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Sunday, 1 September 2013

Random thoughts on GPS: Using absolute vs. relative velocity zones

Posted on 12:43 by Unknown

Random thoughts on GPS: Using absolute vs. relative velocity zones


Suppose you are coaching two distance runners, one with MAS (Maximum Aerobic Speed) of 18km/h and one with MAS of 21 km/h. The former is definitely more aerobically fit and would crush the latter in most middle to long distances (taking into account that threshold is proportional to MAS).

How would you approach their training and analysis of training loads? Would you prescribe threshold pace (tempo runs) of the same speed and distance to both, or would you take into account their individual differences (MAS level)? In other words, would you say do 2x10min @80% MAS or would you say 2x3000m @80% MAS? Or even worse say run for 2x10min at 16km/h to both of them?

In the case where distance is prescribed the slower runner will experience significantly higher workload. (Tip: This is why it is retarded for up and coming runners to copy weekly mileages of high level runners – might be more injury-proof to copy their time spent running although this might also be too strenuous)

Taking this discussion a bit further – would you say they were running at same intensity if they were running at 15km/h for certain time? Of course not – intensity in this case have to be expressed relatively to their abilities. In strength training world we use %1RM to solve this dilemma.

Unfortunately, gold medals are not given to those who express highest effort to their ability (“I got 11th place, but I was running at 95% of my VO2max, while the guy that won  run at 93% of his VO2max – that’s not fair!”) – They are given to those who express highest effort absolutely. In endurance world, races are not 10min time trials, but rather distances, like 5k, 10k and so forth. The winner is the one that covers those distances in least time, not someone that run them at higher %MAS.

What this all mean? It means that athletes and coaches should juggle both individual characteristics and distance demands (in this case distance, terrain, etc). They should plan their training taking into account both, but mostly leaning more toward individual characteristics at least in running world. This is the dilemma that bother a lot of coaches in team sports as I have alluded HERE.  

Long story short – we prescribe training loads and analyze those loads for a single-subject based on relative indicators and compare individuals (give medals) based on absolute indicators. We need to take into account both (again complementarity).

With the recent advances and spread of GPS tracking technologies in team sports one stumbles on the same dilemma. The velocity bands are prescribed absolutely and training/game[1]effort is sometimes judged by distance/time covered at certain absolute zone. As Martin would say “things are simply more complex”.

What I would love is to express some of my random thoughts on this topic.

When it comes to training, coaches[2]would love to know who really gave an effort and who was slacking. Some of them would also love to know if the training stimuli is enough to stimulate adaptation response in, for example, aerobic capacities. Some coaches prescribe extra activities for certain players who didn’t spend certain time in certain [ABSOLUTE] zone during team session [because they believe these were not pushing hard].

This might work as a motivator to the players, but it might be short sighted. First of all, one needs to take positional demands for the activity (in the match for example, position played affect physical aspects more than individual characteristic) and one needs to take individual characteristics. In other words taking into account law of demand and supply (individual characteristics~positional demands).

Using absolute zones we ASSUME that demands were the same for every player, we ASSUME that every player has the same needs for training workload (e.g. time spend at certain zone to yield adaptation), we ASSUME that every player can give the same amount of effort regardless of their individual qualities. In my mind this is a lot of assumptions.

Prescribing time/distance spent in certain absolute velocity bands as a training stimuli or quality/effort control is like saying to a certain lifter that he needs to have 10 total reps over 160kg during a strength workout, without noticing that his 1RM is actually 160kg. In squat. But today he did bench press. But I digress.

So I believe that FOR THIS PURPOSE (workload analysis, quality/effort control) one should use RELATIVE zones. But this is not without problems. It is important to realize to there is no best way –  don’t fall into the saying “if the only tool you have is the hammer, everything starts to look like a nail”. Pick the right analysis for the right job. In this case the task of analysis of individual workloads might demands relative approach.

What should be those zones based on? One could use MAS and MSS (Maximum Sprinting Speed) and ANR (Anaerobic Reserve; MSS – MAS). Or one could use CV (Critical Velocity) and MSS. Or v3mmol (velocity at 3  mmol/L LA) or LT or whatever. I suggest using one you could actually retest easily every once in a while. I like the approach used by Buchheit et al. where they used (Z1) <60% MAS, (Z2) 60-80% MAS, (Z3) 80-100% MAS, (Z4) 100% MAS to 30% ASR, and (Z5) above 30% ASR.

