Perhaps what has been missed is the difference between a single coin flip and a combination of coin flips?
Consider one startup.
f(x) = #fail
succeeds better than 50%, and
f(x) = #succeed
succeeds less than 50%. This is due to the nature of startups.
Sure, it's easy to get about 50% accuracy for one startup by flipping a coin:
f(x) = if rand(1) > 0.5 then #fail else #succeed
But consider the case of two companies A and B. There are now four outcomes:
A = #fail, B = #fail
A = #succeed, B = #fail
A = #fail, B = #succeed
A = #succeed, B = #succeed
If we flip a coin, we have to flip it twice. Our probability that two coin flips match the correct tuple is 25%, and bumping that up to 50% is a massive improvement.
Investors diversify their portfolios. In a portfolio of 100 startups there's probably a winner. Improving the selection of companies means reducing the number of a fund's portfolio companies necessary for a reasonable probability of a winner. More smaller yet successful funds makes capital more efficient.
Better pruning of boolean search spaces has real value. Hence:
When predicating that a company will fail,
he adds, they’re right 88 percent of the time.
That's... not accurate. There are two success conditions (Fail, Fail) and (Succeed, Succeed), and two failure conditions (Succeed, Fail), (Fail, Succeed). If you flip two coins, the chance that they come up both heads is 25%, but the chance that they come up the same is 50%.
The odds of (#fail, #fail) for two startups (A, B) are much greater than 50%. When investing in two companies (#fail, #fail) is not #success.
At a 1% #success probability the failure rate is ~98%. The 1% #success rate is based on the a knowledgeable person choosing A and independently choosing B. If that person can obtain information that lets them improve their selections to %2 #success probability, they can reduce the total number of investments necessary to achieve any particular expected return on investment.
Reducing the number of investments may improve the investor's ability to influencing the outcome of each company in their portfolio, because the investor can allocate more time, energy, and resources to each company in their portfolio [resuming the investor brings business expertise to the table].
The article is claiming that the guy can predict which things are going to succeed and which are going to fail, not make them succeed or fail. His evidence for this is that 50% of the time, he's right (e.g. [success, success], [fail, fail] are both success conditions for him). For him to be adding any information to the system, he has to get it right more often than either choosing randomly or using a fixed zero-information strategy (always bet fail/always bet succeed). You explicitly said that the joint probability matters, but then miscalculated the joint probability of him guessing correctly by random chance.
Consider one startup.
succeeds better than 50%, and succeeds less than 50%. This is due to the nature of startups.Sure, it's easy to get about 50% accuracy for one startup by flipping a coin:
But consider the case of two companies A and B. There are now four outcomes: If we flip a coin, we have to flip it twice. Our probability that two coin flips match the correct tuple is 25%, and bumping that up to 50% is a massive improvement.Investors diversify their portfolios. In a portfolio of 100 startups there's probably a winner. Improving the selection of companies means reducing the number of a fund's portfolio companies necessary for a reasonable probability of a winner. More smaller yet successful funds makes capital more efficient.
Better pruning of boolean search spaces has real value. Hence: