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.
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, I watched the film.
Seeing all those numbers and how they impacted the Oakland A’s made the maths nerd inside me very excited. I love analytics, and I love football and this felt like the perfect way to combine them using my favourite game.
So, what is Moneyball? Well the general idea is to use data and statistics, rather than a generic scouting network, to find undervalued or overlooked players who can be purchased for lower than their market value. Billy Beane was the first to utilise this technique to great effect in baseball as General Manager of Oakland Athletics.
In 2001 the A’s lost the Division Series to the New York Yankees (Football equivalent of play-offs (Kinda)) after winning the first two games of the five game series. They then lost their best three players to free agency. I’d compare this to Leicester’s title winning side losing the Premier League on the final day, then losing Mahrez, Vardy and Kante in the following transfer window.
The following season, due to budget cuts from the owner, the A’s had the lowest wage budget in the league. Billy Beane reasoned that if you had the lowest budget and you did the same as everyone else, you’d finish bottom of the league. And so, out of necessity, invention. Moneyball was born.
Billy Beane, along with Paul DePodesta, used Sabermetrics (the study of baseball using statistics) to source cheap replacements for the stars they had lost. Scott Hatteberg, a former pitcher for the Red Sox was brought in. He had ruptured a nerve in his elbow, and would never throw again but was signed because his on base percentage was very good. David Justice, a great player, but considered past his best, was brought in from the Yankees. The A’s were happy to overlook perceived flaws, trusting the statistics and believing in their principles. Hatteberg went on to hit the home run that would win them their 20th game in a row, an American League record.
So, can this translate to football? Well, it turns out, not so easily. Data is used a lot in scouting and performance management (more on that in the video below if you’re interested), but it can be quite difficult to execute, especially when using it to sign players. Football is a much more dynamic game than baseball. With roles so clearly defined (pitcher, first base etc), it’s easy to see which statistics are relevant. With football, it’s not so easy. However, I have a plan!
Implementing Moneyball in FM
A lot of people interpret Moneyball as buying low and selling high. Whilst you must be willing to let players go, Moneyball is more about finding undervalued statistics and players before anyone else. It’s about using statistics to find players who have more value than the market would suggest. It’s about exploiting market trends to get the most for your money.
So, how do I aim to implement this. I’ve recently moved from Uniao to Crystal Palace, who finished 17th last season (22/23) with one of the lowest wage budgets in the league. Palace seemed like the perfect place to implement Moneyball. With the lowest expectations, there can be no failure.
I have a few pointers to follow, based on the A’s and other sports teams who have been inspired by Moneyball & Soccernomics over the years, such as Southampton. The plan is:
- Get Scout Reports from my Scouting Team
- Separate players into shortlists for each position
- Use Stats to whittle down Scouted players
- Have one or two replacements lined up for each position
It’s important to mention that for each position I am looking at different Key Performance Indicators (KPIs). I will explain this in more detail later on when I discuss my tactics, but essentially the stats I am looking for are dependant on the player.
I will also be attempting to exploit market trends by signing:
- Bosman Players
- Transfer Listed Players
- Players with low Release Clauses
- Old / Injury Prone / Unproven Players
- Players from Lower Leagues / Outside top five European Leagues
Key Performance Indicators
I’ve opted for a Structured 3-5-2 Formation, partially because I want to try it and partially because I can give each position a key role within the tactic. As such, I’ve split my players into 6 main positions.
- Central Midfielders
- Number 10
Each position has different KPIs based on what I want each role to do. For example, my central midfielders need to be comfortable on the ball, able to win the ball back and recycle possession. Therefore, I want players with good passing, tackling, interceptions etc.
After deciding on 10 KPIs for each position, I sorted my already scouted players into separate Shortlists, one for each position. I’ve got a few months to scout players for each shortlist before the January transfer window hits. Hopefully by then I will have several options to choose from in each position, should I need players for my team.
I’ve set up a spreadsheet to record my KPIs so I can compare and analyse data on each player as we approach the January transfer window. We have to be willing to lose players, and when we do, we’ll be bringing in replacements following my rules.
It’s important to remember that this is not a profit making exercise. We are looking to exploit the market and look at the statistics to find undervalued players. We won’t be stockpiling under-23’s with the intention of selling them on at a later date without ever playing for the club. We’re aiming for the best functioning team for the lowest possible outlay.
Before I decided to start this Moneyball save, I signed a few players following my rules without knowing. In future, I’ll be basing all signings on the numbers, rather than attributes.
- Transfer Listed (Req)
- Signed For: £7.5M
- Market Value: £15M
- Release Clause
- Signed For: £6M
- Market Value: £8M
- Free Agent
- Signed For: Free
- Market Value: £24M
- Lower League
- Signed For: £17M
- Market Value: £19.25M
- Unproven / Lower Leagues (Played a handful of games in PL then sold to Rapid Wien)
- Signed For: £11M
- Market Value: £15M
In my next post, we’ll follow my progress through the January transfer window as I line up replacements for my squad, should they be poached by other teams in the league.
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