RACE to Goals Model – The Defence

Prior to the season starting, I introduced the RACE to Goals Model, which you can find here, and I suggest you have a read of that before you continue with this one if you want to have a full description of the different metrics and how they are calculated.

Essentially, I am looking at the same metrics, but this time flipped to a defensive point of view, so the rate of shots conceded, the Defensive Efficiency, and the conversion of chances conceded by each type.

I will describe Defensive Efficiency here though, as it’s calculated slightly differently. Whereas Creative Efficiency attempts to show how good a team is at creating good chances, measured as the proportion of Clear Cut Chances to Total Shots, Defensive Efficiency attempts to show how good a team is limiting the amount of good chances the opposition has, and is measured as the proportion of Normal Chances conceded to Total Shots conceded (%NC). So the higher the number, the lower the percentage of Clear Cut Chances conceded, and the more efficient the defence is.

The benchmark numbers are essentially the same, the slight difference being own goals, and those ‘shots’ by players on the defending team that lead to own goals are also included.

The table below shows how well the teams performed last season against the 4 metrics.

The team that conceded the fewest shots was Tottenham, with only 370 over the entire season, so a touch under 10 shots a game. At the other end of the scale were Reading, who conceded 706 shots, the worst by over 60 shots.

Like with the original article, I feel the raw numbers in the table are a little hard to read, so again I’ll add context and measure each metric as the percentage difference from the benchmark team. From the a defensive point of view, having shots and conversion rates below the benchmark is good, but this is not the case for Defensive Efficiency, so I’ve highlighted this in the table as anything in red as being ‘bad’.

As with Creative Efficiency, Manchester United also had the best Defensive Efficiency, limiting their opponents to only 8.2% of their shots coming from CCCs, with Manchester City being the only other team to have a Defensive Efficiency of over 90%, seeing them perform 6% and 4% better than average respectively. The team with the worst Defensive Efficiency was Newcastle, who allowed over 18.5% of all chances against to be CCCs; however the 2nd worst team, perhaps surprisingly considering how few shots they conceded, was Tottenham, allowing almost 18%, and possibly showing the risk of playing with a high defensive line.

Looking at the conversion rates it becomes clear why Wigan struggled last season. They had by the worst rate of CCCs conceded, in fact at 52.7%, they are the only team over the 3 years of data that conceded more than half the CCCs that they faced. They were also the 2nd worst at stopping Normal Chances being conceded. Reading actually had the best rate when it came to stopping CCCs in the league, but unfortunately for them, when you allow the opposition to create over 100 CCCs in total, you will still concede a lot of goals.

No teams outperformed or underperformed all 4 of the metrics compared to the benchmark. Only 4 teams, the two Manchester clubs, Chelsea and Swansea outperformed on 3 of the benchmarks. Liverpool join Utd, City and Chelsea as the only teams who conceded fewer shots than the benchmark whilst also having a higher than average Defensive Efficiency. Despite conceding the fewest shots, we can see why 7 teams conceded less goals than Tottenham following their underperformance in the 3 other metrics.

Converting the metrics into Expected Goals, we see how badly Wigan performed. Whilst they would have been expected to concede just less than 54 goals from the shots that the opposition had, which was only the 10th lowest, they actually conceded 73 (+19.1 goals more than expected). The other big underperformers were Southampton (+9.7 goals), Newcastle (+9.0 goals) and Aston Villa (+7.9 goals). The biggest overperformers were Everton (-9.5 goals), Sunderland (-8.7 goals), Stoke (-6.4 goals) and Arsenal (-6.0 goals).

In my next posts I will combine some of the attacking and defending metrics together to analyse team’s performances in some new ways, and see how the teams have performed so far this season.

This was originally posted on  EPLIndex  http://eplindex.com/43205/race-goals-model-defence.html

Defending Liverpool's Defence

With the season about to start, I thought I would follow up to piece that I did earlier in the year looking at how Liverpool’s form changed over the season, however whilst that looked at attacking form, this one looks at Liverpool’s defensive form. Again I will look at Liverpool’s performance compared to how the league performed on average, how the top 4 performed, and also compared to Liverpool in the 2011-12 season, as well as having the short term form by having the 6-game moving average. One thing to note is that due to there being fewer observations, for example Liverpool conceded far fewer shots, goals etc., that the graphs show more extreme changes compared to the attacking versions of these graphs

I’ll start by looking at shots conceded per game. Apart from the 18 shots conceded in the first game of the season against West Brom skewing the averages, Liverpool performed more or less in line with the Top 4 teams throughout the season.

In terms of the accuracy of opposing team’s shots, despite the slow start that Liverpool had and perhaps surprisingly, they actually allowed significantly less shots to hit the target compared to the Top 4 teams and the rest of the league over the first half of the season, whilst over the 2nd half of the season, a greater percentage of opponents shots were hitting the target.

