This is the 3rd post in a series detailing my jimmy-rigged athlete monitoring system. If you haven’t read the first couple posts in this series you can catch up here. As I mentioned previously, I don’t have the luxury of a fancy monitoring system and was forced to come up with something myself to monitor the team and individual training and life stresses that players experience.

This post in the series will focus on aggregating the data streams for game days. In my previous post, I discussed what I have chosen to look at and in this post I’ll try to explain how I attempt to make sense of an ever changing stream of data. Note that the streams coming in for any given player on any given day may be unique due to what they’ve done (game vs. rest vs. practice) and how it was monitored (minutes played / prozone data for game analysis; subjective observation / survey data / polar T2 data for practice scenarios). ?This is why it’s particularly important in team settings to be able to quantitatively keep track of each player even if your system is imperfect. Otherwise the variances in physiological load from games or training and life stresses from player to player would be too great for any coach to keep mental notes on.

So let’s start with how the system aggregates the game day data and makes the output somewhat meaningful. As I mentioned before the system basically asks the question of whether the player played in a competitive match that day. If the person was on the game day roster (whether they played or not *see below) that day the following factors can affect their daily stress load:

• How many minutes did they play? I input the number of minutes and the system attributes a stress load on a scale of 1-5. More minutes = more stress.
• Did they travel that day before or after? It’s rare that we travel on the same day we play but it does happen, especially for reserve match games and Darby games against Portland or Seattle. Longer travel = more stress.
• Do we have ProZone data? If so, how do the player’s fitness stats compare to there average outputs on total distance covered, total high intensity run distance covered, and total distance covered in a sprint. The output weights the 3 variables with greater stress placed on sprints and higher intensity efforts.

Because we ALWAYS have data for the number of minutes played, that’s the primary indicator. We only have access to ProZone data at home games and some other stadiums around the league. There’s no PZ data for reserve matches, residency games or many other league games so we can’t count on it. While game minutes don’t always paint the clearest picture sometimes you have to deal with what you’re dealt. As a result, the converted stress load from the minutes played can be modified up or down from the PZ data and travel stress but it’s always the primary indicator.

*I attribute a stress load even to those players who suit for games but do not play for several reasons. The anxiety of a game alone induces stress that must be recognized. Furthermore, because of the warmup routines they do before and throughout the game, they are still experiencing some training stress even if they don’t play.

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Mike Young

Director of Performance at Athletic Lab

Mike is the Head Fitness Coach for the North Carolina Courage and North Carolina FC. He is also the owner and Director of Performance at Athletic Lab sports performance training center. He previously served as the fitness coach for the Vancouver Whitecaps and Carolina Railhawks. He has a PhD in Biomechanics, an MS in Coaching Science, and a BSS in Exercise Physiology and has coached Olympic and professional athletes in Skeleton, Track & Field, MLS and NASL Soccer, PGA Golf, NFL Football, MLB Baseball and Olympic Weightlifting. He has lectured around the world and authored 2 books and dozens of research and coaching articles.

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