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Rosco

Worse than Brendan
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Where The Talent Shone Through In 2012/13.


The close season is always a busy time of the year even if very little action takes place on the field of play. Managerial change, although often planned months before can often drag on for longer than anticipated and transfer deals that seem certain to take place and enhance next years title challenge can also flounder as other sides take an interest.

The end of the season is also the perfect time to assemble team and individual statistics. Team makeup has remained relatively stable and the ageing patterns that can impact on individual players over multiple seasons are largely absent.

This post isn't going to be a data dump, in deference to the many sources which have sprung up to provide a multitude of different types of mainly Premiership data. Nor will it try to make concrete connections between the numbers and various significant match outcomes. Instead I will try to firstly look at how efficiently EPL teams carried out certain tasks during 2012/13. Variation in efficiency numbers will inevitably occur in the relatively short space of a single season, even where there is very little difference in talent between all the sides. Random variation ensures that everything we attempt to measure is a combination of skill and luck, with the former beginning to overwhelm the latter only in much larger datasets.

The wider the actually distribution in efficiency stats compared to that expected from the league average, then the more likely that there are factors involved in the execution of these stats that differ between teams. In short some, teams are more adept at carrying out these actions than others.

I'll then list how strongly these actions correlate to the broad measure of success enjoyed by each side last season. Correlation doesn't naturally lead to causation (leading may cause teams to carry out tasks more efficiently), but knowing there is a correlation is a good starting point for further investigation.

Accurate passes in the attacking half.

Arsenal top the efficiency stats with success rates of nearly 78%, closely followed by both Manchester clubs. Reading prop up the table with 58% and the range of conversion rates on an average of over 10,000 passes per side strongly indicates different levels of team ability in this area of the pitch. Correlation to success in the final league placings is also strong, although Wigan are a notable outlier with top five efficiency recorded from above average frequency of attempts. Similar results are seen for the final third of the pitch, but with even stronger end of season correlation. Wigan slip to near mid table, with below average frequency when we move further up the pitch.

Accurate passes in own half.
Efficiency now ranges from 93% success rate for Manchester United to 84% for relegated Reading and the frequency of attempted passes for United is twice that of Reading. Again there is a likely significant difference in the ability of teams to carry out such passes and correlation to league position is also strong. Wigan are again the best of the relegated sides, but now reside in the bottom half of the ranking.


"We pass better than you do".
Accurate Long Balls.
Last of the stats where there is likely to be a very large, real difference in ability to carry out such on field actions. Given the lack of love for long balls, there is a surprisingly strong correlation between the ability to play them well and league success. The top four in efficiency are the two Manchester clubs, Arsenal and Liverpool, Tottenham and Chelsea are 7th and 8th respectively. Two of the bottom four were relegated, QPR and Reading. Completion rates range from 67% to 43%, but lower overall attempts averaging just below 2,000 for the season, mean a little more regression towards the mean is needed to improve the likely accuracy of these raw figures.

Unsuccessful touches of the ball.
Ball control matters, or perhaps a team which gets ahead can make easier passes and bump up their efficiency numbers. The Manchester clubs and Arsenal have the least unsuccessful proportion of touches, just over 1%, Stoke have the highest percentage of miss controls at nearly double that rate. Correlation with league position is again strong and once again Reading and QPR occupy places in the bottom four and Wigan found themselves relegated, despite another mid table berth.

Successful Aerial Duels.
Still a relatively high chance that one team can be significantly better or worse than another in winning balls in the air, although the correlation to league success is by far the weakest on show so far. Stoke are both the most efficient (59%) and the most frequent of aerial combatants. Reading contested almost as many high balls as the Potters, but succeeded in a league low of 42% and were relegated. Hoofing it, but to no effect and then in turn getting hoofed on? Ground hogging Arsenal contested 800 less aerial challenges than Stoke, but were third in league efficiency.

Changes of possession. Winning possession in the attacking third.
The top six are all in the top eight of teams ranked by their efficiency at winning possession in the final third and the three relegated sides are all in the bottom six. The elite also have frequency rates in excess of three times those at the bottom of the list. Possession alone tells you little, but where and how often you win it seems to add a much needed context to the beleaguered raw measure.

