How To Improve Stroke Cycles to Predict Closing Speed

Courtesy of SwimSwam contributor Chris O’Linger, an assistant coach at the University of the Incarnate Word.

Anyone who swims, or has swum, in my program knows that I choose to focus on research-driven methods pertaining to my training program and performance tactics. One of the most intriguing topics I have run across in my research has been freestyle stroke pulling indices (tempo, stroke length, cycle count), and its ability to predict the average pace and closing speed of eilte 800 and 1500 meter swimmers. Unfortunately, I have not been able to find such a consistent formula for short course swimming just yet, as there will be a much higher concern for control of VO2 max levels, and hypoxic exhaustion when flipturns and underwaters play at least twice the role in the events.

For the purpose of this study, I am using a formula to determine stroke cycles for each swimmer encompassing the amount of cycles taken with a few control factors concerning tempo and distance from the wall during the final stroke extension. At an elite level, these variables can be controlled at a much better rate due to the sheer fact that these swimmers have rehearsed these indices for numerous years, and, for the most part, have settled into a constant rhythm at this point in their career. I believe that stroke cycle, with these minor adjustments, can account for all indices to a degree of confidence high enough (a = <.02).

I analyzed the top 16 male and female miles swam at the 2013 FINA World Championship meet in Barcelona, and came up with an descriptive statistic model which would allow me to find correlational trends and regressional predictive factors for a swimmer’s closing speed based off of their projected stroke cycle counts.  Below are the average statistics for each variable to get a feel for what each individual’s data would comparable with:

N = 32 N = 32 N = 32 N = 32
M = 62.2819 M = 63.0316 M = 18.5122 M = 1.2491
SD = 2.433 SD = 3.450 SD = 2.399 SD = 1.331
Min = 58.68 Min = 54.48 Min = 13.79 Min = -1.16
Max = 66.34 Max = 66.83 Max = 21.83 Max = 4.20

*AP = Average 100m pace

CS = Closing 100m split

D = Difference between average 100m pace and closing 100m split

SC = Projected stroke cycle count

After finding many significant correlations and coefficients high enough to run regressional analyses to determine prediction rates, I found that swimmers with a lower cycle count throughout distance races results in a faster closing speed than swimmers with higher cycle counts. The effect sizes and R-squared values determined that this was a strong enough correlation to predict an elite swimmers closing speed based purely off of their stroke indices.

The regressional line-fit analysis is shown below:

Chris O'Linger, graph, stroke cyclesSimply put, at an elite level where training and technique have been practiced with great emphasis, we are noticing that the ultimately technical swimmers are the ones who are still prevailing. Remember, this data is for the top swimmers in the world, and not anyone can take 15 cycles per lap and swim at this speed, but the trends do show that drill work and awareness of the stroke can lead to vast improvements across all events.



Swimming fast and trying hard is not enough. Technical integration needs to be established in a foundational manner before the workload is unmanageable at proper form.

About Chris O’Linger via UIW


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14 Comments on "How To Improve Stroke Cycles to Predict Closing Speed"

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Fascinating stuff, but it raises questions. Are you sure that the columns are labelled correctly? As it stands, the average closing speed is *slower* than the average pace. Am I correct in my guess that the D=4.2 seconds belongs to Sun Yang? Was there any variability between the 800 and 1500 results?

Have switched the two column headers to reflect Chris’ amendment of his data.

Catherine, The data shown are correct for the differences between average pace and closing split due to the fact that there were more individuals who demonstrated a closing split equal to or higher than their average pace. There was (to my naked eye) not too much variance between the 800 and 1500, although the 8 and 15, however my interest was not to run statistical tests to do so. The variable between men and women, however, is significant. These sample results are based off of the overall calculations (men and women combined), and certainly accounts for the difference between closing and average splits. If you ever want full results, I’m happy to share all that I’m obligated to. My personal… Read more »

On second review, the data are switched between the two columns mentioned prior. I apologize.


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