As the world of technology continues to increase at a torrid pace, the world of professional sports has taken full advantage. At the surface level, most sports are simple in concept. Basketball: they put a ball in a hoop, so they get players who are fast with superior physical attributes; American Football: they move the ball across the field into the endzone, so they get players who can run fast and throw far. However, it’s not quite that simple anymore. Sports teams have always done anything possible in order to get an edge. In this day and age, the edge is obtained by gathering masses of data, which allows team analysts to predict player performance in ways previously thought impossible. If done successfully, a front office can assemble a team of highly talented and productive players for a small price tag.
This is why large scale events called “Hackathons” have been popping up all across the United States. A Hackathon is a competition where statisticians, analysts, and innovators of all ages and experiences gather to present their ideas and projects to a panel of executives that are looking to better their product. The NBA hosted their first ever Analytics Hackathon in New York on Sept. 26, where GMs gathered in hopes of bettering their teams’ “products,” which boils down to their ability to win an NBA Championship. Two USF students participated, senior computer science majors Ray Wang and Courtni Wong. When asked about the experience, Wang said, “It was cool seeing NBA execs, players and coaches. I’m glad Adam Silver is the commissioner. Just being a part of it all was dope.”
College students, graduates, and professional analysts gathered from all across the country, showcasing their projects in hopes of winning the grand prize: lunch with an NBA team’s front office staff and free tickets to one of their games. The projects ranged from the creation of new stats that analyze different aspects of the game, improved algorithms to pre-existing stats, creative ways to visualize data, and much more.
An aspect of the Hackathons that is becoming increasingly needed is data visualization. With all of the new innovations in statistics, visualization methods become just as important. Data visualization is extremely important to analytics, because it allows for many of the new statistics being developed to be shared and easily comprehended. It’s no fun to stare at a bunch of numbers — but to take a look at a chart, web, or graph that is visually appealing allows for the information to be effectively conveyed.
After all of this you may be wondering: why are stats so important?
In the NBA and NFL, there are strict salary caps that an organization’s payroll must stay within when assembling a team; in the MLB there is a hefty fine on the owner if a team’s payroll is over a set amount. There’s a large incentive for discovering talent in small-name or unproven players since they can be signed to play for a small sum of money. A lesser payroll means no stress on the owner’s wallet; and more importantly, the ability to sign established big name players and offer them more lucrative deals.
It all started in 1997 when a young scout by the name of Billy Beane was promoted to become the General Manager of the Oakland Athletics. The A’s had reached the World Series three consecutive times in 1988, 1989, and 1990, but each time did so with the highest payroll in baseball. Owners ordered the previous GM to cut down on the high spending, and thus began the A’s investment in sabermetrics. Beane was mentored by the former A’s GM Sandy Alderson and taught which stats are over and undervalued, most important being on base percentage — if you’ve ever watched or read “Moneyball,” you’ll fondly remember the line “Can he get on base?”
Beane then promptly wove together a team of no-namers and a few bargain stars and took the A’s from a 65-97 record in 1997, to an AL West crown in 2000, and a 100-win team in 2001 — all while having the 6th lowest payroll in all of Major League Baseball. Beane and the A’s front office analyzed the game and its statistics through a different looking glass, which allowed them to sign very productive players for ridiculously low prices. Most teams at the time focused on premier stats like batting average, home runs, runs batted in, and stolen bases; they instead focused on pitcher’s ability to get outs and keep the walks down, or a position player’s ability to get on base, of course. This led to a patchwork team of golden bargains winning the AL West in four consecutive years (2000-2003).
The main reason that the A’s were able to assemble this talent was because the staff was ahead of the curve. No other team’s front office was looking at the game and its players like they were, and it led to the Athletics being able to sign unheralded players with lots of potential for much less than what they were worth. For example, when Barry Zito won the Cy Young in 2002 (23-5, 2.75 ERA) his salary was a mere $500,000. Compare that to his years on the San Francisco Giants where he was making about $15,000,000 a year. The fact that the A’s were able to get huge bargains in award winners like Zito, All-Stars Tim Hudson and Mark Mulder, and 2002 AL MVP Miguel Tejada was incredible.
It pays off to be ahead of the curve, which is why sports teams all across the country are pouring money and resources into finding the best minds that have the capabilities to produce new ways to analyze players and expenses. If a team uncovers something big, then they gain an edge; one that could be valuable to hoisting up the league championship trophy when their respective seasons come to an end.
Photo Credits: Angelhack.com