Accurate through balls.
There was a significant difference in the ability shown by teams when picking a through ball, but that talent differential didn't help them win or more accurately, it didn't correlate to winning in 2012/13. Wigan and Liverpool were among the most efficient and Manchester United and Stoke shared time with the worst, leading to a virtually zero correlation between league position and through ball efficiency. It was raw accurate through ball totals where the strongest correlation with league success was seen. Persistence, rather than efficiency appeared to yield rewards.

Percentage of chances created from set plays.

League leaders, Stoke created almost 20% of their chances from set plays, nearly twice the rate even the most set piece reliant of the top four teams, Manchester City. There was a reasonably strong correlation between set play chance percentage and success in the league. Unfortunately for Stoke it was a negative one. Team's which overly relied on set plays to create a goodly proportion of their chances were competing in a mini league where survival was the primary reward and 40 points the major aim. Cloth was being cut accordingly?

Percentage of chances that are clear cut chances.
What constitutes a clear cut chance will naturally be subjective, but in general more successful sides turn chances into gilt edged ones at higher rates than less successful teams. Manchester United were the kings of the tap in, followed by most of the rest of the top six. Norwich were notable interlopers and while numerically their total chances created were no real match for the title contenders, their efficiency certainly was. (although the advantage may be scouted out of them in subsequent campaigns). The relegated sides were among the bottom eight for efficiently turning chances into clear cut opportunities and that usual cluster of poor sides were joined by Gareth "shoot from distance" Bale's Spurs.

Successful set play crosses.
Another category where the spread in efficiency percentages implied that teams possessed different skill levels. The correlation with end of season success was reasonable and negative, the less efficiently a side found a teammate with a set play cross, the higher up the table they tended to finish. Of course deliberately over hitting a free kick doesn't automatically gain you more points. The likely causation is that players who are better in the air tend to be less adept in other areas of the game and success in these areas possibly lead to more wins for the majority of teams. Football, as Cloughie said, should be predominately played on the ground?

Goal scoring efficiency per shot.
We are now getting into more traditional territory and conversion rates with a regressed 13% for Manchester United at the peak and 7% QPR at the bottom strongly suggests a real talent gap from best to worst. This is reinforced by an efficiency ranking that virtually follows the final league table. Stoke fell to second last in a category that they excelled at in their formative Premiership years, as transition to a more pleasing style proved difficult and only a strong defensive showing, especially pre January kept them afloat.

Percentage of tackles won.
Category topping Manchester City won 78% of their tackles compared to a Premiership low of 74% for Newcastle. Not a huge discrepancy, but with average team tackle attempts topping 2,700 still sufficient evidence to assume that tackling talent does exist in differing degrees for EPL teams. A weak, positive correlation with end of season finishing position possibly indicates that England's premier league is still partly paying due respect to one of it's traditional strengths. Arsenal were the second most efficient tackling side last term, but the presence of relegated QPR and Wigan in the top seven weakened the correlation with finishing position.

Categories where 2012/13's numbers didn't really provide evidence for a real difference in talent levels, included the rate at which clear cut chances were converted (most teams can hit a barn door at ten yards with similar competency) and, to a lesser degree and in a similar vein, shooting accuracy.

The above list isn't exhaustive, but it is sorted in order of the probable biggest disparity in talent levels. It is certain that some teams were much more adept at passing the ball in the attacking half than others in 2012/13 and those more talented sides also tended to be more successful at the end of the season. Tackling ability was much less diverse, that's not to say tackling isn't a talent, but the levels of talent are likely to be broadly similar across the league from a team perspective.

The correlations are presented to describe the broad attributes shown by successful and less successful sides, at worst they provide a crude descriptive measure of the kind of actions successful and unsuccessful teams were efficiently or inefficiently engaged in during 2012/13.

Causation isn't assumed and neither is the direction and teams which perform efficiently, actions which negatively correlate to success may find those talents are essential to their continued survival in the EPL, but the majority of other sides choose to take a different course. Some teams chose to hone a niche approach, to satisfy their limited ambitions?

