Life As a FA

Luck versus Skill June 9, 2016

On my drive home from work late this evening, I was listening to Wharton Moneyball Business XM. They had Michael Mauboussin on, author of The Success Equation: Untangling Skill and Luck in Business, Sports, and InvestingThe Success Equation: Untangling Skill and Luck in Business, Sports, and Investing. Fascinating stuff, discussing the difference between skill and luck, primarily how difficult it is for the human mind to differentiate the two. Humans tend to not just accept something that is, and something that may happen needs a cause or something that created it. There should be a reason for any given occurrence.

In trading or investing, we call some of these events “black swan events”. Companies and markets attempt to assess the risk of these events. Since they’re usually rare and unpredictable, it’s a tough thing to assess risk of something you may not know the cause / effect of at the time it occurs, let alone prior. So we assume that our skill and history will provide the outcome – likely incorrectly, or just by luck we may be correct.

Michael went on to discuss how in business there is a market for possibly being lucky. Using a trader who performed well as an example – recent studies show that it’s increasingly about chance/luck than skill in trading performance. However, if a trader outperforms, they could request a raise or use their performance as the expected amount to move to another company. Depending on the due diligence and statistical/skill assessment of the firms, this creates a market for production by luck.

In reading moneyball and the increasing sports analytics movement, they measure this against regression to the mean in a number of + stats. But in general, pros have a higher skill vs others, and the standard deviation, if you will, of said skills is much smaller. Minor nuances represent the differences in ‘higher’ skill than ‘lower’ at a professional level. A great year by an average pro could result in regression toward the career average. If this is not the case, then that player has probably found an efficiency level that could be affected by actions on their part to reduce the level of variance in that element.

I found the paradox of skill and luck explained very well. Typically, we see the two as a continuum – where on one end luck would play a part such as a roulette table or coins. On the other side, skill – maybe boxing, running. However, it seems to be more array/matrix-like, in that as you increase skill, you increase the dependence on luck. Separation at the most-skilled level involves all kinds of luck.

One author described it using Ted Williams’ .406 batting average in 1941. He had tremendous skill, ahead of most players in the professional leagues. However, that year, he also exhibited a tremendous amount of luck, again more than most players. That combination can attribute to some of the most heralded sporting feats. Our acknowledgement of those streaks come without luck – and that the players were just that skilled. Skilled yes, but also incredibly lucky.

Michael continued to go on about the statistics of lacrosse, and its rules are pulled from hockey and basketball. He noted that Canadian players in college lacrosse are extra efficient. Citing rules of box lacrosse (played usually on a hockey rink, much smaller in comparison to the field as is typical), they aim for smaller goals and have less space to work with. When they get on the field, the added space and larger goal sets them up to be monsters in shooting efficiency. Numbers-wise, 5% of D1 lax players are Canadian. Yet, they make up ~20% of the goals. Additionally, there was a 5% arbitrage between shooting accuracy – overall average was about 28%, non-Canadians shot 27.8% and Canadians? – nearly 33% of shots turned into goals. Astounding.

Statistical notes such as these create fascinating opportunities for further studies and team options, not just in sports but also in business. Taking note and then taking advantage will be easier with the increased abundance in acquiring data, but how much can we direct to noise, and how much is actually signal?