Monthly Archives: June 2013

The Premier League Seasonal Story?

By my standards, this is quite a long piece. Before I get into the graphs and information I have prepared about the league I first want to discuss a couple of areas that irritate/interest me generally in football analysis.

Regression to the mean

I’ve spent the last couple of days digging around and giving consideration to trends in the premier league over the past 13 seasons. Part of the inspiration for doing this comes from what I believe to be overly liberal use of the phrase ‘regression to the mean’ amongst sports analysts which has become a bit of a bugbear for me. I find it troublesome because a ‘mean’ in terms of a football team’s results/shot ratios/goal difference in the short term is dynamic and dependent (to varying extents) on tactics, coaching, injuries and transfers to name a few – all of which change in some cases on a monthly or seasonal basis.

The example that we often see used is Newcastle 2011/12 vs Newcastle 2012/13 and then potentially Newcastle 2013/14 which could serve up another in a long line of ‘regression to the mean’ articles explaining why their 2012/13 results were so bad that obviously 2013/14 was going to be an improvement. But is this actually regression to the mean? Not really. I’m touchy about this in part because I’m a Newcastle fan – but in any case mean regression for an individual team might not ever happen and is of little benefit to the everyday football analyst, who must take a shorter term view (even if short term can be defined as anything up to 5 years or so). Sorry Newcastle fans, but the Toon really could perform worse next season.

Ranting aside, I am being a little pedantic here, because I agree wholeheartedly in the random/luck element to football which can have a drastic effect on a team’s season over 38 games. But there’s a lot of noise in football data that lends itself to making spurious predictions – so I’d prefer we say things like ‘Newcastle had an unsustainably high goals to shots ratio in 2011/12’ or that their goal difference was unusually low for a team finishing 5th in that season. The hockey statistic PDO seems to have a cult following, and although I find it a fairly useful tool in indicating the average team’s luck in football I’ve yet to be convinced by its usefulness in analysing any particular teams over multiple seasons. Why? Because the average team doesn’t really exist. Don’t get me wrong, I love using and calculating averages – but one of their main benefits is to serve analysts as a benchmark by which we can consider individual cases or develop strategies for improvement.

Football by its nature is viewed in the short term and the league is not a closed system – teams are promoted and relegated every year, and there’s a fine line between luck and skill for the majority of teams in the league. However, when we group teams together patterns can emerge.

Macro vs Micro analysis

I’m borrowing use of the terms macro/micro from economics, or if you like top-down vs bottom-up from portfolio analysis. Macro/top-down analysis considers the economy or market or league as a whole in order to inform strategy whilst micro/bottom-up analysis considers the underlying agents involved – in the case of football this is players/teams – in order to form expectations.

The kind of analysis that lends itself to discussion of regression to the mean is macro analysis because it deals with the aggregation of many underlying factors into a league table at the end of a season which for the EPL is the culmination of 380 games, about 8800 shots, 4700 shots on target and 1000 goals. These are sample sizes you can do something more serious with, the problem being that it can be difficult to relate long-term aggregated relationships to single players or teams.

The football manager, on the other hand, has only 38 games to work with and wishes to increase his team’s share of the 1000 goals as much as possible. The football gambler wants to know who will win the next game or score the next goal (we have moved into micro analysis). Micro analysis dominates football research because of our obsession to improve our understanding of the game on the field. The football fan/pundit generally has a relatively short memory and enjoys obsessing about what will happen or has happened in every game.  The analyst reviews thousands of actions collected by data companies and video footage exhaustively in order to gain a competitive advantage over rivals. Micro analysis is absolutely vital to football clubs in order to improve, but the problem with it is that sample sizes are much smaller and noisy – so it can take considerable time and effort to prove an advantage gained from this type of research. Time that a football manager might not want to take because he could be out of a job within 10 games.

What’s my point in all this? Well, I suppose I want to make clear that micro analysis will always lead the way for both innovation and spurious claims in sports but we need macro analysis to keep us in check and help identify how much an achievement/failure can be apportioned to luck or skill.

