Recording the sequence in which events occur enables the analyst to go to far greater depths in interpreting a performance. Now critical events, such as a shot at goal or a winning shot in a racket sport, can be analysed so that the events that led up to them can be examined for repetitions of patterns. These can be very informative for the coach, not only about their own players, but also the oppo-sition. These patterns can also help the sports scientist understand different sports.
First step: You must decide what you want from your system before you begin to design the system.
This does sound a little obvious but the reason for this lies in the fact that nota-tion systems provide masses of data. Unless you have a crystal clear idea about what data you wish to collect, then you will find that your system will collect con-fusing and sometimes irrelevant information. Keep in mind the old adage about not seeing the wood for the trees. Time spent working on what form(s) your output might take can save a great deal of frustration later. Most importantly, it also simplifies the job of defining input. Having once decided what you want, the process of designing your data collection system is simple and straight-forward. Often, the most difficult part is making sense from the mass of data – this is true for all analysis systems. The simplest way of starting is to consider a basic example. A field hockey coach may wish to have more information about the shooting patterns, or lack of them, for the team. Consequently, this coach will need an output from his system consisting of:
1 Position of the pass preceding the shot (the assist) 2 Player who made the pass
3 Type of pass
4 Position from which the shot was taken 5 Which player made the shot
Table 7.1 A simple frequency table for basketball.
Actions 1 2 3 4 5 6
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6 Outcome of the shot, i.e. goal, save, block, miss (wide), miss (high), corner . . . etc.
(Note: If field hockey is not a game with which you are familiar, this method will as easily apply to any field invasive team sport, such as basketball, soccer, water polo, lacrosse, etc.)
The data needed to be notated in this example is relatively simple. The next step is to assign notation symbols for each of the above variables. First, divide the pitch into segments or cells and give each one a code; this could be either a number or a letter, but there are usually advantages in using specifically one or the other.
Deciding upon how the playing surface should be divided is not always as simple as it might appear. Using small cells does enable fine definition of the positions at which actions take place, but the more cells you have, the more data you have to collect in order to have significant numbers of actions in each cell. If in doubt, err on the side of simplicity – the most influential research on soccer was done with the pitch divided into three, the defending third, the middle third and the attacking third. The hockey pitch in Figure 7.4 is at the other extreme of definition, with a large number of position cells.
As position, player, action, etc. are notated, it is often useful to have the codes entered in the system alternating from letter to number to letter; this makes interpretation of the data much simpler. Any saving that can be made in the number of items entered, can also mean a large saving in time – often the dif-ference in being able to notate ‘in match’ or not. It is easy to identify players by
Figure 7.4 A definition of position on a representation of a field hockey pitch
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their shirt number, but if there are more than nine in a team or squad, then you have to note two digits instead of one. Some systems in the past have employed letters for the players ‘10’ and ‘11’. In Figure 7.4, letters have been used to differentiate between respective areas of the pitch rather than numbers. The significance of this is that, for each of the areas above, there is only a single item of information required to be written down, irrespective of which area. This may sound trivial, but when systems can be recording thousands of items of data, each small saving in design, at the developmental stage, will increase the effectiveness of the system many times over.
So let us assume that the coach has decided to use letters for pitch cell divisions and numbers for the players of the team. Does the coding of position cells in Figure 7.4 seem a reasonable layout? A number of potential problems present themselves. (The use of letters ‘I’, ‘L’ and ‘O’ could present some translational problems later. Most notation is done at speed, ‘I’ and ‘L’ can easily be confused both with each other and the number ‘1’, and of course the letters ‘O’ and ‘Q’
with zero, ‘0’).
The main problem with the representation of the playing area is one of defin-ition. Will these pitch divisions give the coach sufficient information on the significant areas of the pitch from which his team are shooting well or poorly? It would seem unlikely. In this situation, previous researchers have used unequal divisions of the playing areas, making the definition finer in the areas of most interest. In this example, this will be around the goal. There are a number of ways of doing this. Figure 7.5 is one simple way using a representation of just half the playing area – this does however negate the possibility of notating shots at goal from the player’s own half.
Another way of doing this would be to use arcs from the goal as shown in Figure 7.6. This has been used in a number of systems, both in basketball and soccer, to good effect. In both games, there is an optimum area from which to shoot, which is more easily defined in this way.
For our example, let us assume that the coach is using the area representation shown in Figure 7.6, and that players are identified by their shirt numbers. The two actions that we are notating are the pass and the shot. There are four different types of pass that our coach has defined:
Flick – F Push – P Arial – A Hit – H
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Now we only have to decide on the possible outcomes of the other action vari-able, the shot, and we have a notation system. The coach has decided at this stage not to differentiate between types of shot, so it is the outcome of the shots that need coding. As we are writing letter and then number as we notate position Figure 7.5 A definition of position on a representation of a field hockey pitch
oriented to analysing attacking moves
Figure 7.6 Another definition of position on a representation of a field hockey pitch oriented to analysing attacking moves
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and player, let us use a letter code for the action outcome. A number of systems involve specifically invented symbols but, for the sake of keeping this example simple, let us stick to recognized numeric and alphanumeric symbols. A simple code would be:
The coach is now able to start notating a match. An example of the type of data obtained will be as shown below. In this way, the coach, or any other operator, can record the position from which the shot was made, who made it and the outcome. Because of the way that the data have been entered, a number, a letter, another number, and a separate line for each shot, interpretation of the data is relatively easy.
Remember that the codes chosen here in this example were chosen for sim-plicity, a number of systems utilize invented symbols that represent actions or outcomes. This is a decision that can only be made by the individual designing the system. Use whatever you are most comfortable and familiar with. Above all, keep it as simple and as easy as possible.
The only problem facing the coach now is processing the data. First, enough data will need to be collected to make it significant, then the distribution of the shots and their assists, with respect to players or position, together with their outcome, can be explored. This form of data processing is very import-ant in most forms of analysis and feedback. Data analysis is a difficult part of notational analysis, a separate section is devoted to it later in Hughes and Franks (2004).
The information above makes it easy to record the data, there is less chance of becoming confused, and it is easier to interpret the data once recorded. It also makes it easier for someone else to understand the data collection sys-tem, should that be desirable. Decide who is likely to use your system; if it is only for your own use only spend as much time ‘dressing it up’ as is necessary.
Note: Always remember that when other people either use your system or are presented with the data from your system, they will tend to judge the whole system by its appearance.
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