I’ve been working on a database that combines a lot of my recent free work, as well as some unseen features, into one manageable spreadsheet. I wanted this to be an upgrade on the current basic athlete monitoring database I have up for grabs. This is a lot more advanced than my previous database, with some more robust A:C calculations, using the EWMA method. Before I go any further, if you are new to excel, and are not familiar with manipulating VLOOKUP, INDEX MATCH and IF formulas, building pivot tables and charts and manipulating charts displays and labelling, or making … Continue reading PREMIUM ATHLETE MONITORING DATABASE – PRODUCT


This is the most extensive file I have released for free to date. I’ve been toying around with the idea of a load planner for a long while. The idea behind this build was so practitioners could plan future loads and see how these planned loads would affect a:c ratios. I think ‘predicting injury’ is going to be a far stretch. If you have previous data already, and have identified some individual risk areas, then by planning loads and managing injury risk, injury occurrences, in theory anyway, could be reduced. This file could be part of a larger workload database, … Continue reading LOAD PLANNER


  In my previous worksheets and demonstrations, EWMA calculations have required you to build an average or copy the first day’s workload to build up some data for the EWMA formula to work from. In previous downloads I have put out, when adding in a new athlete, you would have to change the formulas manually to ensure the first time an athlete’s name appeared, their load in the EWMA column would match their raw score. As seen below, the second time an athlete appears, the formula for EWMA could kick in. I struggled for a while to make this process … Continue reading UPDATED EWMA CALCULATIONS


Most of the donkey work on this was done by Sean Williams @statman_sean, this project was inspired by the work from David Carey @dlcarey88 and co, in his recent paper on Optimizing Pre-Season Training Loads in Australian Football. This paper looked at the daily loads and periodisation strategy that would produce the largest total distance covered across pre-season, whilst keeping ACWR and cumulative loads within specified constraints. Sean put together a mock loading template to replicate this idea by using solver in Excel, the original paper had used MATLAB for its simulations. He was kind enough to send this file to me … Continue reading OPTIMIZING TRAINING LOADS USING SOLVER IN EXCEL


This latest download is something I have been working on for quite some time, having built some similar functions for projects before, but without a 28 day rolling average, standard deviation and z-score. Using Z scores is an effective way to monitor significant deviations away from baseline values for any metric you are measuring. This spreadsheet demonstrates how to calculate an individual rolling 28 day mean, standard deviation and subsequently, Z scores from a bank of wellness raw data. The spreadsheet also uses conditional formatting to highlight raw wellness scores for a given day, based on the rolling z score … Continue reading FORMATTING RAW WELLNESS DATA BASED ON ROLLING 28 DAY Z-SCORES