The Top 7

Are the best teams getting better? Is the battle for 4th more challenging than ever before?

Below are 2 graphs: the first shows the points and goal difference of the top 4 and the second shows places 4-7 since the 2000/01 season. The goal difference is shown on a secondary axis on the right hand side.

Top 4 Pts & GD4-7 Pts & GDThese graphs don’t really suggest any structural changes in the points/goal difference of the top 7 over the past 13 years (although granted, it’s hard to identify structural shifts over 13 years). We can see the mad 2004/05 season, in which Everton snatched 4th place with a goal difference of -1. Otherwise, there is a hint that the 7th best team has improved since 2008/09 as the goal difference of that team has been at a minimum of +5 over the past 5 seasons. This may be a result of Everton managing to consolidate their position in the top 7 and Spurs improving to achieve 4th or 5th in each of the last 4 seasons. In my head I’m counting Liverpool as part of the top 7 because they have been there in every season but 1 over the past 13 years.

Over the past 13 years, the Champions have won with ~87pts, scoring 80 goals and conceding 29.

4th place requires something close to 69pts, 65 goals for and 39 against.

For a relegation candidate, to attain 17th place and avoid relegation a team should aim to gain on average at least 38pts, scoring about 40 goals and conceding about 60.

The Table in Charts

Ave Pts & GD, EPLOver the past 13 years, between them the top 7 have taken about 50% of the total points on offer, scoring about 45% of the goals. We can see that from around pos 7-8, the graph flattens out, dipping again significantly at 19-20. The difference between the team in 8th and the team in 18th is really not that big at all.

Structurally, I don’t anticipate this to change that much in the long term – the top 7 will likely continue to dominate the league, the group from 8-18 will be quite changeable – luck playing a big part in where each team finishes, and the bottom team is more likely to be one of the newly promoted sides whose skill level isn’t up to scratch. That isn’t to say that a team can’t drop out of the top 7 and be replaced by a higher performing team in the lower group from time to time.

In the past 13 seasons, on average 1.15 newly promoted teams have been relegated: occasionally 0 teams, usually 1 team and sometimes 2 of them. And a team in just its second season in the top flight, who perhaps benefited from a little luck in their first season, has been relegated 0.54 times. That’s about 1.7 teams – this year potentially 1-2 teams from Cardiff, Hull, Crystal Palace, West Ham and Southampton.

I think the below graphs give some insight into the potential for luck in the league for the middlish teams.

Ave Shots For & Against, EPLIn the above graph, showing shots for and against, the lines cross over at about pos 8 and then stay pretty flat right up until about pos 17/18.

What about ratios?

Goals per shots, EPLIn terms of goals scored, it’s a little less clear but it does appear as though the best teams (1-7) achieve better scoring rates whilst the worst (19-20) show a significant drop-off.

Ave Goals per shots against, EPLIn terms of goals conceded, it’s a different picture – only the top 3 really seem to have a knack of preventing goals significantly whilst actually pos 14 and below are significant in terms of an aptitude to ship goals.

The pictures suggest that scoring goals is relatively more important to teams at the top of the table, whilst stopping goals is more important to teams in the bottom half.

For example, the average number of goals scored by the team in 18th is 39 goals whilst the average scored by the team in 8th is ~49 goals, a difference of 10. Conversely, the team in 18th concedes about 64 goals per season vs 46 conceded by the team in 8th: a difference of 18. A team expecting a relegation battle is therefore more suited to investing in their defence to reduce the number of goals they concede – as this is the ‘easier’ area for them to make an improvement.

What about the team in 8th that wants to challenge for 4th? Well, 4th place generally scores 65 goals and concedes 39, +16 and -7 respectively compared to the team in 8th. In this case, improving goalscoring should arguably be the priority.

To summarise my thoughts for this piece, really there is a place for both macro and micro analysis in football analytics, despite the clear focus on micro factors by analysts in general. But macro analysis cannot be discounted, and can serve as a helpful guide to direct micro analysis and provide greater certainty that the research we are producing is truly worthwhile.

