The Iceman Cometh – Assessing the Effectiveness of Different Playing Styles at Euro 2016

Executive Summary

  • In Euro 2016 games distance covered had a small positive effect on match outcomes principally through improved defence.
  • There is also evidence that teams that made greater use of a short passing game created more scoring opportunities and scored more goals.
  • Italy averaged the highest distance covered while Spain averaged the highest number of attempted passes. Germany ranked highly for both distance covered and attempted passes.
  • Against Iceland, England dominated possession and created many more goal attempts but lost to a team that compensated by working harder in distance covered. It was a similar story in the Final where France played more but Portugal ran more.
  • No playing style dominated Euro 2016 beyond pragmatic football, playing the style that best suits the players available and most likely to effective against specific opponents.

 

Euro 2016 is unlikely to be remembered as a great festival of football. No team really shone in the way that Spain did with their tiki-taka football style between 2008 and 2012. The headlines were made more by the underdogs most notably Iceland who made it to the quarter finals knocking out England on their way, and of course Wales who reached the semi-finals. The success of both Iceland and Wales certainly made a case for the importance of team cohesion (the subject of my next blog). But did we learn anything from Euro 2016 about the effectiveness of different playing styles? Has the relative demise of Spain seen a swing back in favour of the hard-working artisan over the possession-loving artist?

 

The merits or otherwise of the possession game is of course where football analytics has its origins with the pioneering work of Wing Commander Reep from the early 1950s onwards and his data-based advocacy of a direct playing style. Reep remains a controversial figure and many see him as a strong argument against the use of analytics in the beautiful game. Reep found overwhelming evidence over a lifetime of coding and analysing games that most goals scored came from possessions involving three passes or less, and inferred from this that the long-ball game was likely to be most effective. It took until 2005 for Hughes and Franks to provide the definitive analytical critique of Reep’s conclusions. Quite simply, Reep’s fallacy was to focus only on possessions with a successful outcome. Once you include all of the possessions in the analysis, not just those that ended in a goal attempt or goal scored, there is a tendency for a higher number of goal attempts and goals scored per possession, the higher the number of passes, implying the complete opposite to Reep as regards the relative merits of direct and possession-based playing styles. The ability to complete passes is a general indicator of team quality and teams that complete more passes tend to be better able to create scoring opportunities. Spain’s success with tiki-taka football was not a statistical anomaly but just confirmation par excellence of Reep’s misinterpretation of the evidence.

 

Of course, and not just in (association) football but in all of the invasion-territorial sports, there is no simple linear relationship between share of possession and match outcomes. It is not the quantity of possession that counts as much as the quality of the possession in terms of pitch location and how the possession is used. A similar argument can be made when it comes to distance covered. It is not the distance covered or even the amount of high-intensity work that matters as much as the usefulness of the physical effort. The amount of high-intensity work can often be inversely related to the quality of a player’s decision making. “Reading the game” (i.e. exceptional spatial awareness) can allow players to be effective with minimal physical effort. Indeed much the same can be said about defensive actions in general. Players can defend space effectively by being in the right place at the right time without actually making a defensive action in statistical terms. Tally counts of defensive actions and cumulative totals of distance covered may not necessarily reflect defensive effectiveness.

 

Bearing in mind the obvious limitations of tally counts of passes and total distance covered, were there any discernible patterns in the effectiveness of different playing styles by teams in Euro 2016? I have extracted the data from UEFA’s own published statistics on every game. To ensure comparability I have only used the data for normal time and excluded extra time in elimination games tied after 90 minutes. I have analysed differences across games using win-loss analysis (i.e. comparing mean differences between winning and losing performances using t tests and effect sizes) and correlation analysis across all games, as well as ranking teams by game averages, and using cluster analysis to categorise teams.

 

So what do the data tell us about Euro 2016? First of all there is evidence that distance covered has a small positive effect on match outcomes principally through improved defence. Across all 102 team performances the correlation between distance covered and goals conceded is -0.117. A similar effect is found between comparing winning and losing team performances. Winning teams averaged 108.2 km whereas losing teams averaged 107.2 km. Although the difference is not statistically significant, it is consistent with a small positive effect on winning (Cohen’s d = 0.249).

