How to Predict the Firing of Football Managers

How to Predict Firings of
Football Managers

A Freakonomics Approach
Bernard Leong
A Pragmatic Idealist
http://bleongcw.typepad.com

Saturday, February 28, 2009

Earthquakes
P (E) ∼ E −B
B ≈ [1.8, 2.2]

Introduction to Self-Organized Criticality & Earthquakes (Winslow, Michigan)

Self Organized Criticality (SOC)
The Sandpile Analogy

Bak, Tang and Weisenfeld
(1989)

SOC implies scale invariance and “simple” microscopic
mechanism in general.
Ricepile
Earthquakes History
Model

Objects Soil Rice People

Self- Formation of Formation of the Formation of a
Organization tectonic plates Pyramid social hierarchy
Economical,
Wave Newtonian
Mechanism Social & Political
Mechanics Dynamics
Interactions

Criticality Earthquake Avalanche Political Change


SOC in Social Sciences
• Self Organized Criticality has been introduced
to economics in various contexts:

• Paul Krugman on economic geography &
business cycles (1996).

• T. Schelling on segregation of people in cities.
• P. Bak et al on turnover of commodities.


SOC in history?
• Saslaw and Tuck (2000) looked at
tenure lengths (reigns) of Roman,
Chinese, Korean emperors, Doges of
Venice and Popes.

• They found power law distributions
and suggested SOC as an explanation
correlated to political revolution and
change.

• Their problem is that they do not

If it works on earthquakes, and emperors,
why not try it on CEOs?
The data is available but expensive.


So, I suggested the following dataset....

(From BBC sport)
Football Managers

Empirical Evidence of a power law


Ubiquitous?
Power Exponent
Law
English -1.98
✔
Football
French -2.94
✔
Football
Spanish -2.75
✔
Football
Germany -2.51
✔
Football
Europe -2.21
✔
Chairman
National -2.92
✔
Coaches
American Cricket? Tennis?
-2.63
✔
Baseball
American -2.08
✔
Football

A microscopic model
• Let’s try to use a simple diffusion model:
• Step 1: Initialize 20 managers with two attributes:
reputation and tenure length

• Step 2: Each managers play against each other, using
the simple roundrobin and a simple scoring system.

• Step 3: Use a threshold as a criteria to decide
turnover (i.e. ﬁring the manager).

• Step 4: Run it for a hundred years and optimize
with different reputation and tenure length

Criteria for Turnover
• If a manager’s reputation falls below 0 due to
losing games, he is ﬁred.

• If a manager’s reputation has gone beyond the
maximum, he is poached.

• If a manager’s tenure length has reached the
tenure-threshold (say 30 years), he retires.

You are
fired !!!


Let’s see how badly my simulations ﬁt the observations


The issue about the English League


Something happened in the 1960s....
Wage Cap of players
removed
Start of
TV coverage


Economics and Football
Managers
• First, competitive market forces might bring sports leagues to a 
point of maximum efficiency related to self-organized criticality. 
A team wishes to maximize its profit. This means maintaining 
attendance at games, together with maximizing television and 
advertising revenues.

• Second, competition in a sports league is a zero sum game
where what matters is relative, not absolute performance.
Managers, therefore, find themselves involved in an arms
race and subject to the Red Queen principle that the more
things change, the more they stay the same


Acknowledgements

• Toke Aidt and Daniel Sgroi, Faculty of Economics
• William Saslaw
• Laura Webster
• Thomas Down
• And the many people who I convey and test this
crazy idea to....


“The best way to predict the future is to invent it.”
- Alan Kay


How to Predict the Firing of Football Managers

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