week"

I believe that we are too focused on performance goals and neglect the journey or process goals. Outcome and performance goals give us direction, but sometimes we cannot control reaching of them and can lead to a frustration, burnout, even if they give us purpose and direction. 

Biology of adaptation is complex - we can vary in our reactions to training and adaptations. We can't control a lot of things, beyond training hard and smart, sleeping extra, eating well, etc. If we focus on end-points, especially the end-points we cannot control it might lead to a burnout and disappointments.

Last year I was reading a lot about stoicism (one great book to consider is A Guide to Good Life by William Irvine) and it really influenced me, although it is guide hard to practice and fight the "inner Chimp", but it a great philosophy.

Anyway, the following picture is a pure gold and based on stoic principles of control. I believe we should spend more time on setting process goals and actions we can control and which we enjoy (the journey) instead of tunnel-vision approach to training goals. 

Set the important process goals (aimed at achieving certain outcomes/performance) and get to action. That is your control. You can't control your opponent, how is your body going to react, referees, etc. It is beyond your control and thus not worth of worrying.  

Real-Time Fatigue Monitoring using Metabolic Power and CP/W'

Explanation of the idea

Over the years there were couple of tries to monitor fatigue real-time in team sports, such as soccer. The most simple one was to use heart rate (HR) data and see if the players were getting tired by checking if the HR was getting higher and higher. Long story short - this doesn't work and I have no clue where the idea that increased HR is related to fatigue in the first place.

With the development in technology like GPS used for position tracking of the players, sport scientist showed that the distance covered and distance covered at higher velocities in the last minutes of the game is droping down. Eureka - it has to be fatigue! Or is it?

But then, it has been shown that no matter the fitness level of the player, everybody experience drop in performance during the last minutes of the game. What happens during that time in the game tactically? Depending on the score, leading team will try to put the ball in the corners, protect the ball or get it outside the bounds. In other words slow down the game. So the drop have nothing to do with fatigue, but rather game demand.

Combining the internal (HR) and external work (GPS) might give us some insight regarding the cost of activity. If my HR gets higher for the same level of external work, that might indicate fatigue. I call this the efficiency score. This is definitely interesting idea.

Approach that I am going to present in this how-to article is combining what we know about athletes' potential and the expression of this potential in the game. One such approach is Critical Power (CP) model. I am not going to talk much on CP model, but rather direct you to a great studies and papers, such as:

• Using scatterplots to teach the critical power concept
• Application of critical power in sport
• New Insights into High Intensity Interval Training
• Rationale and resources for teaching the mathematical modeling of athletic training and performance
• 3-min all-out exercise test for running
• Modeling the Expenditure and Reconstitution of Work Capacity above Critical Power

To make this simple as possible, power output over CP starts spending limited anaerobic reserve or W' (read W prime). Using known CP and W' for an individual make it possible to predict distance times and exhaustion times. According to CP/W' model, exhaustion happens when one spends all of his W'. The more W' you spend, the more you are tired. You can find more in the links above

The original real time monitoring idea comes from Philip Skiba. He used this approach in cycling because it is easy to measure power output on the bike. Knowing CP/W' of the cyclist he can visualize loss in W' and make appropriate adjustments in pace. The only problem is that we can't measure power output in soccer for example. Or can we?

I have wrote previously about this concept of measuring/estimating Metabolic Power (MP). You can find more about it HERE. Using this algorithm (combining velocity and acceleration data), Catapult GPS devices estimate and report MP and I was lucky enough to have couple of them at my disposal.

Unfortunately there is still a lot to do when it comes to MP estimates, like validity studies and reliability studies. I will assume that MP estimates are valid and reliable in this how-to article.

Ok - we have MP estimates from Catapult system, but how do we know CP/W' of the player? One approach would include doing 3 or more exhausting runs to estimate CP/W' (see the links above). This is not time nor energy efficient.

Luckily, Dr. Robert Pettitt et al. created the 3 min all-out test (3 MT) for running (see the links above). Using simple 3 min all out (simple, but not easy!) we can get CP/W' for each player. But here is the catch - the 3 MT uses straight running (around 400m track) and the estimates we get are critical velocity and D'. We need to convert that to CP/W' somehow.

Wearing Catapult devices and estimating instant MP might be solution to this. Another modification might be to make the test in shuttle mode, instead of straight line. This will involve more change-of-directions (CODs) and it will make it sport specific.

Not sure if anyone did this though.

