Posts tagged with ‘Statistics’

  • Thoughts on Injury Prediction

    By Mladen Jovanovic on 08/12/2016

    In the following article, I am discussing the famous “J” curve in injury prediction, as well as simulate some data to show how that curve is estimated. I also show the distinction between association and prediction, as well as how to make training decisions based on the different costs of committing false positive and false negative errors.

  • Predicting Injuries Using Banister Model – The Addendum

    By Mladen Jovanovic on 14/10/2016

    Predicting Injuries Using Banister Model – The Addendum A year ago I tried to use Banister model to predict injuries, but I recently realized I made an error implementing link function. So, I decided to do one more try and correct the problem. In the video below I am using three training load metrics (RPE Load, Odometer and High...

  • Masked Relationships and Multicollinearity

    By Mladen Jovanovic on 04/05/2016

    In this video and R workbook I am “playing” with linear regression and I am trying to explain the concept of “controlling” for one variable (this is common in statistics, but I had hard time understanding it until I visualized the problem), masked relationships and multicollinearity.

  • [Playbook] Converting Just Jump to Smart Jump

    By Mladen Jovanovic on 23/04/2016

    Coaches and athletes sometime change measurement/testing equipment. The problem is that the estimates from different equipment might differ (and they usually do). There are couple of soutions to this problem...

  • What Is Propensity Matching and How Can We Improve Validity of Causality Claims?

    By Mladen Jovanovic on 19/03/2016

    Researchers usually try to randomize correctly, but sometimes the effect is not in the treatment, but maybe in the difference between group (pre-treatment) or during the treatment (for example volume of training) which should be similar/same so we can judge the effects of something else of interest (for example novel periodization). The solutions are to involve covariates in the…

  • Predicting Performance Using Rolling Averages

    By Mladen Jovanovic on 08/09/2015

    I wrote before on using Banister model to predict performance and injury from training load. In the video below I am talking about using rolling averages to predict performance, as well as explaining differences between supervised and unsupervised learning and model overfit. Members can download the Excel workbook below the video and follow the “exercises” and play with the…

  • Predicting Injuries Using Banister Model

    By Mladen Jovanovic on 13/08/2015

    Can we predict individual injuries from training load?
    Watch the video to find out more.

  • Workbook: Change Point Analysis of HRV Data

    By Mladen Jovanovic on 02/08/2015

    What is Change Point Analysis and why and how can you use it to help you out to make sense of your monitoring data? Find out more….

  • “Novel” Metric to Compare Athletes Using Their Load-Velocity Curve

    By Mladen Jovanovic on 15/07/2015

    The question is simple: “What could be used to compare athletes across time and among themselves, taking into account the full load-velocity continuum, but taking into account their strength levels and body weight?”. You can find my answer here

  • R Playbook: Introduction to Multilevel/Hierarchical Models

    By Mladen Jovanovic on 09/07/2015

    I wrote about mixed-level models before and I want to expand on it here. I sort-of finished Andrew Gelman’s Data Analysis Using Regression and Multilevel/Hierarchical Models and will continue to play with it. As far as I know Andrew uses the term multilevel models and avoids the terms fixed and random effect. This is a great book to have.

    My…

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