I recently read a column by Gabriele Marcotti about how statistics are becoming more and more important in sports.
Marcotti mentioned a book called Moneyball in his column. This book focussed on the Oakland A's, an American baseball team, and how this team managed to compete with bigger and more wealthy clubs by means of innovative statistical analysis.
The central premise of Moneyball is that the collected wisdom of baseball insiders (including players, managers, coaches, scouts, and the front office) over the past century is subjective and often flawed. Statistics such as stolen bases, runs batted in, and batting average, typically used to gauge players, are relics of a 19th century view of the game and the statistics that were available at the time. The book argues that the Oakland A's' front office took advantage of more empirical gauges of player performance to field a team that could compete successfully against richer competitors in Major League Baseball.
Rigorous statistical analysis had demonstrated that on-base percentage and slugging percentage are better indicators of offensive success, and the A's became convinced that these qualities were cheaper to obtain on the open market than more historically valued qualities such as speed and contact. These observations often flew in the face of conventional baseball wisdom and the beliefs of many baseball scouts and executives.
By re-evaluating the strategies that produce wins on the field, the 2002 Athletics, with approximately $41 million in salary, are competitive with larger market teams such as the New York Yankees, who spend over $200 million in payroll. Because of the team's smaller revenues, Oakland is forced to find players undervalued by the market, and their system for finding value in undervalued players has proven itself thus far.
Basically, the author claims that teams have finite resources (read: money) and are therefore forever looking for value. Value used to be assessed by evaluating traditional parameters. Most of these parameters were subjective, such as for example pace, strength and character. The author claims that one could identify value more efficiently by analysing certain previously obscure statistics.
According to Marcotti, finding a way to apply these principles to football has been something of a holy grail. The most obvious obstacle is that while baseball has easy-to-measure individual match-ups, football is a free-flowing game.
Fortunately, FM Live makes this measuring aspect a bit easier for us, since the game is all about statistics. The attributes of players are displayed in ratings between one and twenty and the game logs pretty much every statistic you would ever want to know.
This means one could apply the aforementioned principles to FM Live. Re-assessing value is becoming more and more important, since your budget is limited and there will be more teams fighting over the same players.
As a result, setting your scouting filters is getting more and more important if you want to end up with proper value for your money. In this article and several follow-up articles, I am going to describe how I set up my scouting filters. In this article, I will describe the general approach I take.
First off, let me tell you that I know what kind of players I am looking for exactly. The game offers various roles for players, even within positions on the pitch. For example, there are five or so roles you can select for your forward, but I know exactly that I only need Target Men or Trequartista's upfront.
Once you know which role or roles your players should cover, you can start by creating your own search template. The basic templates provided by SI give you a nice start to work with. However, following these templates will mean you end up with very broad and generic search results. Basically, you have to narrow the search results down further.
This can be achieved by adding extra parameters to the search. However, how do you know which parameters to add for your role? This is where a combination of common sense and being smart should be applied by a manager. Either read up on forums which attributes to use for which role or look at successful players within the GameWorld and look for common attributes in all of them.
Once you have added these parameters, you will have narrowed down the amount of players you are getting. Now look at the financial means at your disposal. If the listed players are all outside your reach, lower the standards a bit in the search parameters. Ideally, you want to end up with four or five candidates.
Now you can examine these candidates further. How have they performed in the past? What are their statistics like for pass completion or crucial errors? Look at those statistics that are crucial for the role the player should fulfill in your team.
Basically, this is a very generic approach to setting up your scouting filters. In the next few days, I will provide a number of examples of how I scout exactly for players within a specific role.