Alteryx, football, Tableau

The relationship between away team performance and distance travelled in the English football league

If you follow football, you often hear about arduous away trips to the other side of the country. This seems to imply that the further an away trip is, the more difficult it is for the away team.

However, is that actually true? Do away teams really do worse when they’ve travelled a long way to get there, or is there no difference?

The football league season has just finished, so I’ve taken each match result from the Championship, League One, and League Two in the 2016-17 season. After some searching, I got the coordinates of each football league team’s stadium, and used the spatial tools in Alteryx to calculate the distance between each stadium. I then joined that to a dataset of the match results, and you can download and play with that dataset here. I stuck that into Tableau, and you can explore the interactive version here.

First, let’s have a look at how many points away teams win on average when travelling different distances. I’ve broken the distance travelled into bins of 25 miles as the crow flies from the away team’s stadium to the home team’s stadium, then found the average number of points an away team wins when travelling distances in that bin (I excluded the games where the away team travelled over 300 miles as there were only two match ups in that bin – Plymouth vs Hartlepool and Plymouth vs Carlisle).

It turns out that it actually seems easier for away teams when they travel further away:

Teams travelling under 25 miles win just under a point on average, while teams travelling over 200 miles win between 1.3 and 1.6 points on average.

This is surprising, but there could be several reasons contributing to this:

  1. Local rivalries. It’s possible that away teams do worse in derby matches than in other matches; this is something to investigate further.
  2. Team bonding. It’s possible that travelling a longer distance together is a shared experience that can help with team bonding.
  3. Southern economic dominance. England is relatively centralised, economically speaking; most of the wealth is in the south. Teams in the South travel further than average to away games, so perhaps the distance advantage actually shows a southern economic advantage; teams in richer areas can buy better players.
  4. Centralisation vs. sparser regions. England is relatively centralised, geographically speaking; most of the population lives in the bits in the middle, and teams in the Midlands travel the least distance on average. Perhaps teams in more centralised areas (e.g. Walsall, Coventry) have more competition for resources like new talent and crowd attendance, while teams in less centralised areas (e.g Exeter, Newcastle) might have less competition for those resources.

I also used Tableau’s clustering algorithm to separate out teams and their away performances based on distance travelled, and it resulted in four basic away performance phenotypes (which you can explore properly and search for your own team here):

Since I had the stadium details, I had a look at whether the stadium capacity made a difference. This isn’t a sophisticated analysis – better teams tend to be more financially successful and therefore invest in bigger stadiums, so it’s probably just a proxy for how good the home team is overall, rather than capturing how a large home crowd could intimidate an away team.

Finally, this heat map combines the two previous graphs and shows that away teams tend to do better when they travel further to a smaller ground. This potentially shows the centralisation issue discussed earlier; the lack of data in the bottom right corner of the graph shows that there are very few big stadiums in parts of the country like the far North West, North East, and South West, where away teams have to travel a long way to get to.

So, it looks like the further an away team travels, the better they tend to do… although that could reflect more complicated economic and geographic factors.

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R, Uncategorized

Visualising football league tables

I was looking at the Premiership league table today, and it looks like this:

current league table

It’s pretty informative; we can see that Leicester are top, Aston Villa are bottom, and that the rest of the teams are somewhere in between. If we look at the points column on the far right, we can also see how close things are; Villa are stranded at the bottom and definitely going down, Leicester are five points clear, and there’s a close battle for the final Champions League spot between Manchester City, West Ham, and Manchester United, who are only separated by a single point.

Thing is, that requires reading the points column closely. If you take the league table as a simple visual guide, it doesn’t show the distribution of teams throughout the league very well. If you say that Stoke are 8th, that sounds like a solid mid-table season… but what it doesn’t tell you is that Stoke are as close to 4th place and the Champions League as they are to 10th place, which is also solid mid-table. A more visually honest league table would look something a little like this*:

current league table dragged about a bit

*definitely not to scale.

Screen-shotting a webpage and dragging things about in MS Paint isn’t the best way to go about this, so I’ve scraped the data and had a look at plotting it in R instead.

Firstly, let’s plot each team as a coloured dot, equally spaced apart in the way that the league table shows them:

League position right now

(colour-coding here is automatic; I tried giving each point the team home shirt colours, but just ended up with loads of red, blue, and white dots, which was actually a lot worse)

Now, let’s compare that with the distribution of points to show how the league positions are distributed. Here, I’ve jittered them slightly so that teams with equal points (West Ham and Manchester United in 5th and 6th, Everton and Bournemouth in 12th and 13th) don’t overlap:

League points right now

This is far more informative. It shows just how doomed Aston Villa are, and shows that there’s barely any difference between 10th and 15th. It also shows that the fight for survival is between Norwich, Sunderland, and Newcastle, who are all placed closely together.

