penaltyblog

Introduction Expected goals are one of the hot topics in the football analytics community at the moment and it’s a topic I’ve previously written a number of articles discussing how to calculate them.

Introduction I’ve not posted an update on the Pythagorean for the English Premier League (EPL) for a while so the latest figures are below.

Introduction When I introduced my Expected Goals model a few weeks back a number of people commented on the bump in the curve where I had included penalty shots in the data set used to fit the model.

Introduction Since my last article about how to calculate expected goals one question has come up more than any other and that is about the correlation between expected goals and actual goals so here you go: Figure 1: Shots Versus Distance From Goal Figure

Introduction It seems that everybody has their own expected goals models for football nowadays but they all seem to be top secret and all appear to give different results so I thought I post a quick example of one technique here to try and stimulate a bit…

Introduction As there were a couple of presentations at the recent Opta Pro Forum talking about identifying player similarities I thought I’d give a quick example of how to do something similar using k-means cluster analysis.

Introduction Welome back!

Introduction With the UEFA Champions League group stage now underway I took a quick look at what it typically takes for teams to reach the final.

Introduction The amount of football data available is growing rapidly – with every passing week of the season more matches are played and even more data gets collected.

Introduction You may have noticed that my blogging has slowed down over the past few weeks and the reason is that I have joined Onside Analysis as a computational statistician.

Introduction I demonstrated in my last post that the odds calculated using the Eastwood Index were slightly more accurate than the bookmakers over the course of the football season.

Introduction It’s the end of the season so it’s time to review how the Eastwood Index performed over the year and how it compared with the bookmakers.

We have finally reached the end of the season so for the last time in 2012-2013 here are the Eastwood Index’s (EI) probabilities for the English Premier League.

Introduction The latest Major League Soccer (MLS) salaries were released recently by the MLS Players’ Union so I thought I would post a quick summary of the data.

Here are the latest match probabilities for the English Premier League calculated using the Eastwood Index (EI).

Introduction I showed in my last post how Major League Soccer (MLS) is a much more closely matched league than the English Premier League (EPL), with the wage cap and draft system increasing the parity between teams.

Here are the latest match probabilities for the English Premier League calculated using the Eastwood Index (EI).

Introduction Following my recent article for Betting Expert quantifying how large a role luck plays in the English Premier League (EPL) I thought it would be interesting to look at Major League Soccer (MLS) too.

Introduction I’ve written a new article for Betting Expert quantifying how much luck affects football.

Introduction I had an article published at Betting Expert last week looking at how to determine statistically how much data you need to make accurate predictions.

It’s Friday again so here are this weekend’s match probabilities for the English Premier League.

It’s been a busy day but I’ve finally got the probabilities for this weekend’s matches completed.

Here we go with this week’s predictions from the Eastwood Index (EI)!

A couple of weeks back I demonstrated how the EI is more accurate than the bookies based on rank probability scores but a few people have asked if I can do something a bit simpler so Figure 1 shows how often the EI picked the winner as being the favourite…

Introduction The use of Total Shot Ratio (or TSR) seems to have slowly been gaining ground so I thought it would be worth analyzing the statistic in more detail to see what it can and cannot do.

After last week’s international matches, domestic football is finally back so here are this weekend’s match predictions using my EI predictive model.

Introduction Unfortunately time caught up with me last week and I was unable to post any predictions from my Eastwood Index.

Introduction As well as using my EI Index to predict future matches, it can also be used to look back at how team’s performances have changed over time.

Here we go again!

Since last week’s predictions turned out to be so popular I thought I would continue testing the EI index in public so here are this week’s predictions.

The EI Last week was a big test for my new EI Index – it had finally reached the point where I was confident it was working well enough to post its predictions in public.

Introduction My past couple of articles have focused on Elo ratings and how they can be applied to football teams to rank them against each other and to estimate win probabilities.

For a bit of fun, here is a trial run at predicting this weekend’s EPL matches using my EI ratings.

Introduction Now that we understand the theory behind Elo Ratings, let’s take a look at how to calculate them and how to make them more relevant to football.

What are Elo Ratings?

Introduction Having found that the correlation between goals scored and shots on target was the strongest of the various shooting variables I had available to me, I decided to see how well they could predict the outcome of a football match.

With League Two’s Bradford City only one match away from playing at Wembley in the League Cup final I thought it would be interesting to see what the chances were of them getting this far.

Introduction Previously on this blog I have discussed my attempts at using the Poisson distribution to predict the number of goals scored in football matches.

Introduction New article for Betting Expert looking at the current Premier League standings compared with the predictions from my refined version of the Pythagorean Expectation.

Introduction The next stage for developing my refined version of the Pythagorean equation (known as the MPE) is to characterise how much data it actually needs to make accurate football predictions.

Introduction Up until week 12 of the season, Dimitar Berbatov was one of the English Premier League’s top goal scorers and goal creators.

Introduction I showed in my last post that my initial version of the Pythagorean Expectation (MPE) predicted total points for the English Premier League (EPL) pretty well, with an RMSE of approximately four points over the course of a whole season (see he…

Introduction In my previous article, I discussed how to apply the baseball Pythagorean expectation to football and how to measure the error of the predictions using RMSE.

Introduction The Baseball Pythagorean Expectation is a formula originally derived by Bill James to estimate how many games a baseball team could be expected to win over a season based on the number of runs they score and concede (Figure 1).

Introduction Having mentioned the effect disparity plays on determining the league champions in previous posts I thought it would be interesting to look at the actual levels of disparity currently present in football.

Introduction Since taking over as manager of Tottenham Hotspur, André Villas-Boas has been trapped in former Spurs manager Harry Redknapp’s shadow.

Introduction In my previous article I looked at the interplay between luck and skill in determining the league champions.

Introduction How often does the best team win the league?

Introduction In my last article I wrote about my experiences using the Poisson distribution to predict the outcome of football matches.

Introduction The Power Of Goals recently blogged about using the Poisson distribution to predict the outcome of football matches.