Moving on to Opponent Shots Conversion and Shots on Target Conversion, we can see how poorly Liverpool defended and Pepe Reina performed in the opening 5 or 6 games of last season. Basically, Liverpool defended and kept goal more or less like a lower league team when going up against a Premier League side in a cup, but this quickly regressed to the mean, and they performed like a Top 4 team from game 7 onwards (in the moving-average plot, this shows up from match 12). Those first 6 games had such an impact though that the end of season conversion rates were still only in line with the league as a whole.

How do Liverpool, or more pertinently Pepe Reina and Brad Jones, do at keeping out Clear Cut Chances (CCC)? So what is a CCC? It is one of Opta’s few subjective stats that can broadly be described as a chance where the attacker is probably central to goal with only the keeper to beat. So a keeper would hope to either save it, or perhaps attempt to put the attacker off sufficiently that they miss. As I mentioned in the original piece, the conversion rate for CCCs is much more variable than the other conversion rates, this is because in some games there will be few or even no CCCs, which means that both very high and very low single game conversion rates are far more likely, and we see this clearly in Liverpool’s form plot (note that the reason you can’t see the league average plot is because it was the same as the Top 4). Again, Liverpool started poorly, but were better than the Top 4 teams from match 7 onwards, apart from a large peak at match 17 where all the CCCs that Liverpool faced were scored giving a 100% conversion rate. More specifically, it was in fact a 4 match period with the goals coming from Tottenham, West Ham and Aston Villa.

With that in mind, it is interesting to then see the rate at which Liverpool were giving up CCCs per game through the season. Again we see Liverpool started off poorly, giving away on average 1.5 CCCs over the first 10 games, but by match 17, where we saw the 100% conversion rate, the 6-game form had fallen to 0.7 per game. So, it was only 4 out of 4 CCCs conceded in 6 games. As the average conversion rate for all CCCs is around 38%, it is a bit like tossing a coin 4 times and getting 4 heads, so I don’t think we should put it down to poor goal keeping. You’ll notice there is a sharp rise in CCCs conceded from about match 20, but this coincided with an increase with CCCs for Liverpool, and can perhaps be put down to increased attacking leaving the defence more open (Note: Liverpool’s average in 2011-12 was the same as the Top 4’s last season).

Finally I’m going to look at Errors per Game. It should be noted that these are ‘on the ball’ errors, so does not include an error like not marking the run of an opponent from crosses (something that had many Liverpool fans pulling their hair out). Again we see the effect that Liverpool’s poor start to the season had, however it took longer for Liverpool to recover from than compared to the other metrics, but by the end of the season, on the ball errors had almost become non-existent. Over the season as a whole, only Arsenal and Newcastle made more errors than Liverpool’s 36, however if you split the season in half, over the first 19 games Liverpool made 28 errors, over the last 19 games it was only 8. As an on the ball error will often leave the rest of the defence wrong footed, these types of errors tend to have a high conversion rate, and Liverpool conceded 10 goals from the 36 errors they made. If Liverpool can continue to keep the error rate at the level of the 2nd half of the season, then there would be a lot less hair pulled out by the fans this coming season. 

Perhaps it was the tough start Liverpool had, perhaps it was the getting used to Brendan Rodgers system, or perhaps they were just unlucky (probably a combination of all 3), but clearly Liverpool started the season really badly last year. If they can perform defensively as well as they did over the last 30 or so games, they could well turn a few of those losses and score draws into wins, and have a good crack at finishing in the top 4.

I posted this oginally on EPLIndex  http://eplindex.com/37116/defending-liverpools-defence-statistical-analysis-1213.html

RACE to Goals Model: League Predictions

At the end of my introduction to the RACE to Goals Model, that you can read here, I mentioned that I would like to look at how teams performed from a defensive point of view, and to check  how reliable the metrics are year on year.

Whilst I have done those things, I haven't had the time to sit down and write about them. But I have been able to create a model (well, I created a few slightly different versions and picked what seemed the best) to predict this season's league table, so I will at least get that posted prior to the season starting so that I don't let bias from early results get in the way.

The model is a variation of the Shot Dominance model, as coined by @mixedknuts (here), which is itself a variation of the Total Shot Ratio (TSR) model that has been looked at in legnth by @JamesWGrayson (such as this), and a good summary of TSR by @TheM_L_G can be read here. What differentiates the RACE to Goals Model is that it includes the quality of chance that those metrics are missing based on the metrics from my earlier piece, and I hope to go into more detail in later posts.

For the promoted teams I did not have the data available to do the same analysis, so a simply did a regression of goals scored and conceded in the Championship since 2000 for promoted team compared to points scored the following season in the Premier League.

The model predicts that Man City are the clear favourites for the title, and that there will be another very close battle for 4th place, whilst down at the bottom, Fulham, Newcastle Southampton and Norwich could well be in trouble. Of course the model does not take into account any managerial changes or player transfers, which could change team strengths significantly. Personally, I'd expect Newcastle and Southampton to do a little better and be replaced by Sunderland down towards the bottom, whilst common sense would say that Chelsea should challenge at the top, but other than that, I think the predictions are reasonable.

