Off Topic
Amit Hahn And The Intern Play With Numbers
May 14, 2063 – Sitting in his tiny Computer-stuffed Tiki hut on this tiny little atoll the Klub isolated him on, Amit Hahn drew a deep breath and released it slowly. The summer was just starting. The top brass wanted numbers, he was going to give them numbers. But they wanted numbers outside the range of anything he’d seen before, so he knew he was going to have to MSU on this one like he’d never MSUed before. That he was having to deal with new people made it more stressful.
“MSU?” Kara Badiku, this year’s intern asked as he said it. She was a junior out of a school in San Francisco and would be looking for a job full time next year.
“Make Shit Up,” he replied with a sheepish grin. Kara was hot in the way curious people were hot. Her nearness made him sweat in ways that were both uncomfortable and enjoyable. She was going to be on his team for the rest of the summer, which was going to be a distraction. He had things he wanted to accomplish this summer. Models to make and projection systems to refine. He hoped his brain would keep working all year.
Kara laughed. “Make shit up. That’s cool.”
“The Executive suite wants to know how bad their first round pick’s numbers might be if he throws in the majors right away. Specifically what happens if his K-rate is really sub-par.”
“Paul Glass?” Kara said, adding an accentuation mark to the question with a slight turn of her hand.
“Right. The guy throws only 89 at best, and the problem is that there’s just no real data out on the far end to figure out what he'll really do.”
“So you have to extrapolate,” Kara said.
“Right. In other words, make shit up.”
“So what are you going to do?”
“It’s like this,” Amit said as he twitched a muscle in his lip to call up a projection of what data he had. It was all theoretical, but he went on to show her the strike-out rates of pitchers around that league who their baseball people rated at various levels. “The scouts take into account every pitcher’s velocity and pitch repertoire to give them a bogus Stuff rating,” he said.
The K-rated started high, then fell off as the Stuff rating dropped.
“League average is 19.2% of at bats end in strikeouts,” Kara said, catching on to the central idea already.
“Right, so if we round that to 20%, and say a hitter will get 500 AB, that means the league average pitcher would strike that hitter out 100 times.”
Amit didn’t even have to highlight the bottom of the chart for Kara to comment. “Lowest rating we have any real data for is the 4 Stuff,” she said, using the exact right inflection of her perfect voice.
Amit cleared his throat. “Right,” he said. He was feeling more comfortable because the conversation was flowing along with the numbers. “And we have to do the same kind of thing with home runs. Glass is thought to be outstanding at limiting those. League average is just over 3% of AB—let’s call it 3.1 for rounding purposes.” He was pleased to see Kara had already recalled the league’s home run data. “You’re sharp,” he said.
“Thanks,” she replied. “I see the best in the league are giving up homers at maybe 1.7%?”
“Right,” Amit said, realizing he wasn’t nervous at all now and feeling suddenly great about it. “But let’s say Glass isn’t that good. Let’s say he’s only 2%.”
“So he’ll give up ten homers to the guy with 500 at bats.”
“Right. So that’s ten hits.”
Kara caught on directly. “So if Glass were an average strikeout pitcher and a elite level homer preventers, that’s 200 Ks and 10 homers…leaving only 390 AB that turn into balls in play. That means we need to MSU to determine a BABIP number, too, right?”
“Kind of.”
“What do you mean, kind of?”
“Our baseball people think is BABIP is pretty fair, so I think it’s workable to use league average for that.”
“.290?”
“Yes, the BBA is BABIPing .290.”
“If that’s the case, then if Paul Glass had league average Stuff. he’d give up 113 hits on those 390 Balls In Play. Add that to the 10 homers, and a regular hitter would post a .246 batting average.”
“Exactly,” Amit replied, no longer surprised about the speed of Kara’s internal calculations. “But here’s where we need to MSU. Glass’s K-rate isn’t going to be league average. So what do we call it? League average is 20%, and the lowest Stuff rating our baseball guys calculate sits at 16.8%.”
“Well, if glass never struck out a hitter, that would mean you’d apply the .290 BABIP to 490 at bats, which would give us 142 hits, plus the ten homers. So that hitter would hit .304.”
“Right. That’s the worst case. But his K-rate in college was 19%. And BBA hitters are becoming strikeout machines at times, so he’s going to strike out someone.”
“If we say he’s only half as good as the worst we’ve got that makes an 8.4% rate, that would create 129.9 hits on BIP,” Kara said. “Or a .280 average.”
“Which, given is low homer rate and good control, should still be solid. And if Glass’s K-rate is 10% --”
“.275,” Kara noted.
“or 12%--”
“.269”
“Then he’s getting considerably more attractive.”
