By Mladen Jovanovic on 24/10/2017
I am pretty certain that the first one to suggest using Trello (somewhere in 2013) was Jose Fernandez from Houston Astros. Jose is pretty much technology clairvoyant. Another person that directed me to use Paul Dijkstra et al. five-color health and performance risk grading system 1 (see the below picture) was Brian Green from USA Rugby Sevens. We keep...
By Mladen Jovanovic on 11/10/2017
I also love to call this problem “Terminator Problem”, since it is more understandable to coaches, since most of them have seen Terminator movies. The issue is, once we are able to “predict” the future (for example estimating injury likelihood in the next 7 days), can we actually change it?
By Sean Williams on 28/09/2017
Sean Williams was kind enough to provide short review of the recent load monitoring workshop, held at the World Rugby Science Network Conference, as well as to provide full slides and Excel templates. This is tremendous resource for those interested in injury prediction analytics.
By Mladen Jovanovic on 26/07/2017
In this video I am deploying the model to make the decision between 5 variations of weekly plan. The goal is to minimize the morning soreness on the day of the game.
By Mladen Jovanovic on 05/07/2017
In this video I am presenting my “solution” to the problem and provide fake data set, as well as R code for analyzing the data. Having decent predictive model can help us run different optimization algorithms to find the “best” scheduling to minimize/maximize injury risk or performance.
By Mladen Jovanovic on 12/03/2017
Given the prevalence of hamstring injuries in football, a rehabilitation program that effectively promotes muscle tissue repair and functional recovery is paramount to minimize re-injury risk and optimize player performance and availability.
By Mladen Jovanovic on 07/03/2017
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.
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.
By Mladen Jovanovic on 29/06/2016
In the following article I am reviewing very interesting article by Windt, Gabbett et al.: “Training load–injury paradox: is greater preseason participation associated with lower in-season injury risk in elite rugby league players?”.
By Mladen Jovanovic on 02/11/2015
This is the problem I have been wrestling with for some time and I tried to solve it using Bannister model. I even asked for help on Twitter and couple of data scientists suggested taking a look into “functional data analysis” or creating summary variables and performing regression/classification (which I did here).