Hello, welcome to today’s blog which is going to be my second one covering the tidy Tuesday dataset. This week it was looking at a dataset with life expectancy for every country in the world since 1950. I decided you could do some cluster analysis on this dataset and then once you have the clusters can further analyse to understand trends. We are going to use K-means clustering to put the countries together then look for trends and differences between the clusters. So the dataset has country, year between 1950 and 2015 and the life expectancy of that year. Now in order to do clustering, you need at least two measures, therefore, I created one with the change in life expectancy per year. The other measure is going to be the life expectancy in 2015.
In order to find our value of K, I did the below silhouette plot. Now you’re meant to use the value of K with the highest sil width, in this case, it would be 3. However, with so many different countries I feel that would be unfair and group the countries up too much. There are further spikes at 6 and 10.
I decided to do the below plot for different k values both 6 and 10. The plot for 10 is below
10 seems like a good value as there are not too many clusters to deal with but also good variation between the different clusters. We will take k equal to 10 for further analysis.
The comparison above looks at causes of death and i have grouped it up to get the mean for each for cause for each cluster. Conclusions that can be made:
- Cancer is prevalent across all clusters, however, the higher the life expectancy the more prevalent it is. This could be because your more likely to get cancer at older ages.
- Dementia is another cause which seems to increase with older life expectancy.
- HIV is highest in the two lowest life expectancy clusters the same with neonatal deaths
- Finally, road accident is an interesting cause, by far the highest cluster is cluster 7 which seems to be the cluster with the highest increase in life expectancy over the last 65 years. Could this because these are fast developing nations and have not got the safe road infrastructure in as yet.
That’s it for a little intro into reviewing the data this way. Let me know your thoughts and comments. There are lots of dataset on the World Health Organisations website as well as other datasets such as economic growth i can add to this analysis and develop it further.
Hello and welcome to the second part of my mini-series using cluster analysis in order to categorise formula 1 circuits. please go check the first part it outlines the basic data we are using to categorise the circuits and an overview of the method used for hierarchical clustering. Today we are going to go with K-means clustering.
For K-means clustering we have to set our own value for K we are going to do that with two different types of analysis. An elbow plot and silhouette analysis.
The code below is what was used in order to generate the elbow plot. The elbow plot generated is below:
Reviewing the elbow plot it looks like already we are seeing a slightly different amount of clusters then we got when we conducted hierarchical clustering. The elbow of the plot looks to be at 3 but you can also argue there is one at 4 as well as the value for k.
The other way to decide a k value when conducting k means clustering is to produce a silhouette graph. This takes every point which is part of the analysis and rates it on how it fits in with each cluster with -1 being doesn’t fit at all and 1 being fits well. You then produce a graph for each value of k with the average silhouette width and the highest point is the value of k. I have put a picture of the code below and also the silhouette graph produced
Fascinatingly there are two high points. One for a k of 9 and another for a k of 3. I am going to choose a k of 3 as this is closely aligned to what we saw in the elbow plot and 9 clusters are just too many to deal with.
The above graph shows all the circuits in the calendar and where they are for average straight length and average speed, colour by the cluster they have been put in. I am a bit unsatisfied with this. I feel this doesn’t quite fit the different circuits on the calendar. For instance, Singapore is different to China and Germany. Therefore K-means is not going to be the clustering I use in the final blog to look at pace trends across the season. Look out for the final blog which we will look at the pace across all circuits so far for all the teams and we will look at some other metrics like overtakes and pitstops.
Hello there so as you know I’m currently working through the Datacamp course data scientist with R. (If the people from Datacamp are reading this I’m open top sponsorship!) There will be a further update how I’m getting on with this later this week, however, today I wanted to focus on applying something new that I learnt. Cluster analysis. Cluster analysis allows you to take a dataframe of two variables and calculate which are the rows best grouped together. There are two main methods that we are going to look at hierarchical clustering and kmeans clustering. We are going to look at formula 1 circuits. The idea is there are 21 different circuits currently on the calender all different lengths and height profiles and types of tarmac, however, can we group them together with certain characteristics. For me as an avid formula 1 watcher, the differences between the circuits are caused by lengths of straights and speed of corners. Therefore the two metrics we are using are the average straight length and average corner speed.
