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Thursday, 16 July 2015

Keeping Your Defence Healthy.

Recently I wrote up a method to quantify the effect of injuries on a side at Pinnacle. The number of matches a player was unavailable for was weighted to account his value to the team to obtain an alternative squad value for each Premier League in 2014/15.

Therefore, a £20 million player who was absent for 10 matches would in theory be more keenly missed than a £5 million player who was absent for the same span of games.

Under this method Everton were the team who appeared to be most inconvenienced through losing valued players for long periods of the season, while Leicester lost the smallest weighted proportion of their squad value.

On average, a side lost 17% of the value of their squad over a season to injury, although other causes, such as international tournament all ups and suspension also impacted on the availability of players.

So even though injuries are the staple of much Premier League coverage, they are only one part of the reason why a side may be considered to be under strength and even the most accident prone team doesn't stray too far from the baseline injury rate for the league as a whole.

Injuries are a shared experience for all teams and had Everton been more commonly lucky in the treatment room, they might have expected to gain an average of a couple of extra points per healthier season.

The media coverage reaches its most strident when the absentee is a forward. Understandable if the missing star is worth say £49 million, but the majority of teams either don't have the luxury of such players or if they do, squad depth often allows a similar quality of replacement. But less importance is placed on absentees from either midfield or defence.

Where the Injuries Occurred During the 2014/15 Season.

Team
% Forward Value Lost to Injury.
% Midfield Value Lost to Injury
% Defensive Value Lost to Injury
Arsenal
13.8
32.3
15.3
Aston Villa
18.6
12.2
23.5
Chelsea
19.3
11.5
4.9
Everton
17.9
30.7
21.2
Liverpool
30.0
12.2
21.9
Manchester City
15.7
14.6
12.4
Manchester United
17.3
17.5
30.6
Newcastle
9.3
33.0
23.2
Stoke
28.8
8.8
13.8
Sunderland
6.9
28.1
11.3
Tottenham
9.2
4.7
19.2
WBA
14.3
8.9
19.4
WHU
36.3
11.2
21.0

The table above highlights where the largest injury burden fell during the 2014/15 season on teams who have been Premier League regulars over the recent past.

West Ham, Liverpool and Stoke were the sides who saw their offensive value degraded proportionally the greatest by longterm injuries, losing respectively Carroll and Sakho, Balotelli and Sturridge, and Bojan and Odemwingie for in excess of a season's worth of matches.

And Manchester United, Villa and Newcastle suffered greatest on the defensive side of the ball. All bar one of Villa's defensive squad players were unavailable at some stage of the season and the other two sides suffered similar levels of disruption, meaning the teams not only lost squad value from their defence, they also had to continually shuffle their defensive pack.

In contrast, Chelsea had six defenders who were ever present for selection and the most number of Premier League matches one of their defenders was unavailable for during 2014/15 was four games. So the eventual champions had both defensive stability if they so wished, as well as ample choice.

Mame Biram Diouf inadvertently degrades Wrexham's defensive value.

It is tempting to try to see if defensive or offensive injuries have a greater impact in decreasing a side's subsequent performance. However, methodology is problematical. Using bookmaking odds may use estimations which have already been tweaked to account for injuries and creating a proprietary performance model is beyond the resources of most.

A simpler alternative may be to compare the seasonal performance of regular Premier League sides to their historical average and then see how this over or under-performance correlates to their defensive and offensive injury burden.

For example, Arsenal's 75 points in 2014/15 was in line with their average points per season for the previous 10 campaigns, while the two Merseyside teams under-performed their par from the last 10 years, as did Newcastle.

Loss of attacking value due to injury shows little correlation with a the graded performance of Premier League regulars during the last completed season. Liverpool under performed under the sternest of attacking injury loads, but Stoke prospered against their traditional points average despite similar offensive losses.

However, there does appear to be a relatively strong correlation between the amount of seasonal defensive disruption and a poorer than usual performance. 

The five Premier League regular members who lost the most defensive squad value due to injury in 2014/15 each under performed against their 10 season average points total, averaging an under performance of over 17%. While the five sides who largely escaped injuries to defenders all performed either to previous average expectation or exceeded it, on average by 12%.

 
A single season and a general trend linking better performance to a healthier defence is not definitive, but there are sound reasons to support the proposition. For example, organisational skills are often cited as a significant factor in defensive competence and familiarity bred from a settled and consistent defensive unit under a low injury load is likely to assist this.

Injury reports may be only slightly more newsworthy than transfer window speculation, but it may be worthwhile to take note when the list is peppered with defenders. 

