A professional observation was performed at Ware Football Club to critically assess the pre-game methods of research and notation, utilised by the match analyst (MA), who is employed to work with the first team. Ware’s first team currently competes in the Isthmian Division One North, which is seven divisions below the English Premier League.
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Match analysis contributes to performance in sport by helping athletes and coaches make informed choices, and has as a result, assumed a major role in the world of sport. Match analysis denotes the exploration of behavioural events which are objectively documented during competition (Carling, Williams & Reilly, 2005). Its focus may be on the activeness of one particular player, or how the collection of actions of numerous players are integrated during the game. The analysis of one or both teams can be completed, as the behaviour when attacking and defending can be adapted (Carling et al, 2005). This is particularly beneficial for the match analyst as due to the nature of non-league football, full match highlights of opposition games are limited. This allows the analyst to not only gauge important information about an upcoming team, but also gain an insight into an opponent that Ware are not due to immediately play. A description of the team’s tactical pattern of play may be an outcome of the analysis.
Tactical analysis is the area in which the match analyst specialises in. Tactical analysis is associated with tactics and strategy. A strategy can be defined as attempting to limit the effects of any weaknesses, while making best use of the person’s/teams strengths, in a plan which has been established prior to competition (O’Donoghue, 2009). Match statistics and/or video footage are ways in which information can be gathered on upcoming opponents (Carling et al, 2005). Highlighted areas in which coaches could collate match statistics on include for example; the tendencies in the distribution of the opposition goalkeeper, the delivery area preference for corner kicks or whether a team utilises long throws. A scout or an observer can watch the opponent’s matches and collect the aforementioned data.
The coaches who are shown to be more successful in taking advantage of the opponent’s weaknesses while neutralising their strengths are those who have been better advised about the strategy and tactics employed by upcoming opponents (Carling et al, 2005). The MA however, performs the analysis qualitatively by solely describing areas he deems need to be highlighted rather than a notational collation of match statistics. Carling, Williams & Reilly (2009) suggest that this area is surprisingly overlooked within the literature, as emphasis is placed by coaches on their own team’s recent performance opposed to that of upcoming opponents. However, in contrast, the setup at Ware allows the analyst to have considerable influence on match days with the data he received from analysing the opposition, particularly with set pieces and individual player instructions regarding characteristics of the opposition.
Preparation wise, the analyst does organise himself sufficiently as the literature would advise (Carling et al, 2005). The analyst obtains a fixture list of all of Ware’s matches, which he organises so he can choose to watch a date of a fixture between a team that Ware are next due to face. Before arriving, the analyst studies information such as a predicted team line-up to familiarise himself with the opposition players. Other basic factors such as arriving to the stadium in good time and choosing an optimal observation position are also highlighted, which the analyst succeeds in professionally achieving.
Notational analysis is the method in which these events are accurately and objectively recorded. In the eyes of spectators, viewpoints of matches are often conflicting. Some may differ about what happened while other viewpoints may be wholly incorrect. This is due to highly selective human perception and subconscious bias (Patton, 2002). Individuals may see the game from a partisan viewpoint and as such bring their bias to the game. It has been shown that even the best coaches fail to realise where mistakes were made or appreciate where successful plays began and often, are unable to recall sequences of events correctly (Laird & Waters, 2008). If the system of analysis is adapted to the level of play with the data collection methods being reliable and objective, then notational analysis should provide a near enough ‘factual’ record. The MA utilises a pen and paper based system – the most commonly employed (Carling et al, 2005) and includes a limited form of shorthand notation featuring action codes and tally marks.
Once the game has begun, the analyst does not proceed to take any notes within the first 20 minutes, as he feels that it is more beneficial if he can focus solely on the development of the game. The theory behind this is that critical events and distinctive portions of a competition such as exceptional performances and controversial decisions are often easily remembered by coaches and spectators, while non-critical events are likely to be forgotten (Hughes & Franks, 2015).
