Power Laws and Goal Scoring

Is there a normal number of goals scored in a season for a striker? To answer this, one may be tempted to just take the mean of the goals scored of every player in a season. If we do this for last season, the mean is 1.83. Of course, this is misleading. There isn't really such thing as a "normal" number of goals scored in a season. The reason for this is that goals scored does not have a standard distribution, the bell curve we are used to. For example, if you looked at the distribution of heights in a population, you would see a nice bell curve. Most people are right around the average height, and as you go towards the extremes either way (really short or really tall) you find fewer and fewer people. Therefore, the mean of heights in the population is instructive because it gives us the "normal" or "typical" height. The problem is, goals scored in a season does not follow a standard distribution. Instead, most players score no goals at all. The next most common number of goals scored last season? Just one goal, of course. This distribution continues, and it follows a power law distribution.

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Momentum in Bolton vs. Manchester City

Now that some of the advanced data set has been released by Manchester City's performance analysis department it's a good time to start delving in to the data to see what kind of analysis can be done. Although the advanced data set is only for one game-- Bolton vs. Manchester City from last season-- there is still A LOT of data to look at.

The advanced data contains (x,y) location information of every statistic that is kept. This is valuable information, as it obviously tells exactly where each event happened in the game. I was interested in how this information can be used, specifically to look at momentum and passing trends.

Previous Work

Some work has already been done in the soccer analytics community on trying to quantify and analyze momentum. The Analyse Football looked at momentum shifts from this same game, although in a different way. The Soccer by the Numbers blog looks at momentum in football in a much more general way.

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Dealing with the MCFC Analytics Advanced Data Release

I wanted to point out an excellent blog post from the blog Professor Pepper's Assistant.

If you're an R user and are having trouble dealing with the Advanced MCFC Analytics XML data file, the link above provides the code to pull the data in to a data frame in R. After this it is easy to perform whatever analysis you want on it.

I'll admit the code above is beyond my limited R skill level, but I know that it works. I'm excited to start doing some analysis, although the advanced data set is only for one game from last season at this point.

A Simplified Football Prediction Model

I recently wrote a blog post for the Betting Expert site about a simple model I created attempting to predict the outcome of football matches using only very simple statistics.

You can read the full blog post here.

I wanted to point out on here something interesting that I found while working on the model; betting odds do a relatively poor job of predicting football match outcomes. In other words, the percentage likelihood of a win, draw and loss for the home team implied from the odds set by bookmakers is surprisingly inaccurate.

My hypothesis for why this happens is that football is very unbalanced, especially in the EPL. It is very hard to predict when an upset is going to happen, mostly because these upsets are (seemingly) random.

Using just 4 factors in my model, including the home team's goal differential for the season up to that game, the away team's goal differential for the season up to that game, the home team's point total from the previous season, and the away team's point total from the previous season, I could create a model that was as accurate as the bookmakers.

The question that remains is how much more accurate can the model become with the introduction of new variables? Beyond that, what variables should be used?

I am not sure I know the answers to those questions, but I am going to keep playing around with the data.

Visualizing Twitter Data

Inspired from this post on plotting the frequency of Twitter hashtags over time, I was interested in trying to apply this to soccer some way. While not the most technical analysis, I thought it would be interesting to use this tool to analyze transfer rumors.

To summarize the process quickly, there is a package in R (open source statistical software) called TwitteR which allows you to pull Twitter data. It's actually a fairly easy process, especially if you follow the tutorial in the link at the beginning of this post.

As most Twitter users know there is a seemingly unlimited number of transfer rumors circulating Twitter. These range from being fairly plausible to pretty ridiculous ("Ronaldo to the Philadelphia Union???).  As a Manchester City supporter, I was curious at looking at a few popular transfer rumors related to City.

Robin van Persie to Manchester City:

Yes, this is definitely a rumor, and yes, it is probably not going to happen. But I was still curious. Below is a plot of the frequency of the number of tweets that include "Robin van Persie" and "Manchester City". Of course, this is an imperfect method, but it still gives us an idea of what is going on in the Twitter transfer rumor world.

To explain, the graph below measures the number of tweets described above at a 2 hour interval for the past week. This means the height of every line gives us the number of tweets referencing RVP and City in that 2 hour interval.

Carlos Tevez to AC Milan:

After Tevez's past season with the club, there are obviously transfer rumors concerning Tevez all over the place. Because of this, it was hard not to want to look at the data on Tevez. I picked AC Milan because it seemed like the club he had the highest likelihood of going to. Like above, I searched for tweets that included "Carlos Tevez" and "AC Milan". The frequency of these tweets, in 2 hour intervals, is plotted below.

You can try to analyze these graphs to find some meaning, but they are more just a fun exercise than anything else. The TwitteR package lets you do other cool things, like plot the frequency of Twitter mentions for a user. I did this for another site I write for, EPL Index. They tend to get a lot more mentions than @SoccerStatistic does, so I thought it would be more interesting to plot the frequency of @EPLIndex mentions. Again, the intervals are every 2 hours.

Like I said before, this analysis is not very insightful or ground-breaking, but still pretty cool nonetheless. The possibilities for future analysis like this are almost endless, so if people have good ideas of Twitter data to visualize, I'd love to hear them.