Age Profiles for A Team's Best and Worst Premiership Seasons.

The optimum age at which players rise to their peak before making the sometimes rapid decline into retirement or punditry is one of the most neglected areas of football analytics. The lack of readily available data is part of the issue, but assessing player development and then their regression is further confounded by the choice of variables.

Shots, goals and assists are obvious key performance indicators for strikers, although even these will have an aspect of team input, but the choice of which data to assess midfielders and defenders, with their diverse team responsibilities, is more problematic.

Some of the game's best defenders rarely made a tackle.

Therefore, using playing time as a percentage of playing time available as a proxy for player worth may still be the best alternative. A player who is not selected by his manager, either because other squad members are regarded as a better option or who misses playing time through injury, should perhaps be  considered as a less valuable asset, either through lack of developed talent or because of age related decline.

This isn't to say that a 30 year old Frank Lampard is inferior to a peak aged midfielder at a lesser club, but generally we might expect that a team that is stocked with youth or near sell by date talent may perform at lower levels than that same team when it operates with more players at their peak.

In these posts, I looked at when players are most likely to dominate playing time in the Premiership and while goal keepers inevitably defy logical appraisal, the peak for strikers would appear to be in their mid twenties, with midfielders and defenders peaking slightly later.

A logical next step is to see if the results achieved by a team is even casually related to having players at their perceived peak denoted by playing time and whether this performance falls away as less mature and aging performers take more of a centre stage.

I looked at teams which had played at least six seasons in the Premiership and collected the amount of playing time allotted to a range of age groups in the most successful season for that club and then their least successful EPL season.

I then combined the age profiles of these Premiership clubs best season as well as their worst season to see if their lack of maturity or aging may have played a role in their peak and trough of performance.

For, example, Arsenal's best performance in the EPL was unsurprisingly their 2003/04 undefeated season, where their points per game tally was over 2.5 standard deviations above the league average for that season, their worst performance so far followed soon afterwards in 2005/06 when they were just 0.75 standard deviations above average, when finishing fourth.

In total I have a group of 22 sides, comparing their best efforts to their worst, profiled by age related playing time.

The plots have been combined in two year intervals to try to make any conclusions more visible. Defenders perhaps excel as much through experience as raw physical attributes. Defending is as much about organisational skills as it is about speed and stamina. Therefore, generally defenders tend to gain proportionally more playing time later in their career, even if they have peaked physically, compared to strikers or midfielders.

A higher proportion of defenders aged from 25 to 28 played in the combined successful seasons, while more raw youth and 30+ defenders appeared when charting the 22 sides nadir.

Midfielders appear to show a similar trend. The physical demands of a midfield position generally results in players in their mid twenties being afforded proportionally more playing time and the peak at 25-26 years appears to show that a successful season by the standards of each of the 22 teams, was on average also marked by a higher proportion of midfielders from that age group.

Thereafter, older aged midfielders account for proportionally more playing time in every age group on the occasions where the sides under performed most from their usual standards.

  

For strikers, again 25-26 year old predominate in successful seasons. They then see proportionally even more playing time in the next two years, possibly as a result of favourable recent impressions. But much of these appearances by strikers in their late twenties also coincide with a season of dramatic under performance by their side, possibly indicating that some strikers can show sudden and precipitous reductions in talent levels as age creeps up on them.

Raw youth and players who retain some ability, but have seen it reduced by aging may be a necessary component of a team's make up at times because of restricted squad sizes and transfer restrictions. Or they may be selected in the belief that they currently possess more helpful ability than they actually do.

Whatever the reasons for the selection of players possibly removed from their peaks, there does seem to be some evidence that these occasions also correspond, on average, with a large degree of under performance by the side over the course of a season.

To the Lucky the Spoils.

Before the start of the 2014 NFL season I wrote this preview which highlighted the factors that are most strongly correlated with winning.

Turnover differential, the amount of times you take the ball away from your opponent, compared to how frequently you gift the ball to them by interception or fumble, is unsurprisingly strongly correlated with game result.

