For one of my recent posts I needed to add a customized and dynamic table to a ggplot chart. This can often be helpful to display information in addition to the main idea you are presenting in the plot. In my example I wanted to show free kick conversion rates by season in a table format. I found this to be far from straightforward so I will try to outlay the main concepts here in case anybody has the same problem.

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More detailed and readily available data and a thriving analytics community on Twitter have produced a series of new statistics and visualizations for football fans. In this series I will try to give a brief definition of various metrics, background information on charts and sources for where to find them going forward. Expected Goals The concept of Expected goals tries to quantify the quality of chances. To every shot it assigns a value between 0 and 1 based on the historical success rate (i.

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More detailed and readily available data and a thriving analytics community on Twitter have produced a series of new statistics and visualizations for football fans. In this series I will try to give a brief definition of various metrics, background on charts and sources for where to find them going forward. Passing Networks Passing Networks contain two main types of information: average positioning of each player and most frequent passing links between players of each team.

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Quantifying Injury Rates

Being a Bayern Munich supporter injuries have been a big part of past campaigns I have followed. During most of the past season (2017/2018) Manuel Neuer was injured with a broken foot. For the second match against Real Madrid in the Champions League semi finals the team had to additionally compensate the injuries of Jerome Boateng, Arturo Vidal, Kingsley Coman and Arjen Robben. While Pep Guardiola was still coach at Bayern his dispute with the long-serving team doctor Mueller-Wohlfahrt culminated into him resigning after 38 years with the team (Mueller-Wohlfahrt was later reinstated as the team doctor in 2017).

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The possession metric is probably one of the football statistics which is cited most often. It has gained even more media attention since we have seen larger divergence in possession across teams. Frequently game approaches of teams are broadly categorized between possession-oriented and counter-attacking: think about Barcelona/Bayern Munich/Manchester City vs Chelsea/Borussia Dortmund/Liverpool. At first glance the possession statistic seems to be fairly trivial: who controls most of the ball during the 90 minutes?

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The Significant Game

Soccer is not only just the beautiful game; it is also a significant one. This blog tries to find statistical patterns in the sport we all love.

Soccer Analytics

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