Performance Profiling

‘Knowing others is intelligence, knowing yourself is true wisdom’ Lao Tzu

I am almost certain that the analysts and coaches who read this blog will have, at some point, undertaken match analysis of an opponent in order to profile them. It is likely that variables such as shots and entries were recorded and mean values of these variables were presented to players and other coaches as a representative profile. However, how sure are you that a representative profile was presented, and how can you know?

Perhaps the key issue is to assess how and if the data recorded is stabilizing. As Hughes et al. (2001) point out, it is often the case that it is assumed the means have stabilized if enough matches are analysed. However, they further comment that ‘5 matches may be enough to analyse passing in field hockey, would you need 10 to analyse crossing or perhaps 30 for shooting?’. Consequently, there is a need to treat each variable independently, to assess how it stablisises. This would also help analyst in the field determine the minimum number of matches required for each variable to form a representative profile – important due to the time constraints of the job/tournaments etc. So, how do we do this?

One way, presented by Hughes et al. (2001) is to plot the cumulative mean of each variable as well as limits of agreement. Stabilization can be deemed to have reached when the cumulative mean falls within an acceptable limit. The limits of agreement (LoA) are just percentage deviations (+/- 1%, +/- 5%, +/- 10%) of the overall data mean about the overall mean.

Below, I have used this method to assess whether variables have stabilized, and how many matches it took. All the data was recorded by myself, in my role as analyst for Cardiff & UWIC Hockey Club, from our matches this current season.

The first example (below) is the frequency of shots per match.  Although the cumulative mean initially falls within the 10% LoA at the 3rd match, it is not until the 6th that it remains within 5%.

C Mean and LoA for SPG

The second example (below) is the frequency of penalty corners per match. In contrast to the first example, this variable takes longer to stabilise, it is only after the 6th match that it remains within the 10% LoA and by the 9th match had yet to stablise to within 5% of the cumulative mean.

C Mean and LoA for PC's

Although I have only presented the method upon two variables, it will hopefully show that it is not enough to simply assume that by analyzing a large enough amount of matches that a profile of a team can be made, and that it is dependent upon the nature of the data and the variable analysed.

For hockey fans, the method used here has also been used by Boddington et al. (2003), who also discuss some of the above issues raised in more depth.


Boddington, M.K., Lambert, M.I., Waldeck, M.R. (2003). The analysis of skilled performance and game parameters during league field hockey matches. International Journal of Performance Analysis in Sport, 3(2), 121-129.

Hughes, M., Evans, S. and Wells, J. (2001). Establishing normative profiles in performance analysis. International Journal of Performance Analysis in Sport, 1(1), 1-26.

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