In the study I quoted in footnotes they showed that players with higher MAS covered LOWER distance at zones over MAS during a game. Were they slacking? No. They were fulfilling the absolute positional demands by stressing themselves less in relative terms. There is NO direct causality link between being more aerobically fit and running more in a game. Being more aerobically fit might mean that you are stressing yourself less and this might yield less fatigue related errors in technique, etc. One thing to remember is that regardless of MAS levels, distances below MAS were decreased in the second half. So, even if one might increase his MAS, there still might be decrease in distance covered over duration of the game. Again, simply very complex things. We are still clueless what causes this – fatigue or tactical demands, or both.

When it comes to SSGs there might be a ceiling effect [absolute indicators] or even a drop [relative], which mean that players with higher MAS might spend less time/distance[3]in higher intensity zones. We would need both cross and longitudinal studies to confirm this idea.  



What this might mean is that SSG might not be enough to provide an overload for players with higher MAS. On the flip side it might also mean that further increase in MAS might not yield any benefit in distance covered during  SSG/practices/games, so there is no point in increasing it from that aspect. Simply more complex

Speaking of relative zones there also might be a question of their practical use. For example, the MAS and ANR levels might not vary much between players for a given team compared to Typical Error of GPS estimates. So this comes to signal vs noise problem. We might utilize relative velocity zones between players that vary around 0.5km/h, but typical error for GPS velocity estimates might be around that value which questionable practical usability of relative zones approach. It is beyond my statistical knowledge how to evaluate this. We need more research on this and the recent paper by Martin Buchheit is a move to a right direction.

Hope that this post got you thinking because things are simply more complex. Use your brain and use the right analysis to get the job done. IMHO, we need both relative and absolute approach for different purposes.










[1] It is beyond the topic of this blog entry to discuss further implications of individual characteristics (e.g. MAS) to game related performance (expressed in both absolute and relative terms) so I am directing you to the papers by Martin Buchheit et al., like Mendez-Villanueva, A., Buchheit, M., Simpson, B. M., & Bourdon, P. C. (2013). Match play intensity distribution in youth soccer. International Journal of Sports Medicine, 34, 101– 110.
[2]Coaches in team sports should stop worrying too much on the physical aspect of the performance and training loads and worry more about skill acquisition IMHO. A lot of them are pursuing blindly certain drills because players tend to spend X amount of time in this/or that HR/Velocity zone, instead of worrying on what are they trying to coach/teach group or individual (what technical/tactical/team play aspect). In team sports SKILL and team effort kills, especially in more skill related sports like soccer (this might be a discussion on itself regarding the differences between sports when it comes to worrying on physical performance aspects compared to skill acquisition aspects)
[3] I believe this should be expressed as time instead of distance for the same reasons outlined with two runners at the beginning of this blog post. 
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Posted in analysis, conditioning, monitoring, Performance Analysis, Random Thoughts, Research, runnings, soccer, statistics, team sports, Theory | No comments

Sunday, 25 August 2013

Strength Card Builder 1.1

Posted on 10:08 by Unknown

Strength Card Builder 1.1



Because of the interest, I decided to modify and update my Excel workbook I have been using for creating strength programs for groups and individuals. 

In the video below you can see all the features of this workbook. One of the key features is the ability to create GROUP workout cards based on players 1RMs in core/key exercises, along with writing your own set & rep schemes.

This product is not available any more. Please look for the new version







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Posted in Download, Excel, powerlifting, Product, programming, screen cast, strength training, team sports, videos | No comments

Friday, 23 August 2013

Optimizing groups for Small Sided Games (SSGs)

Posted on 04:41 by Unknown

Optimizing groups for Small Sided Games (SSGs)


In the following video I will show you how to

  • Calculate normal scores for any test/statistic (percentiles & z-scores) and the difference between them
  • Calculate composite scores using weighting factors and normal scores
  • Use one great Excel tool – SOLVER to provide optimized solution for groups

The rationale behind this approach is that we want groups of players that are balanced in some way (we decide based on what parameters). In the video below I have used skill rating and MAS score, but you can easily expand that to include daily wellness or some fatigue score, or anything else you want.