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A Hypothetical Team to Challenge for the Top 6

With the season over, transfer targets drawn up and negotiations under way, I thought I’d set myself a brief to build a team from players in the EPL that could reasonably be expected to challenge for a place in the top 6, using stats from my player database (TPOEM).

No budget constraints? That’s easy! Bale, Van Persie, Hazard… Yes, that’s miles too easy, so I’ve set some constraints as follows:

  • Cannot select any player outside of the EPL
  • Cannot select any player from the teams in the top 7 from last season (Man Utd, Man City, Chelsea, Arsenal, Spurs, Everton, Liverpool)
  • Cannot select more than 1 player from the same team
  • Cannot select any player who played fewer than 1500mins

That makes it more interesting. And within the realms of possibility if money suddenly became no object for a team currently outside the top 7. The objective of the team that follows would be to try and oust Everton and Liverpool from the top 6 next season and challenge for 4th/5th.

Goalkeeper – Artur Boruc (Southampton)

I don’t have a great depth of up-to-date stats for goalkeepers – and although I may do some work at a later date to infer their stats from opposition player data, I’ll go with the simple ratings I currently have which placed Boruc just ahead of Krul, Begovic and Mignolet. There is very little really to tell these players apart – and the back line became a bit of a juggling game – my selection effectively hinged on Boruc and Huth vs Begovic and Yoshida towards the end.

Full backs – Danny Rose (Sunderland) and Billy Jones (WBA)

Rangel, Santon and Lowton were just ahead in the pecking order but were dominated by superior players from their respective teams in other positions. The clubs beyond the top 7 aren’t really characterised by strong attacking full backs in general, which tend to catch the eye of TPOEM, but with Rose we have a strong all-rounder who performed well despite his team’s position in the league.

Centre backs – Chico (Swansea) and Robert Huth (Stoke)

José Manuel Flores Moreno (no wonder they call him Chico) was an easy choice as central defender despite a long list of Swansea players contesting for almost every position. His stats, as I have alluded to in recent posts, really were very good last season. Huth, a bit like Jones in the previous selection, is a bit of a ‘plug’ that I made with little else to choose from. Players that didn’t make the cut included Hangeland, McAuley and Williamson (that’s right – the Newcastle player, whose stats were pretty decent when he played).

Defensive Midfield – Mark Noble (West Ham United)

Noble beat Ki, Mulumbu and Delph to take a key place in midfield. In my previous post I published that he was rated 5th best DM in the league last season so it was another straightforward choice.

Midfield – Robert Snodgrass (Norwich) and Yohan Cabaye (Newcastle)

2 more players that were top of the class of midfielders and therefore beat their teammates for selection. De Guzman, Schneiderlin and Sidwell just missed the cut. Both Snodgrass and Kebab -sorry Cabaye- were in the top 10 of my season end player awards due to performances of a very high standard, again despite their team’s inconsistent results.

Attacking Midfield – Adel Taarabt (QPR)

I know that Taarabt divides opinion, having spent 2 seasons in the top flight without reaching the requisite levels of consistency to be mentioned alongside the best attacking midfielders in the league. But he is rated highly by TPOEM and wins his place ahead of Maloney. The reason he is liked by TPOEM could also be a reason to critique the results of the model, because it places a bit more importance on action frequency than the net result of those actions. Still, last season he created 76 total chances (including goal assists) and around a third of his many shots were on target (admittedly he only scored 5). I still think that’s a good result despite playing in a poor QPR team.

Forwards – Christian Benteke (Aston Villa) and Dimitar Berbatov (Fulham)

These 2 pretty much pick themselves, although there was decent competition for places as Koné, Lukaku and Lambert were overlooked. And Michu was even behind them, believe it or not. Benteke and Berbatov (B&B) scored 19 and 15 goals respectively and can each be identified as the major reason that Villa and Fulham didn’t each finish a few places further down the table.

The team in full, plus subs bench:

[click the image below if it looks dodgy…]

Team to challenge for top 6-2The real question then becomes, could this team really stand up to the test against the likes of Everton and Liverpool over the course of a full season?

