The earliest publication in notation of sport is that by Fullerton (1912), which explored the combinations of players batting, pitching and fielding and the probabilities of success. But probably the first attempt to devise a notation sys-tem specifically for sport analysis was that by Messersmith and Corey (1931), who attempted to notate distance covered by specific basketball players during a match. Messersmith lead a research group at Indiana State University that initially explored movement in basketball, but went on to analyse American football and field hockey. Lyons (1996) presented a fascinating history of Messersmith’s life for those interested in understanding the man behind the work.
The first publication of a comprehensive racket sport notation was not until 1973, when Downey developed a detailed system which allowed the com-prehensive notation of lawn tennis matches. Detail in this particular system was so intricate that not only did it permit notation of such variables as shots used, positions, etc. but it catered for type of spin used in a particular shot. The Downey notation system has served as a useful base for the development of systems for use in other racket sports, specifically badminton and squash.
An alternative approach towards match analysis was exemplified by Reep and Benjamin (1968), who collected data from 3,213 matches between 1953 and 1968. They were concerned with actions such as passing and shooting rather than work-rates of individual players. They reported that 80 per cent of goals resulted from a sequence of three passes or less. Fifty per cent of all goals came from possession gained in the final attacking quarter of the pitch.
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Bate (1988) found that 94 per cent of goals scored at all levels of international soccer were scored from movements involving four or less passes, and that 50–
60 per cent of all movements leading to shots on goal originated in the attacking third of the field. Bate explored aspects of chance in soccer and its relation to tactics and strategy in the light of the results presented by Reep and Benjamin (1968). It was claimed that goals are not scored unless the attacking team gets the ball and one, or more, attacker(s) into the attacking third of the field. The greater the number of possessions a team has, the greater chance it has of entering the attacking third of the field, therefore creating more opportunities to score. The higher the number of passes per possession, the lower the total number of match possessions, the total number of entries into the attacking third, and the total chances of shooting at goal. Thus, Bate rejected the concept of possession football and favoured a more direct strategy. He concluded that to increase the number of scoring opportunities a team should play the ball forward as often as possible; reduce square and back passes to a minimum; increase the number of long passes forward and forward runs with the ball and play the ball into space as often as possible.
These recommendations are in line with what is known as the ‘direct method’ or
‘long-ball game’. The approach has proved successful with some teams in the lower divisions of the English League. It is questionable whether it provides a recipe for success at higher levels of play, but these data have fuelled a debate that continued through several decades. Hughes and Franks (2005) tried to demonstrate that perhaps these analyses of the data were too simplistic and that broader non-dimensional analyses give a different answer.
The definitive motion analysis of soccer, using hand notation, was by Reilly and Thomas (1976), who recorded and analysed the intensity and extent of discrete activities during match-play. They combined hand notation with the use of an audio tape recorder to analyse in detail the movements of English First Division soccer players. They were able to specify work-rates of the players in different positions, distances covered in a game and the percentage time of each position in each of the different ambulatory classifications. They also found that typically, a player carries the ball for less than 2 per cent of the game. Reilly (1997) has continually added to this database enabling him to define clearly the specific physiological demands in soccer, as well as all the football codes. The work by Reilly and Thomas has become a standard against which other similar research projects can compare their results and procedures.
Several systems have been developed for the notation of squash, the most prom-inent being that by Sanderson and Way (1977). Most of the different squash
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notation systems possess many basic similarities. The Sanderson and Way method made use of illustrative symbols to notate seventeen different strokes, as well as incorporating court plans for recording accurate positional information.
The major emphasis of this system was on the gathering of information concern-ing ‘play patterns’ as well as the comprehensive collection of descriptive match data. Sanderson felt that ‘suggestive’ symbols were better than codes, being easier for the operator to learn and remember, and devised the code system shown in Figure 5.1. These were used on a series of court representations, one court per activity, so that the player, action and position of the action were all notated (see Figure 5.2). In addition, outcomes of rallies were also recorded, together with the score and the initials of the server. The position was specified using an acetate overlay with the courts divided into 28 cells. The system took an estimated 5–8 h of use and practice before an operator was sufficiently skilful to record a full match actually during the game. Processing the data could take as long as 40 h of further work. Sanderson (1983) used this system to gather a database and show that squash players play in the same patterns, winning or losing, despite the supposed coaching standard of ‘. . . if you are losing change your tactics’. It would seem that the majority of players are unable to change the patterns in which they play.
Most of the data that Sanderson and Way presented was in the form of fre-quency distributions of shots with respect to position on the court. This was then a problem of presenting data in three dimensions – two for the court and one for the value of the frequency of the shots. Three-dimensional graphics at that time were very difficult to present in such a way that no data were lost, or, that was easily visualized by those viewing the data. Sanderson overcame this problem by Figure 5.1 The shot codes, or suggestive symbols, used by Sanderson (1983) for
his data gathering system for squash
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Figure5.2The data gathering sheets and example data of the shot codes, or suggestive symbols, used by Sanderson (1983) for his data gathering system for squash
using longitudinal and lateral summations (Figure 5.3). Not only were the pat-terns of rally-ending shots examined in detail, but also those shots (N-1), that preceded the end shot, and the shots that preceded those (N-2). In this way, the rally ending patterns of play were analysed. The major pitfall inherent in this system, as with all long-hand systems, was the time taken to learn the system and the sheer volume of raw data generated, requiring so much time to process it.
Penalties are now a subject of myth, romance, excitement, dread, fear and pressure – depending upon whether you are watching or taking them. They have either helped careers of footballers or destroyed them. Yet little research has been completed on penalty kicks. Using a hand notation system, Hughes and Wells (2002) notated and analysed 129 penalties with an intention to examine:
the time in preparing the shot
the number of paces taken to approach the ball the speed of approach
the pace of the shot
its placement and the outcome.
In addition, the actions of the goalkeeper were notated – position, body shape, Figure 5.3 Example from some of Sanderson’s data showing frequency
distributions of all shots, winners and errors
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movements as the player approached, his first movements and the subsequent direction, the outcome. Not all video recordings enabled all of these data to be notated, so in the subsequent analyses some of the totals are 128 and 127.
A summary of their findings are presented below:
One in five penalties were saved (20 per cent; 3/15), one in 15 missed (7 per cent; 1/15) and three in four resulted in a goal (73 per cent; 11/15) Players using a fast run up had 25 per cent of their efforts saved, because the player then tried either 50 per cent or 75 per cent power
Best success ratios are from an even run up of 4, 5 and 6 paces
There is no laterality in the success ratios – left footers and right footers have the same success percentages
No shots above waist height were saved
In every case, the goalkeeper moved forward off the line before the ball was struck
Although there is only a small data set, the goalkeepers who did not dive to either side while the striker approached the ball, had the best save and miss ratios.
This is a good example of hand notation providing accurate data in this age of computers, in fact the data were then entered into Access, and analysed through this database, a method used more and more. In addition, because of the nature of these data, and a performance analysis of what is virtually a closed skill situation, the data analysis provides a clear picture of the most efficient ways of penalty taking and saving.
5.3 INTRODUCTION TO COMPUTERIZED NOTATIONAL ANALYSIS