This glossary serves two purposes — primarily, to make your life easier by directly linking you to the data without you having to go through my twitter timeline. Secondly, it will also aim to serve as a glossary, defining some of the data labels that may be confusing to some.
MetaData — Season 20/21 here
ShotData — Season 20/21 here
It’s done, Antonio Conte has actually been let go. And if the shit-show would end there, I would gladly accept it. But, this doesn’t just end with Conte. It seems likely that several staff members will be joining Conte in exiting the club, including Antonio Pintus and possibly Lele Oriali. Various reports, fortunately claim however, that the top management — Marotta, Ausilio among others will be staying at the club, especially after Inter secured a 275 Million Euro loan from Oaktree Capital.
Conte’s departure of course was completely pushed by Conte himself on the valid grounds that if he…
written on Jul 17, 2020
Sassuolo are one of the most exciting teams in the Serie A this season. Dubbed ‘Atlanta’s little brother’, it’s truly surprising how Roberto De Zerbi has got this team firmly in 8th place, and within 6 points off the Europa League spot. Much of their success comes on the attacking front, with Berardi, Caputo, Boga and Đuričić combining for 46 goals this season.
Behind them are the midfield duo of Locatelli and Magnanelli. Locatelli has been crucial for Sassuolo this season. Here’s a graphic showing a few select stats this season.
On a per-90 basis…
we must always redistribute knowledge — Nandy
A few things before I start this guide- I’ll keep this guide short as this one’s intended for those who have a good amount of experience in both Tableau and data prepping. There might be parts you may get stuck in, but for every doubt, there is usually always a Google search to provide a solution and me, who’s always ready to help you out on Twitter.
When I came up with this template, I tired to get it as similar to the folks at StatsPerform and most of the formulas were taken…
Convex hulls are very easy to do in R, the following graphic can be produced in just 5 lines of code. This guide tells you how to make the very same graphic by integrating R with Tableau.
x = df[c('endX','endY')]ch <- chull(x)
coords <- x[c(ch, ch), ]plot(x, pch=19, col = 'blue')
polygon(coords, col = rgb(0,0,1,alpha = 0.1))
Do read the CRAN docs , here if you wish to understand the package in more detail.
‘The gaffer has entrusted him with more freedom today’ , ‘he has been given the creative license, let’s see what he does’. Freedom in football is common parlance. It’s usually associated with creative midfielders who move into channels to receive passes. Still despite being such an easily understandable concept, there isn’t exactly a way to definitively say that ‘player X’ has more freedom than ‘Player Y’.
I propose a new Metric, called FR (Freedom Rating) that puts a mathematical number behind how free a player’s role truly is.
Ok maybe the guys over at statsbomb already have something like this…
Do you want x and y coordinate data for one team’s shots in any particular season. Well, you can do that in just 5 lines of R code. Commence copy-paste
Step 1: Install this library from Ewen and load it up
#install the library from github
Step 2 : Extract team data by mentioning your desired team within the quotations. Also mention the season you want. 2019 corresponds to 2019/20, 2018 to 2018/19 and so on. I chose Real Madrid and 2019.
rm = get_team_players_stats("Real Madrid",2019) #enter team name within quotations and season required
Step 3: Create a…
I recommend reading TabGuide #1 to get you up-to grips with some of the techniques mentioned here. This is a much longer piece and will have more parts to it. Thanks for dropping by :)
To make a pass-map in Tableau, all you need your data to have are four values- X and Y coordinates for the start location of the pass and the end location of the pass. Most importantly, you need a unique identifier like event ID. Ideally, you’d also need the player who made the pass and the player who received the pass and maybe the length…
To get started, all your data needs to have are X and Y coordinates for the shot locations. Anything apart from that like xG and shot outcome is great as well.
If you already have data from another provider, that’s great. Here’s a 5 line code on R from ewenme that can help you scrape shot location data since the 2014 season from understat. All you need to do is find the number for your particular player and then run the code attached below.
#install the library from github
remotes::install_github('ewenme/understatr')library(understatr)data1 = get_player_shots(499) #insert playerID heresetwd("~/Downloads") #set your…