Anyway, here's the predicted table.

Rate of Attack and Creative Efficiency (RACE) to Goals Model

Yes, yet another model looking at the quality of chances and finishing of football teams.

This is something that I had hoped to have finished before last season ended, unfortunately life got in the way and it got delayed. Since then there have been a number of very interesting analyses done, including those by @colinttrainor (like this) and @11tegen11 (like this), which have continued the good work done by @footballfactman (like this), where they have put in a significant amount of work to look at where shots are taken from and what the conversion rates are for shots from those areas. Hopefully I can hang on to their coattails.

Personally, I am far too lazy to collect all that data, so I have let the experts (Opta) decide upon chance quality for me, and I hope to make the model as simple as possible. In my blog so far I have looked at how Liverpool and Tottenham have performed in terms of finishing and creativity, and to add some context, I compared them to the league and Top 4 average. Whilst compiling the numbers, I noticed that the League averages were quite consistent year on year over each of the past 3 seasons, and realised that I could create a theoretical average team that I could use as a benchmark to compare the performance of all the Premier League teams.

I am sure I am not telling anyone anything new when I say that the amount of goals a team scores is essentially dependent on 3 things, the amount of shots they take, the quality of chances they create, and the quality of their finishing, and it’s against these metrics that I will be comparing teams against.

Rate of Attack

This is very simply the amount of shots a team takes, and can be measured on a per game (SpG) or per season (SpS) basis. Yes, I know that not all attacks end with a shot and I am basically just using total shots, but I wanted the model to have a 'racey' acronym, so Rate of Attack it is.

On average, each team takes about 14.5 SpG, or about 550 SpS. Between 9-10% of all shots end up with a goal, and this has been found to be consistent season upon season and across different leagues. For those that don’t know, this is called the Reep Ratio, after an amateur statistician named Charles Reep, who looked at various stats, including the conversion rate of shots, in the 1950s. 

Creative Efficiency (%CCC)

This is a measure of the creativity of the team and quality of chances they have, and this is where I am relying on Opta to decide upon what is a good chance, as I am using their Clear Cut Chance (CCC) for this. A CCC is one of Opta’s few subjective statistics, and whilst a full description is not given, a brief description is given by Opta in their Event Definitions under Big Chance (here) 

“A situation where a player should reasonably be expected to score usually in a one-on-one scenario or from very close range.”

Creative Efficiency (%CCC) is measured as a proportion of Clear Cut Chances to Total Shots.

A team with a high %CCC will, over time, create chances that are easier to score from than the average team. Whilst CCCs make up only about 13% of all shots in the Premier League, they are vitally important, as for each of the last 3 seasons, around 52% of all goals have been scored from a CCC. It should be noted that CCCs include penalties, and whilst I did consider removing them from the analysis as they have their own average conversion rate, I decided to include them for a few reasons, there will be some open play CCCs that will be easier to score from than a penalty, I also think that teams that attack more or are more creative will tend to get more penalties, at least over the long term, and that should be included in their Creative Efficiency, and finally because I want to keep the model simple and with as few adjustments as possible. 

Obviously when you multiply a team’s Rate of Attack by their %CCC, you will get the number of shots which are CCCs. The remaining shots will be what I will call, as I can’t think of a more appropriate term, the Non-CCCs. The two types of shot have their own average conversion rate, and the model analyses the quality of finishing of both types of chance by comparing the goal expectancy (number of chances multiplied by the average conversion rate) to actual goals scored for each type of chance.

CCC Conversion

To give an indication of the average difficulty of a CCC compared to the average shot, it is on average about 4x easier to score a CCC as they have an average conversion rate of just under 38%. It should be remembered though that there is a large range in the probability of a CCC being scored, Sam Green of Opta has said (here) he considers the base probability to start at about 20% and it of course goes up to 100%.

Non-CCC Conversion

The average conversion rate of Non-CCCs is slightly above 5%. The reason why I won’t classify them along the lines of a ‘difficult’ chance is that with the goal expectancy range for individual shots being between 0% and 20%, anything with an expectancy above 10% will still be easier than average.

The Numbers  

Here are the hard numbers I have collected for the past 3 seasons.

 

And these are the benchmark ratios/rates that I have either mentioned or will be using for the theoretical average team.

So, how did each team perform last season? In terms of number of shots, Liverpool lead the way by far with 740 shots over the season, 59 more than Tottenham took, the next best team, and not far off double the amount of shots that Stoke had.

It may not come as much of a surprise to see that Manchester United had the best %CCC, with 21% of the efforts being from a CCC, compared to 18.3% for 2^nd placed Manchester City. To put this difference into perspective, whilst Man City took 98 more shots than Man Utd, they only had 3 more CCCs. Liverpool had 178 more shots, but with a %CCC of ‘only’ 13.6% (still above average) had 17 less CCCs.

