penaltyblog

Introduction penaltyblog v1.11.0 adds batch predictions across the goal-model API, neutral venue support for international tournaments and cup finals, a pre-trained xT model, faster Weibull Copula fitting, and Context7 MCP documentation support.

Introduction A little while back, I made the difficult decision to remove the Bayesian goal models from penaltyblog.

Introduction I'm excited to announce a new feature for matchflow: direct integration with the Stats Perform (Opta) Soccer API!

Introduction In football analytics, we often turn to a simple metric: Goals minus Expected Goals (G-xG) to assess a player's finishing ability.

Introduction If you've ever converted betting odds to probabilities, you've likely noticed they sum to a value greater than 100%.

Introduction The latest release of the penaltyblog Python package brings a major upgrade to your football analytics toolkit.

Introduction The global soccer betting market is a high-volume predictive system, where bookmakers generate and update millions of probability estimates in real time.

Introduction Over the past few weeks, I've been diving into the world of query plan optimization.

MatchFlow: A JSON-Native Query Engine for Football Data Over the years, I’ve spent a lot of time wrangling football data - from open data projects to internal club work.

Introduction What if the metric you've been using to evaluate football forecasts is quietly rewarding worse predictions?

Introduction Football analytics has come a long way in recent years, moving from simple league tables to more sophisticated methods of quantifying team performance.

Introduction I've recently released version 1.1.0 of my penaltyblog Python package, bringing significant improvements to the speed and predictive performance of football (soccer) goals models.

Introduction Imagine you're watching a soccer match and your team's midfielder has the ball at the halfway line.

Introduction I've explored lots of different ways of predicting football matches so far on this blog that have all been backwards looking - they take in historical data and try and use it to predict how many goals will be scored.

Introduction My penaltyblog python package has recently been updated to v0.5.1 so let's take a look at some of the new features.

Introduction Today marks the blog's ten year anniversary and what a journey it's been!

Introduction If you're interested in football analytics or trying to predict football results and odds for betting markets then you are going to need data.

Introduction So far in this series of articles we started off building a basic model for predicting football results using two independent Poisson variables inspired by M.

Introduction In the last article, we built a model based on the Poisson distribution using Python that could predict the results of football (soccer) matches.

Introduction This article is going to walk through building a model to predict football results using the Poisson distribution.

Introduction Over the years, I've shared results from my PlayerRatings model with lots of people, including a number of professional football clubs, and everybody always comes back with the same question - who are the best young players?

tl;dr I've updated the my Player Ratings model to show football players' potential career trajectories.

Introduction A question that often comes up in data science is how you determine the performance of different marketing channels.

Introduction As Edwin Starr famously said, VAR, what is it good for?

Introduction I presented a poster back in 2017 at the Opta Pro forum looking at using tracking data to evaluate footballer’s decisions in and around the box, which I've previously written about on Opta Pro’s blog if you want to find out more about the ori…

Automated Feature Engineering Using Deep Feature Synthesis I recently gave a presentation at the MancML about using Deep Feature Synthesis to automate Feature Engineering and how it fits into a machine learing pipeline - the slides are belowfor anyone int…

Deep Learning in R Using Keras I recently gave a presentation at the Manchester R Users Group on Deep Learning in R using Keras.

Introduction 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.

Analyzing Footballer’s Decisions in and Around the Penalty Box 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.

A Mathematical Approach to Evaluating Footballers With the 2017 Opta Pro Forum rapidly approaching, I thought it was about time I transcribed my presentation from the previous event discussing a mathematical model I’d been developing for rating and ranking

Introduction With the transfer window well under way I thought I'd discuss my footballer recommendation engine for identifying potential transfer targets.

Introduction 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.

Introduction 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 the most famous ranking algorithm of them all yet, Google's PageRank.

Introduction 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

Introduction 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.

Introduction I've have explained the theory behind Massey Ratings on the blog before (here and here) but incase you haven't come across them, they are a way of rating teams that takes into account the strength of the schedule they've faced.

Introduction Due to popular request, here's an updated version of a blog I wrote around this time last year where I used mathematical optimisation to select the best fantasy football team possible.

tl;dr Support Vector Machines provide a viable approach for calculating expected goals Forecasting expected goals conceded seems more difficult than expected goals scored Expected goals have lots of variability associated with them so calculate confidence…

Introduction My last article looked at how well my PlayerRatings model predicted which young players would go on to have successful careers.

Introduction 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.

Introduction Last week I introduced PlayerRatings, a mathematical model I’ve been working on over the past few months to quantify the ability of individual footballers.

Introduction 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.

Introduction At the start of this season I showed how to use linear programming to mathematically optimise your fantasy football team in order to get as many points as possible for your transfer budget.

Introduction 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.

Introduction We all know the league table can lie and one of the common causes of this is strength of schedule.

I’ve not posted this for a while so here is the latest Pythagorean for the English Premier League.

I recently gave a presentation to the Manchester R Users' Group discussing how to predict football results using R.

Introduction 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.

Introduction The Premier League 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 select your team.

Introduction 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' Thereom.