Tuesday, 14 July 2015

38 Games is Not Enough.

The appointment of Roberto Martinez as Everton boss in 2013/14 appeared designed to build on the consistent over achievement characterized by David Moyes' time in charge at the Merseyside club.

Martinez' ability to keep Wigan in the Premier League, on a limited budget seemed ideal to push Everton from the dead ground that exists between the bottom half of the table and Champions League qualification to become consistent top four challengers.

That ambition was nearly reached in his first season in charge. They harried Arsenal for fourth spot, before falling seven points shy. And in a season where titles were perceived to have been lost rather than won, it was enough to get Martinez honourable mentions in the press for manager of the season.

Everton's 72 points exceeded the informed and weekly updated expectation of the bookmaking industry by around 16%. A figure bettered only by Mark Hughes (23%) at Stoke (an unthinkable award winning combination) and Crystal Palace, (31%) who were overseen for the majority of the season by the previously maligned, but deserving winner, Tony Pulis.

Hopes therefore were high that 2014/15 would be the season that Everton moved to a different level. Unfortunately, Everton ended the season in 11th, having spent much of the season looking anxiously down rather than upwards towards the highs of 2013/14.

There were possible mitigation for such a disappointing campaign, although they are most likely overstated. The Thursday to Sunday grind of the Europa League and an injury list that deprived Everton of nearly a weighted quarter of their squad value, the worst attrition rate in the Premier League, for example.

But one ubiquitous presence may have raised expectations and Matintez' stock in 2013/14 giving it potentially further to fall in 2014/15. Namely random variation over a meager 38 matches.

Football teams aren't predictable coin flips, but the well resourced and financially vested interest of the bookmaking industry is likely to produce consistently accurate assessments of a sides individual match outcomes.

And just as coins sometimes run hot or cold in the short term, imbuing them with non existent causation, the same may be partially true in football.

Wigan flip Martinez & he comes down heads to the delight of one brave Latics fan.
The average points expectation based on seasonal individual match odds for a team is just that and we should be wary of assigning managerial excellence as the sole reason for any deviation from this average until we know the extent that actual and expected totals may vary purely through chance over a particular number of matches.

Highest & Lowest Positions in Premier League Simulations Using Match Odds, 2014/15.
  
38 Games High Low Actual Position Expected Position
Chelsea 1 7 1 1
Man City 1 9 2 2
Arsenal 1 13 3 3
Man Utd 1 12 4 4
Liverpool 1 14 6 5
Southampton 1 19 7 6
Tottenham 1 19 5 7
Everton 1 19 11 8
Swansea 3 20 8 9
Stoke 4 20 9 10
West Ham 2 20 12 11
Newcastle 3 20 15 12
West Brom 4 20 13 13
Leicester 5 20 14 14
C Palace 5 20 10 15
Hull 4 20 18 16
Sunderland 6 20 16 17
Aston Villa 5 20 17 18
Burnley 7 20 19 19
QPR 7 20 20 20

The table above shows the best and worst finishing positions of each Premier League team in simulations of the 2014/15 season using the intertwined odds for every one of the 380 matches.

Everton were graded as the 8th best side in the league in the view of the betting odds and as such were one of the select few who may have ended their 38 game season as Champions, although the likelihood was minute. 

They were also part of a larger group of sides who could, if enough hot or cold streaks coincided, find themselves relegated in one of the three lowest positions.


Even with a most likely odds based 8th place finish in Martinez' second season there was a 14% likelihood that his side could have simply got lucky and at least emulated the previous year by placing 5th or better. 

Similarly, a team who might in the long term prove themselves to be the 8th best in the league could expect to do as badly or worse than Everton's 11th place in 2014/15 in 18% of 38 game simulations.

Each of these extremes of finishing positions were achieved without recourse to a Martinez factor, either for good or bad.

So while managerial input, injuries, fatigue and persistent under or over rating by bookmakers may each play a part when a side fails to perform as widely expected, random variation is an ever present that is often ignored entirely in end of term reports.

If the season were doubled in length to 76 matches per side, fewer good teams are able to take advantage of good luck for them and poorer luck for their rivals and finish the extended double season on top of the table. 

But it is only when the season runs for four normal campaigns and 152 matches, that the title is won only by one of the traditional big four. Nominally the best side also only then becomes more likely than not to be crowned Champions and the very poorest find themselves outside of the top half of the table in all simulations.

Highs & Lows When Simulating a Four Year, 152 Match Season Using 2014/15 Game Odds.