I’ll use Everton as the benchmark, considering they actually achieved 6th place.

TPOEM team vs Everton 11-2Player for player, it’s very close ratings-wise. I sorted the starting line-ups side by side (above – click the image if it looks sketchy) and highlighted in green the player who won each head-to-head. 6-5 to the hypothetical team, but only if I match Snodgrass vs Osman and Cabaye vs Fellaini, otherwise it’s 6-5 to Everton. From the picture, it would appear as though Everton have a clear advantage in defence, whilst the hypothetical top 6 team has a stronger attack.

The TPOEM measures back this up overall: suggesting that Everton would be  stronger at winning the ball and retaining possession whilst the goalscoring potential of the ‘top 6’ team would be superior. This hypothetical team has a total score only very slightly higher than Everton – so close in fact that if they played each other at a neutral venue the expected result according to the model would be something like a 37% chance of either team winning and 26% chance of a draw; with a 72% chance of there being more than 2.5 goals in the game but a most likely score of 2-2 (12.7% probability)!

So could the team challenge for the top 6? Yes, I believe it just about could – assuming the players stayed fit all season and the team defended in numbers when appropriate! A rough valuation of the team would be somewhere between £75-100m in transfer fees.

Thoughts and comments welcome.

TPOEM EPL Player Awards 2012/13

I have stripped out players who played less than 500mins and provided some top/bottom 10s based on the TPOEM player ratings model I created earlier this year – calculated from matches in the Premier League only.

It’s a little biased, in more ways than one. The key bias I want to highlight before I start is that the stats for players in ‘busy’ teams are biased upwards – Liverpool and Spurs in particular were relatively active last season. When I say ‘active’ or ‘busy’ I mean that they exhibited high frequencies in certain actions – shots, tackles, ground duels, dribbles, etc. which improved the TPOEM ratings for a team’s players without necessarily providing the gains expected from the average team in terms of goals scored and points won. Hence TPOEM, and many other models to rate players by stats only, ought to be viewed a little critically – i.e. TPOEM suggests that Suarez was the best forward in the league last season, this is due in part to the fact that he attempted a very high proportion of ground duels, dribbles and shots. Calling him the ‘best’ forward is arguable – but there is little question about his results from this particular selection of binary stats.

I will highlight the key metrics I defined earlier in the season, and which players were in the top and bottom 10 for each category. Plus man of the match awards and even tallest/shortest players.

Ball Winning & Defending – this measure depends on things like clearances, tackles, interceptions and how successful the player was at completing each of those actions. Unsurprisingly the worst rated players are mostly attackers, but there are a couple of surprises in the ‘best’ list.

Ball winning & defending TPOEMPassing & Ball Retention – this measure compares how often a player lost the ball in relation to his success rate of passing in different areas of the pitch. You can see the bias towards the top teams who retained the ball very well in the attacking areas.

Passing & ball retention - TPOEMAttacking – goals, shots, dribbles, chances created and a few others in this one. Sturridge did brilliantly after joining Liverpool (Ba’s rating in the table below represent his results at Newcastle only).

Attacking - TPOEMDiscipline – this is effectively a measure of how often a player received yellow/red cards, plus fouling the opposition, minus the fouls he won. The best players are the ‘nice guys’ who seem to get fouled a lot without giving much back. The worst are those with a short temper who flirt with the possibility of a sending off too much for their own good.

Discipline - TPOEMInvolvement – this measures how often a player is involved in various actions for his team, be it passing, tackling, duelling, shooting and so on. The players rated highest are all midfielders, whilst those rated ‘worst’ are generally central defenders/strikers who generally don’t touch the ball that often.

Involvement - TPOEMMan of the Match Awards – the team awards are simply the highest rated player on the team for a given match whilst the overall award is of course the ‘global’ dominant player in a match, for either team. Taarabt and Snodgrass get notable mentions here but Cazorla is the real winner.