Equality of opportunity also isn't guaranteed, especially where individual events for each team fail to reach 4 figures, but the efficiency distributions spread around the league average can still be used to reasonably assume the scale of the different talent levels between sides.
Taken from http://thepowerofgoals.blogspot.ie/2013/06/where-talent-shone-through-in-201213.html
 
age+and+transfer+value.png
 
-axis: PDO. Further right = more luck
y-axis: TSR. Higher = better team
Bubble size: Points. More points = bigger bubble
Bubble shade: Goal difference. Green = positive, red = negative. Lighter shade = further from 0

An explanation of total shots ratio can be found here and here, for PDO see here and here.
A few short summary points that might help with interpreting this:
Spurs were good (high) and unlucky (left). This translated into a decent amount of points (solid sized bubble) and a positive goal difference (lightish green).
United were less good (lower) and luckier (further right). They scored more points (bigger bubble), and had a higher positive goal difference (much lighter green).
At the end of last season I said “Liverpool couldn’t buy a goal this year (darker green). If I’m a manager looking for a new post this is the perfect time to take over at Liverpool, they’re almost guaranteed to score more points next time around, even if they aren’t as good.” Well they
weren’t as dominant
were just as good (edit: h/t Will TGM) with the ball this time around but scored more points.
I also said “If there’s one thing this plot shows us it’s that (with the possible exception of the Manchester teams) there is literally no correlation between TSR and PDO.” Well City regressed all the way to the mean this time around, and there’s still no real pattern here.
Once more the top seven teams formed almost their own tier, but Southampton were excellent too. They’re a team to watch the next time around if they keep all of their players – it’s pretty unlikely that they’ll record a sub 930 PDO next time around.
Reading deservedly were relegated, and it’s somewhat just that Newcastle didn’t go down, they had an horrendous amount of bad luck. The rest were kind of a mess – we’d have had some knowledge about which teams were more likely than others to be relegated at the start of the season but at the end of the day it’s largely a PDO driven coin flip.
Looking at this, if I were an unemployed manager right now I’d have my fingers crossed that an opportunity arose at Newcastle, and I’d be steering as far clear from Stoke as possible.

http://jameswgrayson.wordpress.com/2013/05/28/the-2012-13-premiership-season-in-one-graphic/
 
How much does luck impact the result of a football match and how much does the result come down to talent? Today on the blog Martin Eastwood introduces us to the work of Tom Tango and how it can be applied to football to determine over the course of a season how much of a team's performance is due to luck and how much is due to skill.
RobbenfreekickChampionsLeagueFinal2012a.jpg

One of the more intriguing aspects of football is the role of luck, or random chance. With football being such a low scoring game it instinctively feels that luck should play a large role - it only takes one fortunate bounce or deflected shot to turn a tied match into a victory or loss.
Luck, Talent And Variance

But how can we measure how large an effect luck actually plays in football compared with a team or a player’s talent? Two great posts on the subject have recently been written by James Grayson on his blog, the first discussing how accurate a model for predicting the Premier League could be and the second considering the number of games it takes for talent to become the greater than random variation.
Tom Tango, author of ‘ The Book: Playing the Percentages in Baseball’, suggests that talent can be estimated from the total variance in a league, where variance is the spread of data around its average value.
Total variance = variance due to talent + variance due to luck
The total variance is pretty easy to work out; we can just pick a statistic such as win percentage and calculate the variance over a large enough sample size. Then all we need to do is work out how much of that variance is due to talent and how much is due to luck. As a starting point, Tom Tango suggests that we can estimate the standard deviation due to luck using:
Standard deviation due to luck = Sqrt(0.5 x 0.5 / number of games played)
Unfortunately, this is where we run into a slight problem as Tom is using a binomial model that presumes two outputs – win and loss. Unlike many North American sports, we have three outcomes in football to deal with – win, loss and draw – so the estimation does not work.
The easiest solution to this is to just ignore the draws and use wins and losses but this removes around a quarter of all football results so is not a particularly useful approach. Another option is to consider each draw to be worth half a win and half a loss. This seems plausible until you consider that a draw actually provides one third of the points a win does so instead we will consider a draw to be one third a win and two thirds a loss.
Since we can now calculate the total variance from the win percentage and estimate luck’s variance from Tom’s equation above, we can rearrange the original equation and calculate the role talent plays using:
Variance due to talent = total variance – variance due to luck
Looking at all the 38-match English Premier League seasons to date gives us an average win percentage of 46%, with a standard deviation of 14%. Since standard deviation is the square root of variance, we need to raise this value to the power of two, giving us a total variance of 0.01868.
Next we calculate the standard deviation due to luck:
Sqrt(0.5 * 0.5 / 38) = 0.08111
Again, we need to convert this from a standard deviation to variance by raising it to the power of two, giving us a variance due to luck of 0.0066.
Finally, we calculate the variance of talent:
Variance due to talent = 0.01881 – 0.0066
Variance due to talent = 0.0124
So What Does This Actually Mean?

