Set and Rep Schemes in Strength Training (Part 2)

SET AND REP SCHEMES IN STRENGTH TRAINING (PART 2)

Set and rep schemes in strength training (part1)

Set and rep schemes in strength training (part2)

Here is a second installment of set and rep schemes article for EliteFTS. You can find my blog post on the first installment HERE. 

The purpose of the article is to 'explain' (or at least rise awareness) to the difference between Training objectives, Training parameters and Training progressions and variations. In simple words, training objectives represent description of what needs/can/should to be done to get from point A (current state) to point B (future state), defined by Needs Analysis and  Athlete Characteristic taking into account context at hand. 

Training parameters then represent operational decisions and program in achieving training objectives. 

Training progressions and variations represent a 'wiggle room' within Training parameters ~ since we can achieve same objectives using different approaches. The important point of the article is that there are similar ways to vary and progress training parameters regardless of training objectives. Those commonalities is what the article is set to explore.

In lay terms, if the training objective is to increase upper body muscle mass, we "know" (from research, previous experience or training knowledge) what training parameters generally needs to be performed  (e.g. training upper body 2-3x/wk with 30-50reps per muscle group with 65-80% 1RM), but within those parameters (and constraints) we have a lot of wiggle room to experiment with (art of coaching?). Here comes training progressions and variations that could be explored based on context, individual characteristics, reactions and preferences (e.g. sets across for someone, or waves for someone that hates sets across). 

This is pretty much the same as Tool of Three Levels, just explained a bit differently.

The explored progressions and variations are based on Load/Exertion profile (DOWNLOAD). Also, the new Strength Training Card Builder v3.0 will have ~90 set and reps schemes that are dependable on modifiable Load/Exertion table. If there is an interest will explain how I used Load/Exertion table to devise a lot of set and rep schemes. 

Anyway, follow the links on the top of the page and let me know what you think.

Set and Rep Schemes in Strength Training (Part 1)

Set and Rep Schemes in Strength Training (Part 1)

Here is the latest article I wrote for EliteFTS. The article will be published in two parts. Part one deals more with terminology and something that I call 'intensity trinity'. I hope that this terminology will become standard in our field/industry.

It also explains traditional approach to exercise prescription, know as percent-based approach, along with novel velocity-based approach.

The article purpose is to explain load/exertion table, training process, the difference between "periodization" of training objectives versus training parameters aimed at achieving those objectives and common progressions and variations (set and rep schemes) utilized within training parameters. In my opinion writers tend to confuse those.

I hope that this article will clear up some terminology issues and provide very usable load/exertion table that can be utilized to explain various common progressions and variations on workout, week and block time-frames.

Enjoy the article and tell me what you think.

Click HERE to read the article at EliteFTS website.

F.A.Q. on Team Workouts design and some „news“

I have received couple of emails asking me about percentages on assistance lifts based on core lifts (i.e. split squat 1RM is around 50% of back squat 1RM). I decided to post the answer on my blog instead of responding to each email separately (and you will soon read why).

The answer is: I have used Dan Baker table as a starting point. You can find this table HERE.

If you are interested in calculating 1RMs from reps to (technical) failure, you can refer to another Dan Baker table HERE. Or, if you have access to LPT like GymAware, you can use load-velocity relationship to estimate 1RMs by checking my small experiment HERE.  

Dan Baker tables are really useful and I refer to them pretty often. I tend to have within my reach all the time, along with Joe Kenn’s The Coach’s Strength Training Playbook which I use to implement percent based programs (with some modification). Another great resource in that regard is, you guess it correctly, by Dan Baker:

Baker, D. “Cycle-length variants in periodized strength/power training.”  Strength and Conditioning Journal, 29(4)10-17. 2007.

One note – although I love percent based programs because the way they influence behavior of the players (or me, when I train) by reducing the wiggle room – that same wiggle room might be sometimes needed due individual differences (someone’s 1RM in split squat might be 50% of his 1RM back squat, but someone might have 45-65%, depending on built, experience with split squatting, overall strength levels, etc). Anyway, they (tables by Baker and percent based programs by Kenn) provide a great starting point. And it is always better to under-estimate than to over-estimate, especially when it comes to assistance lifts.