- Average straight length – calculated by measuring each stretch of track which the F1 car would be running full throttle. Removing any lengths of the track less than 100m. an example for Spain below straights is estimated in green.
- Average corner speed – I have calculated this by allocating each corner to either slow, medium or fast speed. (Unfortunately, I don’t have data for the exact corner speed but if any f1 team wants to send it over email me!) so you can see below in the table how many for each circuit was allocated
As I don’t know the exact speeds of these corners I have estimated that a slow corner is 80 km/h, medium speed corner is 150 km/h and a fast corner is 200km/h. This has left us with the following table:
The first thing we are going to look at is hierarchical clustering. The table above is fed into the following code:
this produces the following output:
We have 5 different distinct clusters that the F1 circuits fit into. It’s not too surprising that Singapore, Monaco and Hungary fit into a similar cluster as well as Belgium and Great Britain being similar circuits.
The scatter above you can clearly see the difference between the two main clusters 1 and 2. In cluster one straight length are often shorter, however, the corners are faster. Cluster 2 circuits often have longer straights but slower corners. With a few circuits from each group used so far, this season would be interesting to see if there are any trends with car speed. That’s it for the first part of this series next week we will look at any difference using K-means clustering. In the final part, we will look applying what we have seen so far this year to try and predict who will win in the later rounds.
Hello, welcome to the preview of Group F in the world cup. Thanks for all the support so far on these previews. I would love to hear peoples thoughts and predictions on the competition. Today we will be looking at group F which contains Germany, Mexico, South Korea and Sweden.
The first thing to look at is the age distribution of all 4 teams. Germany seems to have one of the younger squads in the tournament with a relatively small distribution between youngest and oldest players. Mexico has players from the youngest in their 20’s all the way up to near 40. South Korea and Sweden have the same median ages but South Korea has more players clustered around their median and have the lowest amount of players above 30.
Mexico has what looks to be the most experienced squad with players mostly having around 50 caps but they have some players up to 150 caps. Germany has a lot of players with a relatively low amount of caps but also have the trend we have seen with other squads of having a group of players with a lot of caps. I wonder if these players would be a similar age and therefore could be a golden generation. Sweden possibly has the most inexperienced squads in the group with a lot of players less than 50 caps.
On the face of it, Germany seems to have a small number of attackers in the squad. However, they have more midfielders and a few of them are creative attacking midfielders, therefore, I don’t think they will struggle for goals. South Korea also has the same amount of attackers as Germany but seem have picked more defenders. This could leave them struggling to score goals.
Last but not least we look at each teams chances using the probability of implied odds. No surprise really Germany are big favourites to get out the group. However, the fight for second place looks to be a realistic target for the other three teams. It looks particularly close between Mexico and Sweden. They play each other in the last game of the group stage, therefore, it could be a straight shoot-out for second place. Also, South Korea playing Germany who may have already qualified and therefore may make changes could give them an outside chance if it goes to the last game.
That’s it for today’s look a group F please let me know your thoughts would love to start a good debate on your thoughts. Also, check out the other blogs in the series.
Hi there welcome to next in series of little previews ahead of the FIFA World Cup. Today we are dissecting the 4 teams in group C; France, Peru, Denmark and Australia. Please do check out the other previews and further previews are upcoming at 6 pm everyday ahead of the first game.
On the face of it, these look to be some of the youngest squads in the tournament. Australia seems to have players from both ends of the spectrum and a good grouping around peak age players. France has probably the lowest median age across all squads in the competition. Peru doesn’t have too many players between 20-25, however, have a good grouping between 25-28.
Looking at the distribution of caps in each squad it looks like all four teams have relatively inexperienced players. Denmark has the most amount of players which have around 25 caps. They also have the familiar trend of having a spike higher up showing a good amount of experienced pros vital in any squad make up. Peru seems to have the most amount of players with experience in their squad which could stand them in good stead to get out the group. The big question for France is will their lack of experience affect them later in the competition.
Finally looking at squad composition France and Australia seem to have the most amount of attackers. France has done this by bringing fewer midfielders Australia by bringing fewer Defenders. Peru seems to have gone a totally different direction to the rest of the team with a squad overloaded with midfielders. Most are attacking midfielders so they should still have goalscoring options.
