Strat-O-Matic could be played in a basic format but also had options for using advanced rules and statistics. This was decades before SABRmetrics – deep analytics from Society of American Baseball Research members – swept through baseball delivering more insightful ways to evaluate a player’s performance and tendencies in certain situations.
Baseball is the most statistically driven and scrutinized sport and we can now digest piles of data about each game. MLB Advanced Media tracks not only the outcome of each play but the actual path of movement of players, velocity and trajectory of pitches and batted balls, an astounding amount of arcane data.
All of this information changes how players are evaluated, salaries are allocated, and teams are built through draft and development, trades or free agent acquisitions.
There is historical comparison, peer comparison, situational statistics and more. It’s hard to fathom that the information that you used to be able to access in the eight-pound baseball encyclopedia is now a limited set of reference points. Now, sites like baseball-reference.com, fangraphs.com, brooksbaseball.net provide details that a decade ago nobody even thought to ask about.
Investing offers similar weight of data to evaluate company stocks, bonds, economic conditions, investor psychology and so on.
It is one thing to evaluate past returns and current value based on something fairly simple like the price/earnings ratio of a stock. But investment evaluation goes much deeper with formulas, algorithms, and even a measure called batting average that evaluates how an investment manager’s results compare to an unmanaged benchmark.
Unfortunately, many humans aren’t very good at interpreting data and understanding probability. Most of us rely on computer programs to direct us toward conclusions. And most of those conclusions, supported by reams of data, are still informed only by the past. They aren’t very representative of future reality. We can build investment portfolios that optimize for certain past conditions but it’s impossible to be perfectly positioned for what is to come: Will company earnings continue to grow and justify higher stock prices? How far and how fast will interest rates climb – if they ever begin to? Will the global economy be dragged down by Eurozone stagnation or will that be offset by emerging market demographic trends? We can speculate about the future but only make decisions based on past precedent.
Past performance doesn’t stop baseball general managers from offering obscenely lucrative long-term contracts to players on the downslope of their careers so it shouldn’t be too surprising that investors steer a lot of money based on past performance of money managers. This past performance may have no higher likelihood of paying off again than Josh Hamilton will making $30 million dollars for the Angels in 2016 or Robinson Cano will for the Mariners at the end of his 10-year deal in 2023.
Takeaway: Advancements in data management have taken performance evaluation and attribution to a new level of understanding.
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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