When Hall of Fame outfielder Tony Gwynn died in June, I thought of Strat-O-Matic Baseball. Strat-O-Matic is a game where roles of dice determine outcomes documented on each player’s card. The player cards are updated each season. For 30 years, I’ve had the 1983 Strat-0-Matic season in my closet. This was Tony Gwynn’s rookie season.
I pulled out his card the day of his death and was reminded about how probabilities and luck in Strat-0-Matic have a lot of crossover to investing. In Strat-O-Matic you manage a team through a game by rolling dice and consulting the player card for the outcome based on probabilities derived from actual past performance of the player. Tony Gwynn over the course of his career would have a lot more hits on his card than the average player – but not much power. Notice there are no home run results on Gwynn’s 1983 card.
There is certainly luck involved in any single roll of the dice but if you play long enough, probabilities should win out. Just as with coin flipping, you could get heads the first 10 times in a row but if you flip it 1,000 times you’ll be much more likely to end up with close to 500 each.
My friend Jason Hoseney had the 1982 Strat-O-Matic set. We often played All-Star Games pulling our favorite players together. We attempted to build All-Star teams of specialists each filling a role to build a complete team. You wanted a good leadoff hitter who could steal bases, middle of the lineup power but not players who grounded into too many double plays, etc. With the right players in the lineup you should win more than you lose over the course of a season.
Since the 1980s, baseball has evolved even further into a game of specialists filling specific roles. You can’t put nine first baseman on the field and expect positive results over the long run. And you need to use specialists in certain positions more than others. Pitchers, catcher and shortstop have critically specific skill sets.
Investment management is similar in many regards. You use specialists where they add the most value. A balanced investment approach – one that considers the broad universe of return-seeking opportunities and risk management – requires a diverse mix of specialists to play specific roles in the portfolio.
And there is absolutely a mix of luck vs. skill that can be hard to evaluate with investment managers. Sometimes, a single lucky event can be misinterpreted as skill and can be so influential in an investment manager’s success that a single good idea impacts the stated performance of the manager for years to come. But the probability of the investment manager being able to identify these big winners and implement well-timed entry and exit points in these positions is not high.
(For more on the difference between skill and luck as it relates to sports and investing, I recommend that you read The Success Equation by Michael Mauboussin.)
Probabilities of outcomes have become much more prominent in baseball analysis over the past several years due to powerful and swift databases crunching numbers. An example: How does the probability of scoring a run change from having no outs and a runner on first base compared to one out and a runner on second base? This would be a key consideration of a manager who is deciding whether or not to sacrifice bunt.* Of course, the game situation influences the decision. Who is at bat? What is their history vs. the pitcher? Could the runner score from second on a single? Should the manager pinch hit with a batter better suited to the desired outcome of the moment?
*According to Tom Tango’s run expectancy matrix, from 1993-2010 there was a .441 chance that a runner at first and no outs would produce a run during the inning and a .418 chance that a runner on second and one out would produce a run. Maybe the sacrifice bunt is overused, unless it is to move runners from 2nd to 3rd base where the chance of scoring improves by giving up an out to advance the runner.
My partners and I rely on probabilities of outcomes when creating long-term financial planning and asset growth projections. We use a program called Money Guide Pro – the Strat-O-Matic of financial planning – to model how well current assets, future income streams (i.e. Social Security) and expected investment returns can be expected to satisfy the client’s stated goals for retirement income, college savings, travel, health care and so on.
The program’s simulations demonstrate how probable it is that under the assumptions in the plan (annual savings, life expectancy, taxes, inflation, etc.) the goals will be funded for the client.
We’re comfortable if the goals are funded with a 70-80% probability. There’s no need to have 100% probability of success because only the worst possible scenario could cause shortfall in funding the goals. This would be the sort of extremely low probability that isn’t worth planning for.
We know that unexpected, random outcomes will occur along the way. Once in a while, even Rickey Henderson would get caught stealing (although in Strat-O-Matic it would require flipping an 18, 19, or 20 card – a 3 in 20 chance – for the all-time stolen base leader to be thrown out).
Probabilities of some outcomes are much clearer than others. The coin flip is always 50-50. But especially in the investment world, there is always an element of uncertainty that defines the relationship between risk and reward. Probabilities only define the most likely base case. There are alternative outcomes, sometimes with wide-ranging results.
Even if we could accurately gauge probability of investment outcomes, making consistently optimal decisions and having them work out that way will not happen every time. There are many varied factors and uncertainties involved in money management – not to mention the psychological aspects of money and life goals. These factors are fluid and can disrupt the probabilities. Retire a year earlier and the probability of funding retirement income changes. Increase or decrease the required living expenses and the probability moves again.
The challenge in financial planning and investment management is that we can optimize for past results and probabilities very easily and still not have much control over future outcomes. This is especially true when we move beyond numbers in spreadsheets and consider that circumstances and goals change as people transition through phases of life.
Takeaway: Understand probabilities, don’t expect results to follow them exactly
Investing and baseball (Part 1) – What do they have in common?
Investing and baseball (Part 2) – Tony Gwynn and Understanding Probability
Investing and baseball (Part 3) – Statistical Analysis
Investing and baseball (Part 4) – Team building and investment portfolio
Investing and baseball (Part 5) – An insider’s perspective on the game
Investing and baseball (Part 6) – Ongoing re-evaluation and adaptation
Investing and baseball (Part 7) – Keeping scores can be a matter of perspective
Investing and baseball (Part 8) – The rarity of most valuable “players”
Investing and baseball (Part 9) – Fantasy baseball (growth vs value)
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