Man of the Match - TPOEMBest Total Contribution – dominated by attackers, as TPOEM is biased towards goalscoring / creating.

Best Total Contribution - TPOEMBest Players By Position – I have classified players into different positions and shown the best performers below:

Full backs - TPOEM Central Defenders - TPOEMDefensive Mids - TPOEMMidfielders - TPOEMAttacking Mids - TPOEMForwards - TPOEM

 

Tallest & Shortest Outfielders – as published on whoscored.com. Players over 500mins only.

Tallest & Shortest - TPOEM

 

Premier League Fixture Chart In Numbers 2013/14: Adjusted

The same 2 charts published earlier, with adjusted classifications to rate match difficulty.

Fixture difficulty table:

EPL Fixtures in numbers adjustedTrailing 5-game average difficulty table:

EPL Fixtures in numbers 5-game ave adjusted

The pictures may now be a bit more acceptable to some readers, with Newcastle now in the 8-15 group and the top 3 separated from the rest.

And the difficulty scoring chart is no longer linear – I have removed the middle group scores to skew the difficulty distribution:

Group Venue Difficulty
1-3 Away 6
1-3 Home 5
4-7 Away 5
4-7 Home 4
8-15 Away 3
8-15 Home 2
16-20 Away 2
16-20 Home 1

Discussion

Many of the comments I made in the earlier post still apply, although Fulham’s fixture difficulty volatility no longer looks so extreme. Instead, Swansea’s does.

Swansea have arguably one of the most difficult runs of 6 games of any team in the league (through games 17-22) where they play Everton home, Chelsea away, Aston Villa away, Man City home, Man Utd away, Spurs home.  It could be an unhappy Christmas and New Year for the Swans as this run of fixtures lasts about 1 month starting from 21st December.

Premier League Fixture Chart In Numbers 2013/14

EPL Fixtures in numbersI like spreadsheets so when faced with the new season’s premier league fixtures, I was delighted to translate them into numbers and create a table – which I have shared with you above. At the very least I’ve made a reasonably nice-looking heat map.

The games are all shown 1-38 in the current published chronolgical order. We all know this subject to change – but this is the current expected fixture order.

Those of you looking critically at the table will have some questions, so I’ll outline the simple methodology I used – which will either allow you to accept the flawed process or shake your head in mild irritation and carry on with your day.

Process

I split the league into quintiles, which was easier said than done outside the top 4. Spurs could have had a case for being classed as a ‘top 4’ team, perhaps Everton and Liverpool too – considering Everton’s excellent home record last season and Liverpool’s excellent last 19 games. But I stuck with the quintiles rigidly. This gave me a dilemma as to who should be in each pot outside of the top 4 – I solved the dilemma by considering bookie’s odds for relegation and various club ranking systems.

Those of you who know I am a Newcastle fan will scoff at the fact that they are in the 5-8 group (and I do feel guilty about their position) but their odds for relegation were longest after the ‘big 7’. Also sorry Norwich fans for placing your team in the bottom group alongside the promoted teams – blame the bookies for that.

I scored match difficulty in a rather simple and linear way, allocating higher points to away games and matches against the top teams – the scoring chart is below:

Quintile Venue Difficulty
1-4 Away 6
1-4 Home 5
5-8 Away 5
5-8 Home 4
9-12 Away 4
9-12 Home 3
13-16 Away 3
13-16 Home 2
17-20 Away 2
17-20 Home 1

I don’t actually think that team abilities in the league will sit on a linear scale – looking at last season’s table will show the big points gap between 7th and 8th, then a group of teams separated by 9pts between places 8-16. So I might add a new table based on a more considered scale for this reason in a later post.

Pressure Points

Below is a 5-game average difficulty chart for the fixtures throughout the season:

EPL Fixtures in numbers 5-game aveFrom the above, it seems as though Villa, Fulham, Stoke and Man Utd all have the most difficult starts to the season.

Man Utd have away games at Man City, Liverpool and Swansea in their first 5, along with a home match against Chelsea – hardly an easy start for Moyes (their other match is home to Crystal Palace – arguably more straightforward).