 

In the case of passing, the win-loss analysis yields a small to medium effect for the ratio of short-to-long attempted passes relative to the opposition’s ratio (Cohen’s d = 0.315). Winning teams tended to attempt proportionately more short passes than long passes compared to their opponents. Correlation analysis also picks up similar tendencies. More attempted short passes has a small positive effect on goals scored (r = 0.126) but a large positive effect on the number of goal attempts (r = 0.523). By contrast more attempted long passes has a small negative effect on both goals scored (r = -0.147) and goal attempts (r = -0.099).

 

Table 1 reports game averages and rankings for distance covered, total passes attempted and short passes attempted for all 24 teams. Perhaps surprisingly for some, Italy had by far the highest distance covered, averaging 114.7 km per game. The other top teams in terms of distance covered were Ukraine (112.1 km), Czech Republic (112.1 km), Germany (112.0 km) and Iceland (110.3 km). Germany also ranked highly in terms of attempted passes, both all attempted passes (638.7) and attempted short passes (141.0), second only to Spain who averaged 648.0 attempted passes with an average of 181.0 attempted short passes. England ranked third on attempted short passes (127.8) and fourth for all attempted passes (500.3). France, Portugal and Switzerland were the other most highly ranked passing teams.

 

Table 1: Distance Covered and Attempted Passes, Game Averages and Rankings, Normal Time Only, by Team, Euro 2016

Team Distance Covered (m) Total Passes Attempted Short Passes Attempted
Game Average Ranking Game Average Ranking Game Average Ranking
Albania 104,744 20 350.67 19 104.67 12
Austria 107,116 15 456.00 8 113.00 8
Belgium 104,381 21 443.40 11 113.60 7
Croatia 107,028 16 389.75 15 91.75 16
Czech Rep. 112,111 3 317.00 21 83.33 20
England 107,859 12 500.25 4 127.75 3
France 106,550 17 484.86 5 119.71 4
Germany 111,950 4 638.67 2 141.00 2
Hungary 107,227 14 443.75 10 97.25 15
Iceland 110,305 5 259.00 23 60.60 23
Italy 114,656 1 408.20 12 81.60 21
N. Ireland 108,516 8 230.00 24 51.50 24
Poland 108,343 9 370.00 17.5 88.80 19
Portugal 107,885 11 473.86 6 117.43 5
Rep. of Ireland 103,192 24 279.75 22 67.25 22
Romania 103,311 23 346.33 20 97.33 14
Russia 110,014 6 462.67 7 91.67 17
Slovakia 108,968 7 391.50 14 107.50 10
Spain 107,628 13 648.00 1 181.00 1
Sweden 105,354 19 397.00 13 100.67 13
Switzerland 108,300 10 510.75 3 116.50 6
Turkey 104,164 22 370.00 17.5 109.00 9
Ukraine 112,133 2 448.67 9 106.00 11
Wales 105,873 18 388.83 16 89.83 18
All 107,923   424.50 103.40

 

Figure 1 provides a useful categorisation of teams based on distance covered and attempted passes using cluster analysis. What really stands out are the outliers particularly the Republic of Ireland (low distance covered, low short passes), Northern Ireland (medium distance covered, low short passes), Iceland (medium distance covered, low short passes), Italy (high distance covered, below average short passes), Spain (average distance covered, high short passes) and Germany (high distance covered, high short passes). Wales were below average on both metrics while England were average for distance covered and above average in attempted short passes.

 

Figure 1: Cluster Analysis of Attempted Short Passes and Distance Covered, Game Averages, Normal Time Only, by Team, Euro 2016

Blog 3 Graphic

 

Table 2 summarises two specific games – England’s defeat by Iceland and the first 90 minutes in the Final between France and Portugal. In both games the teams that played more lost out to teams that ran more. Both England and France dominated possession, had higher pass completion rates and created more goal attempts yet failed to win. The two winning teams compensated for their lack of possession and limited goal threat by working harder in terms of distance covered. Both Iceland and Portugal covered around 4 km more than their opponents.

