Many people today have heard of the concept of sabermetrics due to Moneyball, a Hollywood film and a book about how it was applied to professional baseball to help teams select better players. More specifically, the manager of the Oakland As in 2002 began using sabermetrics to statistically analyze aspects of various players, either to draft them into professional baseball or trade for existing pro players. Everyone knows the best and better players typically cost the most, but professional teams have limited budgets so they struggle to get them.
The powerhouse teams that historically have performed the best - meaning they have the most world and playoff championships – and often have the largest budgets available to pay the highest-rated players. The Oakland As were not one of those powerhouses, so Billy Beane sought a new method of evaluating players to help him find the ones that could help his team but were also affordable. Moneyball helped define new aspects of a player's value and then assigned numbers to those values in order to make more complex and hopefully more accurate predictions about their potential contributions on the field. Since Billy Beane's success in turning the As around from an average team into a playoff division winner, Moneyball no longer could be considered a secret weapon in the ongoing race to find and draft the top players for the least amount of money.
Now, however, there is another kind of statistical analysis available that might also prove beneficial to baseball, except this time it comes from professional basketball. Muthuball was named after its creator, Muthu Alagappan, a student at Stanford University. Alagappan himself was not good at basketball, but he was intrigued with how aspects of reality could be classified, such as diseases and basketball player functions in games, rather than their traditional roles. Of course, he was also interested in quantitative analysis.
He found that based on player statistics he could define at least 13 different player roles, rather than the five that have been used for many years. For example, Tyson Chandler's traditional position is Center, but Muthuball calls him a paint protector, and Chandler has been selected as the NBA's Defensive Player of the Year. Traditionally, a center is the tallest player who is expected to get rebounds and score in the paint. However, individuals are not simple roles, and individuals can contribute to their teams in more ways than the traditional role definition allowed.
One of Alagappan's points is that his analysis could help teams document the problem of having too many players with the same skill sets, so the complexity in their ability to generate offense and respond to their opponents defensively is compromised. For example, if a team had three big men like Tyson Chandler, they would excel at protecting the paint from easy baskets such as dunks and lay-ups, but they would have little offensive output on high percentage shots, so there would be a disadvantage to having them on the same team. It would be much better to surround Tyson with two players such as a long-distance shooting forward that can defend the wing and a rebounding forward who passes well, but doesn't take many shots while also defending against penetration.
Muthuball is not only an analytical approach; it is also utilizes a software program that can be used to map relationships between players based on their performance statistics. Then they can be compared side-by-side to see which ones are the best buys for teams when they have draft picks or are making trades.
So does Muthuball have any benefit when applied to baseball? It isn't clear yet, because it is a new method so there isn't enough data, but it appears to be promising. The software actually could be used for any a number of analyses within a variety of contexts.
Angie Picardo is a staff writer for NerdWallet, a website dedicated to helping baseball fans find the best credit cards.