Ballin’ on a budget: Heart Rate Monitoring

When presented with “new” ideas on how to implement athlete monitoring, there are a few common answers .

  1. We can’t afford it
  2. We’re not ready for it
  3. We don’t have the time/space/man-power
  4. Ok, let’s do it

In my case, this past year I ran a couple trials with 15 simple heart rate monitors (Polar RS100) that I was able to procure with the help of a faculty member in the exercise science department. Our women’s soccer team runs the Man-U Fitness test a few times per year, and I thought it would be a good opportunity to test drive them. There is typically a great deal of anxiety leading up to it on the part of the athletes, and I’m sure I didn’t help much by adding a restrictive strap to the proceedings.

The first time we did it, there were several things I would do differently, so I made some adjustments and here’s how we did it the second time.

  1. The team runs in two separate groups (one person runs, one person records.)
  2. We go through our warmup, girls hit “start” on the watches as test begins.
  3. Runner reads HR from the watch, recorder notes value at the end of each rep.
  4. Highest HR was assumed to be “MHR” for this purpose. On missed rep in some cases.

If you’re unfamiliar with the “Man U” it’s a 100yd sprint followed by a recovery run back to the start line. The sprint times progressively decrease as the recovery times increase each level/minute.

ManU1

What was I hoping to find?

  1. Graphical representation of data.
  2. Something that looks like an anaerobic threshold.
  3. Relationships between Max HR and levels completed.
  4. Quartile differences relating performance to HR data.

What did I actually find?

RESULTS

ManU2

Level Completed, Max Heart Rates, Average Heart Rates of 19 players on Man U Fitness Test.

  • Thick black line = Median Performance based on Levels Completed.
  • Green, Yellow, Orange, Red = 1st, 2nd, 3rd, 4th quartile respectively.
  • Light Green, Light Red shaded values = Levels below and above 90% “MHR.”

Note: Athlete “17” was considered a massive outlier, and was omitted from analysis.

 

So not terribly easy on the eyes, but now I’ve got a spreadsheet of HR data based on 19 players over the course of a fitness test. In the case of this test “performance” is determined by the number of levels COMPLETED before being unable to make the sprint time, so that’s what I used for my independent variable.

Initial Impressions: Obviously some of the first level heart rates are a bit high. I assume this is primarily a result of the anxiety related to the testing. I’m going to see what I can dig up on that later.

defpart

Although the above paper contests the relationship between heart rate deflection points and anaerobic threshold (Conconi Test,) I decided to use 90% of MHR recorded as our ballpark estimate for a transitional marker. Considering that the first 10 levels are a 1:1 work rest ratio (although of short duration 25s:25s,) it should be safe to assume that the majority of these levels should be completed below 90%.

The following chart details the linear increase of HR for our top 3 performers.

top3avg

Obviously, the top 3 performers had a fairly linear increase in HR over the course of the test, I assume the peak for each athlete respectively to be the highest HR achieved before missing a rep and transitioning into an unsustainable level of energy production.

rawdata

The raw data appears to show some pretty linear relationships between max heart rates and quartiled performance, as well as above and below median performance. Additionally, the levels completed above and below 90% MHR (on the test) seemed fairly strongly related to the performance quartiled groups as well.

I also tried to run relationships between max and average HR’s and levels completed.

MAXHRvslvlavgvslvl

CORREL

Both showed a mild relationship, although the groupings weren’t too tight. (I’m hesitant to use strictly statistical language – I’m working on it.) The objective here, was to expose the raw data possibly presenting a false positive.

Overall it appears that the athletes completing higher levels tended to have a few of the following qualities:

  1. Greater number of levels completed below 90%
  2. Greater number of levels completed above 90%
  3. Higher Max Heart Rates

My logical reasoning here tells me that fitter athletes tend to express higher max heart rates (as predicted,) and are capable of completing more levels below 90% of their max. In other words, the green zone is indicative of aerobic fitness, whereas the red zones are indicative of buffering capacity as the intensity increases. Being that this test was completed in the post-season, it reasons that AEROBIC fitness levels are not comparable to what we might expect to see in the pre-season. I am looking forward to re-running this test in the future and drawing some comparitive conclusions with a greater data pool. Hopefully by then I will be able to run some relationships with more statistical integrity and derive some more legitimate information.

One thought on “Ballin’ on a budget: Heart Rate Monitoring

  1. Pingback: HEART RATE DATA: Part 2 | Alex Carnall: Physical Preparation

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