Athlete Monitoring 1.0

Athlete Monitoring 1.0

This is the MS Excel 2010+ (Windows) workbook designed for data collection and quick analysis and visualization using Pivot Table. The workbook is already set up for quick start-up with data collection such as athlete attendance, sRPE, Wellness, Injuries and Illness details and lot more. In the video at the bottom of the page you can see all the features and how-to of Athlete Monitoring 1.0.

This workbook is designed for small staff usage and sharing. High-end statistical analysis and data mining is possible by exporting the data to R or SPSS software, while the simple (visual) analysis is possible using Pivot Table and Chart.

To use this workbook successfully, users should have basic knowledge of how Pivot Table works and it would be highly recommended for the users to have at least some background in maintaining simple training database themselves.

For further customizations please contact me on my mail. 

The price for this workbook is $35

NOTE: If you don't receive the file immediately upon payment, please be free to send me the email and I will forward it ASAP. 

Strength Card Builder 2.0

Strength Card Builder 2.0

This is the new and updated version of Strength Card Builder with two new templates designed with the influence from Joe Kenn's Tier System workout cards.

The 3x5 Workout Card

Group workout card [LINK] (Produced on Mac)
Tier 3x5 (3 workouts, 5 exercises) [LINK]
Tier 4x5 (4 workouts, 5 exercises) [LINK]


This Excel workbook (both MAC and Windows) allows you to set up your athletes list, their 1RMs in key exercises, set up all other exercises and their relationships with 1RMs, up to 55 set & rep schemes that could easily be used by simple clicking. This way it takes matter of minutes to set up group or individual workouts using your predefined athletes, exercises and set & rep schemes. This is the SIMPLEST way to implement percent based approach to strength training (and it could be modified to be used with RPE approach, velocity based, etc). 

In the video below you can see all the features of this workbook. The video is made with the old version of the file, but all the workflow is the same with the new version PLUS two new beautiful individual templates that are ready to be used with your athletes with minimal set up.

For additional features and customizations please contact me on my mail.

The price for this workbook is $45

NOTE: If you don't receive file IMMEDIATELY after purchase please send me the email and I will send you the copy ASAP.

Percent-based to velocity-based converter

Percent-based to velocity-based converter

“Nothing Is More Practical Than A Good Theory” – Kurt Lewin[1]

In the following video I am explaining how to use Excel converter to convert percent-based programs to velocity-based programs based on lifters load-velocity profile for a given movement.

I am covering some important ‘rules’ and relationships of velocity-based approach to strength training, like load-velocity profiles, minimal-velocity threshold, “reps-in-tank velocity relationship” (not sure if I should patent this one?) – So before watching the following video please refresh your understanding by reading the following posts:

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?

Some great findings and ideas from velocity-based strength training

GymAware user stories: how to track 1RM without actually testing it

Here is the Excel converter I used in this video [DOWNLOAD].

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[1] In the video I stated that Volta said this, but I cannot find it anywhere online. Apparently Kurt Lewin said it. 

Strength Card Builder 1.1

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

Optimizing groups for Small Sided Games (SSGs)

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)

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

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. 

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

Annual Plan for Team Sports v1.0

Annual Plan for Team Sports v1.0

This is the Excel workbook aimed at helping team sport coaches setting up the annual plan and weekly/monthly calendar. 

The workbook is organized in couple of tabs. Two main tabs are Annual Plan and Training Sessions. All other tabs are related to components of the annual plan and include drop down lists and weighting factors used to calculate training load.

The worksheet utilize some VBA code for estimating training load. For example, if one uses 1x4x4’ R:3’ small sided game (4v4), that represents doing one set of four reps of 4 minutes interval with 3 minutes rest. VBA code is used to parse the data from the string and calculate load in A.U. (arbitrary units). Using weighting factors one could assign different weight to different components and calculate total load.