 

Table 2: England vs Iceland and France vs Portugal, Euro 2016, Selected Team Metrics, Normal Time Only

Normal Time Only Round of 16 Final
England Iceland France Portugal
Distance Covered (m) 105,234 109,147 105,749 110,206
Total Passes Attempted 525 243 585 461
Short Passes Attempted 121 59 105 117
Long Passes Attempted 68 61 46 63
Pass Completion 85.9% 71.2% 91.3% 85.9%
Goal Attempts 18 8 17 6
Goals Scored 1 2 0 0

 

Perhaps the winners of the tournament summed it up best by progressing to the Final largely on the performance of their artist supreme, Ronaldo, particularly when it mattered most. But the early loss of Ronaldo in the Final saw Portugal triumph through hard work and defensive organisation. Maybe the lesson of Euro 2016 is that no particular playing style dominated and ultimately it was a triumph of pragmatic football, playing the style that best suits the players available and most likely to be effective against the specific opponents. And one final thought with the new Premiership just around the corner – can we expect Chelsea under Conte to emulate the high work rate of his Italian team at Euro 2016?

 

Endnote on Methods: Cluster Analysis

Cluster analysis is an important exploratory technique that can often generate useful summary categorisations. Cluster analysis can produce very effective visualisations when clustering on two dimensions only as in the case here where I have used distance covered and attempted short passes. If the analysis involves more than two dimensions, it can sometimes be possible to use factor analysis to combine the original metrics into two factors that can then be clustered and visualised. In particular using four clusters and factor rotation can often combine to produce very neat four-quadrant categorisations that are easy to interpret. Two things to bear in mind when using cluster analysis:

  1. It is crucial to standardise the metrics before applying cluster analysis when you are using metrics with very different scales of measurement. In my case, distance covered would have dominated the allocation of teams to clusters if I had not converted both metrics into Z scores before applying the clustering procedure (K-Means clustering). In fact there was a 25% difference in the allocation of teams to clusters between using standardised and unstandardized metrics. Figure 1 displays the clusters in terms of the original units of measurements but the clusters were determined using Z scores.
  2. Cluster analysis, like so many other statistical techniques, can be susceptible to the undue influence of extreme observations (i.e. outliers). This is certainly the case in the analysis of playing styles at Euro 2016. It is always advisable to explore the effects of using different numbers of clusters as well as comparing the effects of excluding the outliers.

 

23rd July 2016

Learning from the Recent Successes in English Rugby

 Executive Summary

  • An analytical mindset has been a key component in the successes of both Saracens under Mark McCall and England under Eddie Jones
  • The analytical mindset at Saracens grew out of Brendan Venter’s evidence-based, people-centred coaching philosophy influenced by his medical background
  • Analytics can never guarantee success but, if properly harnessed, the power of analytics is to improve decision making by replacing guesswork with hard evidence, and ensuring that systematic analysis takes precedence over selective anecdotes

 

Saturday 3rd October 2015 marked an incredible low for English rugby when a 33-13 defeat by Australia at Twickenham saw England crash out of the Rugby World Cup in the group stages of a tournament they were hosting and confidently expected to go all the way to the Final. Wind the clock forward just under nine months to Saturday 25th June 2016 when England completed a historic 3-0 series whitewash Down Under with a 44-40 defeat of Australia in Sydney. And that came on the back of England achieving the Grand Slam in winning the Six Nations in March. What a transformation in a relatively short space of time, and one that English football fans will want to see repeated sooner rather than later following England’s ignominious exit from Euro 2016 after a 2-1 defeat by Iceland just two days after the English rugby success in Sydney.

 

The turnaround in the fortunes of the England national rugby team has been masterminded by a new head coach, Eddie Jones. Beyond the observables – the changes in team and squad selections and coaching personnel, and match performances – along with the head coach’s media pronouncements, it is always difficult from the outside to know exactly how things have changed behind the scenes. But in the case of the regime change under Eddie Jones, it is clear that there are strong connections with the other major recent success in English rugby, the transformation of Saracens in just seven years from a team that had never won the Premiership to multiple Premiership titles and crowned Champions of Europe in May 2016. I was privileged to work with Saracens from March 2010 to May 2015 and saw first-hand how the club transformed its culture, its way of doing things. And one of the critical elements in that cultural change was the adoption of an evidence-based approach to coaching decisions. An analytical mindset is a common characteristic of both Saracens under Mark McCall and England under Eddie Jones. And of course two of Eddie’s assistant coaches at England, Paul Gustard and Steve Borthwick, were key people in the Saracens transformation.