In terms of shot conversion, the team with the best conversion rate for CCCs was, yes you’ve guessed it, the team who scored the most goals, Manchester United with 44.1% of them scored. The team with the worst conversion of CCCs was, yes you’ve guessed it, the team who scored the least…oh, it was actually Manchester City, with only 28.9%, I didn’t guess it either. So, City had 3 more CCCs, but scored 17 fewer CCCs, a significant amount.

The team with the best conversion rate of Non-CCCs was Chelsea at 7.4% leading to goals, and this time we do find the expected QPR at the bottom of the pile with only 3.1%.

I’ll admit that the table above is a little hard to read though, we’ve got different units and magnitudes of measurement and its hard to see how well each team is doing overall, so lets add some context and measure each teams performance as the percentage change from our benchmark team.

Now things become a bit clearer. We can see that despite only taking 3% more shots more than the benchmark, Manchester United’s %CCC was a whopping 62% higher than average, which goes some way to explaining why their total shot conversion was so much stronger than everyone else at 14.2%. However they also significantly outperformed both conversion rate metrics, meaning they scored almost 13 goals more than expected if they had average finishing. If they had scored at average rates, their total conversion rate would still have been the highest in the league though at a touch under 12%.

Only 2 teams managed to beat the benchmark for all 4 metrics, Man Utd and Arsenal. Of the other top teams, Chelsea and Tottenham had a relatively poor %CCC, Man City were poor at converting their CCCs, Liverpool were poor at converting their Non-CCCs, and Everton were poor at converting both types of chances.

At the other end of the table, only 2 teams performed worse on all 4 metrics compared to the benchmark as well, unsurprisingly QPR, with the other team being Newcastle. Reading were very good at finishing their chances, its just that they struggled to create any.

So what does this all look like when we convert these metrics to expected goals and how did the teams compare? There were 3 big outperformers, Chelsea (15.7 goals above expected), Man Utd (+12.6), and Arsenal (+10.4) whilst there was 2 big underperformers in QPR (-12.3)  and Everton (-10.1). For those of you who are into your ‘proper’ statistics, I’ve calculated the Mean Absolute Percentage Error for the model over the last 3 seasons as 10% and the Root Mean Squared Error as 7 goals. Its been a loooong time since I studied statistical methods, so I may have used the wrong error measurements, but I think that shows that the model isn’t too bad.

I’ll finish with how my model differs from those I’ve mentioned which look at shot location. I’ll start with the weaknesses. The first is that my model is far less granular as I have lumped the 87% of all shots that are Non-CCCs with the same goal expectancy, which means that the type of analysis that I can do with my model probably can’t go quite as deep as the others. Due to the creative efficiency element, I think the model is only applicable to teams and won’t be able to do player analysis. There is an element of trust in Opta that they are consistent when collecting the CCC data as it is subjective, particularly as we do not know their precise definition, although having read this (here), I think its fair to assume they are consistent. And because we do not know exactly how Opta define CCC, I think it will be very difficult to see how or if the metrics change depending on the Game state as the info of when a CCC occurred is not available. Whilst on average there are just under 4 CCCs per game, so it might be possible by watching the highlights or reading the match reports to figure it out for most games, in some cases however, as shown by @analysesport (here), it would be very difficult. Another issue is the relative lack of CCC data, it is not freely available (you need to pay for a subscription at www.eplindex.com for the data), it only goes back 3 years, and as far as I am aware, there is no  CCC data publicly available for leagues other than the Premier League

The positives are that it is very easy to collect and analyse the data, you only need the number of games played by a team, their total shots and the number of CCCs they’ve had to be able to estimate the number of goals they should have scored. One of the issues with simply using shot location, as discussed by @mixedknuts (here), is that it does not take into account the positioning of the defenders. For instance a player may take a shot in the central area of the box but have 4 defenders and the keeper between him and the goal, so the probability of a goal would be low, equally a player may break an offside trap and have the ball outside the area but be 1 on 1 with the keeper, so the likelihood of scoring would be quite high. This model at least separates out those chances where the defenders are not making a significant difference to the difficulty of a goal being scored, and whilst these only make up 13% of the chances, they do make up 52% of the goals.

Hopefully, if I get enough time, I’ll look at how repeatable these metrics are and if they could be of use for predicting matches and also look at how the teams performed on these metrics from a defensive point of view.   

You can follow me on twitter at @The_Woolster


Data taken from www.eplindex.com

Liverpool's season in transition: Updated graphs

Thought I would repost some of the the graphs in my piece on Liverpool's season in transition (here) to show the full season. Nothing to say on them other than it seems end of season syndrome perhaps hit once there was nothing to play for.

Data taken from www.eplindex.com

Liverpool: Has there been progression in a season of transition?

In my first post, I looked at Liverpool’s conversion and creativity measures, and noted that when it came to converting chances, Liverpool were not only still lagging behind the average for the Top 4, but also the league as a whole. But I also pointed out that Liverpool, under the new management of Brendan Rodgers, are a team in transition and that an improvement should be expected over time as the players become used to his system, and that the recent goal tally at that point, was probably showing this improvement.

