football
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Building a Football Prediction Model: Week 4
This week I am adding a time series model (SARIMA) to better react to team momentum and dips in form. Again, I am predicting matches days ahead of time to increase transparency. What is SARIMA? Seasonal AutoRegressive Integrated Moving Average (SARIMA) is a time-series forecasting model designed to predict future values based on historical patterns.… Read more
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Building a Football Prediction Model: Week 3
Accuracy: 40% (8/20 teams had predicted goals scored) Home Away xG Home xG Away Actual Score Leeds Arsenal 1.06 1.25 0-4 Wolves Bournemouth 1.61 2.35 0-2 Brighton Everton 0.68 0.72 1-1 Chelsea West Ham 1.99 0.81 3-2 Liverpool Newcastle 0.88 0.77 4-1 Aston Villa Brentford 1.88 0.75 0-1 Man United Fulham 1.69 0.95 3-2 N… Read more
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Ranking clubs by the state of their finances
It’s well known that Premier League clubs make a lot of money. But I was curious about something different: which clubs are actually well run financially? To answer that, I built a small Python model to rank football clubs based on their financial health. Instead of league position or trophies, this looks purely at how… Read more
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Building a Football Prediction model: Week 2
Summary To the nearest whole number, the model predicted the correct goals scored for 10 out of the 20 teams, giving a strong accuracy of 50%. Since last week, I’ve made a few changes to my model. I’ve downweighted past results exponentially and added the factor of recent form (last 5 matches) to influence the… Read more
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Building a Football Prediction Model: Week 1
Passionate about football and data, I want to see if it’s possible to program a model to give me a consistently accurate prediction of a football match. This series of posts attempts to document my experiences in building a model that will predict games. Background One thing I found from reading papers is that a… Read more
