Mladen Jovanovic - Complementary Training - Page 51

Author archive for Mladen Jovanovic

  • FREE Annual Planner for Sports and Strength Card Builder Print Out

    By Mladen Jovanovic on 20/04/2014

    FREE Annual Planner for Sports and Strength Card Builder Print Out I would love to rise an awareness on the two tools for coaches. Yes, coaches – not only strength and conditioning coaches and sport scientists, but coaches in general. Strength Card Builder This tool works under Microsoft Excel 2011 and later, both on Mac and Windows. It allows...

  • What Do You Need to Know as a Sport Scientist Besides Sport Science

    By Mladen Jovanovic on 18/04/2014

    There are new roles emerging in contemporary sports field: sport scientist and performance manager. To be honest I have no clue how to define them – sometimes they are just a way to break out of the normal terminology (see Semantic Stretch and Power of Association in Made to Stick book, which is by the way excellent) and appear...

  • Welcome to the new Complementary Training website

    By Mladen Jovanovic on 16/04/2014

    The new Complementary Training, besides bringing the quality content from the old BLOG, will include updated state-of-the art PRODUCTS that will help you coach more and worry less on Excel, FORUM where you can participate in discussions with other fellow high-performance sport experts and DOWNLOAD where you can download all the important documents, videos and data featured in the…

  • No-Holds Barred Interview with Carl Valle

    By Mladen Jovanovic on 15/04/2014

    It is always fun and insightful to have a no-holds barred talk with Carl Valle. Hate him or love him, whatever you ask him he always provide no BS answer and for that he needs to be respected. I took the chance to ask him 5 nasty questions and he really took his time and energy to answer them…

  • How to (Pretend to) Be a Better Coach Using Bad Statistics

    By Mladen Jovanovic on 25/03/2014

    How to (Pretend to) Be a Better Coach Using Bad Statistics Here is a simple scenario from practice: Coach A uses YOYOIRL1 test and Coach B uses 30-15IFT (for more info see paper by Martin Buchheit, which also stimulated me to write this blog) to gauge improvements in endurance. Coach A: We have improved distance covered in YOYOIRL1 test...

  • Random Thoughts on Injuries

    By Mladen Jovanovic on 17/03/2014

    Random Thoughts on Injuries Few years ago when I was in UK and I’ve visited Leicester City FC on the Performance and Injury conference (#LCFC_PIC). It was great to be in the big LCFC gym with same/similar-minded professionals and listen to their troubles and solutions. I am not planning to provide an overview of the seminar besides mentioning data...

  • Analysis of Metabolic Power data

    Analysis of Metabolic Power Data Using Power-Duration Profile in Team Sports

    By Mladen Jovanovic on 06/03/2014

    This is the idea I got from the Training and Racing with a Powermeter book by Hunter Allen and Andrew Coggan. It is an excellent and must read book on cycling, but also great book about endurance training in general…

  • Set and Rep Schemes in Strength Training – Part 2

    By Mladen Jovanovic on 28/02/2014

    Overview of the process - As I have alluded to in the beginning of this article, training objectives will demand certain training parameters within which we can employ various progressions and variations, termed set and reps schemes.

  • Continuing with Statistical Power simulation in R

    By Mladen Jovanovic on 13/02/2014

    In the last blog post I created a simple simulation of statistical power (probability to identify effects when they are really there) calulation depending on the sample size and effect size (Cohen’s D using Will Hopkins effect levels).

  • “Power” to detect statistically significant effects based on sample size and magnitudes of effects

    By Mladen Jovanovic on 12/02/2014

    I was going through magnitude-based inferences materials by Will Hopkins and I am playing with R simulations. I wanted to see how many times I am able to detect statistically significant effects (p<0.05) depending on magnitude of effects (expressed as Cohen's D, and using Will Hopkins levels) and sample sizes.