I am going to look further into this and track how these measures have changed over the season. I am also going to introduce form into the measures to show how Liverpool has performed over short term periods. To give some extra context, I will also include Liverpool’s average from the whole of previous season, and the same for the League as an average as well as the Top 4 from last season (I have used last season as it’s a complete season with all teams having played each other). For the form measure, I will be using a 6-game moving average as this is what we are used to seeing in the media, it should not give too much or little significance to individual matches, and I also believe that over 6 games you should generally see an equal number of Home and Away fixtures, which is not the case for other even numbered amount of games.

I’ll start by looking at shots per game. In my first post I mentioned my surprise that Liverpool had taken more shots than any other team in the top 5 leagues in Europe, as I had been expecting the number of shots to reduce under a more patient approach. Since then, Liverpool still have the highest shots per game in those leagues. This is one area Liverpool were relatively strong last season, with an average just slightly lower than that of the Top 4. This season the rate of shots started off more or less in line with last season up until Game 17, in which Liverpool had 29 shots against Aston Villa, and there was a big increase in form (the 9 shots against Chelsea also dropped out of the 6-game average equation), since then Liverpool’s shots per game has been at a level above what the Top 4 achieved last season, and as we know, the highest in the top leagues in Europe.

 

Shooting accuracy (this excludes blocked shots, as we don’t know if they were on target or not) started off very poorly, and the season average has only recently started to creep above the level of last season. This has been significantly affected by the poor start however, as the 6-game average has been above last season’s since match 14, although it still has not quite reached the league or Top 4 average from last season yet.

Next I will look at Shot Conversion and Shots on Target Conversion (these conversion rates exclude own goals, as its not clear from the stats alone whether an own goal came from a shot or from something like a misplaced clearance). As you might expect, they follow a very similar pattern. Two things to note are that this season's Shots on Target Conversion ratio is relatively stronger than the Shot Conversion ratio when compared to last season's League and Top 4 averages, and that between match 6 and 11 it was even stronger. As I mentioned in my previous post, I think that last season Liverpool were unlucky in that a number of goalkeepers had outstanding games against them and I think the relative improvement is down, in part, to that. If we look at the peak between game 6 an 11, we see that game 6 was the 5-2 win against Norwich, in which Liverpool had 16 shots, with only 5 on target, but 4 of those were goals (there was one own goal), and 3 were from outside the area, for a massive Shots on Target Conversion of 80%. The other thing to note is that since Match 22 for Shot Conversion, and Match 20 for SOT conversion, Liverpool have been more or less at or above the Top 4 average from last season.

  

Next up is Clear Cut Chance (CCC) Conversion. For those that don’t know what a CCC is, it is one of Opta’s few subjective stats, although they don’t give a full description, it can broadly be described as a chance where the attacker is probably central to goal with only the keeper to beat, however I don’t think it should be thought of as a chance that should be scored, as statistically speaking, just under 40% of all CCCs in the Premier League are scored, so on average they are a bit harder than a 50/50. The conversion rate for CCCs is much more variable than the other conversion rates, this is because in some games there will be few or even no CCCs, which means that both very high and very low single game conversion rates are far more likely. It is clear to see however that there has been a general improvement over the season, following a start of only 2 CCCs scored in the first 8 games, and that Liverpool have been significantly more clinical at taking easier chances than they were last season.

I will now look at a measure of how creative Liverpool have been, which is the proportion of chances that they have which are CCCs. One of the main premises of the possession based football that Brendan Rodgers likes is that the team will be patient and wait for good chances to score rather than take the first opportunity to shoot. But we’ve seen, counterintuitively, that Liverpool are taking more shots, so have they been creating better opportunities? Liverpool started the season not showing the patience that was required of them and rushing too many shots, and after that slow start, Liverpool’s season average proportion of CCCs has only just edged above last season’s in the last 2 games. However in terms of form, Liverpool have been more creative than last season since Match 20, and around or above the Top 4 average since Match 22. It should not be a surprise that conversion charts have a similar shape to this one, as the easier the chance, the more likely it is to be scored. In my view, this is the clearest indication that Rodgers’ methods, from an attacking point of view, are starting to take shape, although I still think they can often show that impatience, as this excellent forensic analysis by the Sports Analysis blog of the Swansea game shows.

The final graph is the CCCs per game, which in essence is a combination of the first graph and the last one shown. The graph has a similar shape to the one above, however due to the increase in the shots per game form from Match 15, relatively speaking, Liverpool are doing even better in this metric, and have been showing Top 4 form since Match 20.

 
If you were to just look at the averages of these conversion and creativity metrics so far this season, they would show somewhat of an improvement over last season, particularly in terms of conversion rates. They would not take into account the poor start to the season that Liverpool had however, or the steady improvements they have since made, or that they have consistently been showing the attacking strength of a Top 4 team for close to half the season (Match 18, the 4-0 win against Fulham, can probably be pinpointed as the game when things really started to click). It seems to me that Rodgers’ has decided to build from the front, and that he may now be close to having the team playing, at least offensively, how he would like. Of course football is about defence as well as attack, and it is Liverpool’s defence that has garnered the most criticism this season, and in my next post I will look to see if there has been any progression in similar stats from a defensive point of view.

