Friday 30 January 2015

Africa Cup of Nations 2015: A dive into the statistics of the group stages...

*** updated 31/01/2015 to include updated PDO vs. SoTR chart

In my last post I ran through some fairly general impressions of the 2015 Africa Cup of Nations so far. Among other topics, I bemoaned the lack of goals and the high number of draws. In this post I develop some analysis of the Africa Cup of Nations by relying heavily on … wait for it … statistics.

By using data provided by whoscored.com I set out to try and understand what aspects of a team’s game have helped them progress to the quarter-finals. In short, I wanted to understand if it is more down to skill or luck.

I also wanted to see if there is any way of guessing how the quarter-finals will go based on trends or patterns in the data.

What I have found out is:

  1. It is a combination of skill and luck;
  2. 4 of the 8 quarter-finalists (50%) enjoyed over 50% of the possession in their group matches --> dominating possession is just as successful as...er, not dominating the possession. It would appear...;
  3. Only 3 of the 8 quarter-finalists (38%) took above the average number of shots per game --> shooting more than your opponent is not always a guarantee for success;
  4. Having a busy attack and quiet defence is no guarantee for progressing to the quarter-finals in this year's tournament; and,
  5. It is definitely NOT good defending keeping the number of goals down in this years tournament.

All of this begins with…

Dominance

One of the first topics I wanted to explore was the concept of dominance. My reasoning is the most dominant team should surely win the game, right?

In the first chart of many below, I compare the shots per game a team takes and the shots per game it defends against. The x-axis (horizontal) shows the number of shots the teams take per game. The y-axis (vertical) plots the shots per game teams defend against. Broadly speaking my logic went like this: “if you take more shots than your opponent, you dominate the game”. In terms of statistics, comparing shots taken to shots defended, we have a simple (but crude) measure of ‘game dominance’.

The dotted blue lines plot the ‘average’  (the same goes for all charts used) which is roughly 11 shots for and 11 shots against per game. Using the example of Senegal, they shoot above average against their opponents and they tend to face fewer than average shots on their goal.

If you think about the chart as four quadrants, the ‘most dominant’ quadrant is that one on the bottom-right. It is the area where teams shoot the most and manage to stifle shots coming against them: in other words, they are strong in attack and have a relatively quiet time defensively. The top-right quadrant is the busiest quadrant. It is where teams have taken many shots but also faced many shots. You get the picture.

Data sourced from whoscored.com and graph idea from Ben Mayhew experimental361.com


So, in looking at this chart, we see that Senegal, Zambia and Burkina Faso all belong to the bottom-right quadrant, but none of these teams progressed to the quarter-finals.

This chart only shows whether the team has attacked its opponents more or has been more on the back foot. It says little about the effectiveness of the attack…

But you have got to score to win

True. The Democratic Republic of Congo are the only team in the quarter-finals to have scored fewer than 3 goals so far. Algeria have scored the most of any team at the tournament so far. They also take the fewest number of shots to score a goal:

Team
Goals Scored
Shots Taken
Shots per goal
ALG
5
22
4.4
TUN
4
35
8.8
CON
4
33
8.3
GHA
4
38
9.5
EQG
3
27
9.0
MAL
3
28
9.3
CIV
3
30
10.0
GUI
3
22
7.3
RSA
3
33
11.0
SEN
3
44
14.7
DRC
2
37
18.5
ZAM
2
38
19.0
GAB
2
34
17.0
CAM
2
30
15.0
CPV
1
32
32.0
BFA
1
47
47.0

But it is not always so simple...

How many shots must a player take, before he scores himself a goal?

The answer is not blowing in the wind, certainly not in Equatorial Guinea. The chart below compares the shots a team takes to the number of shots it takes the team to score. This is a crude measurement of shooting skill, or the potency of the team’s attack. If a team takes more shots to score a goal, generally speaking, they have less skill than a team that takes fewer shots to score a goal.

