Outcome Probability Calculator is back up!

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...

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.

New Site!

I've redesigned the Soccer Statistically site! Instead of using Blogger in the domain name, the site is now www.soccerstatistically.com, which is nice. I've also redesigned the entire website with a new banner design. Here are some of features on the site:

Blog: The blog is exactly the same, and is the also home page of the site. Nothing new here.

Statistical Applets: There is now a menu option called Statistical Applets. Under this are two options, Expected Points Added and Outcome Probability Calculator. The first is a table of the EPL leaders in Expected Points Added, a metric I created a while ago that takes in to account the true value of each goal when ranking goal scorers. For more information, you can read here. The Outcome Probability Calculator lets you enter information about a team in a game, and then gives you the probability of each type of outcome. For example, you could enter the 34th minute, at home, leading by 1, and see the probability of the team winning, drawing, and losing.

About Me: Just an about me page, with a contact us form link.

If you have any comments or suggestions on the design for the new site, I'd love to hear him. I'm working on adding some more statistical applets to that section for the future, which I'm excited about. Hope you like it!

Problems with an Adjusted Plus Minus Metric in Football

Michael Essien

What would be the perfect, all-encompassing football statistic? Something that takes in to account both offensive and defensive skill. Something that measures what value a player adds to his club. All in all, a statistic that quantifies the individual impact a player has on improving (or worsening) his club's ability to score goals and limit (or not) goals against.

Some people have made attempts at this in the past. One example are OptaJoe's tweets (@OptaJoe) about club's winning percentages with and without a player. Here is one example: "10 - Since January 2005, Everton have averaged 61 points per season with Arteta playing, compared to 51 points without him. Lynchpin." These statements are simple, easy to understand, and at first glance seem to be informative. On his blog 5 Added Minutes, Omar Chaudhuri has correctly pointed out that these statements tend to be entirely misleading. As Omar shows, the problem is that these statements are not controlling for the strength of the opponent, the venue of the game, or really anything else, in these games.

My idea was to create a metric that would control for all of these factors to truly understand every player's worth to their club. Being a big ice hockey fan (specifically the Boston Bruins, if you are wondering) I thought that the plus minus statistic might be able to be applied to football. For those of you not familiar with this statistic, plus minus basically measures a club's net goals when that player is on the ice/field. When the team scores a goal when the player is playing, the player's plus minus increases by one. Conversely, when their team concedes a goal when they are playing, their plus minus decreases by one. The idea is that, over the season, the best players will have the highest plus minus.

I faced the same problem as before though, as this does not control for the strength of the opponent, the strength of the team the player is playing with, and where the game is being played. For example, a poor player on a top club would naturally have a higher plus minus than a good player on a poor club.

To fix this, I applied an analysis used in basketball to create an adjusted plus minus statistic. This was created by Dan Rosenbaum, and if you are interested the explanation can be found here

Without going in to too many technical details, the adjusted plus minus metric is created using a massive regression. The right hand side variables are variables for every player, while the left hand side are goals for. Each observation is a unit of time during a game where no substitutions are made. Each player variable is a 1 if the player is playing at home during that unit of time, a -1 if they are playing away, and a 0 if they are not playing. The significance behind this methodology is that it controls for each player's team, venue, and opponents. If you want to know more about the methodology, read the link above. The data is from the 2010/2011 season and is provided by Infostrada Sports  (@InfostradaLive on Twitter).

The main problem with this as some, including Albert Larcada (@adlarcada_ESPN), pointed out on Twitter, is that there is multicollinearity in the regression. This arises because, unlike in basketball, there are not many scoring events. What happens is that many players are highly correlated in the model. This throws off the adjusted plus minus values for each player, so we should not take anything from the results.

With that in mind, here are the results that I came up with. Again, these results are likely not correct, but I thought people might be curious to see them anyways:

Because I (a) spent a lot of time on this and (b) think it is important post work even if it doesn't necessarily work out, I went ahead and wrote this post. Keep in mind that the results above don't really mean much. The values are also not statistically different from 0. In other words, the standard errors on all the values are large enough so that we cannot say that they are statistically different from 0. This is another reason why the results are not very reliable. However, I think that the adjusted plus minus statistic could be the first step to creating metrics that truly capture the actual value of a player. Most statistics used (assists, goals, etc.) can be thrown off because they are highly dependent on the team the player plays for.