The idea is that having a balanced groups might produce higher level of competition and on a same quality level. This might be interesting research project to see if balanced vs. unbalanced groups show different performance readings (GPS, iTRIMP, etc) and what option might be better for certain goals.



You can download the workbook HERE (I have updated it a bit compared to the one in the video)





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Posted in analysis, conditioning, Download, energy system development, Excel, screen cast, soccer, team sports, videos | No comments

Wednesday, 14 August 2013

Individual Qualities vs Positional Demands

Posted on 11:26 by Unknown

Individual Qualities vs Positional Demands


One frequent question I get from coaches and try to resolve myself is whether the conditioning should be based on individual characteristics (MAS, YOYO, VMAX, etc) OR based on position demands?

Regarding the position demands: how do we quantify them and what is the worthwhile difference (SWC) between positions that warrants different training prescription? Most of the studies focused on p values instead of SWC and TE. Speaking of TE (Typical Error) there is a huge %CV (coefficient of variation) in game-related data (which means that distances covered vary as much as 30% from game to game). This further complicated applicable positional differences from practical and physical preparation standpoint.

Ok, suppose we know the positional demands for our level of playing - should we focus on where we are in these demands or where we want to be (play)?

Suppose that we don't take individual qualities into consideration and we impose positional demands based on where we want to be on the players. How are we certain they are not being loaded too much or too little?

In ideal world player physical qualities should coincide with his positional demands. Look at this as the law of supply and demand (supply~demand) or potential~expression. Sometimes this might not be the case because players don't play certain position solely on their physical qualities, but also technical, decision making, mental and so forth.

I believe in complementary approach. I also love the approach by Carlo Buzzichelli -

INTENSITY: Individual Quality
VOLUME: Position Demands
ORGANIZATION: Positional Demands
WORK:REST: Positional Demands and Individual Quality
SITUATION/POSITON: Positional Demand

Again certain drills might be more 'suited' for certain positions, but in short if FB are running longer distances (see GPS data on duration and length of efforts in certain velocity zones) than FW, then FW might perform conditioning in shuttles and FBs in straight line [for example]. Another might be the volume - all positions run at certain %MAS, but MFs might do some extra set.

Again, this is not static picture - the emphasis might shift over time and over pre-season/season.

When it comes to blending technique with conditioning [e.g. doing conditioning with finishing for FWs, conditioning with heading the ball out for CDs, etc] I believe positional demands will dominate [and not his technical/tactical qualities, yet again it depends]

Another way to look at this [dichotomist] problem is to use SSGs and games overall to put certain individual into the most position specific context. Thus, if this is solved with practices, is there a need to do it with conditioning too? And why are we splitting these two anyway? [see my presentation on Periodization Confusion].

How much specific work is too much? When does the specific work fails to provide overload and adaptation? When does the adaptation/overload fails to bring transfer to specific work?

Unfortunately I don't have THE answer, except stating that coaches should learn to reconcile and juggle with these two dichotomies [I solved it by using Squiggle Sense] and not to lean too much on pre-made solutions and philosophies. What I mean by the latter is that one needs to take complexity of biological adaptation and skill acquisition of each individual into consideration instead of pursuing certain rigid approach. The solution is smart monitoring and predictive analytics for each individual. This is the work in progress - take the empiricist/experimental stance and test your hypothesis for each individual, instead of rationalizing things based on who said what.

Unfortunately again, taking a stance of empiricist is not easy - it demand knowing what to measure, how to analyze it, how to compare it to other measures and how to make reliable action steps.



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Posted in analysis, conditioning, energy system development, general vs. specific, monitoring, Performance Analysis, Random Thoughts, soccer, statistics, team sports, Theory | No comments

Naming the HIT drills

Posted on 08:42 by Unknown

Naming the HIT drills


I love the idea of naming certain drills/workouts (like Crossfit does using female names) because it is easier for the athlete to ‘personalize’ them and remember them. So, instead of saying we are going to do 30:30 intervals at 100:70% MAS, it might be easier to say “Boys, get ready for Bloody Mary”. 

In the table below I have presented couple of HIT variations, including SIT (Sprint Interval Training, a.k.a Anaerobic Power/Capacity) and RST (Repeat Sprint Training). Think of those at tools in your toolbox – Long HIT, Short HIT, SIT, RST. 

