Loser's versus Winner's game

Simon Ramo identified the crucial difference between a Winner's Game and a Loser's Game in his excellent book on playing strategy, Extraordinary Tennis for the Ordinary Tennis Player. Over a period of many years, he observed that tennis was not one game but two. One game of tennis is played by professionals and a very few gifted amateurs; the other is played by all the rest of us.
Although players in both games use the same equipment, dress, rules and scoring, and conform to the same etiquette and customs, the basic natures of their two games are almost entirely different. After extensive scientific and statistical analysis, Dr. Ramo summed it up this way: Professionals win points; amateurs lose points. Professional tennis players stroke the ball with strong, well aimed shots, through long and often exciting rallies, until one player is able to drive the ball just beyond the reach of his opponent. Errors are seldom made by these splendid players. Expert tennis is what I call a Winner's Game. Amateur tennis, Ramo found, is almost entirely different. Brilliant shots, long and exciting rallies, and seemingly miraculous recoveries are few and far between. On the other hand, the ball is fairly often hit into the net or out of bounds, and double faults at service are not uncommon. The amateur duffer seldom beats his opponent, but he beats himself all the time. The victor in this game of tennis gets a higher score than the Opponent, but he gets that higher score because his opponent is losing even more points.
As a scientist and statistician, Dr. Ramo gathered data to test his hypothesis. And he did it in a very clever way. Instead of keeping conventional game scores---"Love," "Fifteen All," "Thirty-Fifteen," etc.-~amo simply counted points won versus points lost. And here is what he found. In expert tennis, about 80 percent of the points are won; in amateur tennis, about 80 percent of the points are lost. In other words, professional tennis is a Winner's Game--the final outcome is determined by the activities of the winner--and amateur tennis is a Loser's Game---the final outcome is determine d by the activities of the loser. The two games are, in their fundamental characteristic, not at all the same. They are opposites.
From this discovery of the two kinds of tennis, Dr. Ramo builds a complete strategy by which ordinary tennis players can win games, sets and matches again and again by following the simple strategy of losing less, and letting the opponent defeat himself.
Dr. Ramo explains that if you choose to win at tennis--as opposed to having a good time--the strategy for winning is to avoid mistakes. The way to avoid mistakes is to be conservative and keep the ball in play, letting the other fellow have plenty of room in which to blunder his way to defeat, because he, being an amateur (and probably not having read Ramo's book) will play a losing game and not know it.

Rule of the game (example tennis)

When we watch a sporting event like a tennis tournament we might hope for a favorite player to win and even feel upset if he loses. However, we don’t often question the rules of the tournament. Lewis Carroll, better known as the author of Alice in Wonderland, explains why the typical tournament structure often fails to award best players the top prizes and offers an alternative method.

In an elimination tournament, each player can only advance along a certain path toward the final. As each player moves through this space, the field is narrowed, until the top prizes are determined. The structure of the space critically influences who finishes well in the tournament. A competition that seems at first glance to be fairly structured to filter out the weaker players may, in fact, not be good at all at selecting the best competitors. In any competition it is not just skill and lucky breaks that determine the winners; the rules of the competition itself determine who will finish well. Many competitions are structured to correctly determine only first place; the second and third prizes are very much subject to chance.

Let us take, as an example of the present method, a Tournament of 32 competitors with 4 prizes.

On the 1st day, these contend in 16 pairs: on the 2nd day, the 16 Winners contend in 8 pairs, the Losers being excluded from further competition: on the 3rd day, the 8 Winners contend in 4 pairs: on the 4th day, the 4 Winners (who are now known to be the 4 Prize-Men) contend in 2 pairs; and on the 5th day, the 2 Winners contend together to decide which is to take the 1st prize and which the 2nd -- the two Losers having no further contest, as the 3rd and 4th prizes are of equal value.