One way to fix the problem of mulitcollinearity is to use a different statistic that occurs more often, and is highly correlated with goals. I think the best option for this would be shots on goal. This way, you could create a statistic that controls for the player's team, opponents, and venue, and measure how many net shots on goal occur when they are on the field. Just a thought on a possibility of something to look at in the future.

The data used for this is provided by

Infostrada Sports

 (@InfostradaLive on Twitter). Special thanks to Simon Gleave (@SimonGleave on Twitter) for helping me with the data.

How to Succeed in the EPL: Chances Created and Chance Conversion

A common statistic that many people have begun to value and notice a lot recently is the chances created statistic. Chances created, according to Opta's website, is defined as "assists plus Key passes" where a Key Pass is "the final pass or pass-cum-shot leading to the recipient of the ball having an attempt at goal without scoring" (Opta is a company that tracks and generates a ton of data in soccer). So basically, any pass that leads to a shot is considered a chance created.

Swansea's Mark Gower is a perfect example of a player highlighted by the chances created statistic.

Chances Created

The appeal of this measure is that it can value players that play on weaker teams better than assists do. For a player on a weaker team, it is harder to record assists since they are playing with teammates that are less likely to score. Chances created is a fairer statistic because it does not value the strength of your teammates as much. Overall, it can highlight creative players that are often overlooked because they are on weaker teams and do not have as many assists.

Do Chances Created Actually Matter?

With all this in mind, I was curious to find the actual worth of the chances created statistic. One way to measure this is to look at how chances created and wins are correlated. To make it a little easier, I looked at the relationship between goals scored and chances created for EPL teams. In other words, do teams that have more chances created score more? Do teams with less chances created score less? The answer, in short, is yes, they are correlated. Below is a scatterplot of the relationship. There is a clear positive relationship between chances created and goals in the EPL last season. The coefficient is statistically different than 0 (p=.000), which tells us that there is extremely strong evidence that there is a positive relationship.

Chance Conversion Percentages

This is only half the story though. Some teams get a lot of shots off, but either because they are not good at shooting or are taking shots that have a smaller chance of going in, some of these teams have a low number of goals because they have a poor conversion percentage for shots. The conversion percentage is defined as the goals divided by the total number of shots (excluding blocked shots). Below is a scatterplot similar to the one above, this time with conversion percentages on the x-axis. The conversion rates are rounded to 2 decimal places, hence the bunching. Again, this shows a positive relationship between conversion percentage and goals. Teams with higher conversion rates tend to score more and vice versa. This relationship is also statistically different from 0 (p=.002). A quick note: the product of chances created and conversion rate is very close to the number of goals a club has scored. I'm pretty sure the discrepancy comes from including blocked shots in shots attempted, but not in conversion rates.

EPL 2010-2011, Chances Created and Conversion %

With this in mind, I created a scatterplot of conversion rates and chances created for EPL teams last season. The plot shows that clubs found scoring success in different ways. The Manchester clubs did it by being efficient scorers; they had conversion percentages of .15 and .16. Chelsea and Tottenham were on the other end of the spectrum with higher chances created, but lower conversion percentages (.12 for both). The graphic also shows that West Ham did not struggle because they were not creating chances; they struggled because they had a low conversion percentage (.10). On the other hand, Birmingham struggled because they failed to create enough chances to score, despite a decent conversion percentage of .12.

EPL 2011-2012 thus far, Chances Created and Conversion %

What about this year? Below, I created the same scatterplot as above, this time for the current season. City's dominance is really highlighted. They are leading in both chances created AND conversion percentage, hence the massive number of goals this year. Again, United seems to be scoring because of their high conversion percentage. QPR and United actually have very similar number of chances created, United just finishes their chances with a much higher percentage. Liverpool sticks out because of their high number of chances created, but really low conversion percentage (.09).

Conclusion

The bottom line is that creating chances and conversion rates are the key to understanding goal scoring. A club can succeed with a high conversion rate (United) or by creating a lot of chances (Liverpool). A club can really dominate by doing both well (City). The graphic above can also suggest what kind of players each club needs. For example, Manchester United and Newcastle would benefit by picking up a creative midfielder who creates more chances, and Liverpool and QPR would benefit by picking up a more efficient scorer. The scatterplot also tells us why some clubs struggle. Wigan needs to up their conversion percentage (currently a dismal .06) and Stoke needs to create more chances. City, on the other hand, should just continue to buy all the best players.

All data comes from eplindex.com (@EPLIndex)