One interesting ‘finding’ is that ‘playing’ with work:rest and active:passive we are able to come up with different variations. This might be important boredom wise, if nothing else.

What I was thinking to do is to name these drills/exercises using (a) girl names, (b) cocktail/spirit names, (c) work tools, (d) guns or (e) anything else manly. If you have some ideas be free to put it down in comments.


MAS stands for Maximum Aerobic Speed

Mean %MAS is average/mean intensity of the drill taking duration and intensity of both rest and work periods

For passive rest I have took walking and approximated it to 30% MAS (which is later used in Mean %MAS calculus)

Also, make sure to read the best review paper on HIT by Martin Buchheit and Paul Laursen 

High-intensity interval training, solutions to the programming puzzle: Part I: cardiopulmonary emphasis.

High-Intensity Interval Training, Solutions to the Programming Puzzle : Part II: Anaerobic Energy, Neuromuscular Load and Practical Applications.


HIT me with the names/ideas! :)


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Posted in conditioning, energy system development, IE20-10, team sports | No comments

Saturday, 10 August 2013

Analyzing Time-Series of Individual Data #2: Using Harmful/Trivial/Beneficial chances

Posted on 09:22 by Unknown
Analyzing Time-Series of Individual Data #2: Using Harmful/Trivial/Beneficial chances


Just a quick update – after a comment by John Fitzpatrick (@JFitz138) on the use of Will Hopkins’s approach of calculating chances I decided to give it a shot.

For a Typical Error (used together with SWC to calculate chances) I used SD of the Rolling Average. 

To calculate chances I have coded simple VBA function that use NORM.DIST function of Excel. 

To calculate chances, one must assume that data points in Rolling average are normally distributed (and that might not be the case!) around their mean with SD. Here is my sketch: (see papers by Hopkins for more)

My wonderful drawing skills

Even simpler approach than this might involve pure counting of days (data points) above, within and below baseline and SWC, especially for non-normally distributed data set (someone correct me if I said something stupid). Using Box and Whisker plot and interquartile ranges might also seem possible solution.



Anyway, here is the short video of the workbook and below you can find update download link. 




Click HERE to download Excel workbook. 
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Posted in analysis, Download, Excel, HRV, Performance Analysis, screen cast, statistics, videos, wellness questionnaire | No comments

Friday, 9 August 2013

Analyzing Time-Series of Individual Data

Posted on 11:44 by Unknown

Analyzing Time-Series of Individual Data

If you haven’t been living in a cave for the last couple of years, you definitely noticed an increase in data collection, data mining and visualization. HRV tracking, jump output tracking, estimating 1RMs from velocity-load data, game statistics, performance analysis, various testing statistics, body weight, Run Keeper, Run Tracker, and all that quantified-self movement. 

Collecting data is getting easier and easier – even without one being aware of it. What is still falling behind is making sense of all that data. For example, you might have been collecting HRV or rest HR every morning for the last couple of months, or even better training load using session RPE and duration. How do you analyze this? How do you visualize this data? How do you make sense of it? How much certain statistic need to drop to provide any worthwhile change and real-world effect?

Luckily, the statistics we learned in school didn’t help us. Too much reliance on Fisherian approach (using p value) and too much usage of statistical significance that doesn’t mean much to a coach. Even worse, they (lay people with no formal education in inferential statistic) misinterpret term statistical significance as real-world significance, instead of low chance [p<0.05, p<0.01, p<0.001 etc] of acquiring such an extreme score if null hypothesis is true. If this sounds confusing – it is, and unfortunately, according to Geoff Cumming (author of excellent Understanding the New Statistics book) even the researchers don’t get these concepts right. 

If you are interested in these subjects you should definitely read everything ever written by Will Hopkins – and I will give you a quick-start presentation one need to read to understand the important concepts of magnitude base statistics and SWC (Smallest Worthwhile Change) and TE (Typical Error):

How to Interpret Changes in an Athletic Performance Test [Very Important read]
A New View of Statistics: Applications of Reliability
Client Assessment and Other New Uses of Reliability [Very Important read]
Making Meaningful Inferences About Magnitudes
A Socratic Dialogue on Comparison of Measures
Progressive Statistics
Understanding Statistics by Using Spreadsheets to Generate and Analyze Samples 

Couple of great researcher, like Martin Buchheit (@mart1buch) are pushing the envelope in using magnitude-based statistics (SWC and TE and chances) – but as far as I know a lot of journal editors are still resistant to forget about p value.