Looking ahead to the final 8 games of the season, 3 of the remaining matches are against teams from the bottom 4, with only 2 teams being higher than Liverpool, so if Liverpool can maintain their recent form (losses against West Brom and Southampton aside), then they should have a relatively good run in. One thing that could of concern though is that in the last 3 games Liverpool have had only 8, 12 and most recently 10 shots in those games, which are their worst, 4^th worst and 3^rd worst totals of the season respectively. Liverpool fans will hope that this is a short term blip rather than a downward change in form.

Data taken from www.eplindex.com

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Liverpool's Annual Accouts: Will the Reds stay in the red?

Since Liverpool’s latest annual results were released last week, I’ve seen lots of questions regarding how good (or bad) they are, should we be concerned about the losses or the increasing debt, how to compare with 2011 with the reporting period now being only ten months, and what will the results for this current season look like, so I’ve decided to use what I do for my day job to attempt to show what the results might have looked like if the financial year end had not changed, and then to have a guess at what this year's results might be like.

My aim with this is not to get what our financial position will be exactly right, but to give a broad overview of what might be expected, particularly to those who don’t have a financial background, and I hope that I explain everything in a way that can be understood. I won’t be explaining all of the terminology however, but I'll assume that if you are reading this then you do at least have a partial interest in the subject

If you do have an interest in Liverpool's finances, and football finance in general, then I can highly recommend both The Swiss Ramble and the andersred blogs if you haven't come across them already, as those guys really know their stuff (and far more than me!).

A few caveats:

I’m not an accountant, so I might get some of my accounting assumptions wrong,

I’ve never analysed football accounts professionally, and they can be particularly difficult to interpret, so there may be some nuances I am missing

It’s been a couple of years since I did pure corporate analysis, so I may be a bit rusty.

I am making some guesses,

I have done this pretty quickly, and,

I have a 5 month old baby and am not getting much sleep…!

Basically, I’ve probably made some mistakes, so I am covering my arse!

But I am sure there are a number of you out there who have a bit more knowledge about these things than me though, so I am happy to be put right and make changes.

As I said above, football finance is quite difficult to interpret especially as the accounting treatment relating to, amongst other things, the amortisation of the value of player registrations, as this leads to significant non-cash expenses affecting profit levels, and in turn how to account for the profit (or loss) on player sales. A decent basic description of this can be found here.

Making a profit is of no use if you are not generating cash to cover your expenses, similarly a year on year accounting loss, whilst not great and probably not sustainable in the long term, may not be a serious issue if there is a positive operating cashflow to be able to fund the business. In terms of Liverpool’s results for 2012, a loss of £40.5m has been affected by the non-cash amortisation and impairment of player registrations by £42.7m.  

For that reason, I have stripped out all non-cash items from my analysis in an attempt to give a slightly clearer picture. In P&L terms, this means I am only going down to EBITDA, before moving on to the cashflows and then debt levels. The table below shows the last 4 years of results, with my forecast for 2012 for 12 months and 2013, and I will try to explain the adjustments I made to get these figures. Where possible I have used known information to make these adjustments, or changed figures so that they are in line with historical numbers, but in some cases, they will be plain old educated guesses. Where I have made adjustments I have tried to err on the side of caution and be conservative. I am also assuming that no work has started on the redevelopment of Anfield or any associated financing. I’ve also highlighted some of the key figures that people tend to look out for, these are Turnover, Wages, EBITDA, Operating Cash Flow, Net Debt and Wages to Turnover. I have also added a line for Cashflow before Transfers and Financing. This, in my view, is how much net cash the club will have generated after all outgoings to spend on transfers without the need for any Financing (ie increasing debt or an equity injection from the owners), and as the owners have stated we will be self financing, this is a key figure.

2012 forecasted results if they had been for 12 months

As Ian Ayre said in his FAQ on the official website, they have decided to change the accounting period to align with the football season, which made this years accounting period only 10 months. But we can make a few adjustments using information that is out there.

Turnover

In his interview with the Telegraph, Ayre mentioned that the unaudited revenue figure they gave to Deloitte for their annual football finance review was £188.7m, so I will use this as the starting point. The accounts tell us that both Media and Matchday revenue have not been affected the accounting period change, which means that the Commercial revenues must increase from £63.9m to £83.6m.

Costs

There will be costs associated with the revenues generated over the 2 months, so we should also increase Cost of Sales. Towards the bottom of the table I have given the Cost of Sales % for each period, which at 10.3% for the 10 months, is a bit lower than the average for the previous 3 years. Perhaps Liverpool have become more efficient, but my guess is there are lower profit margins on the types of revenues earnt over the summer months, so have increased the Cost of Sales % for the 12 months to 11%.

Other Cash Admin costs should also increase over those 2 months, so I have increased them to £24m to be in line with the previous 3 years.