Possessions are roughly equal numerically during a game. So if you end an opponent’s drive by a turnover, you inevitably deprive them of potential points, while often increasing your own scoring potential from your subsequent drive because of good field position.

A side having a turnover differential of +2 in a match will see that team winning over 80% of such games. Any higher and the win percentage rises to 90%+.

Therefore, the importance of turnover differential is both huge and widely recognised. Former Ravens coach Brian Billick writing for NFL.com uses turnover differential as a major component of a statistic he calls “toxic differential” which also charts big plays, in excess of 20 yards allowed and gained.

Billick describes this combined statistic as controllable, which implies that a team that has done well in such categories as turnover differential in the past will continue to do well in the future and in doing so will reap the expected positive results.

However, if we look at season to season correlation between turnover differential for teams, there appears to be virtually no persistence.

That is not to say that there is no talent associated with turnovers. For instance an experienced NFL quarterback may be consistently less prone to turning the ball over than a rookie and some side may encourage a gambling cornerback to go for interceptions. But there is also likely to be a large degree of luck and randomness involved in turnovers.

This is perhaps most visible when sides are attempting to recover or secure a fumble. The ball can pass through numerous hands before it is finally claimed.

In 2013, seven sides had a turnover margin of +10 or more averaging a turnover differential of +14 and their combined winning record was 0.65. This season, the same seven teams are on course to average a turnover differential of -1 over a 16 game regular season and their current combined win% has fallen to 0.53.

At the other end of the scale the five sides that had turnover differentials of -10 or worse have improved their average differential from -15 to a projected -5 and their winning % from 0.36 to a current 0.42.

So a big driver of game outcome, turnover differential is likely to be partly a product of luck, especially at the extremes and a side that has benefitted from extreme splits may not be as fortunate in the future.

The team with the best current record in the NFL is Arizona. Their 7-1 record in one of the NFC’s toughest divisions the NFC West has been achieved with a +9 turnover differential compared to their -1 in 2013.

The Cardinals may have worked to improve their turnover differential. The narrative from within the dressing room is understandably one of renewed confidence and positivity. And Billick, who has won more Super Bowl than most people on the planet, may be correct in that turnovers are largely controllable. But consistently, turnovers do appear to be at least partly due to luck for the majority of teams.

Arizona may not be quite as worthy of their current 7-1 record. Pythagorean estimates, which chart points scored and conceded and partly account for the perceived luck that exists where teams win lots of close matches, has the Cardinals as a 5-3 team.

The NFL season is of course geared towards the Super Bowl and Arizona are currently fifth favourites trailing behind division rivals Seattle, who currently trail the Cardinals by two games. So there is an acceptance, even within their own division that Arizona are perhaps not the 7-1 team that they appear, especially if their turnover differential returns to more normal levels.

However, this may not matter to Arizona, even in the post season against quality opponents. If they play 0.5 football for the remainder of the season, their 11-5 record should get them into the playoffs and if they play to their Pythagorean estimates, a 12-4 could land them a top two seeding.

The turnover record of seeded teams and the implication that turnover differential is a transient quality, appears to highlight the amount of influence random chance has on deciding the destination of the Super Bowl.

Over the last ten seasons, number one seeded sides had an average regular season turnover differential of +13.5, number two seeds, 11. The figure was 8 for number three seeds and around 5 for 4^th, 5^th and 6^th seeds. Playoff teams need to be good, but top seeds need to be good and perhaps also lucky.

Average Turnover Differential For Seeded Teams Since 2003.

Post Season Seeding.	Average Turnover Differential.
1	13.5
2	11.0
3	8.3
4	5.1
5	5.5
6	5.4

So turnovers and possibly, by implication, luck played a role in gaining a team a high seeding. And that seeding comes with huge benefits. A top seeded team has two guaranteed home field games to reach the Super Bowl, while a 6^th seed needs to negotiate three road games to reach the same destination.

If we assume (probably unrealistically) that the difference between 1^st seed and 6^th seed has come about purely by chance, the rewards of the playoff schedule will see the number one seed attempting to overcome odds of 5.0 to lift the trophy, compared to a road weary 25.0 for the similarly talented sixth seed.