152 Game Season High Low
Chelsea 1 4
Man City 1 5
Arsenal 1 6
Man Utd 1 7
Liverpool 2 8
Southampton 3 11
Tottenham 4 11
Everton 5 15
Swansea 7 18
Stoke 7 19
West Ham 7 20
Newcastle 7 20
West Brom 8 20
Leicester 8 20
C Palace 8 20
Hull 9 20
Sunderland 9 20
Aston Villa 9 20
Burnley 11 20
QPR 11 20

A 152 game season would guarantee Chelsea, 2014/15's best team in the view of the bookmakers, a Champions League spot and all but do the same for Manchester City and Arsenal. While such is the clustering of ability in the lower half of the table, that still over half of the Premier League teams could find themselves relegated, despite having a core ability that might be greater than many rivals.

The logistics of having a 152 game season is of course untenable, as is perhaps the desirability of ensuring a side is more likely to finish were their talent dictates they should. But judging a manager on a single season, while understandable is also incomplete without at least hinting that chance, as well as skill may have been a factor in a single, Pulis like over achievement.  

Sunday, 21 June 2015

Growing Old Together.

Age profiles of sides have become a hot topic in football, particularly since Manchester City's squad of mainly peak age players began to approach 30 and potentially slip into gentle decline.

The subject is rife with many pitfalls. Survivor bias is well known in more data friendly sports, whereby only the best older players remain active later in their careers and so elevate the apparent abilities of more mature players.

An approach that attempts to account for this bias by measuring the yearly change in ability has drawbacks, most notably even the decision of which metric to chose in a fledgling data environment, such as exists for football.

Even more fundamental is how to express a side's age profile once we have settled on which parameters to measure. A sport that mixes tactical awareness, skills and physical fitness typically sees a player peaking in their late 20's.

In the case of football, different positions appear to have slightly different peaks, keepers having the most longevity and this has been covered elsewhere in this blog.

Average age, adjusted for playing time, is an obvious figure to quote to describe a team's age distribution. But this does invite misconceptions. For example, a team equally packed with youngsters and veteran players may through pure chance have a weighted average age that coincides with the accepted peak of around 28, yet without including one such player in their lineup.

A graphical representation adds more information, but the absence of numbers hinders the ability to quantify the impact of a changing age profile on a team's fortunes.



Manchester City's route to Premier League success was fuelled not only by money, but also a squad that was talented, but also played either approaching or at their peak age.

If we ignore positional variation and for simplicity assume that a Premier League player peaks at between 28 and 29, the plot above clearly shows how the age profile from City's initial Premier League winning squad moves from being predominately comprised of players approaching their peak to players at their peak when they regain the title in 2013/14 and then began to consist of players moving away from their peak during 2014/15.

As an extrapolation I've aged the squad by a year for 2015/16 and plotted how the profile would change to one consisting of a majority of players who would be likely past their prime if the share of playing time was similarly distributed in the upcoming season among the same players from the 2014/15 season.

The various graphs illustrate well the changing profile, but comparative figures would also be useful.

One simple way would be to chose a peak age and then see how far removed the weighted playing time is from this ideal peak for each age group of players. For example, an ever present 20 year old would be further away from the chosen peak age than a similarly, ever present 26 year old.

Under this scale, a side consisting of entirely peak age players would score a cumulative zero, as all minutes played would fall to peak aged players.

Manchester City's 2011/12 team score 2.5 under this method to measure the weighted proximity of their players appearances to the peak age of 28-29. Balotelli contributed 1331 minutes as a 21 year old and at the other end of the age scale, Kolo Toure played 852 minutes as a 31 year old.

As the squad converges towards their peak age by 2013/14 the arbitrary score of 2.5 has fallen to 1.1, reflecting the compression of the distribution around 28-29 years, but begins to rise to 1.7 during 2014/15, as more players move away from their peak. If 2015/16 followed the extrapolation, with no fresh blood added, City's proximity quotient (for want of a better term) will hit 3.1, compared to an average of 2.6 for the previous six Premier League champions.

Of more interest is to use this number in conjunction with a figure to describe the proportion of players approaching the peak age to those who have passed the peak age.

In 2011/12, City's weighted playing time that was outside the peak age was made up predominately of younger players. The ratio of weighted playing time for young players to those past their prime was 9.7. By 2013/14, younger players still narrowly predominated, but the ratio had fallen to 1.3, indicating near parity.

2014/15 saw City fielding a raft of 30+ year olds and relatively few minutes allocated to players aged younger than 28 and the ratio of younger non-peak players to older ones fell below one to 0.36. Older players now predominated outside of the peak age group. (Title winners average a ratio of 3.6, indicating a degree of planning from within the current playing staff).

So, as the plot for 2014/15 illustrates, City's squad has aged, moved away from the peak age and has very few replacements within the current set up.