Moneyball: Applying Sabermetrics to Football Manager

#1 Johnno

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Group: Members
Posts: 3
Joined: 21-August 10

Posted 17 July 2011 - 04:48 PM

For those who don't know Moneyball: The Art of Winning an Unfair Game is a book about American baseball coach Billy Beane. He is coach of Oakland Athletics and they struggled to compete with the likes of big spending New York Yankees. He developed a statistical model, known as sabermetrics, which allowed him to identify undervalued players and compete with the best teams in the league.

Fenway Sports Group bought the Boston Red Sox and applied a similar transfer strategy which resulted in winning the world series. Since then they have purchased Liverpool FC and are trying to emulate the successes of the Red Sox buy buying players based on rules outlined in Moneyball and using statistical analysis to identify players.

Sabermetrics is based around the premise that the traditional way of determining a players worth is flawed. It also aims to find an individual players contribution to a winning performance.

Baseball consists of many discrete events (hundreds per player per season), each measurable as a contribution to run scoring. In football, the relevant events are goals, but they are rare. And, for more common events, like "passes completed," the exact contribution to goal scoring or goal prevention is often not obvious.

Historically the following stats have been used to evaluate how well a player has performed.

Goals

Assists

Clean sheets

Pass completion %

Shots on target %

Tackles completion %

All of these are dependent on the skill level of other team mates. Imagine if you put your favourite premier league midfielder into a non league side, do you think his assists would be as high as if he was playing for the league champions? Somebody has to finish those chances for him.

What stats are available in Football Manager that allow us to more accurately evaluate a players contribution.

Tackles per game

Passes completed per 90 mins

Shots on target 90 mins

Headers won 90 mins

Conceeded per 90 mins

Distance covered per 90

Dribbles per game

Using a combination of the above stats with the desired position we should be able to identify some undervalued players.

Rules of Moneyball

In addition to identifying players based on statistics there are rules on transfer policy.

1. Draw opinion from several sources.
So you have identified a player using sabermetrics, now you need to scout him and then scout him again with a different member of staff.

2. Sell a player before buyers see a deterioration in his game.
Arsene Wenger is the example to follow here, can you think of a player Arsenal have sold who has gone on to be as good as he was for Arsenal?

3. Sell any player for the right price.
Every player you buy is an investment, and its about maximising your return before that players value will start to decrease.

4. Buy players in their early twenties.
Teenagers are underdeveloped and may never reach their potential and as such are high risk investments. Older players have less chance of an increase sell on value. Players in their early twenties are sufficiently developed to gauge whether they are likely to reach their potential, and will have increased sale value when they reach their peak.

I plan to play a career game using these principles and perhaps ill update this thread to let you know how it works, i would be interested if other people used these rules and to see how much success they had.

I started a game and holidayed a year to compile a first eleven using the stats to identify players and filtering out anyone over 25. So if money was no object this would be my first 11.

(I used an updated transfers db)

GK - Neto - Fiorentina
DR - Rafael - Man utd or Dejan Lovren - Lyon (stats almost identical)
DL - Gael Clichy - Man City
DC- Gerard Pique - Barcelona
DC- Mamadou Sakho - Paris Saint Germain
MCd - Yann M'Villa - Stade Rennais
MCa - Javier Pastore - Palermo
MR - Lionel Messi - Barcelona
ML - Stevan Jotevic - Fiorentina
ST - Edinsaon Cavani - Napoli
ST - Diego Costa - At Madrid

I hope to apply all of this to the lower leagues and see how far i can go.

UPDATE:
I decided to start my game after the first season holidayed so i have some stats to start with. Southampton have been relegated to League 1 so I thought, they would be perfect to test the system out and have the infrastructure in place if i move up the leagues quickly.

I am using a basic framework to identify players:
Only players who have 1800 mins played will be considered for analysis

GK
G/90 < 1.25

DC
H/90 > 5.0
T/pg > 4.0

DRL
T/pg > 3.5
DC/90 > 5.0
Pc/90 > 20.0

DM
T/pg > 4.0
DC/90 > 5.0
Pc/90 > 25.0

AM
Pc/90 > 25.0
Dr/pg > 2.0
ST/90 > 1.0

ST
Pc/90 > 15.0
Dr/pg > 2.0
ST/90 > 1.5

Anyone who meets the criteria is then scouted by each of my scouts

#2 Xulu

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Group: Coaching Team
Posts: 215
Joined: 03-June 10

Posted 18 July 2011 - 01:16 AM

I read Moneyball some years ago. It was pretty good. In addition, I have played, coached, and officiated both baseball and football. I believe that while principles of Moneyball are useful for football, using it as a whole strategy is flawed.