For Wages, I have divided the figure in the accounts by 10, and then multiplied by 12 to annualise it. I think Wages may well come in below this as I presume that part of the players salaries are made up of performance bonuses for things like appearances, which won’t be paid in the summer without any games, but I’ve said I will try to be conservative and I’ll leave it at that.

This has the effect of decreasing EBITDA from £11.1m to £2.5m

Cash Flows

There will of course have be some cash flows over the 2 months, so I will adjust some of these figures as well

Changes in Working Capital

The figure for the 10 months of a Working Capital decrease of £12.2m looks out of place when compared to the previous 3 years, so I will normalise this. Year on year degree of change in working capital has been increasing, but I will assume the change to be in line with what we saw in 2011.

Net Interest Paid

We will have paid some interest over the 2 months, so I’ve increased this a bit.

Capex

Again, £2.3m looks on the low side compared to previous years, and I presume that a lot of any Capex work carried on the stadium/Melwood/The Academy is done during the off season, so I have increased this to £4.5m.

Deferred Income/Accruals

This is the biggest assumption I have made to the 2012 results. A football clubs cash flow is very seasonal, with a big increase in cash when payments for season tickets are received. The deadline for season tickets I am told is 28th June, and assuming that most people pay up during June, then much of this cash inflow will not appear in the 10 months accounts. However, season ticket revenues are recognised on a pro rata basis as matches are played, so an item in the balance sheet is created for Deferred Income. Note 1 of the accounts describes this as comprising “amounts received on sales of season tickets, sponsorship income and hospitality income. These amounts are released to the profit and loss account over the period to which income relates”.

There are also Accruals, which are expenses incurred in the period for which no invoice had been received at the end of that period. These could be things like utility usage (electricity etc), although to be honest, I am not sure why the numbers are so large (image rights perhaps?).

Looking at these two entries in the accounts (Note 16) and compared to historical amounts, 2012 again looks out of place, so I have made adjustments to normalise them. I have increased Accruals by 3% compared to 2011 to account for inflation, and I have increased Deferred Income by 4% on 2011 in line with the season ticket increase. So the adjustment I make to cash flow is the increase of Deferred Income between the audited accounts and my adjusted figure, less the difference in Accruals over the same period, which is £10.5m. It is possible that some of the Accruals have been invoiced but not paid, in which case they would become creditors and still be a liability on the balance sheet, but I will again be conservative and assume the difference has all been paid. This has the knock on affect of reducing the financing requirements (debt levels), by the same amount.

Financing

After making the above adjustment, we have a decrease in cash of £2.5m, which leads us to have only a slightly positive cash position. In reality we would probably have a little more cash in the bank and we would have made use of the banking facility, increasing our financing, but as this has no affect on the net debt figure, I have not made an adjustment here.

Overall View

Revenues increased by 2.8%, which is not too bad considering that there was no European football. The wage ratio has fallen only marginally from 70% in 2011 to 69%, and up from the ten month ratio as wages outstripped revenues in those two months. A wage ratio of 70% is considered as a rule of thumb to be the maximum sustainable level without a club needing to increase debt levels. Despite clearing out a lot of players in the summer of 2011, Liverpool also made some big money signings and had a full season from Suarez and Carroll, and probably paid a significant amount in wages for one or two players who were on loan. The biggest difference is in the Net Debt level which has now seen a more modest increase of just under £8m as opposed to the reported increase of £22m. FSG did take out £8m to pay back some of the shareholder loan, which suggests that they will look to get back some of their cash invested back over the short to medium term.

2013 Forecast

Turnover

2013 is much harder to forecast as we have to make a lot more educated guesses, starting with our revenues.

For Media revenues, I have decided to use the 2011 figure as Liverpool was also in the Europa League in that season. Really, the number of televised games should be looked at, but I don’t have that data.

For Matchday revenues, as ticket prices were frozen for this season, it is simply a case calculating the average revenues per home game last season and multiplying by the amount of home games this season (25 in 2012 Vs 26 for 2013). Again this is not perfect due to not knowing how much was received out of the gate receipts from the the two cup runs and three games at Wembley in 2012, although my guess is that its not a massive amount.

For Commercial revenues I have used my 2011 forecast as the starting point. To this I have added £12m due to the impact of the Warrior deal (a £13m increase on what Adidas paid, but 1 month of which was already in my forecast, so I’ve taken off £1m). As part of this deal, Ayre said that Liverpool now have much more control over non-branded goods, which could add another £25m per year. I think this sounds very optimistic personally, and would take a long time to generate as it would require the opening of international club shops, so I will take a stab in the dark and say this added £2m of revenue this year. We also signed a number of other sponsorship/partnership deals, most notably with Chevrolet and Garuda Airlines. No details have been given for the size of these contracts so I have assumed £4m, but an article in the Daily Mail earlier this year gives an indication of the type of size of these contracts.

This sees Liverpool break the £200m revenue mark for the first time on the back of very strong increases in commercial revenues.

