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.
