With transfer deadline day in full-swing it seems like as good a day as any to complete our look at the relationship between football (soccer) and big data (part one here).

Today is the last chance for a few months for football clubs to outsmart their rivals by buying the players that they hope will give them a competitive advantage for the rest of the season. How will data play a part?

Whereas the 2002 Oakland Athletics provided a clear example of how statistics can be used to gain competitive advantage in baseball player recruitment, evidence of similar success in football is harder to find. As indicated in part one, a prime example is Botlon Wanderers, which arguably punched above its weight for years and was one of the first Premier League teams to use statistics to influence strategy and player recruitment.

As Simon Kuper details, one of the key examples of the latter is the club’s 2004 signing of the late Gary Speed, who at 34 would have been judged by many as being too old to compete for much longer at the highest level.

Kuper reports how Bolton was able to compare data related to Speed’s physical data with younger – and more expensive – players in similar positions, and determine that his performance was unlikely to deteriorate as much as would be assumed. Speed played for Bolton for another four years.

While there are other examples of successful purchases being influenced by data, those more sceptical about the potential for data to influence the beautiful game can also point to some high-profile failures.

If a moneyball approach was going to be successful within English football it had the perfect chance to prove itself at Liverpool in recent years. Since October 2010 the club has been owned by Fenway Sports Group and John W Henry, who once tried to hire Billy Beane as the general manager of the Boston Red Sox and in November 2010 hired the closest thing European football has to Billy Beane – Damien Comolli – as Liverpool’s Director of Football Strategy.

Statistical relevance
Quite how much Liverpool’s subsequent transfer policy was influenced by statistics is known only to Liverpool insiders, but certainly Comolli was cited as being responsible for the signings of Luis Suárez, Andy Carroll, Jordan Henderson, Charlie Adam, Stewart Downing, and José Enrique – for an estimated total of £110m – with the £35m spent on Carroll making him the most expensive British footballer of all time.

Either way, statistics have been used to judge the wisdom of those purchases with the scoring record of striker Andy Carroll (6 goals in 44 Premier League games) and winger Stewart Downing’s record of goals and assists (0 and 0 in 37 Premier League games) coming in for particular scrutiny.

Carroll yesterday joined West Ham United on loan, while Downing looks likely to have to adopt a more defensive role to stay at the club. Comolli left Liverpool in April 2012 by mutual consent.

While Liverpool’s transfer dealings are hardly a ringing endorsement for the applicability of statistical analysis and football, it would be wrong to judge their compatibility solely on the basis of transfers alone.

Network theory
We have also seen growing evidence of interest in statistical analysis to football tactics, with a number of academic research reports having been published in recent months. These include Quantifying the Performance of Individual Players in a Team Activity, which originated at the Amaral Lab for Complex Systems and Systems Biology at Northwestern University and provides the basis for Chimu Solutions’ FootballrRating.com, and A network theory analysis of football strategies by researchers at University College London and Queen Mary University of London.

Source: A network theory analysis of football strategies

Both of these use network-based analysis to understand and represent the value of players within a team’s overall strategy. As the researchers behind ‘A network theory analysis of football strategies’ explain:

“The resulting network or graph provides a direct visual inspection of a team’s strategy, from which we can identify play pattern, determine hot-spots on the play and localize potential weaknesses. Using different centrality measures, we can also determine the relative importance of each player in the game, the `popularity’ of a player, and the effect of removing players from the game.”

Looking at this from the perspective of someone with an interest in analytics it is fascinating to see football analyzed and represented in this way. Looking at it from the perspective of a football fan, I can’t help wondering whether this is just a matter of science being used to explain something that footballers and football fans just instinctively understand.

Another research paper, Science of Winning Soccer: Emergent pattern-forming dynamics in association football, certainly falls into the category of over-explaining the obvious. Based on quantitative analysis of a frame by frame viewing of a soccer match the researchers concluded that “local player numerical dominance is the key to defensive stability and offensive opportunity.”

In other words, the attacking team is more likely to score if it has more players in the opposition’s penalty area than there are defenders (having “numbers in the box“), while the defending team is less likely to concede if it has more defenders than there are attackers (or has “parked the bus”).

What’s more: “The winning goal of the Manchester City match occurred when a Queen Park Ranger [sic] fell down”. “She fell over!”

Which isn’t to say that there is nothing football can learn from big data – just that there are clearly areas in which statistical analysis have more value to contribute than others.

But we’ll conclude by looking at what can data management learn from football – particularly total football – the soccer tactic that emerged in the early 1970s and inspired our concept of Total Data.

In our report of the same name we briefly explained the key aspects of Total Football…

Total Football was a different strategic approach to the game that emerged in the late 1960s, most famously at Ajax of Amsterdam, that focused not on the position of the player, but on his ability to make use of the space between those positions. Players were encouraged to move into space rather than sticking to pre-defined notions of their positional role, even exchanging positions with a teammate.

While this exchange of positions came to symbolize Total Football, the maintenance of formation was important in balancing the skills and talents of individual team members with the overall team system. This was not a total abandonment of positional responsibility – the main advantage lay in enabling a fluid approach that could respond to changing requirements as the game progressed.

This fluidity relied on having players with the skill and ability to play in multiple positions, but also high levels of fitness in order to cover more of the pitch than the players whose role was determined by their position. It is no coincidence that Total Football emerged at the same time as an increased understanding of the role that sports science and diet had to play in improving athletic performance.

… and outlined four key areas in which we believe data management as a discipline can learn from Total Football in terms of delivering value from big data:

• Abandonment of restrictive (self-imposed) rules about individual roles and responsibility

Accepting specialist data management technologies where appropriate, rather than forcing existing technologies to adapt to new requirements. Examples include the adoption of non-relational databases to store and process non-relational data formats, and the adoption of MapReduce to complement existing SQL skills and tools.

• Promotion of individuality within the overall context of the system

This greater willingness to adopt specialist technologies where appropriate to the individual application and workload does not require the abandonment of existing investments in SQL database and data-warehousing technologies, but rather an understanding of the benefits of individual data storage and processing technologies and how they can be used in a complementary manner – or in concert – to achieve the desired result.

• Enabling, and relying on, fluidity and flexibility to respond to changing requirements

The adoption of alternative platforms for ad hoc, iterative data analysis enables users to have more options to respond to new analytic requirements and to experiment with analytic processing projects without impacting the performance of the data warehouse.

• Exploitation of improved performance levels

The role of more efficient hardware, processor and storage technologies is often overlooked, but it is this improved efficiency that means users are now in a position to store and process more data, more efficiently than ever.