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Outcome Probability Calculator is back up!

April 22, 2015Ford Bohrmann

After about a month of downtime I have the outcome probability calculator back up and running. Shiny (made by RStudio) is great but they decided to start charging so I rewrote it all in Python. I used Bokeh, which is great. If you're trying to do some data visualizations online it's a great way to go. The formatting looks a bit different but the data and models are exactly the same. Check it out here.

If you want to see how I created the models, check out this post.

And if you haven’t seen the Economist blog post from a couple weeks back comparing Messi to Ronaldo using the data, read it here

A lot of people have reached out to me asking for the data or have been trying to manually gather it from the applet. If you’re interested in using the data then just reach out to me at soccerstatistically@gmail.com and I’d be happy to send all the raw data to you, provided you reference this blog when you use it.

Finally, now that the calculator is fixed I can focus on some other work I’ve been doing. I’ve admittedly been absent from posting here for a while. I have a few posts I’ve been working on recently, so expect some new stuff coming soon...

tags R, outcome probability, Bokeh, Python, data

World Cup Performance by Continent (Lots of graphs)

July 11, 2014Ford Bohrmann

Much has been made of the inter-continental games so far this World Cup, especially considering the presence of 3 of the 4 CONCACAF countries making it past the group stages, including the US getting out of the group of death and Costa Rica going much farther than anyone predicted.

To see how various (FIFA defined) continents have done compared to past World Cup results, I used past World Cup data collected from 11v11.com. I looked at the past World Cup results (here is an example from the United States’ page http://www.11v11.com/teams/usa/tab/stats/comp/978). These results include all World Cup and World Cup qualifying games, which is what I limited my analysis to. World Cup qualifying games are a little different than World Cup games, but considering these are almost always between countries that are in the same continent, I think its OK because I drop intra-continent games anyways. What defines a continent is pretty hazy, so I just stuck with FIFA’s definitions. This means that Australia is actually a part of Asia, and some other anomalies. This division of the world is the best way to stay consistent, though. The continents I ended up using were Africa, Asia, CONCACAF, Europe, Oceania and South America.

If you want to look at the code I wrote to do the analysis (the data scraping, the actual analysis, and the visualization) head over to here https://github.com/fordb/wc-continent-headtohead

There’s nothing too crazy going on in the analysis, just a lot of graphs to look at.

tags world cup, R, viz, Python, ggplot

Outcome Probability Calculator (Updated)

January 21, 2014Ford Bohrmann

I've updated the site's Outcome Probability Calculator. I updated and added more games of data, changed the methodology somewhat, and created a new online app. The first iteration of the app was featured on the Wall Street Journal's website in a blog post called Arsenal Beats Reading and Math

tags outcome probability, betting, regression, R, expected points added, ggplot

Power Laws and Goal Scoring

January 20, 2013Ford Bohrmann

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.

tags power law, goals, R, ggplot, regression, EPL

Dealing with the MCFC Analytics Advanced Data Release

September 17, 2012Ford Bohrmann

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.

tags MCFC, advanced data, R

Visualizing Twitter Data

July 19, 2012Ford Bohrmann

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.

tags viz, twitter, R, ggplot

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