“Yeah,” Kara said. Her eyes were glistening as she looked at Amit. There was an awkward pause, then she took a breath and said: “This MSU is kinda fun.”
“Yeah,” Amit said as he smiled, knowing now that it was going to be a great summer even if Paul Glass flamed out. “It is, isn’t it.”
“MSU?” Kara Badiku, this year’s intern asked as he said it. She was a junior out of a school in San Francisco and would be looking for a job full time next year.
“Make Shit Up,” he replied with a sheepish grin. Kara was hot in the way curious people were hot. Her nearness made him sweat in ways that were both uncomfortable and enjoyable. She was going to be on his team for the rest of the summer, which was going to be a distraction. He had things he wanted to accomplish this summer. Models to make and projection systems to refine. He hoped his brain would keep working all year.
“The Executive suite wants to know how bad their first round pick’s numbers might be if he throws in the majors right away. Specifically what happens if his K-rate is really sub-par.”
“Paul Glass?” Kara said, adding an accentuation mark to the question with a slight turn of her hand.
“Right. The guy throws only 89 at best, and the problem is that there’s just no real data out on the far end to figure out what he'll really do.”
“So you have to extrapolate,” Kara said.
“Right. In other words, make shit up.”
“So what are you going to do?”
“It’s like this,” Amit said as he twitched a muscle in his lip to call up a projection of what data he had. It was all theoretical, but he went on to show her the strike-out rates of pitchers around that league who their baseball people rated at various levels. “The scouts take into account every pitcher’s velocity and pitch repertoire to give them a bogus Stuff rating,” he said.
The K-rated started high, then fell off as the Stuff rating dropped.
“League average is 19.2% of at bats end in strikeouts,” Kara said, catching on to the central idea already.
“Right, so if we round that to 20%, and say a hitter will get 500 AB, that means the league average pitcher would strike that hitter out 100 times.”
Amit didn’t even have to highlight the bottom of the chart for Kara to comment. “Lowest rating we have any real data for is the 4 Stuff,” she said, using the exact right inflection of her perfect voice.
Amit cleared his throat. “Right,” he said. He was feeling more comfortable because the conversation was flowing along with the numbers. “And we have to do the same kind of thing with home runs. Glass is thought to be outstanding at limiting those. League average is just over 3% of AB—let’s call it 3.1 for rounding purposes.” He was pleased to see Kara had already recalled the league’s home run data. “You’re sharp,” he said.
“Thanks,” she replied. “I see the best in the league are giving up homers at maybe 1.7%?”
“Right,” Amit said, realizing he wasn’t nervous at all now and feeling suddenly great about it. “But let’s say Glass isn’t that good. Let’s say he’s only 2%.”
“So he’ll give up ten homers to the guy with 500 at bats.”
“Right. So that’s ten hits.”
Kara caught on directly. “So if Glass were an average strikeout pitcher and a elite level homer preventers, that’s 200 Ks and 10 homers…leaving only 390 AB that turn into balls in play. That means we need to MSU to determine a BABIP number, too, right?”
“Kind of.”
“What do you mean, kind of?”
“Our baseball people think is BABIP is pretty fair, so I think it’s workable to use league average for that.”
“.290?”
“Yes, the BBA is BABIPing .290.”
“If that’s the case, then if Paul Glass had league average Stuff. he’d give up 113 hits on those 390 Balls In Play. Add that to the 10 homers, and a regular hitter would post a .246 batting average.”
“Exactly,” Amit replied, no longer surprised about the speed of Kara’s internal calculations. “But here’s where we need to MSU. Glass’s K-rate isn’t going to be league average. So what do we call it? League average is 20%, and the lowest Stuff rating our baseball guys calculate sits at 16.8%.”
“Well, if glass never struck out a hitter, that would mean you’d apply the .290 BABIP to 490 at bats, which would give us 142 hits, plus the ten homers. So that hitter would hit .304.”
“Right. That’s the worst case. But his K-rate in college was 19%. And BBA hitters are becoming strikeout machines at times, so he’s going to strike out someone.”
“If we say he’s only half as good as the worst we’ve got that makes an 8.4% rate, that would create 129.9 hits on BIP,” Kara said. “Or a .280 average.”
“Which, given is low homer rate and good control, should still be solid. And if Glass’s K-rate is 10% --”
“.275,” Kara noted.
“or 12%--”
“.269”
“Then he’s getting considerably more attractive.”
“Yeah,” Kara said. Her eyes were glistening as she looked at Amit. There was an awkward pause, then she took a breath and said: “This MSU is kinda fun.”
“Yeah,” Amit said as he smiled, knowing now that it was going to be a great summer even if Paul Glass flamed out. “It is, isn’t it.”