Data sourced from whoscored.com and graph idea from Ben Mayhew experimental361.com


The measurement in this graph is of ‘attacking effectiveness’, i.e. how effective the attack of one team against the other. Generally speaking, the teams in the bottom right of this chart are in the sweet-spot. They take a lot of shots, so they play with a high intensity AND they don’t take many shots to score a goal. It seems fairly reasonable then that Ghana and Tunisia – two of the stronger teams in this tournament – are in the sweet-spot. But, what about the other two favourites: Algeria and Ivory Coast? Well, both Ivory Coast and Algeria don’t take as many shots as Tunisia and Ghana but the four teams are similar in the aspect of not needing to take many shots to score a goal. Interestingly, seven of the eight quarter-finalists take below average number of shots to score a goal. In other words, generally speaking the teams with highest attacking skill have progressed. The Democratic Republic of Congo is the exception: it has progressed to the quarter-finals taking an above average number of shots to score a goal.

What is a good chance?

Alright, so shooting accuracy is one thing, but it is also important for attackers to be supplied with plenty of chances by their teammates. So, I was sitting there thinking (wahey): "how could I come up with a stat simple enough to understand that shows a ‘good chance’ objectively?"

Most of us would agree that if a team advances play to a position where they can shoot from inside the opponent’s box, they have created a good chance to score. So, I developed a measure of what a good chance might be: 

I have taken the number of shots a team makes inside the box as a proportion of the total shots they take (per game). So, if a team takes 8 shots inside the box per game and takes 10 shots per game, its “good chance” proportion is 80%. Make sense?

In the chart below, the ‘sweet-spot’ is in the top right-hand corner. Here, teams have a lot of possession and more than half of their shots are inside the opponent’s box. 

Senegal, Burkina Faso and Gabon all populate this quadrant, however, none progressed to the quarter-finals. If we look to the bottom-right quadrant, we see three quarter-finalists - Equatorial Guinea, Ghana and Congo - populating the area. These teams create good chances (>50% of the shots they take are inside of the opponent's box) but have less than 50% of the possession.

Data sourced from whoscored.com graph idea my Charles Low (my own)
So, then, it is not all about creating "good" chances...

The Importance of Possession

My belief that possession is key to winning games (as well as scoring, obviously!) probably reveals my bias approaching the data, so I should make it clear. My belief is based on seeing the way the tiki-taka style popularised by Barcelona, (i.e. possession is king) has come to dominate the game in recent years. When I think of “teams of my generation” very few could compare to Barcelona under Pep Guardiola or the Spanish World Cup winners of 2010.

I wanted to dwell on this thought for a little longer. Think about it like this – how does a team convert its possession? Does it create a high number of chances but has a weakness of conceding a lot of chances on the counter-attack? Think Arsenal…

So, to try and translate this into figures I developed a "shot dominance ratio". I called it this way as I constructed a ratio of shots taken by a team to the total number of shots in a game. So: 

shot dominance = shots for per game / (shots for per game + shots against per game). 

If shot dominance > 0.5 the team has taken more shots than its opponent.

Possession is the average possession figures over three games. Again if possession is greater than 50% the team had more possession than their opposition. So, the teams in the top-right of the chart (i.e. with over 50% possession and 50% shot dominance) dominated the game in terms of possession and shooting – broadly a measure for chances to score. Only five teams – Democratic Republic of Congo, Cameroon, Ivory Coast, Burkina Faso and Senegal – had more possession and shot more than their opponents. Three of those teams are going home.

Data sourced from whoscored.com and graph idea Charles Low (my own)

What about the defence?

Less is made of the defence, but it is interesting to look at nonetheless. What this chart below reveals is really interesting. Remember from my last post I said there have been a pretty low number of goals in these games? Well, that is NOT explained by good defending. So, the explanation is either excellent goal-keeping, or poor skill in front of goal – because it is certainly not due to lack of chances being created.

