Moneyball and Risk Analytics
With the World Series wrapping up, it reminded me of Moneyball, a 2011 film based on an account of the Oakland Athletics baseball team's 2002 season and their general manager Billy Beane's attempts to assemble a competitive team. In the film, Beane and assistant general manager Pete Brand, a math whiz straight out of Yale University, were faced with one of the league’s lowest budgets for players, yet they built a team of undervalued talent by taking a sophisticated sabermetric approach to scouting and analyzing players. This approach flew in the face of traditional scouting made up of men who believed that they could predict a player’s future success simply by observing how well they could hit a ball, throw a pitch, or steal a base.
After Beane’s wheeling and dealing for players that fit the mathematical profile, the A’s were reborn, going on to qualify for the playoffs and win the AL West Division with a 2002 regular season record of 103-59—just behind the Yankees for the best record in all of Major League Baseball.
What does this have to do with risk management?
One of the traditional ways of evaluating risks is on a qualitative scale, such as high/medium/low, 1 – 5, - the typical approach to batting, pitching or stealing bases. However, as David Vose of Archer points out, “when (should) the probability of a risk be described as low? Below 10%? How about very low? Below 1%?” He goes on to say, “Qualitative terms describing risk are far too ambiguous, too difficult to challenge and agree upon, make poor use of available data and do not allow us to work out the most efficient risk management strategy.”
This qualitative approach is like the baseball scouts that rated batters as ‘superior’ or ‘average’. Both ways of rating risks and batters are inherently biased. Though these measures are useful under some circumstances, they don’t tell you about the potential impacts in dollars and cents; terms decision-makers can act on. Billy tells the old-school scouts that they must do something differently if they’re going to win with the salary restrictions they have.
Billy and Pete took a different, quantitative approach to arrive at the outcome they wanted, which was to win. They calculated the interim goals that would get them there, like average runs they needed per game, on base percentage, etc. Then they selected the least expensive or most undervalued players with the right performance metrics that met their criteria which maximized their budget.
Businesses need to make money, turn a profit, and meet revenue goals and market expectations. Executives make decisions every day on business growth strategies, competitive moves or organizational changes based on the financial benefit or cost. For these executives to evaluate whether they should spend resources to address a risk versus seize a business opportunity, they need to compare the cost and benefit against each other – in “apples to apples” terms. In its most simple terms, what’s the cost and the benefit of the risk? Risk quantification is the art and science of understanding the monetary impacts risks could have on the organization’s goals and strategies.
Risk quantification puts risk management into the language executives need to evaluate risks against the business’ strategic and operational goals and is particularly important when risks are present that threaten the organization’s ability to meet its goals – just look back at the impacts the pandemic had on businesses and industries of all type and size.
The sabermetric approach to scouting and analyzing players, and the quantitative approach to measuring risks both start with the end in mind, and that’s wins and achievement of strategic goals – both of which are why the game is played.