Now, if we divide the list of competitors, arranged in the order in which they are paired, into 4 sections, we may see that all that this method really does is to ascertain who is best in each section, then who is best in each half of the list, and then who is best of all. The best of all (and this is the only equitable result arrived at) wins the 1st prize: the best in the other half of the list wins the 2nd: and the best men in the two sections not yet represented by a champion win the other two prizes. If the Players had chanced to be paired in the order of merit, the 17th best Player would necessarily carry off the 2nd prize, and the 9th and 25th best the 3rd and 4th! This of course is an extreme case: but anything within these limits is possible: e.g. any competitor, from the 3rd best to the 17th best, may, by the mere accidental arrangement of pairs, and by no means as a result of his own skill, carry off the 2nd prize. As a mathematical fact, the chance that the 2nd best Player will get the prize he deserves is only 16/31sts; while the chance that the best 4 shall get their proper prizes is so small, that the odds are 12 to 1 against its happening!

Therefore don't assume that the rules of the game favor the best. The rules of the games are oftentimes predetermined and one can either influence those or even change the entry point into the competition.

Source: BCG, Strategy institute

Fishing and startegy

The pike is one of the most efficient, lean predating machines in freshwater. If you put a small pike in an aquarium with a bunch of minnows it will demonstrate its predatory skills with frightening efficiency. If you separate the pike from the minnows using a sheet of perspex the pike will continue to launch its attacks for a little while. And then it will just give up. You can then remove the sheet of perspex and the pike will still believe that it can no longer catch its prey - and will simply starve to death.

This little tale is similar to the "Learned helplessness" theory which can also be found in my blog and is posted at an earlier date.

Flies, Bees and strategy

Imagine putting half a dozen house flies and half a dozen bumble bees in glass bottle. The bottle is placed with its base towards a window and the open end towards the middle of the room. The bees are strategically aligned to fly towards the sunlight. The presence of the glass is a mystery to them. They buzz and buzz away at the bottom of the glass driving towards the sunshine - until they too die. The flies on the other hand are much less ’strategically aligned’. They fly in far more random patterns and within a few minutes most of them will have found their way to freedom.

Are you thinking aligned liked bees towards the goal or are you trying various options, some which seem to be absurd?

Hiring and landlords

Whenever you hire someone be cautious if you speak to their current employer as a reference. If the candidate is a non-performer chances are that the current employer dislikes him/her and is likely to get rid of the person. Consequently, the reference would be good to expedite the transition out of the company. Therefore don't only ask the current employer but the one before (!) the current position. That employer is much more likely to give you the unbiased truth. Same holds true for renters that want to move into one of your properties. Don't stick to checking their current landlord. If they are messies chances are that their current landlord wants to get rid of them and is giving you a great reference for them.

Robert Frank @ Google

Data centers and airports

How would you create an effective network from scratch? Sure, you could experiment and then evolve over time. But what if you had to build a good network right now and couldn't experiment?
One idea came to my mind on my bike on the way to work: could you copy the size and location of airports for the efficient placement of distributed data centers? This might be a good predictor for suitable places. Why? One assumption is that airports are located at places where there is a need, either direct need or indirect need as a hub. One can also argue that over time more effective and better located airports grew whereas airports at unpopulated areas decreased in size and vanished. Finally, the cost structure is similar: high fixed costs and little variable costs.
Here are two examples: my previous hometown Charlottesville has only about 40.000 inhabitants and has a small airport. Considering the fixed costs of an airport (btw, you can compare those costs to a data center) one would only invest if there's enough return, i.e. traffic. Smaller town won't probably have an airport or only for recreational purposes. Of course large cities such as San Francisco have large airports to serve the local population. Now let's look at hubs. For hubs the economics are similar and to a part totally different from regular airports. Hubs channel traffic and require a larger size. Hence, location between centers and size are important. It is no surprise that Atlanta and Chicago are major hubs. Certainly real estate prices are cheaper than in Manhattan than in Georgia and it helps that both airports are in the middle of the country.
So why should we care? Both categories (data centers and airports) are totally different but share in their core the same characteristics. One can certainly draw useful conclusions from the case above and I challenge you to find similar cases for your business / situation.

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