Saturday, 8 December 2007

Testing team sports athletes and analysing data

Many strength and conditioning coaches and/or exercise physiologists are nowadays employed to work with team sports. Testing and monitoring training is now becoming standard practice and data analysis, data mining and the ability to produce meaningful reports is a necessary skill of the elite sports science support staff. I this short post I will not discuss the main aspects to consider when performing a test and/or the limitations of testing procedures. I will just present simple examples of reporting data using Microsoft Excel.

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When dealing with large squads, single athlete's scores should be analysed and continuously monitored to make sure the athlete is progressing and improving. However, in order to profile areas of improvement it is important to compare the single athlete to the group or to a known group of elite performers.

A very simple way for doing this with excel is to collect all the data in a single sheet with the name of the athlete in the first column and all the tests scores in the following columns. Then, when the average values and the standard deviation for the team is calculated, all scores of each individual player can be transformed in Z-Scores. In Excel this is possible using the function STANDARDIZE which returns a normalised value from a distribution characterised by mean and standard deviation.

The syntax is the following:

STANDARDIZE(x,mean,standard_dev)

X   is the value you want to normalize.

Mean   is the arithmetic mean of the distribution.

Standard_dev   is the standard deviation of the distribution.

Once each score is normalised, spider charts can be used to see how each individual player scores as compared to the team scores. Two examples are given here. Zero is the team score, every score higher than zero means that the athlete scored better than the average value, every score below zero means that the athlete scored less than the average value.

Figure 1: This is an athlete that outscores the team average values in all tests

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Figure 2. This is an athlete outscored team results only in sprinting.

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When we plot the results in this way we can clearly identify areas where we need to make an impact with a training programme. So, while in athlete JL we need to put a lot of emphasis on sprinting abilities, on athlete H we need to do a lot of work on strength and power. With this approach we can then track not only athlete's development in different areas but also how they evolve in comparison to his/her team scores. Individualization of training is the key aspect to take into consideration when working in team sports. Data analysis allows the coach, the physiologist and the sports scientist to profile each individual player and provide appropriate training interventions.

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