Statistics - Complementary Training - Page 3

• # Netflix Prize and Injury Prediction Prize

The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings without any other information about the users or films, i.e. without the users or the films being identified except by numbers assigned for the contest. Which made me think – why don’t the “rich” clubs, such...

• # Uncertainty, Heuristics and Injury Prediction

I was asked by Rod Whiteley and Nicol van Dyk to contribute to the Aspetar Journal targeted topic issue that just got released off the press. I tried to combine my knowledge of predictive analytics, machine learning, philosophy of science, heuristics and practical experience as coach & sport scientist into one article. Hopefully I managed to create readable narrative.

• # Power BI Course for Sport Scientists – Video 1

I decided to create a course for sport scientists, to get you started with PowerBI. My bet is that PowerBI will become new Excel (if not already) and having the skills needed to produce reports will be needed as Excel skills are needed now. So, knowing how to learn PowerBI will put you ahead of the curve and...

• # Thoughts on Injury Prediction

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

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.

• # Masked Relationships and Multicollinearity

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.

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?

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…