I built a predictive model for football match stats (shots, corners, fouls) across 20,000 matches. The strongest predictor ended up being ELO from chess. [OC] Visualization

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This data visualization displays "I built a predictive model for football match stats (shots, corners, fouls) across 20,000 matches. The strongest predictor ended up being ELO from chess. [OC]" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on For the past few months I've been working on a personal project: a predictive model for per-match football statistics. Not the final score, but the behaviors: how many shots each team will take, corners, fouls, cards. The dataset covers around 20,000 matches across five seasons and the top 5 European leagues.

I started with hundreds of variables: rolling shot averages, foul rates, corner frequencies, home/away splits, opponent profiles. Everything you'd expect. The first results were decent, but the model was essentially regressing toward each team's historical mean without any real understanding of match context. It could see that Team A averages 14 shots and Team B averages 11, but it had no concept of the gap between the two sides. It didn't know that tonight Team A is so much stronger they'll pin Team B in their own half for 70 minutes and probably end up with 19 shots while Team B scrapes together 6.

Historical averages are built against opponents of all quality levels. They encode nothing about the specific match being played, and that contextual read is exactly what every football fan processes automatically before kick-off. The hard part is giving a model a number for something so intuitive.

I ended up turning to chess. ELO ratings were invented in the 1960s by Arpad Elo to classify players more precisely than tournament standings alone. Beat someone stronger and your score rises significantly; lose to someone weaker and it drops. It updates after every game, with the only inputs being the result and the relative strength of the two players — no performance quality, no expected goals, just who won and against whom.

I built an ELO system for all clubs across the top 5 leagues, initialized from external sources and updated match by match through five seasons. When I added the ELO gap between the two teams as a predictor, things shifted immediately.

**Bivariate Spearman correlation with shots:**

|Predictor|Correlation| |:-|:-| |ELO gap|**0.377**| |Rolling shot average|0.273|

The chess number outperformed every football-specific variable in the model. And when you break it down by bucket, it's obvious why:

|ELO gap|Avg shots| |:-|:-| |< −200 (much weaker)|9.2| |−200 to −100|10.5| |−100 to −50|11.0| |±50 (balanced)|12.8| |\+50 to +100|13.0| |\+100 to +200|14.4| |\> +200 (much stronger)|**17.4**|

*Global average: 12.7 shots*

From 9.2 to 17.4 driven entirely by the strength gap — and no rolling average captures it, because rolling averages don't know who those shots were taken against. A team that faced three weak sides in a row will have inflated numbers; the ELO gap adjusts for that automatically.

200 variables, five years of data, six leagues, and the most important feature had nothing to do with football.

Happy to get into the methodology or the initialization choices in the comments., which allows us to understand complex relationships and insights within the data through visual storytelling.

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