As argued in one of my previous blogs, building on the “Decision Rule” from Myers, Premier League managers could increase their chances of turning around a game if they would make earlier substitutions. As can be seen here managers on average implement their first, second and third substitutions in the minutes 62. 72 and 81 respectively.
Liverpool and Man United played a close but rather dull game at Anfield Road last night (17/10/2016). The Red Devils held the Reds to only 9 shots, their lowest tally in a Premier League home game in the last 2 seasons (the current and last season that is), while they themselves also could not get more than 7 attempts.
Tactics have been transmitted to the players, set pieces have been prepared (or more likely not..) and opponents have been thoroughly studied. Then it’s game time and the manager sees his influence slip out of his hands. There seems to be only so much he can do during the 90 minutes. Walking impatiently along the sideline, shouting at the fourth official and handing notes to throw-in takers is probably not going to do it, but one of the obvious things he can do to try to impact an on-going game is to bring on substitute players. Little has been written on this matter, so here is my (first) try.
In this previous post I described my expected goals model for the Primera División (Argentina). Here I will quickly show some first examples of the resulting game maps:
Rosario Central – Newell’s Old boys
This is an example of the xG map and corresponding information on the clásico of Rosario of last February 14th. As you can see on the pitch Rosario Central trashed their local rivales: they took a lot of shots, and the majority from very good locations.
For the last couple of months I have been (slowly) working on an expected goals model for the Argentine league and I am very happy to finally present a first version here. I will explain the basics of the model here and I will also try to show some of the underlying numbers, something I haven’t seen so much in posts from others.
For the ones that want to skip this blog and just see the results: click here.
Seeing Cristiano Ronaldo score yet another insignificant hat-trick against Espanyol last night I wondered how many of his goals actually matter. And with “matter” I refer to goals that actually have a clear impact on the game and are not just goals in the margin (when already winning 2-0 for example). Within the same effort I could might as well check the same statistic for all players in Europe’s top 4 leagues. So that’s what I did and I also added the goal data from the Champions League and Europa League.
In another blog I am keeping track of my work on a expected goals model. All variables and assumptions are largely based on other people’s models, but here I’ll discuss something I have not yet seen (or missed..): the underlying shot numbers. While working on the analysis for the bigger model I found a lot of interesting statistics on the shot conversion rate (goals/shots) per, for example, game state, number of shot in the game, time in game, and more. Where for the model I will dive deeper into the numbers and use statistical testing these are largely just descriptive tables. But no tests, no significance levels. And yes, each of the paragraphs would need a blog of its own..!
It’s the most wonderful time of the year and the 10 Premier League games on Boxing Day make it even more beautiful! This is the schedule:
I recently wrote a short piece about Liverpool and how much time they spent games in a drawing state. After having calculated the same statistic for each team in the top-6 European leagues + the Eredivisie (there are probably no valid arguments for the inclusion but I like the league) I noticed that there are much more interesting stats to be found. Just some screenshots and key facts.