 

The Saracens transformation dates back to 2009 and the appointment of the South African internationalist, Brendan Venter as Director of Rugby (in succession to Eddie Jones – it’s a small world). As well as a wealth of rugby experience in both South Africa and England, Brendan also brought a coaching philosophy strongly influenced by his medical background. As a medical practitioner, Brendan is committed to an evidence-based, people-centred approach. As he told me once, “I treat people not diseases, and I make my decisions using the best available evidence.” Brendan created a culture at Saracens that put an emphasis on really looking after the players as individuals, supporting and encouraging their personal development, and promoting a strong tribal bonding between players, coaches and support staff. An important appointment in this respect was the sports psychologist, David Priestley, who in a very quiet, unassuming and professional way did an incredible job in creating the people-centred Saracens way.

 

Brendan also instigated a more systematic approach to the analysis of games by the coaches. Supported by the performance analyst, Matt Wells, again another true professional massively respected within the club, the coaches recorded their observations for their own areas of responsibility from the game video to create expert data on player performance. It showed incredible commitment to painstakingly go through the game video to analyse every contribution of every Saracens player. Paul Gustard, the defence and lineout coach, would study every tackle and defensive play, and every lineout and then systematically record his observations. The other coaches – Alex Sanderson, Dan Vickers and Andy Farrell (and subsequently Kevin Sorrell and Joe Shaw) – did the same for their areas of responsibility. Gradually a mass of expert data was being built up and I was brought it to interrogate the data and analyse patterns across games. A reporting structure was created to feed into the coaches’ review of games. A central component of these reports was a set of team and player key performance indicators (KPIs) colour-coded using a traffic-lights system.

 

After Brendan returned to South Africa in January 2011, Mark McCall was promoted to Director of Rugby. Brendan had played with Mark at London Irish and brought him to Saracens. The transition was almost seamless particularly as Brendan retained a senior advisory role as Technical Director. Like Brendan, Mark embraces an evidence-based approach – he’s a law graduate. Under Mark the use of analytics greatly expanded into game preparation, particularly opposition analysis.

 

It said a lot about Saracens that they had the confidence to bring in an outsider, a university professor with no previous experience in professional rugby albeit I am a qualified football coach (UEFA B License) with many years of experience in applying analytics to sport. Brendan and Mark encouraged me to think out of the box and use analytics to challenge the coaches. Both of them were acutely aware of the problem of groupthink when a group of people work so closely together over an extended period of time and develop a collective view of the world. I remain in awe of just how hard the coaches at Saracens worked to get the best out of themselves in order to facilitate the players to get the best out of themselves. They embodied the notion of servant leadership, leading by serving the collective. And importantly they were open-minded, and like all great teachers (coaches after all are teachers), always looking to learn more so that they could become better coaches.

 

And it wasn’t just the coaches and the other support staff – sports scientists, medics, strength and conditioning, and psychologists – who embraced analytics and an evidence-based approach, the players did so too, no more so than the team captain, Steve Borthwick. Often known as the Professor of the Lineout, Steve was meticulous in his preparation for games. He worked closely with Paul Gustard in developing the lineout strategy for games. Steve also received all of my team and opposition reports, and regularly followed up with questions and comments. It is absolutely no surprise that Steve has formed such an effective coaching partnership with Eddie Jones, first at Japan and now at England. And it is no surprise either that Paul Gustard has also become an integral member of Eddie’s coaching staff. All three share an incredible commitment to excellence, capacity to work and care for detail. All three have an analytical mindset just like the coaching staff at Saracens. Analytics can never guarantee success but, if properly harnessed, the power of analytics is to improve decision making by replacing guesswork with hard evidence, and ensuring that systematic analysis takes precedence over selective anecdotes. Perhaps English football should heed these lessons.

 

14th July 2016