Sports Fans Want Data, Fantasy Sports Fans Need It
GEOINT Foreword presentations discuss the use of geospatial data for human performance
A bit of advice—don’t play Aaron Baughman, a principal data scientist at IBM, in fantasy football.
The average fantasy sports players use only about 3.9 data sources in research to pick their teams for the season and starting lineups from week to week, but Baughman employs thousands of data points with help from IBM’s Watson, the Jeopardy-winning supercomputer.
Fantasy football is only one among a growing list of use cases for data analytics explored Sunday during a series of presentations on GEOINT in Sports at GEOINT Foreword—the Symposium’s pre-conference science and technology day.
Baughman, who is with IBM’s sports and entertainment division, works with major professional tennis tournaments (such as the Australian, French, and U.S. Opens as well as Wimbledon) and golf’s Masters and Pebble Beach tournaments, in addition to the Grammys. He applies artificial intelligence and machine learning to crunch numbers and derive algorithms to create varied products such as highlight reels, statistical analysis for television use, and even fashion trends.
He and IBM recently struck a chord with ESPN to contribute to its fantasy football program. Baughman’s fantasy statistics last season: a 13-0 record in a 14-team NFL fantasy league, in a season in which he scored 1,472.2 points, nearly 200 points more than the league runner-up. He finished the season by winning the playoffs, outscoring the runner-up by nearly 80 points.
The next panelist, Rocco Pecora, a BI solutions architect with SME Solutions Group Inc., uses data in a different way, and often has to articulate its value to skeptical clients.
“Nobody wants to be first, but there’s a race to be second,” said Pecora, a soccer enthusiast from his days at Florida Atlantic University.
The reason for the dichotomy of analytical data adopters in sports, according to Pecora, is “culture, both as an influencer and obstacle.” The key is the ability to effectively apply data, as opposed to merely collecting it.
Pecora offered Super Bowl-champion Philadelphia Eagles and the Cleveland Browns as examples. Both collect data, but “the Cleveland Browns have proven that, no matter how many Ivy League data scientists you have, it won’t get you into the playoffs. He added, “I think the Philadelphia Eagles … within two years, made not one change in the analytical staff. The only thing that changed was the way (former coach) Chip Kelly created processes to extract data and provide decision-making from the way (current coach) Doug Pederson did.”
Pederson coached the Eagles to the Super Bowl title in 2018, while the Browns have lost 31 of their last 32 games.
Dr. Dave Warner, director of medical intelligence with MindTel, gave the final presentation, in which he spoke of analyzing the careers of every Major League Baseball player with more than 500 at-bats since the 1800s to determine when performance-enhancing drugs were most pervasive.
More important was Warner’s work in Afghanistan, where he ran a human terrain counterinsurgency operation using geospatial techniques. Children, Warner learned, wanted to play cricket.
“When cricket games were happening, it sort of tamped out the violence between Taliban and Pashtun,” he said.
So Warner’s organization decided to fund youth cricket teams. Yet war in Afghanistan persists, with intermittent and brief periods of peace.
“Turns out by using the geospatial information, by using the sociocultural information, I was able to do precision-strike cricket tournaments right in the heart of some really bad places,” Warner said, eliciting a laugh from the audience, which stilled when he added: “Sometimes you just need to buy a day or two [of peace] to tamp things down, to let things go. It’s stunning.”
Posted in: GEOINT Foreword, GEOINT Symposium Tagged in: 2018 Show Daily Day 2, Data, Humanitarian Issues, Machine Learning & AI
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