Insights, research, and analysis on football analytics
Why most finishing metrics are flawed and how a Bayesian approach gives us a truer picture of a player's finishing ability...
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A simple guide to stripping the overround and finding the real probabilities behind the bookmaker's odds...
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v1.5.0 delivers interactive charts, faster models, upgraded football probability grid, and a powerful Flow query language - all designed to make your analysis sharper and quicker...
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A data-driven deep dive into how accurately bookmakers price global soccer markets...
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MatchFlow just got smarter, friendlier, and more powerful for optimizing your pipelines, visualizing your data flow, and keeping your data clean...
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MatchFlow is a JSON-native query engine for football data - no flattening, no fuss...
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Why the Ranked Probability Score might be misleading your football model evaluations, and what to use instead....
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A smarter, football-focused alternative to Elo — using Pi Ratings to track team strength and predict matches...
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Comparing football goals models — Poisson, Dixon-Coles, and more — to see which predicts best and how to optimize them...
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This article walks through how to calculate expected threat in Python using linear algebra instead of the original convergence method...
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This article walks through how to estimate goal expectancies from bookmaker's odds using Python...
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My penaltyblog python package has been updated to v0.5.1 to include new Bayesian football (soccer) models and web scrapers...
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It's been ten years since I started this blog...
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This article shows how to use the penaltyblog python package to scrape football data...
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This article looks at how to predict football results using a Bayesian hierarchical model built in Python and PyMC3...
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Building on the last article, we upgrade our Poisson model with the Dixon and Coles adjustment and time decay...
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A tutorial about predicting football results using Python and the Poisson distribution...
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Using my Player Ratings model to identify the best footballing prospects...
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Predicting what player's potential career trajectories look like...
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Reattributing xG using multi-touch attribution modelling...
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As Edwin Starr famously said, VAR, what is it good for? Well, let's find out...
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I finally wrote about the presentation I gave at the Opta Pro 2018 Forum....
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I recently gave a presentation on automated feature engineering at MancML....
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I recently gave a presentation at the Manchester R Users Group on Deep Learning in R using Keras....
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I've long been critical of the use of radar plots for visualizing football data and was recently challenged by a reader of this blog to come up with a better alternative so here we go...
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I wrote up the poster presentation I gave at the 2017 Opta Pro Forum for the Opta Pro blog looking at using machine learning to quantify footballer's decisions....
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With the 2017 Opta Pro Forum rapidly approaching, I thought it was about time I transcribed my presentation from the previous event....
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With the transfer window well under way I thought I'd discuss my footballer recommendation engine for identifying potential transfer targets....
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My Twitter feed seems to be increasingly taken up with discussions of Expected Goals in football yet there always seems to be something important missing from the discussion, and that's uncertainty...
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I've discussed various techniques for ranking football teams on my blog before, such as using Massey Ratings to account for strength of schedule, but I've not covered Google's PageRank yet...
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John Burn-Murdoch and the Financial Times have used my PlayerRatings model to analyse the recent flux of players moving to teams in the Chinese Super League - you can read the full article here...
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There has been some discussion and misunderstanding around the low frequency of draws in football on my Twitter feed recently so I thought I'd just give a quick recap around why the probabilities of draws are so low...
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Updated Massey ratings for the English Premier League showing how well teams are really doing when you account for their strength of schedule...
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The Premier League’s fantasy football is back ready for the new season so I thought I’d run through an example of how linear programming can help you mathematically select your team.
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I've written about expected goals on this website before but I've changed approach recently so I thought I'd write up some of the different ideas I've been playing around with...
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My last article looked at how well my PlayerRatings model predicted which young players would go on to have successful careers. This time we explore false negatives - which top players may have wrongly had low PlayerRatings in their youth...
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Following my last article discussing my PlayerRating model for quantifying footballers, JackIO challenged me to test the model by looking at what young players it recommended...
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My previous article introduced PlayerRatings, a mathematical model I’ve been working on over the past few months to quantify the ability of individual footballers. One of the nice characteristics of this approach is that player ratings can be aggregated together to create team...
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I originally submitted the idea behind this article to the recent Opta Pro Forum and although it was turned down I thought I’d write it up anyway incase anyone else was interested in the results...
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It's transfer window time so here's your mathematically optimised fantasy football team...
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In part one I introduced Massey Ratings and how they can be used to rank football teams in a way that accounts for their strength of schedule. Next, we’ll take a look at how Massey Ratings can be extended further to look at team’s attack and defence strength separately.
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We all know the league table can lie and one of the common causes of this is strength of schedule. Take Southampton, at the time of writing they are currently second in the Premier League twelve matches in yet still haven’t played...
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I’ve not posted this for a while so here is the latest Pythagorean for the English Premier League.
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I recently gave a presentation to the Manchester R Users' Group discussing how to predict football results using R. My presentation gave a brief overview of how to create a Poisson model in R and apply the Dixon and Coles adjustment to it to account for dependance in the scores.
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My last article on expected goals introduced the concept of using exponential decay to estimate the probability of scoring based on the shooter’s distance from the goal. The article received lots of feedback (thanks everyone!!), with a couple of common comments standing out th...