Visualizing workloads using sparklines is a neat way to see the progression (overall, or within the components – use check boxes to select what you want to be calculated and visualized) in load over time and see the differences between weeks. The formula for load in A.U. is quite simple: it is Load = Work_Time * Density, where Density is Work_Time / Total_Time. Not perfect, but get's the job done. For this reason it is crucial to assign different weighting factors for different drills within each training component (strength, speed, power...) and for each component in the total load. I have set up a table for Strength, Specific conditioning and General conditioning tab where one could see calculated loads. Using Solver tool (add-in) in Excel or Goal Seek one could select certain drills to have certain load. I will cover this in one of the how-to videos. 

The annual plan (Annual Plan tab) and daily calendar (Training Sessions tab) are connected in a way that you can see key constraints (games related data, camps, travel, vacation and testing) devised in the annual plan in the daily calendar for a given week. This allows for easier planning of a given week (microcycle).

The basic version is set up to be used in soccer, but by modifying the names of the boxes in the Annual plan and drop down lists for each training component one can adjust if for their own needs and their sports. 

On the bottom of the page I posted four instructional videos on how to use the Annual Plan for Team Sports v1.0, although I believe that most of it is quite intuitive especially for coached used to plan/work in Excel. 

Because the whole Excel book is not protected, that means you can customize it anyway you want, but it also means you can screw things up. Thus, I suggest making a copy of the workbook as soon as you download it.

For further customizations of the workbook please be free to contact me.

I am offering the Annual Plan for Team Sports workbook at  $45

Instructional videos - please use full screen and HD option

NOTE: If you do not receive the file immediately after the purchase please be free to contact me on my email. PayPal is supposed to work, but I had couple of customers reporting not getting the file. 

Using PowerTool/GymAware: short video and explanation

Using PowerTool/GymAware: short video and explanation

We have been using GymAware for the past year, but recently we acquired the Pro version of the software that allows automatic data collection, data-basing, analysis, reports and what else on the cloud.  This makes life much easier and also allows for tracking much more than a single parameter.

What we have been using it for the past year is to track mean external power output in the countermovement squat jump with 20 kg. This allowed to see trends with the players and gain some ideas on their freshness and readiness.

Statistical methods used to analyze the trends were rolling averages of the last 6-10 measurement used to get Z-Scores of the each individual. This way we can see trend, but also how much is one away from his normal variability (Z-Scores). The yellow flag was usually set to -1 to -2 and red flag for everything under -2 for Z-Scores. This is used besides visually checking for the trends over time (one could use rolling average to smooth the curve).

One thing with this method is that one needs to accumulate enough data to get more reliable estimates. But that’s always true with anything related to statistic. To make more confident claims, one need better and bigger data. 

Random data to show the calculus and visual representation

What we plan doing this year is doing the same, but with Pro software we can track more variables to gain insight which one is more sensitive to readiness changes. Things like mean power, peak velocity, dip, jump height. It is easy because everything goes directly to the cloud. No need to write things on paper.

Besides this simple use, we use it to track improvements in squat with estimating 1RMs on load-velocity profile. I wrote about it here. You can check the short reportage from one gym session in Boson Olympic Center in Stockholm, Sweden.

The beauty of this approach is that it is submaximal (except for the speed of movements which should be intended to be as fast as possible within technical limits of the exercise). There are two ways to assess 1RM – actually finding 1RM or doing reps-to-technical failure and estimating 1RM from the tables (makes sure that the reps are around 3-6). Using velocity approach is novel method and I am still figuring out what is the best way to do it.

Basically one could do 3 reps at 55%, 65%, 75% and 85% as fast as possible. Research states that it is more reliable to track mean velocity instead of peak velocity of the rep. Also, one could use best rep or set average. I would use set average since it is less prone to errors I guess. The velocity difference (fastest – slowest) should be more than 0,5 m/s (talking about mean velocity method) – you might need to change % a bit to make this possible. This is important to get more valid and reliable regression coefficients. One could also play with standard error and get confidence intervals  and thus more magnitude based statistics to assess (real) change over time.

Load-velocity profile

 Next comes the selection of the speed for 1RM estimate. It is usually around 0,2 – 0,4 for mean velocity (not peak velocity). This depends on the lifter, exercise, depth of movements, etc. One could also use LD0  (resistance at velocity = 0) but not as real/training 1RM, but rather as an indicator of change happening.

