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| PECOTA's Doppelganger Reports have helped players like David Ortiz. |
In
2008 Derrick Rose was the first pick of the NBA draft, promising stardom and
success for the Chicago Bulls. Only 2 years later, at the age of 22, Rose was named
the youngest ever MVP of league; averaging 25 points, 8 assists, and 4 rebounds
per game. Everything was looking up for the bulls with a rising star and a slew
of draft picks coming in, that was until the worst happened. In 2012 game
against the Philadelphia 76ers Rose tore his ACL, causing him to miss the rest
of the season and the entirety of the next season. Rose never played to the
same caliber again.
The story of Derrick Rose is just a drop
in the bucket. So many times, teams have lost star players due to catastrophic
injuries causing them not only to lose the player but team chemistry, more
games, and fan attendance. Catastrophic Injuries are the achilleas heal of
professional sports franchises, but by using data and analytics teams can now act
to help prevent injuries from occurring in the first place.
The largest innovation in injury analysis
that has made its recent debut in professional sports is called PECOTA (Player
Empirical Comparison and Optimization Test Algorithm). PECOTA is a sabermetric
system that was introduced in 2003 to Major League Baseball in 2003 and has
changed the sport dramatically. The sports analytics system, created by Nate
Silver, forecasts player’s performance in a number of areas and is now being
utilized to prevent catastrophic injuries.
PECOTA’s potential in injury
prevention can best be seen in the use of their doppelganger reports. By taking
the complete history of thousands of Major League Baseball players from past to
present, the sabermetric system is able to compile a list of players most
similar to an individual which allows franchises to accurately zoom in on a
player’s data and get more accurate performance forecasts.
One example of this is shown in how
the Boston Red Sox managed star slugger David Ortiz, before the introduction of
PECOTA into the league it was a consensus that when big hitters decline that
they decline hard and fast. In 2009 Ortiz went into a slump hitting
consistently worse than previous season. By the standard way of thinking the
Red Sox should have cut their losses and traded him away. However, using
PECOTA, the organization was able to get the most bang for their buck. Using a doppelganger
report, the Red Sox were able to accurately zoom in on smaller more accurate
set of data. They saw that players like Ortiz often followed a path of underperforming
in their early thirties and soon make a comeback. The Red Sox decided to take a
chance on the data and it worked out. In 2013 Ortiz lead the Red Sox to world
series batting .688 in the series.
The use of PECOTA has clearly shown the
promise of big data’s use in professional sports. By using doppelganger reports
like the Boston Red Sox, teams can now predict a player’s performance forecast
and prevent injuries much more accurately. By learning from Baseball, I fully
expect other leagues to start investing in similar systems leading to less
catastrophic injuries and overall better performance from athletes.
Sources:
Askew, Luke. “Chicago Bulls History: On This Day in 2011, Derrick Rose Was Named MVP.” Pippen Ain't Easy, FanSided, 3 May 2018, pippenainteasy.com/2018/05/03/day-2011-derrick-rose-named-mvp/.
Baseball Prospectus | Glossary, legacy.baseballprospectus.com/glossary/index.php?search=pecota.
Stephens-Davidowitz, Seth. Everybody Lies: Big Data, New Data, and What the Internet Reveals about Who We Really Are. Dey Street, 2018.
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