Analyzing Time-Series of Individual Data

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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Some great findings and ideas from velocity-based strength training

Some great findings and ideas from velocity-based strength training

I have been thinking more and more about velocity-based lifting recently as a method of prescribing load and volume for an individual. I have wrote couple of blog posts and article on this topic that you might want to read first: 

Estimating 1RM using load-velocity relationship

Velocity Loss as an Indicator of Neuromuscular Fatigue during Resistance Training

Velocity-based strength training: Short Q&A with Mario Marques

Using PowerTool/GymAware: short video and explanation

Does Speed Work work? My response to Mike Tuchscherer’s article. Part 1

Does Speed Work work? My response to Mike Tuchscherer’s article. Part 2

Is power/speed reading in clean and snatch counterproductive (and other rant)?

Percent-based training vs. Auto-regulatory? Can they be complementary?

The mentioned links should cover the bases. What I want to do now is to discuss possible applications and some interesting findings.

I have re-reading great paper by Izquierdo et al. Effect of loading on unintentional lifting velocity declines during single sets of repetitions to failure during upper and lower extremity muscle actions and I came to couple ideas. 

What the researchers did is to perform Bench Press and Parallel Squat exercises to failure with 60%, 65%, 70% and 75% of 1RM, while trying to perform each rep as fast as possible. 

These graphs are very interesting and I will get back to them, but first I want to convey some of my ‘insights’ using velocity measurement in the gym over the last year and something.

Here is the load-velocity table for my ATG pause back squat and pause bench press I did somewhere in February this year. This is based on the first rep for each load which is usually the fastest. 

I we visualize %1RM used in bench press and squat and mean velocity reached, we get the following graph:

What is immediately apparent is that the slope of the curve for bench press is steeper. We can calculate those and we get -1.46 for bench press and -0.89 for squat. In plain language, one tend to lose more speed with increasing loads in bench press than in squat. 

Another apparent feature is that velocity at 1RM (we are going to call it MVT – minimal velocity threshold) is lower for the bench press (~0.1 m/s) than for squat (~0.3m/s). We can talk all that why this might be the case – but at the end of day it is not really important. What is important is to remember that there is different MVT for every exercise and that they differ from individual to individual (not much thought). 

What might be interesting to find out is weather MVT changes when some improves his 1RM? According to study by González-Badillo and  Sánchez-Medina (Movement Velocity as a Measure of Loading Intensity in Resistance Training) it is not changing over time, at least for bench press

 This is important for couple of reasons – we can use velocity to prescribe intensity. Even more important is that 1RM might vary from day to day due readiness or normal variability, thus using %1RM might be misleading and not taking into account improvements or decrements in strength. Using velocity might solve this issues, along with being auto-regulatory in nature.  Long story short, instead of prescribing 5 reps with 80% 1RM, one might prescribe 5 reps with 0.5m/s starting speed. What is also interesting is that providing immediate feedback might increase motivation, competition, stability of performance and higher improvements – based on the studies by Randell et al. (study1, study2), at least for jump squats – but I believe that this might be true for non-ballistic exercises as well. 

Ok – this is what happens to the velocity of the first (best) rep across loads. But what happens to velocity when we repeat sub-max sets to failure? That is also what the study by Izquierdo et al ought to find out. 

What we can see from the graphs (see at the beginning of this article) is that the velocity across reps is falling down quicker in the bench press than in squat. I will get back to this soon and why is this important. 

What is VERY INTERESTING is the finding that mean velocity in 1RM load is pretty much the same as Mean velocity in the last rep of nRM test. What does this mean is that my last rep in 5RM is probably going to be very close to 0.3m/s for squat and 0.1m/s for bench press.  How can we apply this to practical settings? Well, the closer we come to our MVT in multiple-rep sets, the closer we are to failure. According to a study I blogged about here, the closer we are to failure (indicated by loss of velocity) the higher the neuro-muscular fatigue. Hence, by monitoring last rep velocity we might produce different levels of fatigue in a given set. 

I am interested to see if this prediction holds true across loads (or reps-per-set), or if I have 2 reps in the tank with 12RM load would the velocity be same as when I have 2 reps in the tank with 5RM load? Since I don’t have data for this, I ought to digitalize the data point from the graphs in this study. Here is what I got for bench press

And for squat

What is interesting to note here is that number of reps done with same %1RM is higher in squat than in bench press. 