What is nice about this approach is that it could be done ANYTIME. It doesn't need to detract you from your normal workouts. Could be done with the warm-up sets and some working sets. And using this, athletes get the idea why it is important to be strong to lift fast and be fast/explosive overall. It is also easy to see trends (with strength athletes this might be their monitoring tool to see the effectiveness of training/block and judge when to switch or modify the program – more on this in some future articles).  Also, it removes the idea of grinding the weight, especially with the team sport athletes. Only technical and fast reps.

The negative side is of course errors in calculus. I am searching to find the best method (valid, reliable and sensitive) of doing it to assess real changes and correlate it with real 1RM. Athletes should also strive to lift as fast as possible and by lifting light weights slower one could easily “cheat” to get higher estimate, since that would flatten the curve. Anyway, until then I advise not to use estimated 1RM as training 1RM, but rather as an indicator to increase training 1RM. For example, if an athlete uses 140kg as his training 1RM (one that is used to calculate all the weights based on percentage – see more here) and over a training cycle his estimated 1RM at 0,4 m/s improves for 5-10kg, it might be a good idea to increase his training max for 5 kg. This brings me back to developing vs. expressing concept I alluded in numerous blog posts.

Having Pro account of GymAware it is now easy for me to collect data and do my own research to find the best method of load-velocity based estimates of 1RMs.

Stay in touch because more is on the way….

Creating team workout using Excel

Here is the screen cast of a rather simple solution in Excel for creating individualized team strength training workouts. 

Make sure to increase quality before watching

Estimating 1RM using load-velocity relationship

I recently wrote an article regarding the use of velocity of the lifts to predict 1RMs for the official GymAware website. You can read it by simply clicking on the image below.

I wanted to thank Rob Shugg for posting it and the whole Kinetic Performance company for creating such an amazing tool - PowerTool/GymAware.

I also hope that the article will bring some more  food for thought and stimulate more work in this direction.

Excel Tricks for Sports (YouTube channel)

I don't know why and how I haven't stumbled on this channel before - it is full of Excel tips for everyday sport coaches problems. Make sure to check it out. I want to thank Darcy Norman for giving me a heads up.

 

http://www.youtube.com/user/ExcelTricksforSports

Interested in learning statistics and R? Start here!

Just a quick heads-up. I recently came across Coursera – a website offering FREE education:

About Coursera We are a social entrepreneurship company that partners with the top universities in the world to offer courses online for anyone to take, for free. We envision a future where the top universities are educating not only thousands of students, but millions. Our technology enables the best professors to teach tens or hundreds of thousands of students. Through this, we hope to give everyone access to the world-class education that has so far been available only to a select few. We want to empower people with education that will improve their lives, the lives of their families, and the communities they live in.

I cannot express how much this project is important. The knowledge is out there – all you need to do it get it. For free!

Here are the couple of courses I found interesting and actually started watching.

Statistics One by Andrew Conway (I actually started with this one)

Computing for Data Analysis by Roger D. Peng

Mathematical Biostatistics Boot camp by Brian Caffo

Data Analysis by Jeff Leek

 They might be very interesting watch for the readers interested in learning statistics, data analysis and R. 

You may wonder why am I posting this - or even - why I am writing about data analysis and things like that on physical preparation blog? Well, I honestly believe that in next couple of years, even now, these skills (statistics, data analysis and decision making, even basic programming in VBA for Excel for example) will make a big difference in your skill sets and will make you light years ahead of other strength and conditioning coaches that might have more single leg exercises variations than you. At least if you plan working for a serious organization. And besides it is fun to learn new stuff, especially with the recent interactive tools and state-of-the-art presentations and lectures. What are you waiting for?

Another courses I find interesting personally are the following:

Fundamentals of Personal Finance Planning by Don DeBok

Introduction to Philosophy by a bunch of professors and lecturers

Introduction to Guitar by Thaddeus Hogarth

Learning was never been so easy.  Hell even learning guitar seems VERY interesting (Note to myself: keep it simple and stick to the basics).