Here is the outline of the whole idea:

• Use Catapult GPS (or other that support MP) to get instant MP
• Perform 3 MT in shuttle mode (e.g. 20-40m shuttles) to get players' CP/W' while wearing Catapult GPS devices
• Use CP/W' together with instant MP from Catapult GPS to get level of W' and thus instant level of fatigue

Here is the how-to algorithm using exported MP data from Catapult Sprint and R for doing calculus.

Calculus in R

Load the exported data from Catapult Sprint software and do the basic data manipulation. Data comes from one small sided game (SSG) that lasted slightly less than 7 minutes. Data is for one player and sampling frequency is 10Hz. You can download the data set HERE.

sampling <- 1/10

# Load CVS data from Catapult Export File, Sampling = 10Hz
Catapult.data <- read.csv("Catapult GPS Data.csv", header = TRUE, skip = 7, 
    stringsAsFactors = FALSE)

# Create time vector, since we know sampling frequency of 10Hz
Catapult.data$Time <- seq(from = 0, by = sampling, length.out = length(Catapult.data$Metabolic.Power))

Here is the look of the data in Catapult.data data frame

str(Catapult.data)

## 'data.frame':    3931 obs. of  3 variables:
##  $ Time           : num  0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 ...
##  $ Metabolic.Power: num  0.003 0.057 0.59 2.481 2.994 ...
##  $ X              : logi  NA NA NA NA NA NA ...

We have that X logical element that I don't know what it is doing there so we will remove it

Catapult.data$X <- NULL

Here is the Histogram (density plot) of Metabolic Power data

library(ggplot2)

ggplot(Catapult.data, aes(x = Metabolic.Power)) + geom_density(fill = "blue", 
    color = "darkblue", alpha = 0.5)

As can be seen from the plot, most of the scores are <25 W/kg (note that VO2max or MAP - Maximal Aerobic Power is usually associated with 20 W/kg – see this blog post ), although there is a certain data spread (min = 0 and max = 86.62). Some of it might be the measurement error ( those over 100 W/kg) [That's why we need validity and reliability studies]

Here is the box plot. Note the outliers.

# create boxplot that includes outliers
ggplot(Catapult.data, aes(y = Metabolic.Power)) + geom_boxplot(aes(x = 1), fill = "steelblue", 
    alpha = 0.4, outlier.colour = "blue", outlier.shape = 23, outlier.size = 1) + 
    stat_summary(aes(x = 1), fun.y = "mean", geom = "point", shape = 23, size = 3, 
        fill = "white") + labs(x = "")

I am not going to clean the outliers for now, but we usually should do it. For the sake of this example it is not necessary. Please note that outliers might be measurement error or really hard acceleration. This is why we need validity and reliability studies.

Here is the Metabolic Power over the duration of the sample (in this case SSG for 7 minutes)

# create boxplot that includes outliers
ggplot(Catapult.data, aes(y = Metabolic.Power, x = Time)) + geom_area(fill = "blue", 
    alpha = 0.4, color = "dark blue")

Let's assume that critical power of the athlete is 13 W/kg. What we need to create is the difference between CP and MP and create a factor that tells wether we are in aerobic or anaerobic zone (according to CP/W' model, all work done over CP uses W' or anaerobic capacity - hence the call anaerobic, although metabolism is not that black or white)

Athlete.CP <- 13  # Critical Power (This is individualized)
Catapult.data$CP <- Athlete.CP

# Calculate the difference between CP and MP
Catapult.data$difference <- with(Catapult.data, (CP - Metabolic.Power))

# Code the phases into aerobic or anaerobic. When difference between MP and
# CP is <0 that's anaerobic work. If it is >= 0 that's aerobic work
Catapult.data$Energy.Source <- factor(with(Catapult.data, ifelse(difference >= 
    0, "Aerobic", "Anaerobic")))

Now we can compare Aerobic and Anaerobic work comparing the time spent in each (in this case number of samples)

ggplot(Catapult.data, aes(x = Energy.Source, fill = Energy.Source)) + geom_bar(stat = "bin", 
    alpha = 0.5, color = "black")

Now it is the time to calculate spending and replenishing of W'. Please note that because MP and CP are expressed relatively to athletes BW then W' will also be expressed relatively to athlete's bodyweight. One assumption used here is that replenishment of W' is THREE time slower than W' depletion.