What I had to do to test my hypothesis (that the velocity for the same number or reps left in the tank across %1RM is similar) is to re-organize this table and visualize it. Here is what I got for bench press and for squat

As you can see from the graphs this relationship between reps left in tank and velocity is sound (it is beyond me how to quantify magnitudes for this – my statistic knowledge is medium); average SD for velocity across reps left in the tank is 0.02 and average %CV is around 5% for both squat and bench press.  

What this means, and is VERY INTERESTING, is that we can estimate proximity of failure based on rep speed (taking into account that the effort to lift fast should be 100%). Not with 100% accuracy, but pretty close. 

I have mentioned that velocity drops a lot faster across reps for bench press than for squat. This is important since certain authors in velocity-based strength training circuits recommend using % drop as a threshold to stop a set. For example, they prescribe starting velocity (e.g. 0.5 m/s) and velocity drop of 10% (stop doing set when velocity drop for more than 10%, and in this case that is 0.45 m/s) for both bench press and squat. Based on the data I have presented I think this is not that smart (I have tried it also, with me and with some players once). It works for bench press, but for squat you might end up doing a lot more reps (especially if there is a bit of bouncing in the hole). Over aprox. 75% 1RM velocities for squat are faster (see bench vs squat load-velocity graph), his velocity drop across reps is slower and hence using % is not the way to go. 

The solution might be prescribing absolute velocity stop. For example starting with 0.45 and finishing when velocity reach 0.4 m/s. 

There is still a lot of practical trial-and-error left to be done, but here are some recommendations

Estimate load-velocity curve for each individual and each core lift when you do 1RM testing Estimate MVT (or velocity at 1RM or last rep) for each individual and each core list If you perform 3-5RM test, estimate the slope of the velocity curve – this might be used later to predict proximity of failure and velocity stops (when to stop the set).   Use associated velocity with certain nRM and/or %1RM (e.g. 0.5m/s for 5RM or 85% 1RM; 0.3 for 1RM or 100% 1RM) and use the velocity to prescribe instead of nRM or %1RM because it is more reliable plus you get a lot more other benefits To manage fatigue in the set, prescribe velocities stops (should I put the trade mark on this one?) for certain reps-left-in-the-tank.  Manage volume (number of sets) by time allotment (e.g. 20min for squats); or by comparing the average velocity of the sets across reps (need to ‘research’ this approach); or by prescribing number of sets based on cycle; or by allowing certain drop in reps from set to set.   When doing multiple sets, one could stick to the weight associated with certain velocity intensity and velocity stops (in this case that might result in drop in reps across sets) or one might decrease weight to maintain starting velocity. I am clueless what method to use – might depend on the cycle or be rotated from time to time.  So the prescription might be something along these lines: 20min time allotment slot for squats (or prescribed number of sets) Starting velocity 0.4m/s  Keep that weight across sets Velocity stop 0.35 m/s If your first rep is equal to or less than 0.35, stop completely even if time is still available Damn, I am becoming to sound like DB Hammer

Anyway, I urge coaches to try velocity-based approach. Make sure to check the best LPT system on the market today – GymAware. Data collection and analysis is walk in the park, along with the setup. 

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Measuring external workload in Boxing using accelerometers

For the last month we are having a pleasure of using MiniMax GPS devices (two of them) which we got for three months testing by courtesy of Catapult. I am short of amazed by the simplicity of it’s use and great software that comes with it – Sprint. 

I have been playing with normal features and uses of Catapult GPS, but my wild (or weird?) spirit won’t let me alone so I wanted to experiment a bit with the units and software while we have them and try something unorthodox to GPS devices.

Actually, the GPS devices by Catapult are equipped with accelerometers (among other great sensors) which are used to get Player Load statistic. Player Load is used to estimate non-running based loads using change in acceleration (three axes). It is very usable in quantifying changes of direction, hits, feints, jumps, etc. So I decided to put the devices in indoor mode and use Player Load feature to try to quantify boxing load.

Disclaimer: Accumulated Player Load also depends on something that is called ‘dwell time’ (someone correct me if I am wrong and if this is only applied to efforts analysis) and various filters, that are used to smooth out curves and get rid of errors (like knocking the unit, or dropping it). I have decrease ‘dwell time’ for Player Load to 0,2s compared to usual 1,0s. This is used because units are meant to be wore in a ‘bra’ between shoulder blades to represent whole-body movement. Playing with these parameters might affect the analysis. This is important since the units are not meant to be used for hitting the heavy bag. It would be also more valid to have couple more units that could be wore on ankles, head gear and between shoulder blades to get the full body movement (or head pounding in sparring). Maybe next time. 