As for fast starts, don’t be surprised if Spurs, Man City, Arsenal, Norwich and West Ham get a few good early results to shoot towards the top of the table.

We can also see the likely pressure points of the season – Arsenal’s season could be over unless they can navigate games 15-19 safely (Everton home, Man City away, Chelsea home, West Ham away, Newcastle away). And if Mourinho’s Chelsea are still in the hunt for the title with 7 games to go few should bet against them.

Lastly I’ve added the range and standard deviation to the chart to attempt to give some insight into how consistent the difficulty of each team’s games is through the season. It suggests that we might expect Fulham and Arsenal to fluctuate more between hot and cold streaks (e.g. headlines might read ‘Jol is a genius’ and ‘sack Jol’ at various times, ditto Wenger) whilst Newcastle are a bit more likely to achieve a more constant haul of points during the season (expect to hear ‘sack Kinnear’ regardless throughout the season!).

Premier League Review 2012/13: Wigan Athletic

Cumulative pts - WAFCIt’s hard to write this very objectively, considering how much THAT FA Cup victory sticks in the mind – an example of availability bias. I suppose the historic cup win represented a glimpse of what might have been, had Wigan got their act together in the same way for the majority of their matches in the league. They started the season quite indifferently, and ended the same way: nothing like the strong finish they had in the previous season although those memories certainly stirred up panic amongst the teams above them!

Goals For - WAFCGoals Against - WAFCPretty much anyone who saw Wigan last season (in the league, not the cup) will realise that offensively they were ok, but defensively they were poor. They outscored both Sunderland and Newcastle, but, with an average of 1.92 goals conceded per game, they equalled Reading’s poor total of 73 conceded – the worst in the league.

Home & Away - WAFCHonours even in terms of points at home and away – a better points total away from home than Sunderland, Newcastle and Stoke. That means that, yep you guessed it, their home record was one of the worst – only superior to QPR in fact.

Relative strength - WAFCNot a lot to shout about in terms of ‘giant-killings’ against the top 5 here. What about the cup?! I’m not talking about the cup. Wigan needed to do just slightly better against the teams around them to have stayed up.

Vital Stats - WAFC

[click the above picture to view Vital Stats]

Players over 182cm - WAFCI’m intrigued by these stats. They show that by weighted average playing time, Wigan were just short of the league average in terms of height, but they fielded something like 2-3 players per game less than the opposition in terms of players over 1.82m. It might simply mean that there are loads of Wigan players standing 1.81m tall, which may not have been too much of a problem.

% Playing time - WAFCMPS vs League TPOEM - WAFCWith over 60% of the team having played over 2400mins, the chart above suggests that Wigan’s team was largely settled and perhaps they didn’t have too many serious injuries. That’s not true, as highlighted by the article I linked to for the Newcastle review here. Crusat, Ramis, Scharner and Caldwell were all missing for significant periods, plus Figueroa and Beausejour in the closing games, forcing Wigan to use their squad to its full extent – when you look at it that way, they can count themselves fairly unlucky not to have finished the season a little higher up. However, Wigan were still able to field the arguably more important Koné, Figueroa, Boyce, McCarthy and Maloney for over 2800mins.

In general, Wigan’s squad applied itself very well, attaining higher than average performance scores which belied their position. The same occurred with QPR. I think this may be a result of Wigan having to chase games more often than the average team – i.e. the effect of game states on match statistics – TPOEM only sees the end results, not what happens before/after goals. But there’s also little to doubt the excellent contributions from Koné, McCarthy and Maloney.

General stats:

  • 5th highest tackle success ratio (78.4%)
  • Involved in the lowest total aerial duels (835) and a below average success ratio (47%)
  • Lowest total headed clearances (415, less than half Spurs’ total)
  • 2nd lowest total clearances (894, behind Arsenal)
  • 2nd lowest total losses of possession (5568, behind Swansea)
  • 3rd highest number of long balls attempted (2048) and a good success rate of 64%

Players - WAFCMaloney and Koné really dominated Wigan’s stats. Maloney took the lion’s share of TPOEM team man of the match awards, taking 11 in total of which 4 were good enough to be overall man of the match. McCarthy’s overall contribution to Wigan was similar to Ramsey’s for Arsenal – which I mean as a compliment – all the more impressive considering the differing fortunes of the 2 teams. Although Koné was profligate at times, he managed an impressive 11 goals in the league and Wigan will struggle to keep him – particularly with reported interest from Martinez’ Everton.