The Dance of p values 

Anyway, as coaches we are not interested in group averages and making an inferences to a populations (at least we shouldn’t if we are not thinking about research career). We are interested in individual response and unfortunately we had a lot of flawed thinking over the years using flaw of the averages and thinking that all individuals will respond in a similar and predictable way. Welcome to the biological complexity. 


Presentation slides from WindSprint 2013

Luckily a lot more studies are leaned toward showing inter-individual variability, quantifying it and visualizing it, besides worrying only on the group averages and whether they get statistically significant effect of the treatments.   

What we need to do is start thinking in terms of individuals and their unique reactions. All training is single subject experiment, even if you work in team sports (a bit harder to implement, but still very important). 

Taisuke Kinugasa (@umekinu) is one of the few researchers focusing on single-case research design and analysis of single-subject time-series. If you are wondering what are single subject time series it is all that data you collect on yourself (quantified self), like HRV. 

Speaking of HRV, recent papers coauthored by Martin Buchheit and other great researchers, brought into light some very applicable tips for coaches to be used on a daily basis. Part of that applicability is using SWC and TE (progressive statistics, magnitude-based approach) and single-case design (in some papers). 

Evaluating Training Adaptation with Heart Rate Measures: A Methodological Comparison. Int J Sports Physiol Perform. 2013

Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison. Eur J Appl Physiol. 2012 Nov;112(11):3729-41

Training Adaptation and Heart Rate Variability in Elite Endurance Athletes: Opening the Door to Effective Monitoring. Sports Med. 2013

Cardiac Parasympathetic Reactivation Following Exercise: Implications for Training Prescription. Sports Med

What they showed is that having either week averages or rolling 7-days averages “appears to be superior method for evaluating positive adaption to training compared with assessing its value on a single isolated day”. 

I have wrote about rolling averages and Z-scores in evaluating wellness data HERE so I won’t go into details too much. 

Another interesting approach was to estimate BASELINE for each athlete and estimate SWC of that baseline. The researchers did this by taking first two weeks of the intervention as baseline. Then this baseline and SWC of it (usually 0.3 to 0.5 of intra-individual SD) is used to estimate ‘context’ to 7-days rolling averages.

Sometime this approach is used in sports and for baseline is taken certain period of the year. Another option is to have ‘rolling’ average as well and that might include longer time frame than 7-days rolling average. Again, there are pros and cons of each approach and analyzing time series is more an art than it is a science. Not sure if there is a right thing to go about it. 

The idea is to get baseline and SWC, and then to use Rolling averages and TE (it is beyond me how is this calculated, except using rolling 7-days SD) to get chances for beneficial/trivial/harmful changes (see links above from Will Hopkins). 

The simplest approach might be to use percent change between last score and rolling average (or longer baseline). Unfortunately this approach doesn’t take individual variability into considerations (see more HERE). 

Another approach that takes this into account is to get daily Z-Score which is number of rolling 7-days SDs that last score is different that rolling average [Z-Score = (Last_Score – Rolling_AVG) / Rolling_SD ]. I believe that this is the approach behind iThlete HRV coding system. If you are out of your normal variability then you get a flag. 

What we want to achieve with all these approaches is ‘flags’ – what is a normal score and what is abnormal. Again this is more art than it is a science, but I believe the right analysis is a must – one just need to put it in the right context. 

Long story short, I have created a Excel workbook that analyses time-series using some of the approaches above. I wanted to thank Andrew Flatt (@andrew_flatt) for providing me with his HRV data and to Andrew Murray (not the tennis player - @cudgie) for giving me an idea of using Effect sizes for comparing Baseline and Rolling average (same as daily Z-Score). 

Here is the video of me demonstrating the software and below you can find a link for downloading the Excel workbook.




Click HERE to download Excel workbook



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Posted in analysis, dashboards, Download, GymAware, HRV, monitoring, screen cast, statistics, Theory, videos, wellness questionnaire | No comments
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