The first point to note is that the variation due to luck accounts for around 35% of the total variance leaving around 65% attributed to talent. This is good news for the skilful footballers as talent has approximately double the effect on a team’s win percentage that luck has.
However, this still means that pretty much one third of a team’s win percentage is purely down to random chance. Since we know the average win percentage is 45%, an average team can expect to achieve 51 points per season but this could potentially vary anywhere between 34 – 68 points due to luck.
Taking last season’s league table as an example, the average team could have ended up anywhere between 19th and 5th place just because of the random variance of their results.
Another calculation we can do is to work out at what point in the season luck and talent have an equal effect. For the English Premier League this works out at twenty matches, meaning that at around the half way mark of the season an average team’s league position is as much due to luck as it is to talent.
This then leaves eighteen matches for talent to overcome luck. Obviously, the first twenty matches are not completely random with the next eighteen determined purely on talent. Rather it is a gradual effect over the course of the season as the more talented teams slowly exert their dominance over random chance as shown in Figure 1.
LuckTalent.jpg

So next time a team goes on a winning streak or an attacking player fails to score for a few matches remember that football is one third luck and it may well be variance you are looking at.
 
This means nothing without the "number of nutmegs per game" stat
 
Billy Beane

A guy who’s poor at math(s) tries to understand soccermetrics.

It’s strange that a book about the first mainstream application of analytics and sabermetrics in Major League Baseball general management was made into a movie. Clearly Columbia Pictures saw something in Michael Lewis’ book Moneyball—on Oakland Athletics GM Billy Beane’s use of statistics when buying players—right off the bat (chuckles); they optioned it a year after its release in 2003.

The movie is structured around Beane’s redemption as a failed baseball player, relying on revolutionary player analysis as a kind of revenge against the Old Guard that fed him the same lies about his long term prospects as a pro. But the means whereby Beane made his name in baseball is, cinematically-speaking, quite compelling, even though in the film it’s generally treated as a subplot.

Beane essentially relies on a Paul DePodesta construct whom the movie refers to as Peter Brand to use advanced statistics involving on-base percentages to assemble a championship team on an average wage bill. This is what much of the sporting world knows today as Moneyball. So when someone mentions player metrics or analytics in soccer, this is generally how it’s popularly understood.

Unfortunately, the term is now so widespread it’s trotted out whenever a player bought on the relative cheap plays extraordinary football regardless of what we know about the rationale behind their acquisition. Hence observers have often spoken of “Moneyball” in passing when referring to Newcastle United’s Papiss Cisse, purchased for around £10 million in the January transfer window, and who subsequently scored 13 times in 13 appearances since January. This despite the fact we know little about what sort of statistical rubrics were used by the club when considering the player, other than Alan Pardew’s words at the time: “He is a finisher with an already established CV in the Bundesliga, where we have monitored him for the best part of two years.”

I write this to illustrate that the popular understanding of the potential application of statistical analysis in soccer is limited by this narrow view. It is highly unlikely, for example, that soccer analytics will dramatically change how football is played, or allow managers to buy players on the cheap that will render a whole team greater than the sum of its parts and increase a club’s win percentage. The integration of advanced analytics in soccer will be more evolutionary than revolutionary.

As Sarah Rudd, Vice President of Analytics and Software Development at StatDNA, told me, soccer is similar to basketball in that the numbers confirm rather than contradict conventional tactical wisdom. Best practices in football are fluid and change over time to adapt to present circumstances and trends, as Jonathan Wilson’s history of football tactics and formations Inverting the Pyramid illustrates. In practice, advanced soccer metrics simply allow players to tweak their performances and adjust from one game to the next based on subtle but measurable weaknesses on the opposing team, for example. See Jen Chang’s recent look at Everton’s use of performance analytics.

