400m Hurdles Analysis Follow-Up

Posted on 07 August 2012 by Darrell Cobner

Hi Mike,

To extend on our conversation on your blog site, here are some thoughts on 400m hurdles from a lecture I delivered in March 2011. Through conversations with Wynford Leyshon and data extracted from Dai Greene’s dissertation, I compiled a workflow to extract the key contact times from within the race. I conducted the exercise on a sample of 12 performances in mid to late 2010.

I extended the traditional split points of the first touch beyond each hurdle to incorporate contact points of the two strides either side of each hurdle. This was thought to generate not only an indication of flight time, but also multiple derivatives of timings around the barriers to explore.

This data was compared to a model of performance for split times and also against normative performance data accessed for the athlete concerned. Crudely, I took a mean of 5 competitive races and calculated a ±0.05 band to be sensitive enough to provide an indication of performance for the section time hurdle (+1 to +1). This value was used to provide the tricolour conditional format feedback in the dashboard.

 The absolute difference between -1 and +1 contact points were used to calculate flight time. On reflection, this should have been expressed to 2 decimal places, as the data collection procedure is not sensitive enough to deliver this accuracy.

The section times were calculated as the differences between the +1 contact points, and presented in the same fashion as your graph (but as an area graph). The same data was presented in two ways in this dashboard; I felt they were both important visual representations (conditional format and area graph). The dual axes plot also showed the flight times as they were potentially linked to the section time. The sample illustrated a couple of interesting features, such patterns of flight time differences with alternate lead leg take-off in the example below (blue line zigzagging up to H5).

The comparisons with the modelled performance helped to show relative pacing strategies adopted by different athletes in different races. This example shows an athlete who was running quicker than the time he achieved throughout the race, but could not maintain this speed endurance and fatigued at the latter stages.

When comparing the same athlete over a series of races, it highlighted there is not necessarily a consistent pacing strategy adopted in different races. Based on the modelled performance to run a 48.15s race, the line graph show three derivatives of racing strategy. Red = slower starting pace which did not accelerate to convert to a quick time, Yellow = quicker starting pace which was not maintained, Green = controlled starting pace with an increase in pace after H7.

This methodology requires more rigorous exploration as it could be addressed a multiple levels. It may require scrutiny of the modelled parameters applied in this instance. Or is the scale of difference too small to even warrant exploration in the first place? I am looking forward to supervising an Undergraduate dissertation in this area in September to explore it further. Hopefully, this student will be able to share his thoughts and findings this time next year?

I am an advocate of the dashboard summaries of performance and offer the final slide from the lecture to close the blog… the overall dashboard can be seen here.

Mike: I hope this was a useful exercise for you to undertake…



2 Comments For This Post

  1. james.hillier Says:


    I like the layout of the dashboard for representing data for 400m hurdles analysis. I think that it is extremely hard to model race distribution against a ‘norm’. This is due to the fact that in my opinion there are 3 very distinct types of 400m hurdlers:

    1) Speed – These guys are very quick on the flat, but don’t have good natural rhythm. They invariably go off hard and fast and ‘hold on’ at the end of races. They tend to be very inconsistent and They also aren’t technically great at hurdling, and they don’t like to train too much over the hurdles, preferring to do fast flat runs that they enjoy more. They have limited understanding of race distribution and stride patterns. They don’t normally run too well at championships where they have to run rounds. However, they can run some very quick one-off races, so they tend to have very fast PBs. Kerron Clement fits into this bracket.

    2) Strength – These guys aren’t especially quick in the classic sense (i.e 100 and 200 m times wont be great), but they are very strong and probably train like middle distance runners. They run more even paced than the speed guys, and can be quite consistent if they get their stride patterns down to a tee. Felix Sanchez fits into this bracket

    3) Rhythm – These guys possess qualities of both speed and endurance, but most importantly have great natural rhythm. They find a way for their stride pattern to ‘fit’ into the spaces between the hurdles. They tend to have very good self-awareness and like to do a lot of specific hurdles sessions. They won’t give you electric flat 400m times, but will always run consistently (especially over the hurdles). Dai Greene is a rhythm guy.

    Therefore, I think when doing analysis of 400m hurdlers, it is actually of more value to compare an individuals races and look at trying to find an optimum race distribution and/or stride pattern. However, tread with care; elite Athletes are only at their very peak for perhaps a few weeks a year, so some sort of caveat would need to be considered in comparing early and late season runs for example.

    A very exciting topic that warrants further investigation. I will be doing a technical report of the 400m hurdles from London 2012. I will post a link here when I have completed it.

  2. Darrell Cobner Says:

    The following statement is direct feedback from Wynford Leyshon, who asked me to share his thoughts on the blog. Wynfords background is summarised here (http://www.worc.ac.uk/wcpas9/684.htm).

    “The comments by James on 400mH analysis make for interesting reading and I look forward to his analysis of the 400mH at the Olympics.

    I am a firm believer in the use of performance analysis to aid coaching and find the dashboard approach of displaying data much better than having to plough through loads of tables of data.

    The questions that I would like to pose are:

    1) What is the core data that a coach wants from the analysis? Is there too much data displayed for the coach? For me the core data is: Touch down times, stride pattern and PI 1 (1st 200m cf to second 200m)

    2) To date the analysis has been carried out on world class hurdlers. Could the performance analysis approach be utilised for sub-elite/club athletes?

    3) How useful would the approach be in the coaching situation where the athlete may only run to H5, H8 or perhaps H10, but may repeat these runs a number of times?

    4) Would it be possible to develop a mobile analysis system whereby the analysis was produced straight after the race. Coach would then add comments on areas of good practice and areas of improvement. Athlete would then receive immediate feedback (analytical and subjective) on his/her performance.
    Possible Research Project
    Project would take 1 / 2 years and be a longitudinal study following a group of athletes in training situation (Mid Feb – April 2013 ) and in competitive situation (Mid May – July 2013). Project researcher would need to be present with coach in training situation. Most cases training would take place in Swansea.

    Race situation researcher would need to present with coach in some of the races (Races could take place all over the country).

    Decisions on what core data to be included together with the technical development of mobile analysis system to be developed at Cardiff Met by Feb 2013? and then trialled in coaching/race situation.”

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