Quantifying external work in boxing was always been difficult, so the coaches relied on notational analysis, HR data (internal work), bLA (internal work) and RPE (subjective indicator). Using small accelerometers one could quantify load. This was I tried to do (as a beta self-experiment)

So I have put two devices (named LEFT and RIGHT) on my wrists and put a bandage over:

I decided to do 9 ‘rounds’ of 2minute duration and 1 minute rest. I have done the following activities just to see the possible differences:

1. Run on treadmill at 8km/h at incline 1%
2. Run on treadmill at 12km/h at incline 1%
3. Jump Rope (easy)
4. Jump Rope (hard; every now and then 10 faster high knees)
5. Shadow Boxing (two round)
6. Heavy Bag (three rounds)

I have put 14oz glove for heavy bag work. First heavy bag round was light contact, 4-6 hits, nothing hard – just warming up. Second round were 1-2 very hard strikes with a nice reset and pause in between. Third round I have tried to throw hard punches in combinations and just move around like in a real sparring (at least as my shape allowed me). 

Here is the picture of Player Load and HR for different rounds for LEFT arm.

On this one you can see one heavy bag period in higher zoom:

As you have guessed correctly each spike is a hit to the heavy bag. 

Here is the table with some numbers pulled out (for LEFT and RIGHT arm)

This is also an example of CRT (Customized Team Report) provided by Sprint software. I have used this Excel to graph some data further.  

I have summarized load for LEFT and RIGHT to get TOTAL and I did some simple descriptive statistics. 

On the following picture you can see relationship between Mean HR and Player load for different rounds. Please make sure that correlation might be wrong because I have put running and jump rope as rounds and that might affect the relationship. 

Same as in the picture above there is relationship (but lower) between Player Load and HR Exertion Index (something like TRIMP score). Again each dot represent one 2min round. 

On the following pictures we can see different rounds and the HRmax, Mean HR and Player Load

Max HR

Mean HR

Player Load

There are couple of interesting insights. For Player Load, Jump Ropes get a bit lower position compared to HR mainly because the arms are almost stationary to the side of the body. Same thing for Running at 12 km/h. Again Player Load in this example represent the movement of the hands. 

Compare to HR data, Heavy Bag 1-2 (hard punches in 1-2 with longer reset and rest in between) get lower score in Player Load for couple of reasons: the short re-set time decrease the score, or because of dwell time and filters hard hits are not taken into account. As all the researchers admit - we need more data. 

On the following graph I have created the ratio between Player Load and Mean HR

The idea for this type of analysis comes from cycling world (see blog entry by Joe Friel on this). By dividing Player Load with Mean HR we get some idea of efficiency and cost (short term; between activities comparison) or how the athletes are adapting/improving (long term, time series; within activity comparison). 

Since the accelerometers are on hands, the activities that create lot more arm movement per internal load (penalty; mean HR) get better score.  We can use this between activities comparison to give us some insights into the differences in external work and internal penalty for that work. We might be able to compare individuals and see who might be more efficient (if mean HR are expressed at %HRmax) as a boxer (if we compare sparring or heavy bag data).

In the case we keep tracking extrenal/internal data for one activity, we could see how the athlete is adapting (as Joe Friel did) and when is he starting to plateau (or start showing some possible drop due overtraining or detraining). This might aid in the design of the training block. Again more research is needed.

I hope that this article gave Boxing, MMA, Karate, KickBox, Tae-Kwon Do coaches and researchers some ideas that they might try expanding upon, start using or start experimenting. 

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Random thoughts on coaching software

I have wrote on this topic couple of months ago (HERE) and I have been spending that time in trying couple of them. Now I would love to provide some general comments and thoughts to both developers and coaches looking into those solutions. 

Before I even start please consider the following picture

The point of the picture is that you cannot ever satisfy everybody’s needs, being that the needs of your athletes, customers, friends or what have you. 

Talking about software solutions in mind, this mean that it is foolish to try to do that. Coaches will always prefer to use couple of different solutions (calendaring tool, video analysis tool, notational analysis tool like SportsCode or Sideline, workout builder, GPS software, EXCEL, Access, R, you name it) and software companies trying to fulfill all of those will end up like that pair on the picture, plus with a software (donkey) that no one know how to use due all that different templates, modules, features, graphs and everything. 

