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Extending the Classical Test Theory with Circular Performance Model
By Mladen Jovanovic on 13/01/2020In this video, I will go more into these details and also provide an extension of this model, which I termed Circular Performance Model (CPM). CPM, in my opinion, better represent performance phenomenology we experience as coaches and athletes.
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Predicting non-contact hamstring injuries by using training load data and machine learning models
By Mladen Jovanovic on 17/12/2018Research has shown that there is an association between training load and likelihood of suffering non-contact injuries. But can we predict the injury? In this paper I have tried to predict non-contact hamstring injury by using two seasons day-to-day training load data.
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Smallest Worthwhile Change: Individual vs Group
By Mladen Jovanovic on 10/11/2018Smallest Worthwhile Change: Individual vs Group If you haven’t been living under a rock over past few years, you must be familiar with Will Hopkins work on magnitude-based inferences (MBI). One of the basis behind MBI is defining smallest practically meaningful change, or smallest worthwhile change (SWC). Together with Typical Error (TE) of a test, SWC is very needed...
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Data Preparation for Injury Prediction
By Mladen Jovanovic on 23/08/2018I have recently wrote a technical note (actually a video) for Sport Performance and Science Reports journal regarding Data Preparation for Injury Prediction. Both data and R core are available on GitHub repository.
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Fantastic Sport Analytics Papers & Resources
By Mladen Jovanovic on 03/03/2018I have recently stumbled on a few great papers that outline very useful statistical techniques, that are VERY applicable to sport and training analytics. If you are interested in analytics, this is a gold mine.
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Few Simple Improvements to Monitoring Data Analysis
By Mladen Jovanovic on 22/02/2018Most of us are collecting and analyzing training load data and readiness metrics (i.e. GPS training load, sRPE and wellness). The most common analysis method is to use two rolling averages windows – acute (around 7 days) and chronic (around 28 days) and to calculate their ratio, referred to as ACWR (Acute to Chronic Workload Ratio), or TSB (Training...
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How to Analyze Training Load and Monitoring Data?
By Mladen Jovanovic on 21/11/2017In this video I am explaining two methods to deal with missing values and why you should pay attention to the way you are aggregating data (daily, team averages, body soreness, etc).
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Latest on Load Monitoring [Video & FREE Templates]
By Sean Williams on 28/09/2017Sean 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.
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To Turf or Not to Turf, That is the Question [Part 2]: Applications
By Mladen Jovanovic on 26/07/2017In 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.
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To Turf or Not to Turf, That is the Question [Part 1]
By Mladen Jovanovic on 05/07/2017In 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.
