Performance Analysis Challenges in Professional Football Practice

Chris Carling

Institute of Coaching and Performance, University of Central Lancashire, Lancashire, U.K.

Lille Football Club, Lille, France

Over recent years, the progressive bridging of the

gap between sports science and applied football

coaching practice has led to greater recognition of

the need for and subsequent benefits of objective

systematic processes for monitoring player

performance in training and competition (Strudwick,

2013). The on-going assessment of performance in

competition notably using performance analysis

techniques such as match and time motion analyses

provides opportunities for the design and

prescription of evidence-based practice frameworks

for optimising training and match preparation

(James, 2006). Many professional clubs now

formally employ performance analysts and sports

scientists to provide factual and permanent records

of events underpinning both individual and team

technical, tactical and physical performance in

competition. Innovative state-of-the-art computer

software and video technologies are used to generate

unprecedented masses of data to profile and

benchmark match-play performance. Analysis of

how teams and individuals perform in competition

aids in identifying specific strengths and weakness

and over a period of time can create a benchmark

against which instances of future performance can

be compared in order to pinpoint positive and

negative trends in play (Carling and Court, 2012).

However, there are many challenges when using

performance analysis. These include technological,

analytical and cultural concerns as well as evaluating

its actual impact on practice and most importantly

on competition (Carling et al., in press). Gathering

information on physical, tactical and technical

components of play that is objective, reliable and

accurate and can be generated on-demand are

paramount. In addition to the craft skills and

experience of the analyst, challenges also concern

the wide range of technologies currently available

and employed in professional football club settings

(Carling et al., 2005). Questions on how data are

acquired, stored, accessed and processed before

subsequent visualisation and presentation to staff

and players must be asked. Indeed, innovations in

software and hardware have greatly streamlined the

entire match analysis process providing accurate

real-time analysis. The development of multiple

camera semi-automatic tracking systems for

example enables collection of a multitude of data on

every player’s on and off the ball movements over

the course of competition (Glazier, 2010). Real-time

analysis as the match unfolds is now a reality and

there are possibilities to access specific information

on performance at any time during or immediately

after competition. Cost and quality of systems do

vary however and investments need to find the

balance between needs and budget to ensure a

positive return on investment and subsequently

make an impact in practice and competition.

Selection and definition of the different

components or key indicators of performance that

are to be collected and how the results are

subsequently analysed, interpreted and presented are

also major concerns (Mackenzie and Cushion,

2013). This process depends greatly on the craft

skills and experience of the analyst. It also depends

on the real world needs and willingness of coaching

staff to impact on the process (Wright et al., 2012).

An effective analysis process not only relies on

deciding what information should be recorded and

for what purpose but how it can eventually impinge

on practice and improve match performance. Can

data that aid understanding of how players have

performed in key areas of play eventually be

translated into practical applications to impact on

player learning, training session prescription and

performance in competition? There is a need to

focus on areas of performance where results will

have maximum impact and advantages can be

gained both in the short and long term. An effective

service also depends upon the performance analyst’s

ability to take into account and determine what

factors might have directly impacted performance

when interpreting data. Consideration should be

given not only to ‘how frequently’ an action is

performed but also ‘how well’ or the difficulty

involved especially when comparing individual

players. There is also need to account for the

constant interaction between physical, technical,

tactical and psycho-social factors. For example, is a

team’s impaired passing proficiency towards the end

Carling C..

Performance Analysis Challenges in Professional Football Practice.

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

of games related to a drop in physical performance

or is it simply due to the team frequently chasing the

game and therefore taking more risks? Contextual

issues such as current form, changes in coaching and

playing staff, playing home or away as well as

opposition quality, playing style and team formation

and environmental conditions must also be

accounted for (Lago, 2009).

A practical question that frequently drives the

match analysis service is what are the key trends

‘statistically’ or the ‘key performance indicators’

that stand out (or not!) when winning, drawing or

losing games. While use of frequencies of key

performance indicators is common, analyses of

‘efficiency’ have more relevance from a practical

viewpoint (Hughes and Bartlett, 2002). For example,

a team might regularly create more scoring

occasions compared to other teams in the League but

the latter might have a better ratio of chances to

goals scored. Another question is based on the

relationship between key performance indicators and

the ability of teams to recover from a losing position

or maintain a winning position. For example, are

more points recovered by top compared to lower

ranked teams across the season after conceding a

goal first and which key match events can be

identified as having influenced the result (Carling et

al, in press)? Data comparing performance in home

versus away games or when playing against Top 6

teams or peers lower down in the table respectively

are pertinent. The former point can provide useful

indicators on how teams adapt according to game

location especially when a different playing style or

formation is employed. The latter can give an idea of

a team’s ability to ‘raise their game’ against the best

teams or ‘grind out’ results against peers fighting

relegation. It is also important not to simply

concentrate statistical analyses on attacking play and

neglect defensive performance. Distinguishing

physical performance in relation to team possession

(in other words during defensive or attacking play)

in particular can be helpful in understanding

differences in running distance (Bradley, Lago-

Peñas, Rey et al. in press). For example, is a wide-

midfielder but not a centre-forward working hard

‘physically’ off the ball to both create space for team

mates and to close down opposition players who are

in possession? Match analyses of technical

performance of a potential recruit can provide clues

as to how the player performs in relation to the

benchmark data of the player they might replace.

Data can also be exploited to identify potential

trends in performance across match periods (e.g.,

between halves or towards the end of games) in

order to judge a team’s or an individual player’s

ability to be ‘consistent’ over the course of play.

Similarly, the benchmarking of progress at different

milestones during the playing season is relevant (e.g.

first- versus second-half of season). This work can

be extended to investigate performance specifically

during periods of match congestion and determine a

team’s ability for example, to maintain standards in

domestic League matches directly after European

competition. The analyst might also track intra-

seasonal variations, for example, passing and

crossing completion rates over the first 5 games in

the present compared to the previous season.

Finally, information obtained from large-scale

match and motion analysis investigations can be

used to impact on the way teams generally train and

prepare for games. For instance, general exercise-to-

rest ratios or low- to high-intensity exercise ratios

calculated using time motion analysis data are used

to represent the physical demands of the game and

provide objective guidelines for optimising the

conditioning elements of training programmes

(Reilly, 2007). In addition, when statistical analyses

have enabled identification of a problematic issue,

the coaching staff can subsequently prescribe

adapted training drills and then reanalyse

performance to determine if there is improvement in

the frequency or efficiency of actions over following

games (Carling and Court, 2013). For example, the

effect of a modification in team formation on the

number of opportunities created for wide players to

cross the ball or a change in warm-up strategy to

improve high-intensity work rate in substitute

players immediately on entering play. Future work

will no doubt move towards the development of

intelligent systems to aid in the development of

optimum training prescriptions and predictive

modelling of ensuing performance using the

intelligence gathered from analyses of performance.

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