Since the information is out there, it’d also be interesting to see how this applies to league position over time. Sadly, Premiership matches aren’t all played at 3pm on Saturday anymore, they’re staggered over several days. This means that the league table will change every couple of days, which is far too much to plot over most of a season. So, I wrote a webscraper to get the league tables every Monday between the start of the season and now, which roughly corresponds to a full round of matches.

Let’s start with looking at league position:

League position over time

This looks more like a nightmare tube map than an informative league table, but there are a few things we can pick out. Obviously, there’s how useless Aston Villa have been, rooted to the bottom since the end of October. We can also see the steady rise of Tottenham, in a dashing shade of lavender, working their way up from 8th in the middle of October to 2nd now. Chelsea’s recovery from flirting with relegation in December to being secure in mid-table now is fairly clear, while we can also see how Crystal Palace have done the reverse, plummeting from 5th at the end of the year to 16th now.

An alternative way of visualising how well teams do over time is by plotting their total number of points over time:

League points over time

This is visually more satisfying than looking at league position over time, as we can see how the clusters of teams in similar positions have formed. Aston Villa have been bottom since October, but they were at least relatively close to Sunderland even at the end of December. Since then, though, the gap between bottom and 19th as opened up to nine points. We can also see how Leicester and Arsenal were neck and neck in first and second for most of the season, but the moment when Leicester really roared ahead was in mid-February. Finally, the relegation fight again looks like it’s a competition between Norwich, Sunderland, and Newcastle for 17th; despite Crystal Palace’s slump, the difference between 16th and 17th is one of the biggest differences between consecutive positions in the league. This is because Norwich, Sunderland, and Newcastle haven’t won many points recently, whereas Swansea and Bournemouth, who were 16th and 15th and also close to the relegation zone back in February, have both had winning streaks in the last month.

One of the drawbacks with plotting points over time is that, for most of the early part of the season, teams are so close together that you can’t really see the clusters and trends.

So, we can also calculate a ratio of how many points a team has compared to the top and bottom team at any given week. To do this, I calculated the points difference between top and bottom teams each week, and then calculated every team’s points as a proportion of where they are.

For example, right now, Leicester have 66 points and Aston Villa have 16. That’s a nice round difference of 50 points across the whole league. Let’s express that points difference on a scale of 0 to 1, where Aston Villa are at one extreme end at 0 and Leicester are at the other extreme end at 1.

Tottenham, in 2nd, have 61 points, or five points fewer than Leicester and 45 points more than Aston Villa. This means that, proportionally, they’re 90% along the points difference spectrum. This means they get a relative position of 0.9, as shown below:

Relative league position over time

This is a lot more complicated, and perhaps needlessly so. It reminds me more of stock market data than a football league table. I plotted it this way to be able to show how close or far teams were from each other in the early parts of the season, but even then, the lines are messy and all over the place until about the start of October, when the main trends start to show. One thing that means is that however badly your team are doing in terms of points and position, there’s little use in sacking a manager before about November; there’s not enough data, and teams are too close together, to show whether it’s a minor blip or a terminal decline. Of course, if your team are doing badly in terms of points and position and playing like they’ve never seen a football before, then there’s a definite problem.

To make it really fancy/silly (delete as appropriate), I’ve plotted form guides of relative league position over time. Instead of joining each individual dot each week as above, it smooths over data points to create an average trajectory. At this point, labelling the relative position is meaningless as it isn’t designed to be read off precisely, but instead provides an overall guide to how well teams are doing:

Relative league position over time smooth narrative (span 0.5)

Here, the narratives of each team’s season are more obvious. Aston Villa started out okay, but sank like a stone after a couple of months. Sunderland were fairly awful for a fairly long time, but the upswing started with Sam Allardyce’s appointment in October and they’ve done well to haul themselves up and into contention for 17th. Arsenal had a poor start to the season, then shot up, rapidly to first near the end of the year, but then they did an Arsenal and got progressively worse from about January onwards. Still, their nosedive isn’t as bad as Manchester City’s; after being top for the first couple of months, they’ve drifted further and further down. It’s more pronounced since Pep Guardiola was announced as their next manager in February, but they were quietly in decline for a while before that anyway. Finally, looking at Chelsea’s narrative line is interesting. While they’ve improved since Guus Hiddink took over, their league position improvement is far more to do with other teams declining over the last couple of months. Four teams (Crystal Palace, Everton, Watford, and West Brom) have crossed Chelsea’s narrative line since February.

I don’t expect these graphs to catch on instead of league tables, but I definitely find them useful for visualising how well teams are doing in comparison to each other, rather than just looking at their position.

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