Now we look at each teams probability of getting out the group and winning the tournament. Finally, we have a group that on the face of it could be quite competitive for second place at least. Denmark is a clear favourite but both Peru and Australia seem to have good outside chances at least according to the bookies. France has a decent chance of winning the whole tournament and is currently 4th favourites, so it will be interesting to see how they do with their young squad.
That’s it for today’s overview let me know your thoughts how far do you think France will go and who you think will get out the group?
Today we are going to look at group B in the World Cup. This is part of my series reviewing each squad in the World Cup in order to asses strengths and weaknesses and understand squad make up. If you haven’t seen the other Blogs go check them out group A went live yesterday and the other groups will follow over the coming days. Group B consists of Spain, Portugal, Iran and Morocco.
The first thing to look at is the age composition of the 4 squads. Interestingly Iran seem to generally have the youngest squad in the group with the lowest median. Also Spain seem to have the largest grouping around peak age between 27-30. Morocco despite having the highest median have the lowest age players in the group. Portugal have some young players but also have some of the generally older players with a lot of squad members above 30.
Looking at the experience of the players Morocco looks clearly the least experienced squad. This could be because of the high amount of lower age players compared to the other teams. Spain and Portugal have similar caps profiles with a group of inexperienced players but also complimented by a few experienced players.
The main thing that’s interesting with the squad composition of the 4 teams is that Portugal and Spain have the same composition. Is this a template the the bigger countries seem to be following? Also there is an increase in attacking players in these 4 squads compared to Group A which should mean these are all better balanced. In fact Morocco, Portugal and Spain have the same amount of attackers. With Iran having more Midfielders then any other team could this give them more options however lack of attackers could harm them if chasing a game.
Finally we look at the chances of team qualifying from the group and the chance of winning the World Cup. Qualification for the group looks like a pretty much over and done deal. Portugal and Spain look to have by far the strongest chances of qualifying from the group. This could make this group not too interesting for spectators. Portugal and Spain do play each other in the first game which if there is a loser could add extra pressure when they come to play Morocco or Iran. I’m surprised the low chance compared to Spain of Portugal winning the title. Portugal are the reigning European champions and have mercurial talent Cristiano Ronaldo. Spain however look to be one of the big favourites so it will be interesting to see how they do after last World Cups total failure.
Thats it for group B overview any questions or comments let me know or if you have any ideas of other things i should look at let me know.
Hello welcome to the first of my blogs looking at each group in the world cup. Over the next 8 blogs I hope to dissect each country’s squad and finally look at their chances of progressing and winning the cup. So today we start with group A which contains hosts Russia, Uruguay, Saudi Arabia and Egypt.
The first thing to look at is the age range of each squad in group A. All 4 teams have a median around the same area. As you can see Egypt have a 45 year old player, one of their GK’s who is the oldest player in any squad in the tournament. Uruguay and Egypt tend to have some younger players than Russia and Saudi Arabia. Saudi Arabia look to have generally one of the older squads in the tournament.
Next we look at the caps all the players in the squad have received. It’s clear Russia has the most players with the least amount of international experience. Will they struggle to cope with pressure from playing in front of home crowd. Saudi Arabia despite having the older squad of the 4 teams seems to have generally the least experienced team. Uruguay however seem to have a good balance with experience at all different levels.
Looking at each teams squad composition clearly all have the same percent GK as 3 GK is stipulated in the rules. One thing that’s clear is Egypt, Russia and Saudi Arabia have a low amount of strikers within there squads. All three have just 3 recognised strikers will this leave all them struggling to score goals. Also Egypt seem to have the lowest amount of midfielders compared to the rest of the group with an increase in defenders. This will give egypt lots of options in defence in case of injuries however could leave them exposed if they need to make changes from the bench to try and win games.
Finally we use the implied probability from the betting odds to look at the chance of each team getting out the group and winning the tournament. Overall group A seems to have no teams really capable of mounting a serious challenge for the world cup. With Russia with home advantage rated lower then Uruguay. Also it doesn’t seem to be a particularly close group for qualification the clear favourites are Uruguay and Russia. The wild card in this is Egypt if Mo Salah is fit for the tournament then expect there chances compared to Russia to increase considerably. If he isn’t expect this could be a pretty straight forward group.
Thats it for today’s look at group A please let me know your thoughts do you think any teams in this group can go far? let me know your thoughts in the comments. Group B will follow tomorrow.