So Arizona may have been fortunate so far. But they have already banked a 7-1 record and a tangible, if perhaps undeserved reward awaits their lucky run if they can use their present record to secure a top seed.

It is perhaps ironic that in the most popular sport in the country that widely acknowledged the influence of random chance on sporting events, the luckiest teams are rewarded with a big post season advantage.

Subbing Your Striker.

Substitutes provide a fascinating look into the changing dynamics of a football game.

Scoring accelerates as the game progresses and in this post from 2012 I looked at the tag of "super sub" that had become attached to Edin Dzeko and how it owed much to the higher goal scoring environment in which he commonly played.

Individual players may tend to produce elevated scoring rates as a substitute not only because of the higher goal environment, but also because they will usually be playing against a minimum of seven tired opponents. Playing time for substitutes also tend to result in smaller sample sizes, leading to extremes of good or bad scoring rates.

Small sample sizes will inevitably throw up prodigious scoring rates for individual players, whereas those which inevitably fall well below normal scoring rates will tend to be neglected. The former, high scoring group of players will therefore often be used to represent the scoring feats of substitutes as a whole.

To attempt to remedy this, it seems sensible to firstly compare the records of all starting strikers who play for the entire game, all those who are subbed out and all those who take their place, to see if there is the expected benefit from playing exclusively during the later minutes of a match.

Peter Odemwingie looks forward to a period of elevated match scoring.

These results are taken from the 2011/12 EPL season and are restricted to players who were designated as out and out strikers. I looked first at the scoring rate per 90 minutes for the three different groups of strikers, as well as their conversion rates.

Striker.	Goals Per 90+ Time Allowed.	Goals Per Attempt.
Plays Entire Game.	0.356	0.126
Subbed Out.	0.346	0.139
Subbed In.	0.387	0.113

The average amount of playing time for the subs in this sample was 18 minutes, added time probably stretches this to 21 minutes. And as suspected, as a group they score at rates that are above either those of the strikers who were substituted and those whom played the entire game.

However, there are a multitude of factors that may alter the scoring rate of this minority group of strikers. They may be thrown onto the pitch in place of a midfielder to help chase an game, which may lead to them scoring at a high rate than usual, even allowing for the late stage of the game. But additional goals may be ceded at the other end.

Similarly, strikers subbed out of a match may have been replaced by a defensive midfielder, to help close out a game where more goals and the possibility of an increased scoring rate were there for the taking.

Allowing for these micro details of game state and managerial intention would require painstaking analysis of each individual match, but we can perhaps design a proxy to reduce the effect of goal environment and these other variables.

There does appear to be some method behind substitutions in the EPL. In this post I suggested that younger, less experienced players are proportionally substituted out of a game more frequently than more experienced players, even with such likely factors as a fitness advantage.

So there may be perhaps a systematic overall approach from EPL managers to substitutions.

Therefore, I looked just at substitutions where a striker replaced another striker, matched the pairings together and treated the combined statistics of the departing player and the newly introduced substitute as those of a single 90  minute + injury time playing event. Albeit made up of two different individuals.

This partly eliminated occasions where a side was aggressively attacking their opponents. If we compare this composite "player" combined from a subbed out and subbed in striker to a striker who plays for the entire game, we might see if the manager is getting optimum return from his ability to make substitutions by looking at like for like changes.

There were just over 300 occasions where a striker replaced a striker in 2011/12. We could speculate that more often a less talented striker was replaced by another less well thought of attacker, but with fresher legs, while the side's premier striker remained for the entire 90 minutes.

The "subbed out/subbed in" group of matched attacking players scored 115 goals in 309 matches of 94 minutes allowing for injury time. A rate of 0.356 goals per 90 minutes.

Strikers who played the entire 94 minutes, scored 277 goals in 746 games. Also a rate of 0.356 goals per 90 minutes.

Either through accident, design, a quirk of this data set or a combination of all three, in 2011/12 EPL managers were able to get goal scoring returns from a substitute striker and the striker he replaced that were identical to those returns from a striker who was considered worthy of the full 90 minutes, under broadly similar match conditions.

A case of expertise and experience eking the optimum return from a side's strike force?