This is not to say analytics can’t be used to deduce whether a club is overpaying mediocre players. At the MIT Sloan Sports Analytics Conference held in Boston this past march, Michael Fotopoulos and Andrew Opatkiewicz released a paper titled “Salary Allocation Strategies for Major League Soccer“, a means to measure ability against wages using former Chelsea and Watford manager Gianluca Vialli’s geometric framework for player evaluation.

It’s not a perfect science by any means, but as both player analysis technology improves managers and coaches will be better able to measure player performance against wages, particularly important in single-entity, salary-capped MLS.

Chances are however there will be no single metric that will change the game, no hidden on-base percentage or other sabermetric tool that will forever alter the way the casual fan, or player, or manager, views the sport. Rather analytics will be used to tweak improvements a number of diffuse areas in the game, like optimal mix between offense and attack, best practices in formational play based on available personnel, the proficient execution of set-pieces, understanding whether crossing the flanks is an efficient use of possession, and which of the many newly available and accepted metrics are actually useful for the layperson (or blogger) in evaluating either a team or player performance, like pass completion percentages or the dreaded “assist number.” The latter will be particularly important in the evolution of soccer media, although it’s unlikely it will provoke the same kind of split we see in baseball between the narrative-driven romantics and the small-sample size-obsessed numbers nerds.

Each of these in turn will be enhanced as player analysis technology improves. It will be a slow, arduous process, but the fun part is there is still a lot to learn. Unfortunately many of the most advanced metrics are kept secret both by large analytics firms and by clubs, which largely leaves the average joe out of the loop on more complex player and team metrics. That will be the subject of a future column…
 
Sabermetrics work much better in baseball than other sports simply because there is so much volume from which you can derive stats. Over the course of a season, almost every scenario possible plays out dozens of times for each player, allowing for some very in depth comparisons and projections.

It's much harder in footy I would say because of how team oriented the game is: one player's stats can be massively inflated because he receives the bulk of the opportunities, or deflated because he gets none for numerous reasons. Not discounting the theory, but a lot more thought needs to(and is) be put in, as system and style play so much more of a role.
 
I didn't read the whole first post and have no clue what it's about.

But thought I'd contribute anyway and make this thread a bit more interesting.

bouncing_tits_11.gif
 
This kinda explains why we have so many shots, but often no goals:

Where did the Strikers shoot from in the EPL?

Posted on June 10, 2013 by statsbettor
In a previous article I plotted the average shot location of the leading strikers.
I’ve had another look at this, but decided to present the data a little differently.
Instead of just plotting one average point for each striker I have broken down the proportion of shots that each player took from smaller sized zones, this way we can get a good idea of the volatility of the shooting positions for each of the players.
Data Rules and Housekeeping
Penalties have been excluded (but headers included) from these figures, and I am showing the shot breakdowns for all players that had more than 70 shots in last season’s Premier League. This amounts to 30 players in all.
Each player’s percentage will add up to 100% as all we are doing is looking at the breakdown of where they took their shots from (as a proportion of their total number of shots).
In order to aid analysis I have attempted to group the players into bands. The bands have not been created scientifically so arguably players could feature in different groupings from what I have shown here.
Please note that Excel is probably not the best place to create vizuals (but, hey I’m an accountant and it’s my perogative!!) and so the locations of the penalty area, and the figures in the zones within the penalty area may not be exact. What will be correct is the general shape and positioning of the shot locations.
Images
The images should be self explanatory, with the player shooting into the goals on the right of the pitch, but I’ll talk through the first image – Nolan.
59% of his shots were taken from central locations inside the penalty area
24% of his shots came from the corners of the side / corners penalty area, and only 16% of all his shots were struck from outside the penalty area.
I’m not going to talk through the other 29 images, you’ll be able to arrive at your own conclusion.
Hopefully, you’ll find these images interesting and helpful in terms of understanding where players typically shot from.
So without further ado…….
Foxes In the Boxes
We’ll start off with the players that seen the majority of their shots come from prime central, fairly close positions.