The long story short is KISS – Keep It Simple Stupid. There is one Catch 22 though: CONNECTIVITY. 

The main feature of all good software are quick and easy import and export functions, where one could export their data into different format, like CVS or EXCEL and import them easily into the software. This could be solved by having “import template” which one could use to enter their bulk data and import them painlessly. One example could be entering player info – coaches usually have this in EXCEL or what have you, and it is major pain in the ass to enter the same data between different software packages.

Another crucial feature is drag-and-drop. In the era of touch screens and iDevices we are used to “physically” (I mean by mouse) manipulate objects, lists, data or what have you instead of typing. One simple example is that if I want to assign a player to a given session or vice versa, I should be able to drag and drop instead of typing in an input box. One could check how Trello works to get some insight. This also give some feeling of “control” over everything and ease of use.

The need for optimal flexibility and pre-made entries needs to be taken into account. I love to figure out the system by having a ‘blank slate’ option and building it from the ground up (yes, when I play video games I first goes to Settings menu). Even with the software solutions people love to stick to “invented here” solutions (see the Switch book). This by all means doesn’t necessary mean not providing any key examples or key data (like exercises). Definitely not providing plenty of them so users are lost or suffer from “not invented here” syndrome. If these are provided for learning phase, there should be an easy option to delete everything pre-made.

This could be also said for data analysis and/or forms. If the software only provide pre-made built-in options – guess what – I will choose another one. Maybe their build in solution is the best there is, but I would still need to create my own (“not invented here” syndrome). What I need is flexibility and guidance with some key examples. 

This balance between flexibility (to modify everything) and pre-made solutions (that could be deleted after learning period or easily modified) can be applied to all modules of the software. Again, too much flexibility is wrong as is too little. I don’t care about flexibility of 100+ options, like font colors in reports, graph colors (those fancy graphs and templates, like 20 different 3D and shading options) – that just paralyzes me. What freaks me out is inability to modify forms, names and staff like that that I am going to use and present to players. Even worse if I can’t deselect the data players need to input or see.  Keep flexibility to modify what is important, but stick to pre-made solutions to the parts that don’t need much user attention (or at least provide templates/schemes, like color scheme instead of me being able to modify every single color type).

Dashboard and reports should be neat and visual, without a need to read a book to understand it. There are numerous books on good design of dashboards and graphs, like work by Stephen Few, to name a few (did you notice what I just did there?). Use of bullet graphs, good tables, sparklines, colored bands (don’t overdo it), and simple indicators (I don’t want to see rainbow colors on indicators, stick to red only, or orange and red). 

The analysis of the data is a bit tricky. There are numerous ways to calculate and visualize things. One thing – forget about team averages for monitoring individual players. I don’t care about their between individuals Z-score for their sleep quality rating – it doesn’t tell me jack sh*t. Develop good time series analysis. I also want to be able to modify analysis types and set my own flags. Too much flexibility and I would need a math degree. Too little and I will chose different software. Learn how to calculate SWC and TE and probably chances (if you don’t know what these mean you shouldn’t be in this business of making analysis software anyway) and how to visualize them. See work by Will Hopkins on this. I don’t care about p values since I am not making inferences to a population. I care about a single athlete in my group, not if an average effect on a population is statistically significant. Leave that to researchers. 

In workout builders most coaches don’t want their players to carry iPhones in the gym to populate set and rep schemes. Not unless you are working with a distance client or a strength dominant athlete. Even them can fill the workout card and enter data later to compare planned vs. realized. Make this simple and an option. For me being able to create workout cards is enough along with managing their 1RM data (or any other testing data with easy importing function). I don’t need any fancy iPhone app for my guys to see workouts. I want that for a simple data collection (like Wellness, sRPE, HRV, BW, injury, nutrition, etc) where it actually matters. 

I also don’t want my athlete to read a book on how to use their part of software. It should be simple and intuitive, or else they ain’t going to do it. If they need to log in every time the higher the chance they won’t. Making an app with saved login info with two clicks away of data input is the way to go. The more they need to write or click the higher the chance they won’t do it. We need higher compliance and honesty.

It would be great if the software is completely in the cloud (Web based) and I don't need special software to be installed. This allows safer back-up (higher chance of your computer crashing that a server), quicker software update and use between platforms.

