Football has now moved into the information age, or more precisely an advanced technology age and as such ‘hyper-niche’ knowledge is driving major changes in all areas of the game, especially in football analytics (Memert & Raabe, 2018). The pace of change in football analytics is not subsiding but instead accelerating and this will get even faster as technology gets more advanced (Bradley, 2020). Figure 1 illustrates the swift change from simple manual analyses to fully automated integrated analyses alongside the computation reliance through this journey.
To enable selected football organisations to gain a competitive edge, some have intentionally set up research and development (R&D) based ‘think’ tanks or departments that work in either silos or alongside traditional front facing disciplines such as performance analysis and sports science etc (McCall, 2021). R&D is typically associated with innovative work that develops new knowledge and products or optimises existing ones through a systematic process. Although most will agree that R&D can accelerate the pace of innovation across most industry sectors, what is the typical process and how can this be applied within a football context. This blog aims to delve into the area of R&D in football with special reference to its specific stages in a football setting, adoption by elite clubs and summaries some case studies of best practice. Other critical issues are explored such as the barriers associated with such practices.
Stages of R&D in Football
Four key phases can be found within Figure 2 that broadly represent some of the R&D processes that organisations go through and the subcomponents below them. The initial phase for a Club is defining the research scope through the creation of club specific performance questions that are ideally proposed by key stakeholders. An example of this process could be ‘How can we effectively leverage data analytics alongside our tradition scouting tools to improve our future recruitment?’ or ‘How can we use R&D alongside our football initiative/IQ to reduce injury occurrence?’ Both questions above are two of the biggest questions that teams will ask themselves given that English Premier League clubs will lose around £180 million per season due to wages paid to injured players and this equates to around £9 million per club (Eliakim et al., 2020). Obviously, there is biased towards top EPL clubs in terms of lost wages paid to injured players due to the higher wage demands of players at top clubs (Football Injury Index, 2019). Thus, keeping them on the field is of primary importance. Moreover, Deloitte Sports Business Group found that the recruitment of players into the Premier League in the summer transfer window of 2021 totalled around £1 billion and thus having a greater confidence level that new players will fit into a club and ultimately perform is of paramount importance. In contrast to club specific performance questions, more generic human questions could be posed along the lines of ‘How can we help our players?’ This is not an either-or option, as that would be a false dichotomy as both approaches are very relevant. The problems that resonant with key stakeholders are usually of interest to each club (Lacome et al., 2018; Bradley et al., 2019). Once questions have been solidified, then idea generation occurs to visualise how this can be realistically implemented to maximise value and return on investment.
The second stage is the most convoluted and complex stage as the idea is pushed into development in which design, implement and integration occur. For instance, staff may develop computer algorithms for recruiting players that use multiple data feeds, including technical events, tactical dynamics and physical tracking data and thus creates key performance indicators that link to the characteristics of the player that is being replaced or the playing style of the team. This may be accompanied by immersive data viz for effective communication to the key stakeholders. If deemed to be effective, this can then be automated to implement and integrate this into the clubs’ processes. Although many iterations of these types of tools are common place in R&D, the point comes when a final version is needed in the stabilisation process.
The delivery to the end users or key stakeholders can be smooth if they have been part of the initial discussions and thus deployment in this context can be a natural evolution of the project. As more is known about the new area researched, it is imperative that ongoing improvements are feedforward to create new variants of the initial idea and refinements and optimisation are key. This should therefore create a perpetuating R&D system that consistently refines with each iteration of the R&D wheel in Figure 2. The reader should be aware that this four-part process has been used by Football Analytics HQ consultants successfully in various club settings, but little is known about the processes used by other football clubs and organisations. The lack of dialogue on such an area could be related to the unwillingness of clubs to share information in an attempt to gain a competitive edge especially in relation to recruitment and injury risk mitigation.
Applying Moore’s Curve to Football R&D
Moore (2014) used an idea diffusion curve to highlight how new products move through selected consumer populations. These ideas follow a curved trajectory, starting with innovators and early adopters, before growing into early and late majority and after sometime they finally reach the laggards. This curve was primarily applied to technology products by Moore (2014) but the implications of this work are far reaching and can be applied to just about every product or service within the market place including R&D in football (Figure 3A & 3B).