Costs

Again I have used a Cost of Sales % of 11% and cash admin costs of £24m.

The forecast for wages is a bit of a leap into the unknown as exact individual player wages are not published, although ballpark figures can be guessed if you believe what you read in the press. In the summer of 2012 Liverpool sold some of the higher earners on the books in Kuyt, Rodriguez, Aquilani, and Bellamy, with Joe Cole leaving in the winter transfer window. It is rumoured that some of these players have received pay-offs from the club as their wages will be lower at their new clubs, but I have accounted for these in the Exceptionals line as they are a one-off cost. The players have been replaced by younger players who will be one less wages. Back in September Rory Smith of The Times tweeted that we had saved in the region of £450k a week in wages, or £23m over the season. Now this may not be completely accurate, but it sounds about right. However since then we have seen Suarez, Agger, Skrtel, Shelvey, Kelly, Sterling and Suso sign renegotiated contracts, whilst we have also seen Sturridge and Coutinho signed in January. My guess based on rumoured wages for players is that we will have saved in the region of £12-14m this year on wages, but I’ll be conservative, especially in light of the recent reports of Suarez’ bonus payments, and say we have saved £10m.

Finally we have Cash Exceptionals. Unfortunately Exceptionals at Liverpool have become the norm, but hopefully this will be the last season we see them at a significant level. I am basing this number on rumours or figures given in the press, which are the £5m to Swansea to get Rodgers and his staff, the £3m pay off for Joe Cole, and the £5m pay off for Aquilani. I’ve added another million for any other costs, such as other back room staff pay offs and the legal costs with regards to Damien Comolli’s unfair dismissal case.

This gives an EBITDA of £28.6m, a massive improvement.

Cash Flows

Changes in Working Capital

I will be conservative and assume that the changes in Working Capital are the same as the previous two years.

Net Interest Paid

We know from the from the “Post Balance Sheet Events” comments (page 5 of accounts) that FSG injected £46.8m into the club in the form of a non-interest bearing loan, and used the funds to repay some of the bank debt that was in place. This means that we will be paying less interest over the year. I do not have the pricing details of the bank facility, but assuming a nice round number of 5%, that would be a saving of £2.3m on the previous year.

Capex

I’ve simply assumed £4.5m again.

Net Payments from player registrations

In case this is not clear, it is the cash from buying and selling players. Payments for transfers are not always made in full at the time that the player is bought or sold, they can often be spread over 2 or 3 years, so for my forecast I have assumed that 60% of the transfer fee is paid up front, whether we have bought or sold. I have used the transfer fees as given by LFCHistory.net. Further to the payments made from this season’s transfers, there will also be amounts owed/due from previous season’s transfers. In note 16 of the accounts we see an amount for Trade Creditors falling due after more than one year (page 24) for 2011 of £18.8m, with the description below of “Trade creditors falling due after more than one year relate to the contractual payments due on the acquisition of players’ registrations”. For simplicity I have assumed this to be all paid in 2013 and this is a cash outflow. We will also be owed money for transfers, and Note 15 tells us that £12m was due after more than one year at 2011.

You’ll notice that net payments from player registrations is a couple of million more than Cashflow before Transfers and Financing and I've said these will more or less match going forwards, but at least it is in right range. I will assume that this difference is covered using the bank facility and have increased financing by £2m.

Deferred Income/Accruals

I am assuming that Accruals and Deferred Income will now be in line with the year end 2012 accounts, and have assumed them both to increase in line with inflation of 3%, and making the same adjustment as for 2011, which sees an outflow of £9.2m, and as there are not have sufficient Cash Flows to cover this directly from operations, I have assumed this will be financed via the bank facility and have increased financing by the same amount.

Financing

After the adjustments above, £12.2m of the bank facility will be utilised, but we will be in a similar position as the last set of accounts, in that we will soon be receiving the cash from season tickets and will be able to pay the facility down.

Overall View

The strong increase in revenues and the fall in wages sees the wage ratio fall to 57.4%, which is much more healthy and gives the profitability required for future transfers. Lower financing needs sees Net Debt fall by £3m compared to year end 2011, but the majority of this is now owed to FSG and is interest free.

Looking forwards to year end 2014, the new TV deal will be in place, and we could expect an increase in Media revenues of perhaps £30m or more depending on where we finish in the league. As the whole league will also see a significant increase, I expect this to be inflated away over the space of a few seasons despite the new Premier League rules, but it will leave us in a very strong position compared to our European counterparts. All else being equal, and with no more of those exceptionals, Liverpool could well have a budget of between £50-70m including wages to spend over the next 2 transfer windows, which I think should please all Liverpool fans.

Thanks to the hard work behind the scenes, as well as the marketability of the Premier League, Liverpool are now in a much healthier financial position, and despite not being in the Champions League, despite having a smaller stadium than some of our competitors, despite being run on a self financing business model, with the introduction FFP and the new Premier League rules, Liverpool should be able to compete for top quality players in the transfer market.