Data is a powerful tool in the world of football. It can be even more powerful when utilised within Football Manager. This makes using data in FM19 essential. FM is essentially a huge database of attributes and statistics, which can be manipulated and analysed to produce better results for your team. In this post I’m going to outline my tips for utilising this data to improve your save.
As mentioned in our previous post we are going to take a step away from the sterile lab setting (though we will be back) and look at some more natural data. Data that has been occurring in my saves. As a result we will be able to use statistics around key performance indicators (KPI’s) to actually determine what our club DNA should be for for each of our units: defence and attack. Recruiting players FM19. There are some great articles,…
After my first full season in the Premier League, we are back with another instalment of Moneyball. Last season we finished 15th, with data driven signings making a bit of an impact. This time around we’ll be taking a quick look at my scouting process, as well as all the ins and outs this summer, and preview the upcoming season. Moneyball in FM19.
Welcome back to the FM Statistics Lab. What’s the importance of decision-making in FM19? In our first post we covered how we set up our lab environment to give is as much control as possible over our variables, and we also dipped our toes into the statistical water by investigating what sort of impact the professionalism attribute can have on overall team performance. If you’ve not had a chance to read it you can find it here – spoiler alert…
Statistics and myth-busting are the aim of the game in our FM Lab. We have set up a database and league to let us test what impact changing certain variables have on the actual game, to gather lots of data, and then test it statistically at the end. Specifically Professionalism in this first post. If like me you like seeing simulations and experiments, but also like me you want more hard figures then this is the experiment series for you….
First I want to start out by saying thank you to everyone who read Moneyball Part 1. The response was really overwhelming and while I tried to respond to most comments, there are undoubtedly a few I missed. As promised, in this post we will be looking at my January Transfer Window activity, the stats for the players I signed, and a closer look at my KPIs and statistical setup. Moneyball in FM19.
I start a Masters Degree in Data Analytics this September, and recently I’ve been looking at how I can use Analytics to impact my favourite game, Football Manager. Cleon recently tweeted about Moneyball, and how no one has replicated it correctly when it comes to FM. Moneyball in FM19. Now, I had heard of Moneyball, and knew it involved using Analytics to scout players, but I didn’t know the true extent of Moneyball, or where the definition came from. So,…
As a manager, I always look for strikers with the highest finishing attribute and then assess their other attributes. However, I wonder what would happen if I created two identical players and only adjust their finishing in FM19 attributes and see what the result is like. In my last article where I looked at age, which can be read here – Does Age Make a Difference in a Striker? | Football Manager Experiment. It was slightly difficult to create a conclusion on…
Typically, when you search for a new player to join your team on Football Manager, age can be a big factor in whether or not you will sign them. I want to create an experiment that looks at two identical players in terms of attributes, but completely different ages. I want to find out whether age is a defining moment in the signing of a player. In this particular experiment, I am going to create a player who would be…