Lastly, despite playing relatively little, Scharner was the highest rated defender in terms of pure defensive contribution – no doubt they missed his aerial presence for most of the season in defence.

Premier League Review 2012/13: West Ham United

Cumulative pts - WHFCNewly promoted West Ham’s season started brightly, taking 18 points from their first 11 games. There were no serious moments of anxiety throughout the whole season really – West Ham continued to get results throughout the term, at most having to wait 4 league games for a win.

Goals For - WHFCGoals Against - WHFCThey scored consistently just above 1 goal per game throughout the season, with and without Carroll in the team. They generally defended well too, apart from a few blips away from home around the start of 2013 where they conceded 3 at goal-shy Sunderland then 5 more at Arsenal and another 3 at Fulham.

Home & Away - WHFCThe Hammers’ record on the road was well short of their good home form. In fact only Everton had a bigger disparity between their home and away points total. West Ham’s goal difference was incredibly +12 at home, -20 away.

Relative strength - WHFCNothing too strange going on here though, West Ham slightly underperformed against the bottom 5 teams and didn’t score enough against them but nothing for Allardyce to lose sleep over.

Players over 182cm - WHFCVital Stats - WHFC[click vital stats table above to view]

A higher than average team height and weight, aided by the 1.93m tall Carroll and the improbably heavy Guy Demel at 88kg (Whoscored.com). Allardyce had more players than average at his disposal over 1.82m and he generally fielded 1 more than the opposition too.

% Playing time - WHFCMPS vs League TPOEM - WHFCWest Ham’s squad, in general, remained injury-free and available for most of the season – as shown by the first chart above, where about 8 players managed over 2400mins in the league. Jääskeläinen, Nolan, Reid, O’Brien and Diamé played the most minutes onfield. Carroll and Noble each played about 2000mins – not disastrous – but both contributed very well when they did play so were indeed missed when unavailable.

General stats:

  • Involved in 1472 aerial duels (3rd highest), with the 2nd best win ratio (55%)
  • 3rd most headed clearances (788)
  • 3rd fewest touches on the ball (20658)
  • Highest total goalkeeper saves (166)
  • 2nd most yellow cards (74) but =least for red cards (1)
  • More attempted crosses than any other team (1013) with best accuracy: 27%
  • 14th in the list of total long balls attempted

Players - WHFCWest Ham’s strong performance in the league depended on a strong spine starting with Jääskeläinen, Reid, the midfield trio of Noble, Diamé and Nolan plus the physical Carroll or Cole up top.

Despite playing relatively few minutes, it’s hard to overlook the contributions of Noble and Carroll to the team when they played, illustrated by the TPOEM man of the match awards as noted below:

Player Minutes Played Team MoM Overall MoM
Mark Noble 2286 7 3
Kevin Nolan 2990 6 2
Andy Carroll 1939 6 1

Noble’s contribution as a DM was not dissimilar to Carrick’s contribution to Man Utd and Schneiderlin’s to Southampton – certainly in the top 10 of the league.

Reid also played very well, and was named the club player of the year, which I won’t argue with. Actually West Ham are one of the few teams whose full backs have worse performance scores than the centre backs, which is likely to be a result of them venturing forward less than average. And Jarvis didn’t catch the eye of TPOEM at all – not to say he had a bad season per se – he can probably take some of the credit (shared with Nolan/Carroll) for West Ham’s good crossing accuracy, but little else!

Wish list for Allardyce this summer? A replacement for Carroll (or the big man himself) is paramount and why not a more penetrative player who can improve on the current full backs or wide midfielders.