Again, if we think about the chart as four quadrants, the top-left quadrant is where good defensive teams are situated. It is the quadrant where teams don’t allow many shots to be taken on their goal and it is also where many shots must be taken before a goal is scored against them. If we consider Equatorial Guinea way up there in the right-hand quadrant we could view it that their defence has done well – it takes their opponents many shots to score a goal. However, their defence (and goalkeeper) are probably well over-worked as the team faces a lot of shots, on average, in games.

Data sourced from whoscored.com and graph idea from Ben Mayhew experimental361.com



Something Else?

So, I sat there thinking (wahey): “how can it be that the dominance of possession and shots cannot explain the team’s performances?” How is it that teams who have more of the ball and shoot more than their opponents do not qualify for the quarter-finals? It could be down to luck. But, it could also be down to skill.

In football analytics there is a way of providing a rule-of-thumb to quantifying skill and luck. Skill is described as the Shots On Target Ratio (SOTR) [shots on target for/total shots on target]. Luck is described as shooting % + saving %. It is taken as a given that it requires great skill to shoot a high percentage on target. In the luck category, it is considered a great fortune to save a high percentage of shots against you. [NB: it makes sense right – you ever been watching a game and you thank god the opposition striker couldn’t hit a barn door?!] I owe a great deal of praise to Seth Dobson’s fantastic blog 2-2-6 for explaining these concepts.

Basically, the higher the PDO, the greater the fortune a team has. And, as luck is subject to laws of gravity this spells bad news for teams with PDO of above 1000. In the graph the teams are grouped by colour according to where they finished in the groups. The quarter-finalists are the gold and silver marks. What we can see broadly is that 6 of the quarter-finalists have benefitted from luck - as PDO would have it - so far. How long will it last?

Conclusions

So, what have I learned? I have learned that making charts is a great way to waste time and provides some insight into the beautiful game. However, I still have a gut feeling Ghana are going to win this thing…



2 comments:

  1. I like the structure of this - it's much easier to read when broken into a "story" with sections. To save repeating yourself the next time you follow a similar approach (and ensuring that less patient readers make it all the way to the end), I'd recommend creating a glossary / explanations page so that you can refer new readers there rather than re-explaining your rationale.

    There's a caveat needed with the vertical axes of the charts you've adapted from my blog (shots taken / faced per goal scored / conceded) over a small number of games, which is that they're very "luck"-driven (you're combining them - more or less - to calculate PDO). The charts overall work best in a league competition where everyone eventually plays everyone else (reducing the effects of varying opposition strength) and over a large number of matches (reducing the effects of short term luck, player absence etc) - it may well be the luck factor of small tournaments that are responsible for some of your findings.

    A small graphical suggestion - I like the way that @stats_snakeoil colour codes the clubs on his scatters (e.g. https://twitter.com/stats_snakeoil/status/561264210832011264/photo/1) and maybe colouring the teams here by which stage they've reached or how many games they've played (same thing I guess) would make it easier to spot patterns between stats and success.

    Lastly on PDO, is there a reason why yours doesn't centre on 1000 in your final graph?

    Overall though good work and I hope you keep this going.

    Ben (Experimental 361)

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  2. Ben, thanks very much for your feedback. I have already learned a lot from reading your comments.

    I totally agree with you on the glossary idea, I think this sounds like a good approach. I am maybe limited in what I can do under the blogspot options but something like an explanations page is good idea.

    Really agree with your point on the longer term nature of leagues and its effect on the PDO. The relevance of the PDO may be slim in this year's African Cup of Nations, I wonder if it will play out in the figures?

    Thanks for recommending to speak to @stats_snakeoil - he explained he does a lot of his graphs with the Tableau Software. I have yet to get to grips with that but it certainly seems to allow nice customisation.

    I have updated the PDO figures in my new post (http://sattherethinkin.blogspot.be/2015/01/luck-or-skill-updated-chart.html) but I'll Tweet a version later.

    Definitely going to keep this going. I appreciate it.

    Charlie

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