Data metric development – Outswinger FC

Goalkeeper Sweeper Pass Score: Measuring how a sweeping action can contribute to progression

It’s been a few months now since I’ve started looking at data at a different way. Instead of focusing on the metrics that are generated, I’ve been looking at the raw data more and create my own metrics. Part of the reason of that is that I’m never completely satisfied with the metrics given by the different data providers. Especially, when it comes to goalkeeper’s data.

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Using Standard Deviation and Mean Absolute Deviation to rate Goalkeeper’s shot-stopping

For the last few years I’ve been dabbling with data in football, especially with data visuals that show performance or intention. For the most part I have only focused on that was familiar within the data and focused on representation of data and the manipulation of it. But since I’ve worked more and more with data in big datasets, I’ve also realised that differences in outcome also have a lot to do with the methodology you are using.

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Gamestate xG Score: Expected goals adjusted by game state

Expected goals. You might think oh no here we go again, but I think it might be the one metric that has become part of normal conversations, without actually knowing the power or versatility of it. That also means we often talk about expected goals or xG and make wrong assumptions/conclusions. This can lead to a completely distorted point of view and discredit the work data people do in sports.

Free kick Threat Score: Measuring the threat a player generates from indirect free kicks

For me, 2024 is going to be more about creating and applying existing data metrics across football. The first genre of metrics is the metrics that are useful for set pieces because I feel that’s where a lot of improvement can be made and also because I’m just a nerd for set pieces.

Corner Threat Score: Measuring the threat a player generates from taking inswinging and outswinging corners

In football, there are many things to look at from a tactical or coaching perspective and a data perspective. One of the things I love looking at are set pieces, corners in particular. There are not many corner-specific metrics out there and the available ones, mostly focus on the result of the corner in terms of expected goals (xG). In this article, I will explain the new data metric that I have created: Corner Threat Score (CTS), which is divided into Corner Threat Score Inswingers (CTSI) and Corner Threat Score Outswingers (CTSO).