I've discussed some of the ways to blend percent based programs with auto-regulatory programs HERE.

Also, I have been asked about a good book recommendation when it comes to learning Excel. This one I found a MUST read:

 Now some (unrelated) news….

I decided to switch to Mac.  I am getting Mac Book Pro 13’’ Retina pretty soon and I am leaving my HP Pavilion DV-7 that served me so well since 2009 to my mom.

So I decided to re-install Windows so she can have “clean” comp. I tried Windows 8 for couple of hours – and damn – I am lucky for switching to Mac. It is totally weird and un-intuitive system.  So I removed it and re-installed Windows 7.

In the process of backing things up and re-installing key software packages I managed to format my external hard drive with ALL the back-ups (luckily I had work related documents on DropBox), pictures, college stuff, writings, books, movies, music  – EVERYTHING. Yet, I somehow managed to recover most if not all by using un-format tools. It took me 3 days though. 

That’s one of the reason I haven’t been responding to emails. My Outlook is now up and running. I will probably stay a little *quiet* during December while I am moving to Mac for the first time, along with having a year break. The pre-season starts on 15^th January 2013 and I will try to get some Sun since Stockholm is already pretty dark.

 I will definitely give my best to avoid reading or writing anything related to training to give my head a break. If you have some fiction novel recommendations please be free to post it. In the mean time I plan checking book by Jules Evans on philosophy for life.

I wish you all great Holidays and learn on my mistakes – always have DOUBLE back-ups.

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).

 

Dashboard design

I have received quite a few emails on my last blog post.  Most of the authors asked me  how to create the dashboard that conveys clear and simple message to the viewers. Honestly, I am new in this area myself but I can point you to the following two papers by Stephen Few I found a must read:

Graph design for effective communication

Effectively Communicating Numbers

I am thinking about getting some of Stephen Few books: 

 

The thing is that I don’t want to move too much from actual coaching. I know that the skill set of physical preparation coaches is expanding from basic squat progression, but I think the line between sport scientist (that might also have a position in a pro club)  and physical preparation coach is getting really blurry lately and I now wonder are they actually the same? The role of the physical preparation coach now includes nutritional advices, rehab, prehab, monitoring, testing, analyzing data, visualizing data… Where the hell is all of this going anyway? 

Not to rant too much – the next thing I pinpoint the authors of the emails is the study by Stuart Cormack et al. Stuart was kind enough to send me the full paper. I modified the Wellness Questionnaire and statistical methods to analyze it a little bit, but you should check this study if you are interested in the process. 

Int J Sports Physiol Perform. 2010 Sep;5(3):367-83. Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players. McLean BD, Coutts AJ, Kelly V, McGuigan MR, Cormack SJ. Abstract INTRODUCTION: The purpose of this study was to examine the changes in neuromuscular, perceptual and hormonal measures following professional rugby league matches during different length between-match microcycles. METHODS: Twelve professional rugby league players from the same team were assessed for changes in countermovement jump (CMJ) performance (flight time and relative power), perceptual responses (fatigue, well-being and muscle soreness) and salivary hormone (testosterone [T] and cortisol [C]) levels during 5, 7 and 9 d between-match training microcycles. All training was prescribed by the club coaches and was monitored using the session-RPE method. RESULTS: Lower mean daily training load was completed on the 5 d compared with the 7 and 9 d microcycles. CMJ flight time and relative power, perception of fatigue, overall well-being and muscle soreness were significantly reduced in the 48 h following the match in each microcycle (P < .05). Most CMJ variables returned to near baseline values following 4 d in each microcycle. Countermovement jump relative power was lower in the 7 d microcycle in comparison with the 9 d microcycle (P < .05). There was increased fatigue at 48 h in the 7 and 9 d microcycles (P < .05) but had returned to baseline in the 5 d microcycle. Salivary T and C did not change in response to the match. DISCUSSION: Neuromuscular performance and perception of fatigue are reduced for at least 48 h following a rugby league match but can be recovered to baseline levels within 4 d. These findings show that with appropriate training, it is possible to recover neuromuscular and perceptual measures within 4 d after a rugby league match.