Beane was quite a success with the A's, but the Athletics never did win the World Series. Why? Beane and others attribute that to the randomness in the postseason. Moneyball is designed to work over a long period of time (the regular season in MLB), but it will often fail to work in the completely random MLB postseason. I would assume the easiest correlation would be that Moneyball principles would work well for a League competition, but do less well in Cup competitions (including Champions/Europa Leagues and MLS/promotion playoffs).

As a personal note (and the beef of my counter-argument)- Gael Clichy. Clichy no doubt had excellent statistics according to sabermetric principles. Clichy had a higher match rating than Bacary Sagna, and Sagna is regarded to be among the top 2 or 3 right backs in the EPL. Only Leighton Baines had a higher match rating for EPL left backs than Clichy. Clichy registered a single point the entire campaign (1 assist), but took care of the ball much better and broke up more attacks than almost any other EPL left back on a per game basis. So Clichy is pretty damn good according to the stats, what's the problem?

The problem arises because baseball and football are two very different games in regards to measuring statistics. Baseball is - fundamentally - a duel between pitcher and batter. It is extremely easy to isolate the statistics of a baseball player and quickly evaluate their true talents in most fields besides outfield defense. Hitters can be easily tracked through OPS instead of BA and pitchers through WHIP instead of ERA, while all players can get a nice clean VORP rating. All of this is possible because the individual players in baseball are so easily isolated statistically that they can be evaluated on their performance alone much easier.

So what of football? Well the statistics play a big part, but why does Gael Clichy make so many more tackles, interceptions and clearances per game? In baseball, this would be easily isolated by VORP. Would Leighton Baines, Ashley Cole, Patrice Evra, Kieran Gibbs, whoever, be better at LB for Arsenal? The answer is much more unclear than trying to replace Derek Jeter at shortstop. Why is it so much more unclear? Because football is a fluid game in which all players are moving around and interacting with each other. This does not happen in baseball.

On Arsenal's right flank, Theo Walcott (usually) would work very hard and track back to assist Sagna. Walcott played much wider than his AML partner, and that allowed Walcott to cover the opposition LB. On the other flank, Samir Nasri and Andrei Arshavin would play much more narrow and track back less. That would free the opposition RB from his marker and allow the opposition MR/AMR to place more pressure on Clichy. That means that Clichy has to do more work than Sagna and can often be targeted more for attacks because he is so vulnerable. To further hinder Clichy is the style of the central midfielders. Jack Wilshere plays on the left and bursts forward quite often as a passer. He is less defensively solid than the right central midfielder - Alex Song - who plays further back to provide more defensive stability. So the two players in front of Sagna are more aware and capable of attending to defensive responsibilities (Walcott/Song) than Clichy (Nasri/Wilshere).

Hence the problem with Clichy. He is called into action more than Sagna or most other EPL LBs due to the team shape and the attitudes of the players in front of him. Therefore, he has more chances to put up statistics. He does this well. However, other factors come into play. Clichy is terrible at positioning himself to lay an offside trap. Very often, Clichy plays an opposition attacker onside. This often leads to a chance, goal, or penalty. Clichy has fundamental weaknesses in his game like that. Many of those weaknesses do not show up on an individual stats sheet. Instead, they show up on the team sheet as something far worse.

American football is a good example of statistics not always being right. Nnamdi Asomugha is the most sought after free agent and will sign a huge contract despite barely registering any statistics on a poor team (Oakland). Despite registering almost no statistics, he would undoubtedly be the first choice Cornerback for almost every NFL team. Why? Asomugha is so good at covering his man in the Oakland man-to-man scheme that opposing teams do not throw to the receiver covered by him. The result is that Asomugha sees very little action despite being one of the best players in the entire league.

While I agree that there are ways to be more efficient than other teams and gain advantages, statistics alone will not do. Other methods will need to be considered before individual players are "moneyballed".