Bespoke R&D departments within elite football are few and far between so this curve could be something that the game observes in the years to come. For instance, if we observe the trends in Figure 3B, we can see the bulk of the teams start applying serious R&D processes only after this has been adopted by the clubs that were willing to take a chance on something new, different and most importantly useful. Thus, as Figure 3A indicates, these clubs get the most value as they effectively took the most risk early on in this process.
The clubs that are the innovators and early adopters are usually visionary clubs which are also well funded. This latter point is not always the case as necessity is a key driver for innovation and the richest clubs can typically spend themselves to success but other clubs that are less financed have to be even more innovative to catch up and compensate for the lack of finance they have. Once the word gets around the industry regarding the innovative approaches of these visionary clubs and the added value they reap, this progresses to a stage that the pragmatic clubs in the early and late majority feel safe investing their resources in specific R&D infrastructure. The laggards will typically only adopt this new approach only when it is necessary. The clubs in the innovators and early adoptors group usually have limited resistance to new ways but this resistance increases when a new R&D approach is presented to the mass majority and thus creates a lag. The difference in viewpoints or resistance between these two groups is the chasm (Figure 3B). The sea change in the perception of the value and usefulness of R&D only comes when the left side of the curve is completely sold on the benefits of R&D in football. The reader must be aware that applying technology marketing principles to R&D practices in football is novel, it also has numerous limitations. For instance, the diffusion of innovation models like the one in Figure 3B does not integrate the overlapping effects of different contexts of the individual, community or industry (MacVaugh and Schiavone, 2010) thus the reader must be aware that this section is purely speculative in nature.
Figure 3. Adaptation of Moore’s (2014) idea diffusion curve and applying it to R&D based practices with elite football clubs.
A real-life example of best practice in R&D can be found at Liverpool Football Club, which are renowned for their innovation in this area. Once John Henry bought Liverpool, the club brough in a new director of football and as a result Michael Edwards and Dr Ian Graham were employed to kick start the new R&D analytics revolution at the club. The R&D work at the club is carried out by numerous full time staff who vary from physicists to experts in technology and data management. Recruitment was the primary focus of the initial R&D work of this department but this is much more far-reaching now and includes scouting the opposition prior to matches using data and video. Although it is very difficult to assess from the outside the success of this R&D work, if one focusses on the recruitment of players such as Mo Salah and the likes of Virgil van Dijk it is easier to see the impact data has had on successful recruitment. One most also be mindful that data is not used in a reductionist manner but synergised with normal scouting process to create a hybrid approach. Success in football is highly multi-factorial and complex with many technical, tactical, physical, psychological and cultural factors playing a significant role in teams winning. Since adopting this data analytics approach, Liverpool FC had a resurgence in form recently by winning both the EPL and UEFA Champions League in the period between 2018-20. Although cause and effect cannot be established in these examples due to the complicated nature of football performance, it seems that adding a data analysis approach to selected elements of the club could be advantageous.
One criticism associated with most clubs that use innovation is the lack of openness to share ideas. This is obviously understandable given the competitive edge one gets from R&D and also the added financial expenditure spent on this work. FC Barcelona could be an exception to the rule given the launch of the Barca Innovation Hub in 2017 which is a centre for knowledge, innovation and technology (Witts, 2019). This aimed to collaborate with the sports world in key areas such as performance, technology and medical sciences to improve the performance of its teams. FC Barcelona staff and consultants have been relatively open to publishing details on the training practices of its team (Martin-Garcia et al., 2018) among other areas that are not usually published. One could view this from numerous perspectives as open and progressive or that the information released is stripped down to older ideas/methodologies and thus it excludes any novel practices to maintain an edge over their competitors.
In summary, it is envisaged that R&D departments are going to become commonplace within the football industry in the future. This will occur due to the continued attempts of clubs to gain more of an advantage over competitors. Thus, using some of the Football Analytics HQ frameworks described in this brief blog could help teams to understand the process. Moreover, based on Moore’s (2014) curve, the elite teams that embrace R&D more quickly as innovators and early adopters will reap the most value from the process other the bulk of the majority and laggards.
If you are interested in developing your R&D process within a football organisation please see our Football Analytics HQ consultancy services.
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