I am also thinking about doing a screen cast explaining the Excel method of doing the data collection, analysis and visualization or maybe providing a service of designing the custom made Excel sheets. Don’t know how much people might be interested in them, but if you are please be free to leave a comment below.

Mike Young’s Soccer Fitness: A Science Based Approach

Just a quick heads up for the presentation slides by Mike Young who is a strength and conditioning coach for Vancouver WHITECAPS FC and the owner of the EliteTrack website.  His Fit for Futbol  blog is a must follow for all coaches interested in physical preparation of soccer players.  

Soccer Fitness: A Science Based Approach from Mike Young

Playing with statistics part 3

After a general rant in part 1 and part 2 I am about to start actually doing some stats.

Ok, the first 30-15IFT test we did was in January and here are the scores.

		TEST
		January 14th, 2012.
Player		v30-15IFT	Z-Score
1		19,5	-0,89
2		19	-1,57
3		19,5	-0,89
4		20,5	0,45
5		19,5	-0,89
6		20	-0,22
7		21	1,12
8		21,5	1,79
9		21	1,12
10		19,5	-0,89
11		19,5	-0,89
12		19,5	-0,89
13		20,5	0,45
14		20	-0,22
15		21,5	1,79
16		20	-0,22
17		21	1,12
18		20	-0,22
Mean		20,17	0,00
SD		0,75	1,00
Min		19,00	-1,57
Max		21,50	1,79

As you can see from the table I have calculated Mean and Standard Deviation. Z-score (or standard score) for each athlete is the number that shows how many standard deviations players score is above/below mean value. Having 0 in z-score is equal to mean value of the group. Standard score is great because it take variability of the group scores into account, yet again this might present a problem since some outliers can skew the score by shifting the distribution. This skewness stuff can be calculated too and it deals with normal distribution. Again I am not an expert on this. Anyway, here are the histogram and scatter gram of the scores. 

Standard score is great for comparing the athletes and creating the rankings. Here is the graph of Z-scores of the player.

 

Now it is easier to identify outliers or guys who are above/below 1SD or 2SD or whatever (which one to choose is beyond my statistic knowledge at the moment). This might guide training prescriptions for certain athletes. For example guys with Z-Score below -1 can/should do more conditioning volume (and less intensive due their lower v30-15 score which I use to determine running intensity in the intervals). After some time we can see how they respond to training (this will be covered later) and identify responders vs. non-responders and thus give some feedback to planning/programming process and individualization of training loads in general. 

Basically we can group those players in four major groups: 

Low initial score, low training response	Low initial score, high training response
High initial score, low training response	High initial score, high training response

Based on this we can judge on talent of certain players regarding certain quality. But we still lack one parameter that it is hard to measure and that is the ceiling, but this is beyond this article. 

It would be interesting to see the distribution of the athletes in these 4 quadrants over time. Again this might help with the training prescription and individualization within team sport.

If we create z-scores for more than one physical quality (like sprint time, broad jump, % of body fat, etc) we can create spider graph for each player. Here is an example of spider chart from Marco Cardinale’s blog.

The spider graph can be based on Z-score if we want to compare the player to the team average, or it could be based on absolute values if we want to compare it to a certain model of player or certain test standards (make sure to check BioForce by Joel Jamieson). Again, this can guide us with training prescription and individualization.

Another interesting graph could be equalizer. I got this idea from Marco’s blog.

 

To summarize. In this part I covered some basic descriptive statistics of static score (one test). In the next installment I will cover re-test and the statistical analysis of the change and will probably create a 4 quadrant’s graph. Until then check the links posted. Stay tuned….