Let's assume that W' is 300 J/kg

One trick that needs to be covered is that W' needs to be capped to 300. In other way, if instant W' is 300 and MP < CP then the W' cannot increase. It can only decrease, and not below zero

Athlete.W.prime = 300
Catapult.data$W.prime = Athlete.W.prime

for (i in seq(from = 2, to = length(Catapult.data$Metabolic.Power))) {
    w.change <- Catapult.data$difference[i] * sampling

    if (w.change > 0) 
        w.change <- w.change/3

    new.w <- Catapult.data$W.prime[i - 1] + w.change

    if (new.w < Athlete.W.prime) 
        Catapult.data$W.prime[i] <- new.w else Catapult.data$W.prime[i] <- Athlete.W.prime
}

On the following chart we can see what is happening with W' over time

ggplot(Catapult.data, aes(x = Time, y = W.prime)) + geom_path(color = "blue")

Now we can write a function that calculates W' for a given data set and use it later in a nice simulation. We will add additional parameter W.start that defines the level of W' at the beginning of the sample. By the default this is same as athletes W'.

calculate.W.prime <- function(MetabolicPower, Time, Athlete.CP = 13, Athlete.W = 300, 
    Start.W = Athlete.W, replanishment.ratio = 1/3) {

    # Create vectors and everything else needed for calculus
    data.length <- length(MetabolicPower)
    W.prime <- rep(Start.W, data.length)
    dt <- c(NA, diff(Time))
    MP.difference <- Athlete.CP - MetabolicPower

    # Loop through elements and calculate W'
    for (i in seq(from = 2, to = data.length)) {
        w.change <- MP.difference[i] * dt[i]  # Calculate change in W

        if (w.change > 0) 
            w.change <- w.change * replanishment.ratio  # Adjust ratio

        new.w <- W.prime[i - 1] + w.change  # Calculate new W 

        # Check that we don't go over Athlete.W.prime
        if (new.w < Athlete.W.prime) 
            W.prime[i] <- new.w else W.prime[i] <- Athlete.W.prime

        # Check that we don't go below zero or exhaustion
        if (new.w < 0) 
            W.prime[i] <- 0
    }

    return(W.prime)
}

Simulation

Now when we have function to easily calculate W' from data let's make a small simulation. Using the existing data from Catapult.data data.frame, let's assume that we have four players with different levels of CP, but the same levels of W'. In other words guys that differ in their aerobic fitness. What we want to see is how the same external work influences their W' levels over the duration of SSG drill.

Simulated.W <- data.frame(Time = Catapult.data$Time, MP = Catapult.data$Metabolic.Power)

Simulated.W$Very.Low.Aerobic = with(Simulated.W, calculate.W.prime(MP, Time, 
    Athlete.CP = 11, Athlete.W = 300))

Simulated.W$Low.Aerobic = with(Simulated.W, calculate.W.prime(MP, Time, Athlete.CP = 12, 
    Athlete.W = 300))

Simulated.W$Medium.Aerobic = with(Simulated.W, calculate.W.prime(MP, Time, Athlete.CP = 13, 
    Athlete.W = 300))

Simulated.W$High.Aerobic = with(Simulated.W, calculate.W.prime(MP, Time, Athlete.CP = 14, 
    Athlete.W = 300))

Let's plot the scores, but before that we need to convert wide data format to long data format that is used by gglplot2. We will use melt function from reshape2 package.

library(reshape2)

drawing.data <- melt(Simulated.W, id.vars = c("Time", "MP"), variable.name = "Group", 
    na.rm = FALSE, )

levels(drawing.data$Group) <- c("Very Low Aerobic", "Low Aerobic", "Medium Aerobic", 
    "High Aerobic")

ggplot(drawing.data, aes(x = Time, y = value, color = Group)) + geom_path() + 
    labs(x = "Time", y = "W'", fill = "Aerobic Fitness") + guides(color = guide_legend(reverse = TRUE))

As can be seen from the picture, athletes with lower CP will reach exhaustion, and won't be able to achieve such a work rate. Higher the CP, the less one spends W' for the sample supramaximal work and quicker he replenishes W' for work under CP.

Utilizing this approach might allow coaches real time fatigue monitoring of the players during the game or training.

Validation

This is very interesting approach to fatigue estimation. But it needs to be validated. One approach might involve measuring levels of performance before, in the half time and at the end of the game and comparing this drop to W' drop. If this is correlated, then W' could be used for real-time estimation of fatigue.

One thing to note though - if we use very short burst type tests, like 10m sprint or vertical jump, we might not get the correlation with W'. One interesting study by Marcora et al. showed this. Researchers need to pay attention what they use for criterion measure. I believe that the drop in W' will mostly correlated with drop in a criterion test that has some duration like Wingate 30sec test on the bike, not necessarily drop in vertical jump height or power for example.

Conclusion

I guess this represent one very interesting method with a lot of potential applications. This could easily be PhD thesis and a source of ideas for research. All I hope is that the potential users (companies, researchers, clubs) of this approach will remember to reference this blog and it's author.