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: 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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Can Tottenham continue to be Bale'd out in the chase for Champions League?

Apologies for the corny title, all the hyperbole about Gareth Bale over the last few weeks in press is obviously having an effect on me. Along with the over excitement, there has also been numerous Bale related stats that have been mentioned in the press and on twitter at various points, including the following;

• “Gareth Bale has scored 10 goals in his last 12 Premier League appearances”
• “Gareth Bale is 1st for PL shots on target this season (51)”
• “No player has scored more Premier League goals in 2013 than Gareth Bale”
• “Gareth Bale has scored each of Tottenham's last five PL goals”
• “Gareth Bale: Has now scored Tottenham's last 6 goals in all competitions”, and
• “Gareth Bale has been directly involved in 80% of Spurs’ goals in all competitions so far in 2013 (9 goals & 3 assists)”
    

So despite the hyperbole, it’s fair to say Bale is currently in the best form of his career. Looking at his output in the League, he has scored 15 goals in 11 games so far, of those 11 games, 6 were won by a single goal, 2 of which he scored 2 goals in, and one other was a draw, so those goals have been very important. Whilst it would be too simplistic to say that he has won them 15 points, it’s clear that he is playing a significant role, especially when you consider that he has had 22% of their shots, 29% of their shots on target, and 34% of their goals.

A look at Tottenham’s conversion rates also shows Bale’s outperformance compared to the rest of the squad. We can also see that the rest of the team are not only performing worse than the average of the top 4 teams last season, but also worse than the average of all the teams last season (I have used last season as it’s a complete season with all teams having played each other, and with Tottenham currently in the top 4, comparing to this season’s top 4 would be a bit circular with them in it).

Conversion rates exclude own goals. Shooting Accuracy excludes blocked shots. Chance = A shot on or off target, excluding blocked shots. CCC = Clear Cut Chance

However, there is nothing wrong with relying on one player if that player is an outstanding attacking talent, as it could be foolhardy not to. Suarez has taken up that role at Liverpool for most of this season, van Persie also did at Arsenal last season, and despite the quality of players throughout their squads, it can be argued that both Barcelona and Real Madrid rely on Messi and Ronaldo from a goal scoring perspective.

What should be of greater concern for Tottenham is the quality of the chances they have been creating and the narrow margins in which they have recently been winning. Positively, their shooting accuracy is the 2nd highest in the league this season, behind only Manchester United, and due to also having the 2nd highest shots per game, they have actually fired more shots on target this season than any other team. But digging deeper, we also see that they are the team with the highest proportion of shots taken from outside of the area this season at 55%. Tottenham like to break quickly using their pace and are one of the best counter attacking teams in the league, and the number of shots taken from outside the area could be as a consequence of teams sitting back, denying them the space to break into, and they are then lacking the creativity to get in behind the lines. The fact that when playing at home they are taking 60% of shots from outside at the box, and they have scored 7 more goals away from home than they have at home backs up this point of view. Other indications that they are creating poor quality shooting opportunities are that they rank 2nd lowest in terms of Clean Cut Chances as a proportion of total chances. Consequently they only rank 7th in terms of the number of clean cut chances per game.

However despite the number of more difficult chances they attempt, they are relatively good at them. Prior to the last round of games, they had the highest shooting accuracy in the league for shots from outside the area, although it probably helps that Bale is actually as accurate from outside the area as he is from inside, as shown in this blog from Squawka. In terms of conversion of those attempts from outside the area, they have scored the most in the league with 11, although this is a product of taking significantly more shots than anyone else. When you look at their conversion rate of shots from outside of the area it is only 4%, however this is still relatively good, when you take free kicks out of the equation, they score 1 goal for every 27.4 shots from outside the area, which compares favourably to the rate of 1 to 33 for all shots from outside the area since the beginning of last season as calculated by the Differentgame’s SPAM analysis. Tottenham are also slightly above average this season in terms of the conversion of chances that are not clear cut, with a rate of 8.1%, which ranks 6th in the league.

On the face of it, Tottenham are carrying some good form, having been unbeaten in 11 league games. However that run included 7 of the bottom 9 teams, and the last 6 games in the League has seen them outscored by 11 other teams and a positive goal difference of only 3. They have also left getting their results until late in the game recently. In their last 8 games in all competitions, they have scored 4 goals to win or equalise until the 90th minute or later, with a further 2 goals getting results coming after the 75th minute. Whilst this may be a sign of increasing mental fortitude and give them the confidence that they can always get a result, there is a risk that if they continue to leave themselves chasing the game into the closing stages, that the bounce of the ball does not go their way.

Obviously football is about defence as well as attack, and Tottenham’s defence is clearly playing very well having conceded the fewest goals in the league in those last 11 league games and conceded fewer shots than any other team so far this season. But they are about to start their most difficult run of games this season, facing 6 of the top 9 in their next 7 games, and whilst the teams they are playing should have a more expansive style of football, possibly giving Tottenham more space to play their natural game, they may need Bale to continue his rich vein of form if they are to get the wins needed to remain in the top 4.




Data taken from www.eplindex.com, www.whoscored.com, & www.squawka.com