It is an implicit assumption in notational analysis that in presenting a performance profile of a team or an individual that a ‘normative profile‘ has been achieved. Inherently this implies that all the variables that are to be analysed and compared have all stabilised. Most researchers assume that this will have happened if they analyse enough performances. But how many is enough? In the literature there are large differences in sample sizes. Just trawling through some of the analyses in soccer shows the differences (Table 1).Table 1 Some examples of sample sizes for profiling in sport.ResearchSportN (matches for profile)Reep & Benjamin (1969)Soccer3,216Eniseler et al., (2000)Soccer4Larsen et al., (2000)Soccer4Hughes et al., (1988)Soccer8 (16 teams)Tyryaky et al., (2000)Soccer4 and 3 (2 groups)Hughes (1986)Squash12, 9 & 6 – 3 groupsHughes & Knight (1993)Squash400 ralliesHughes & Williams (1987)Rugby Union5Smyth et al., (2001)Rugby Union5 and 5Blomqvist et al., (1998)Badminton5O’Donoghue (2001)Badminton16, 17, 17, 16, 15Hughes & Clarke (1995)Tennis400 ralliesO’Donoghue & Ingram (2001)Tennis1328<rallies<4300 (8 groups)There must be some way of assessing how data within a study is stabilising. The nature of the data itself will also effect how many performances are required - 5 matches may be enough to analyse passing in field hockey, would you need 10 to analyse crossing or perhaps 30 for shooting? The way in which the data is analysed also will effect the stabilisation of performance means - data that is analysed across a multi-cell representation of the playing area will require far more performances to stabilise than data that is analysed on overall performance descriptors (e.g. shots per match). It is misleading to test the latter and then go on to analyse the data in further detail.This study aimed to explore strategies in solving these problems in two sports, squash and badminton, in depth and then present further examples from a multiplicity of types of sports. A computerised notation system (Brown and Hughes, 1995) was used to record and analyse play, post event, for elite (N=20), county (N=20) and recreational (N=20) players. T-tests were used to examine the inter- and intra-reliability of the data collection processes. In addition, to establish that a normative profile had been reached, the profiles of 8 matches were compared with those of 9 and 10 matches, using dependent t-tests, for each of the categories of players. This method clearly demonstrated that those studies assuming that 5, 6 or 8 matches or performances were enough for a normative profile, without resorting to this sort of test, are clearly subject to possible flaws. The number of matches required for a normal profile of a subject population to be reached is dependent upon the narure of the data and, in particular, the nature of the performers.A notation system, designed to record rally-end variables in Badminton, was shown to be both valid and reliable. Inter and intra reliability ranged from 98.6% (Rally length) to 91.3% (Position). Percentage differences between data from side, and end observations of the same match were not greater than for the intra-reliability data thus different court viewing angles had little effect on notation. Previous literature declared profiles of performance without adequately tackling the problem of quantifying of the data required in creating a normative template. The badminton notation system was used to examine the cumulative means of selected variables over a series of 11 matches of a player. A template, at match N (E), was established when these means became stable within set limits of error (LE). T-tests on the variable means in games won, and games lost established the existence of winning and losing templates for winners and errors. Match descriptors (rallies, shots and shots per rally) were independent of match outcome. General values of N(E) established for data types, (10% LE), were 3 matches (descriptive variables), 4 (winners/errors (w/e), 6 (smash + w/e), 7 (position + w/e). Respective values at 5% LE were 7, 5, 8 and 10. There was little difference in the values of N (E) when variable means were analysed by game than by match. For the working performance analyst the results provide an estimate of the minimum number of matches to profile an opponent’s rally-end play. Whilst these results may be limited to badminton, men’s singles and the individual, the methodology of using graphical plots of cumulative means in attempting to establish templates of performance has been served. Further examples will be presented from different sports.For the working performance analyst the results provide an estimate of the minimum number of matches to profile an opponent’s rally-end play. Whilst the results may be limited to badminton, men’s singles and the individual, the methodology of using graphical plots of cumulative means in attempting to establish templates of performance has been served.