This is both inaccurate and unreliable as a subjective observation process, even for experienced football coaches who have been shown to recall a mere 59.2% of critical events that occur over the course a 45-minute half (Laird & Waters, 2008). This inexact ability to recall critical events can lead to a distortion of the coach’s perception of performance by events they can remember – also referred to as highlighting (Hughes & Bartlett, 2008). This form of highlighting, can cause an inaccurate viewpoint of the game in total, particularly when coupled with a personal bias and the emotions of the observer (Hughes & Franks, 2004).
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Summaries of studies involving eyewitness statements of crime can provide some explanations of inaccuracies which may be relevant to the subjective observation of competitive performance (Maslovat & Franks, 2008). These explanations revolved around errors in attentional focus, observer bias and an increase in arousal level. Ultimately this lack of accuracy has a knock on effect within both decision making and coaching feedback. This can be improved by utilising video data, which provides unbiased, comprehensive and objective information (James, 2006; Hughes & Bartlett, 2008). Furthermore, using a combination of computer and video technologies enable coaches to use functions such as slow-motion and replay which results in a reviewable, retrievable and unbiased analysis of individual and team performance (Lee, 2011).
Areas of strengths and weaknesses can then be highlighted based on the selected performance indicators, providing an exhaustive representation of what can be anticipated in forthcoming matches (Carling et al, 2005). In training, these formulated strategies can then be worked on and analysed by the coach. The usefulness of trying out some of these performance aspects (such as using different formations & utilising short corner kicks) and behavioural aspects (attitude and commitment) can be evaluated by the teams, and influence team selection (Carling et al, 2005).
A performance indicator (PI) is defined as representing some relevant and important aspect of play (O’Donoghue, 2009). PIs have been widely used within a coaching context (Hughes et al, 2012) and the academic literature (McGarry, O’Donoghue & Sampaio, 2013). As football is such a dynamic and multi-faceted sport, it has become very difficult to objectively and universally define PIs. As such, PIs of importance will differ from one coach to another (Hughes et al, 2012). Within these academic (Mackenzie & Cushion, 2012, Hughes et al 2012) and practical (Wright, Atkins & Jones, 2012; Wright, Carling & Collins, 2014) areas of interest there has been a development of the expression Key Performance Indicators (KPI). These KPIs are judged to be more narrowly associated with success for teams or individuals than basic PI’s (Wright et al, 2014).
Although the PIs and KPIs used by the analyst appear to be mainly in line with the literature, such as aerial strength, tackling ability, reading the game and pressing for central defenders for example (Carling, 2005), there is also an emphasis placed by the analyst on the range of passing in this position. This is an interesting KPI which does not seem to be consistent with what would be expected of this particular position in the literature (Hughes et al, 2012; Carling, 2005). One reason may be that because the analyst has experience in the non-league, if a central defender can play accurate long balls it is a perceived as a good offensive tool for the opposition due to the more direct nature of non-league football in comparison to professional football. It has been shown that analysts and coaches may use the term KPI, to characterise a feature of their playing philosophy/strategy that they perceive as central to their success (Wright et al 2012; Wright et al 2014).
As aforementioned, the analyst should devise a quantitative notational system adapted to that standard of play. Utilising straightforward analyses (conversion rates and simple frequency counts) helps to supply a ‘snapshot’ of performance as a whole, and is of the highest significance to working match analysts within an elite setting (Carling, Wright, Nelson & Bradley, 2013). This will eliminate the various issues surrounding the current subjective nature of the analysis. This should begin on the commencement of the game, instead of after 20 minutes where a considerable amount of data is already lost. In addition, combining video and computer technologies enables post-event analysis to produce unbiased, reviewable and retrievable analysis of team and individual performances. PIs should be discussed with the coach and while these can vary from coach to coach (Hughes et al, 2012) it is important not to deviate too much away from the norms.
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