The Next Band
Now we move on to the players who, whilst they had plenty of shots from central locations, weren’t afraid to mix the locations where they chose to shoot from.


More Variation
This next group of players had a much more even distribution of where on the pitch they were likely to shoot from.


The Long Range Shooters
We now arrive at the group of players who actually seemed to specialise in shooting from long range, or perhaps even in some cases, from some pretty silly angles.

Last, and perhaps least
And bringing up the rear…..

The locations that Taarabt shot from really take some believing.
Most Varied Striker
It’s not a very scientific test, but the player that had shots from the greatest number of zones as shown in in the above charts was Bale with shots from 33 locations. The Welshman was closely followed by Suarez with 32 different shot locations.
Easiest to Predict Striker
Using the same methodology, West Ham’s Kevin Nolan was the most predictable striker as his shot locations were covered by just 18 of my zones. Osman and Cisse were next with 19 different shot locations.
 
After posting this article on the breakdown of shot locations I was asked by a number of people if it was possible to show the absolute number of shots and goals for each player in a similar format.
So what I have done is look at players in the EPL who scored at least 12 goals last season, and plotted their shots (on the left hand side) and their goals (on the right hand side) side by side.
As with the previous article, penalties have been excluded.

The players have been sorted (approximately) by their total goals total. I created the images in batches of three which is why Bale has been uploaded before Van Persie.
I’d probably be stuck for words if I tried to describe what is happening in each image, but I’ll comment on anything of note as we pass down through the images.

The vast majority of Van Persie’s goals came from relatively close in, especially compared to Bale’s image above.

It appears that Suarez is happy to shoot from almost anywhere inside the opposition’s half. For the most part, it appears that these shooting decisions are not very wise due to the infrequency of those long range shots finding the net. This can be seen by the amount of red zeros in the right hand image.

Benteke’s shooting choices were very much controlled, with a lot in the central areas.

Michu barely scored a goal outside the area last season. Looking at his goal locations he seemed to be in the right place at the right time a lot.

Berabatov may as well not have set foot outside the opposition penalty box, with all his goals coming from fairly close range.

Dzeko ensured that, for the most part, he was in good positions before he decided to shoot.

Walcott’s lack of returns from outside the penalty area surprised me somewhat, especially given the amount of times that he shot from long range.

Not only were all of Le Fondre’s goals scored from inside the area, but that the vast majority were scored from very close range.

If I was Arsene Wenger, I think I’d be having a word with Cazorla. For all of the long range shooting he insisted on taking last season he didn’t trouble the scoreboard operator very often. Those numbers are quite stark and given his silky skills you can’t help but think that Arsenal would have benefited had the Spaniard made one more pass.

As was noted earlier today, Rooney has a very nice set of shooting numbers. In fact, the general shape of his shot locations is quite closely correlated with those of Van Persie. I guess we can make of that what we wish.
 
Shot Location by Teams in the EPL

Posted on June 12, 2013
For this last look at shot locations I have decided to look at the breakdown of shots per zone taken for each team. As before, I am including shots and headers, but excluding penalties from the images and analysis.
In order to aid analysis I have included the teams in the order that they finished in the league, so we start off with the Premier League winners for 2012/13 – Man United.

Man United took 45% of their shots from the absolute centre areas of the penalty area, but more importantly only 12% (which is a league best number) of their shots came from poor positions. For the purpose of this piece, “poor positions” are defined as any zone beyond the penalty area which has been extended to the dotted line in just the Man United image.
As an aside, if the figure is 0% that means that at least 1 shot was taken from that zone, but not enough to be rounded up to 1%.

In terms of shot location, Man City were not that very different from their Manchester rivals. They had a handful more shots from further out and didn’t manage to have as many of their shots from very close in range.

22% of Chelsea’s shots originated from poor positions, whilst their proportion of shots from very central locations inside the area was at 35% – a decrease on the top 2 teams’ numbers.

Arsenal were very conservative with their shot selection, with any long distance shots being taken from central locations. Santi Cazorla would have been a huge influence on the long range shots that they took, exclude him and their shooting locations would be even more impressive. The 31 zones that they shot from was the least by any team; in fact Gareth Bale and Luis Suarez shot from more individual zones themselves (33 and 32 respectively).

