Posts tagged with ‘Performance Monitoring’

• Smallest Worthwhile Change: Individual vs Group

Smallest 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...

• Data Preparation for Injury Prediction

I 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.

• Fantastic Sport Analytics Papers & Resources

I 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.

• Few Simple Improvements to Monitoring Data Analysis

Most 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...

• How to Analyze Training Load and Monitoring Data?

In 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).

• Latest on Load Monitoring [Video & FREE Templates]

By 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.

• To Turf or Not to Turf, That is the Question [Part 2]: Applications

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.

• To Turf or Not to Turf, That is the Question [Part 1]

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.