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The Premier League’s fantasy football is back ready for the new season so I thought I’d run through an example of how linear programming can help you mathematically select your team.
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In my last article on expected goals I showed how to incorporate the distance from goal along the Y axis into the expected goal model using Pythagoras’ Theorem. This all worked pretty well, giving us an r squared value of 0.95. However, while the r squared value was good there...
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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 on discussing how to calculate them. If you haven’t read those pieces yet it’s probably worth taking a quick look to set the...
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I’ve not posted an update on the Pythagorean for the English Premier League (EPL) for a while so the latest figures are below.
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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...
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Since my last post 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..
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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 of chat about the best way to model them.
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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.
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Welome back! Now that I'm no longer part of Onside Analysis I'm free to start blogging again so let's start off by taking a look at how my football Pythagorean is doing for the English Premier League so far this season.
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With the UEFA Champions League group stage now underway I took a look at what it typically takes for teams to reach the final.
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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. This is great as it allows us to increase our understanding of the game but it also means we quickly end up with more inf...
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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.
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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. My next goal is to work out the optimal way of using this edge to make a profit, starting off with the Kelly...
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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...
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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...
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Here are the latest match probabilities for the English Premier League calculated using the Eastwood Index (EI)...
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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...
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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...
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Here are the latest match probabilities for the English Premier League calculated using the Eastwood Index (EI)...
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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...
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I’ve written a new article for Betting Expert quantifying how much luck affects football...
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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...
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It’s been a busy day but I’ve finally got the probabilities for this weekend’s matches completed....
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It’s been a busy day but I’ve finally got the probabilities for this weekend’s matches completed....
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Here we go with this week’s predictions from the Eastwood Index (EI)!...
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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...
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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.
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After last week’s international matches, domestic football is finally back so here are this weekend’s match predictions using my EI predictive model. Let’s see if it can keep up its good form and continue to beat the bookmakers!
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Unfortunately time caught up with me last week and I was unable to post any predictions from my Eastwood Index. However, since then I have been busy validating the results to see how accurate the predictions really are using the 296 matches played in the English Premier League...
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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. An interesting example is Liverpool, who sacked Kenny Dalglish at the end of the 2011–2012 season to bring in Brendan Rogers from Swansea City.
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Here we go again!
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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. Fingers crossed they turn out well again!...
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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.
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For a bit of fun, here is a trial run at predicting this weekend’s EPL matches using my EI ratings. I haven’t compared these with anyone else’s odds yet but they generally look about what I would have expected....
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On the whole, the Elo system works okay but it was not designed with football in mind and so there are some issues with it - for example it can only handle two distinct outcomes – winning and losing...
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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...
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The Elo rating system was originally devised by its creator Arphad Elo as a way to calculate the average skill levels of two chess players. Although the system was created specifically for chess it has also been adapted to many other games and sports, including international f...
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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....
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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...
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Previously on this blog I have discussed my attempts at using the Poisson distribution to predict the number of goals scored in football matches. So far, the results have been...
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New article for Betting Expert looking at the current Premier League standings compared with the predictions from my refined version of the Pythagorean Expectation.
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The next stage for developing my refined version of the Pythagorean equation is to characterise how many weeks of data it actually needs to make accurate football predictions...
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Up until week 12 of the season, Dimitar Berbatov was one of the English Premier League’s top goal scorers and goal creators. However, since then he has gone 450 minutes without registering either a goal or an assist, coinciding with Bryan Ruiz’s injury...
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The next stage for my Pythagorean's development is to testing whether it can be applied to leagues outside the EPL. Having one Pythagorean equation that could be used globally is preferable to having to create specific equations for each league.
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Part two of guide to applying the baseball pythagorean to football.
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Introduction to applying the baseball pythagorean to football.
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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.
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Since taking over as manager of Tottenham Hotspur, André Villas-Boas has been trapped in former Spurs manager Harry Redknapp’s shadow. Every tactical decision or team selection Villas-Boas makes is seemingly compared with Redknapp’s previous achievements. And after Tottenham’s...
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In my previous article I looked at the interplay between luck and skill in determining the league champions. There is another parameter though that also interacts with luck and that is the structure of the league itself.
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How often does the best team win the league? Probably not as often as you think as it is not just talent that is required for success; a decent amount of luck is needed too.
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In my last article I wrote about my experiences using the Poisson distribution to predict the outcome of football matches. The results so far have been rather disappointing so I thought I would have a look at where things were going wrong.
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The Power Of Goals recently blogged about using the Poisson distribution to predict the outcome of football matches. I have been evaluating the predictive ability of the Poisson for the English Premier League (EPL) this season so I thought I would share my experiences too.
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To make much sense of the statistics available for football we need to have an understanding of their context so I am planning on starting off simple by looking at baselines for various events and statistics while I build up the information required to start a mathematical model.
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There are plenty of analytical football blogs already out there on the internet so I thought long and hard about whether to bother with pena.lt/y..
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After Manchester United’s recent defeat to Tottenham, Sir Alex Ferguson was once again furious about the amount of injury time played. He even went as far as to claim the four minutes Chris Foy added was an ‘insult’.
Read moreHello!! Hopefully this blog will be live soon once I have worked out how to use it…
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