Steve Ballmer breaks down the numbers behind the LA Clippers’ historic NBA playoff comeback win

Steve Ballmer breaks down the numbers behind the LA Clippers’ historic NBA playoff comeback win

7:28pm, 17th April, 2019
Steve Ballmer, former Microsoft CEO, records an episode of the Numbers Geek podcast earlier today at his office in the Seattle region. (GeekWire Photo / Kevin Lisota) With even more than normal, Steve Ballmer might not have seemed like an analytical guy to casual fans watching his LA Clippers come back from a 31-point deficit to defeat the Golden State Warriors in Game 2 of their first-round NBA playoff matchup earlier this week. It was the biggest comeback victory in NBA playoff history. But as listeners to , the former Microsoft CEO has a passion for numbers, as well. Earlier today, before recording a future episode of the show about the upcoming annual report on the U.S. government from Ballmer’s nonprofit civic data initiative , we took the opportunity to have him analyze the stats from the Clippers’ historic win. Listen to this short bonus episode below, or subscribe in your favorite podcast app, and continue reading for highlights from his comments, along with a copy of the box score from the game. “We were down 73-50 at halftime and we won 135-131, which tells you we outscored the opponent by 27 points in the second half, scoring over 40 points in (each of the final) two quarters, which is essentially unheard of,” Ballmer said. But “the thing that really flips is the shooting percentage” in the second half, he said. The Clippers shot 66.7 percent from the floor in the second half, and ended up shooting 56.5 percent for the game, vs. 53.3 percent for the Warriors. The Warriors “had a major rebound advantage at one point” earlier in the game, but by the end of the game, the Clippers were at 34 rebounds vs. the Warriors 38 rebounds, “which was a big deal,” Ballmer said. He added, “I would say the most important thing to take a look at, at the end of the game, was how many turnovers both teams had. Both teams had a lot of turnovers, 22 for the Warriors, 19 for us. I worry sometimes about us two ways. Turnovers and rebounding, sometimes offense, but mostly turnovers and rebounding. And we wound up pretty close to the Warriors on both sides. They had a couple more rebounds. And they also had a couple more turnovers, which means we both got about the same number of possessions. We just put the ball in the basket better.” Of course, this was just one game. The series resumes Thursday night at Staples Center in LA with the teams tied at one game apiece. Also check out , with audio from Ballmer on the baseline at Staples Center. We’ll be back soon with another episode of the show.
Koala-sensing drone helps keep tabs on drop bear numbers

Koala-sensing drone helps keep tabs on drop bear numbers

9:24pm, 1st March, 2019
It’s obviously important to Australians to make sure their koala population is closely tracked — but how can you do so when the suckers live in forests and climb trees all the time? With drones and AI, of course. A new project from Queensland University of Technology combines some well-known techniques in a new way to help keep an eye on wild populations of the famous and soft marsupials. They used a drone equipped with a heat-sensing camera, then ran the footage through a deep learning model trained to look for koala-like heat signatures. It’s similar in some ways to an earlier project from QUT in which were counted along the shore via aerial imagery and machine learning. But this is considerably harder. A koala “A seal on a beach is a very different thing to a koala in a tree,” , perhaps choosing not to use dugongs as an example because comparatively few know what one is. “The complexity is part of the science here, which is really exciting,” he continued. “This is not just somebody counting animals with a drone, we’ve managed to do it in a very complex environment.” The team sent their drone out in the early morning, when they expected to see the greatest contrast between the temperature of the air (cool) and tree-bound koalas (warm and furry). It traveled as if it was a lawnmower trimming the tops of the trees, collecting data from a large area. Infrared image, left, and output of the neural network highlighting areas of interest This footage was then put through a deep learning system trained to recognize the size and intensity of the heat put out by a koala, while ignoring other objects and animals like cars and kangaroos. For these initial tests, the accuracy of the system was checked by comparing the inferred koala locations with ground truth measurements provided by GPS units on some animals and radio tags on others. Turns out the system found about 86 percent of the koalas in a given area, considerably better than an “expert koala spotter,” who rates about a 70. Not only that, but it’s a whole lot quicker. “We cover in a couple of hours what it would take a human all day to do,” Hamilton said. But it won’t replace human spotters or ground teams. “There are places that people can’t go and there are places that drones can’t go. There are advantages and downsides to each one of these techniques, and we need to figure out the best way to put them all together. Koalas are facing extinction in large areas, and so are many other species, and there is no silver bullet.” Having tested the system in one area of Queensland, the team is now going to head out and try it in other areas of the coast. Other classifiers are planned to be added as well, so other endangered or invasive species can be identified with similar ease. .