Tottenham had 25% of all their shots from poor positions. Well, they should have been poor positions, but Gareth Bale made sure that they managed to get an an unreal percentage of them on target.
Only 27% came from central inside positions, which is very low for the team that finished 5th in the league. Quite simply, their shooting was from another planet this season – the question is can they do it again next season. One aspect they have in their favour is that per @simongleave Bale posted some pretty amazing shooting accuracy numbers in previous seasons.

Everton couldn’t really have asked for better shot locations than the ones the received this season. 39% were central inside, with only 15% coming from poor positions. Unsurprisingly, the left hand side of Everton’s attack seen much more heat than the other side.

So Liverpool managed to hit shots from 40 different zones this season, that is the most for any team (tied with Wigan) in the Premier League. I had shown in the individual player numbers that a certain Uruguayan can take most of the credit for Liverpool earning that particular prize.
20% of Liverpool’s shots came from poor positions and 31% from inside central ones.


Swansea showed great control in their shooting for a mid table team, with only 17% of shots from poor positions and 36% from inside central spots.

As has been noted by the author in previous pieces on this site West Ham did a brilliant job with their shot locations. At only 12% of shots from poor positions they tied with Man United, and their figure of 48% of shots from centrally inside the penalty area was a league high number.


Norwich and Fulham had similar shot profiles, both did well to keep their shots from poor positions to less than 20%

A little like West Ham, Stoke make a big play on shot location with 43% of all their shots coming from great central inside locations. Their shot profile, at least on paper, looks like that of a much more successful team. What more successful teams do, however, is have more than 10 shots on average per game over the course of a season.


Aston Villa had a nice heatmap in terms of their shot locations, although they struck 18% of their shots from what I defined as poor positions earlier, the majority of them were at least central. Also, with just 32 zones covering all the shots that they took they match Man United in that regard for the league’s second lowest number, just behind Arsenal.

Newcastle had a wide range of shot locations and with 27% of them coming from poor positions they would need to attempt to work into closer positions next season. The figure of 27% was the worst in the league. Just 30% was from inside centrally.


Wow, Wigan really seen most of the park. With shots from 40 different zones they had as many varied locations as Liverpool (they shared the league high number). However, they still had less of their shots from poor positions than Newcastle did.

Reading’s issue wasn’t the breakdown of their shots, it was the fact that they didn’t have nearly enough of them. Just 18% of shots from poor positions and 44% from inside central areas aren’t indicative of a team that were relegated. However, just have 391 shots from 38 games is.

Conclusions
Despite having to go all the way down the league table to the 8th placed team until we find a team that had less shots than Man United, the heatmap of Man United goes a long way to showing why they won the league. They had a hugely measured approach to where they pulled the trigger.
Everton’s huge reliance on their left hand side is apparent and one wonders want their Merseyside neighbours, Liverpool, might have achieved had they calmed their head a little before shooting from all locations.
Tottenham’s shooting locations are just plain ugly, but they do have Gareth Bale who, it seems, can undo a lot of ugliness with his left foot.
It is interesting that there are a couple of teams in Stoke and West Ham who place great importance on pitch location. It’s a good job that Sam Allardyce hasn’t had the pleasure of managing Luis Suarez – that would be an interesting battle of minds!!
Given how many shots Newcastle were (presumably) forced into taking from poor positions they can perhaps count themselves lucky to be able to call themselves a Premier League team again next season.
 
I bet the people that made these spreadsheets have even more complex ones to explain why they have no friends & are yet to lose their virginity at the age of 36.
 
I bet the people that made these spreadsheets have even more complex ones to explain why they have no friends & are yet to lose their virginity at the age of 36.


That type of spreadsheet involves all sorts of nerd hard-on inducing macros and multivariate analysis that we can't even begin to imagine.
 
Wow. Those two torpedoes almost seem to drag her behind them as she tries to clear that bar.

It's the arch of the back that fucks it up. See how the torpedoes give her the initial momentum, only then to act as a dead weight when she dives into the horizontal position. There's a lesson to be learned here folks.
 
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