So, in short these are some of the features I am looking for

1. Managing athletes and athlete data (e.g. testing, playing data) with simple import/export and drag and drop
2. Drag and drop calendar where I could create event and participants (and easy get the attendance report)
3. Assign individual training workload by using different indicators (sRPE, TRIMP, GPS data, tonnage…) either by easily importing data from other software or by in-system built collection (sRPE on iPhones)
4. The ability to manipulate and get some form of the analysis between training load and reaction (like testing data, wellness data, performance rating or injury data). The software designers should understand this simple Banister Impulse-Response model. 
5. Having a neat way to categorize my workouts, sessions, their types. I should be able to import pictures from other coaching software. 
6. When assigning workout I should be able to assign tags to different components, and easily be able to create report on the distribution of those tags (i.e. how much time did this player spend on finishing activities this month, and compare that to what was planned)
7. Having an ability to make strength workouts that are linked to testing data. For example I should be able to assign 5x5 at 80% to athletes, and then I get immediate weight based on their 1RMs and exercise relationship with the 1RM test (i.e. lunges to squat). Same thing for conditioning (i.e. using MAS). 
8. I should be able to quickly assign team sessions, yet quickly modify them to suit individual needs
9. There should be Annual Planner where everything should be interconnected. 
10. I don’t want to sell my kidney to buy the software. 

I will probably expand further on this in the next couple of weeks. There are definitely some great pieces of software out there, but most of them don’t fulfill all my needs (remember the picture above? It is applied to me too) and I would probably need to use couple of them – one as workout builder (AccelerWare and VisualCoaching for strength training, EDGE10 for skill sessions [looks great btw with all that tags], SessionPlanner for soccer drills, etc), one as monitoring and testing tool (Kinetic Athlete, GymAware, iThlete team version, Swift Speed Light, SpeedMat, FusionSport timing gates, jump mats) one for database (EDGE10, Smartabase). 

The only problem is money. Software for these purposes is still expensive. Excel might be poor man’s option for now. Till next time…

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Periodization confusion – Slides from WindSprint 2013

I had a pleasure to be one of the lectures at WindSprint 2013 held in Sundsvall, Sweden. I have finally met French sprint coach Pierre Jean Vazel who I know for years from Charlie Francis forum.

Among other presenters were Aki Salo (great presentation on relays), Takanori Suyibiashi (Japanese sprint methods), Roland Lööv (his presentation was on Swedish, so I didn’t understand much).

My presentation was probably too meta-physical, but I have tried to cover some theoretical/philosophical aspects of periodization. This was the first time I was presenting, and I did it on English language, so I was a bit nervous (plus being on stage form me is everything  but walk in the park). Anyway, I was pleased by the reception even if could definitely done it better. Maybe next time J

I have covered a lot of topics/concepts and it was hard to go into details on each. The goal was to present the bigger picture and to provide different framework to think about things.

Here are the slides

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Player Profiles v1.0

Player Profiles v1.0

In November 2012 I have created this Excel workbook to manage testing data or any other statistic one would love to keep track of for individual athlete.  You can find more info in the following links:

Finally done – Player and Test Report/Dashboard

Player and Test Report/Dashboard Screencast

The aim of this workbook is the following:

•          To manage tests and statistics and provide context for the data (poor, average, excellent score)
•          To manage player information
•         To visualize the data using bullet graphs (which provide context for the scores), trend lines  (history of scores), compare players to other within the squat and to identify strong and weak aspects
•          To create a one page report for each player
•          To  create testing report

 Here is the screenshot and the screencast is at the end of the page 

Since I ceased to develop this product I will not be providing any  type of support beyond the screencast above. There are some things you need to keep in mind before using it:

•         Always have a copy of the file with your data. Always
•         When you refresh the database, make sure to close all other open workbooks in Excel (I am not sure what it report an error when other workbooks are open, but it works perfectly when it is the only one workbook open)
•         If you change the test from more-is-better to less-is-better, you will need to adjust the bullet graphs manually and this involves setting up the x-axes (making them reverse) and changing the colors of the zones. This involves some knowledge of Excel though and trial and error.

Taking all these shortcomings into consideration I am still offering this workbook for intermediate Excel users since they might be able to modify it and maybe use some of the ideas to make their own templates.  Because I stopped the development of it and because I am not offering any support for it the price is  $25.  Also, to run it you would need to have Excel 2010 or later.

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