I was asked by Josh Bryan to post an article from my own blog http://mikehainesperformance.wordpress.com on a simple piece of analysis I conducted on a 400m Hurdles heat during the Olympics and my thought on the process. The purpose of the analysis itself was to challenge myself to investigate a performance in an event I have no previous experience in, whilst attempting a quick turnaround as expected in the industry. So, here goes:

Capturing the footage

The first thing I noticed was that it may be quite difficult to analyse a single performer for the whole race due to the different camera angles used and the constant zooming in and out. At times the camera angle was tight, focussing on just one or two athletes, meaning that others were being missed.

Tight angle with only a selection of athletes in view

Whilst at other times a very wide angle was used, making it difficult to identify when toe off and landing occurred.

Wide angle, with some athletes obscured from view

The footage focussed on Angelo Taylor primarily, meaning that he was one of the few athletes always on screen, therefore I chose to analyse his performance.

Dashboard

After some quick research into the most useful parameters for studying hurdling performance I decided to collect data on:

• total race time
• 200m time
• the time differential between the second and first 200m
• the time of the approach run and number of strides from the start to the first hurdle
• the flight time over each hurdle from toe-off to landing
• the split times and number of strides between hurdles (hurdle units)
• the time and number of strides between the final hurdle and finish line
• the time differential between the worst and best hurdle unit

I tested two different methods of collecting the data, both using Dartfish Connect Plus. The first utilised continuous event buttons representing the different hurdle units and flight times, the second utilised the timer function within the Analyser module. Both worked equally well although perhaps using continuous event buttons within the tagging module would allow split times to be exported to an excel spreadsheet linked to the dashboard to speed up the process, which wasn’t considered in this instance.

Select the following link to view the dashboard: Angelo Taylor Olympic 400mH R1

* It is important when looking at the dashboard to consider the fact that the race used was just a heat in which Taylor cruised to victory and didn’t have to expend much energy. This is reflected in the 4.05 seconds difference between his 200m times. It would be more interesting (and useful) to conduct this analysis on a more competitive race as the data may then be useful to indentify strengths and weaknesses. I will look apply this analysis to the final, as well as apply it to the 110m hurdles where flight times are considered more significant.