Professor Saber and the Evolution of Moneyball
– by Mario Crescibene
Man, was the snow coming down! I sat lazily on my couch, legs slung over the backrest, with my computer resting on the coffee table, and Moneyball on the TV acting as background noise. Anything to scratch that baseball itch and escape the cold, bleak January winter that only Cleveland can muster. Why must Cleveland be so cold?
I stared at the Fangraphs page as Jonah Hill explained how sabermetrics would give Billy Beane an edge over every other team in baseball. “He gets on base.”
The revolutionary insight that changed everything.
I glanced back at my screen. WAR. wRC+. xwOBA. Exit velocity. Barrel rate. Every sabermetric that helped give the A’s an advantage was now right here in front of me, public for anyone from MLB front offices to casual fans.
“Hold on,” I said to myself as my idea took form.
If everyone’s looking at the same numbers… where’s the advantage? Sabermetrics was Billy Beane’s secret weapon because he had statistics no one else was using. But now? Now they’re just standard. So what were teams actually doing to separate themselves?
I needed to talk this through with someone who knew more about statistics than I did. And I needed some color in my life. Literally. Everything outside was white and gray – pure drab. But with the need for positive energy and a statistics-minded question, I knew exactly where to go. I grabbed my coat and headed to Case to talk with Professor Saber.
Campus was a ghost town. I made my way through the math department halls: gray carpet, white walls, fluorescent lights humming in the silence. It felt like an episode of Black Mirror. My footsteps echoed down the hallway as I made my way to Professor Saber’s office. I knocked and from inside sang out an enthusiastic voice that practically sparkled:
“Come iiiiiin!”
The door swung open and there was an EXPLOSION of color!
Professor Saber’s office walls were painted deep navy blue, dotted with stars, planets, and comets… like a child’s bedroom ceiling. A massive whiteboard covered one entire wall, with equations scrawled across it in every color marker imaginable: red derivatives, purple integrals, orange probability distributions all tangled together like some kind of mathematical rainbow. Bookshelves overflowed with textbooks, creating their own spectrum from mathematics to statistics to data science.
And there, sitting at her desk in the middle of it all, was Professor Saber.
She had wild, curly, blond hair that seemed to defy both gravity and any reasonable attempt at organization, and wore a black sweater covered in tiny embroidered normal distribution curves in every imaginable color. Her desk was an organized chaos: scattered exams, scientific journals, a laptop running some kind of statistical simulation, stacks that only made sense to her.
“Mario!” she exclaimed, raising both arms in the air with genuine delight. “Perfect timing! Winter break is so boring without students to teach. Come in, come in! What statistical adventure brings you to my classroom today?”
I plopped down into the beanbag chair on the other side of her desk. It was turquoise blue and made a satisfying whoosh sound as I sank into it.
“So,” I started, trying to organize my thoughts, “I’ve been thinking about sabermetrics. They’re objectively better than traditional stats, right? wRC+ is more useful than runs, WHIP is better than hits for pitchers – all that stuff actually measures what matters.”
“Absolutely!” She nodded enthusiastically, her curls bouncing.
“But here’s the thing,” I continued. “In Moneyball, Billy Beane had an advantage because only the A’s were using these metrics. They could find undervalued players that other teams missed. But now? Everyone’s looking at the same FanGraphs leaderboards. Every team has the same Statcast data. If we’re all using the same numbers to make decisions, then—”
“—then there’s no competitive advantage!” she interrupted excitedly, practically jumping out of her seat. “Exactly, Mario. Very good!”
She spun toward her whiteboard and grabbed a green marker. She drew a large equals sign in the middle of the board, then started writing team names on both sides: “Guardians = Dodgers = Yankees = Rays = All 30 Teams” with arrows pointing to a box labeled “Same Public Data.”
“You’ve identified the paradox perfectly, Mario. Sabermetrics succeeded so well that they became universal. And when everyone has the same information, the playing field levels out.”
“So what happened next?” I asked.
“Well,” she said, capping the blue marker and grabbing a blue one, “let me tell you a story about your very own Cleveland Guardians… back when they were still the Indians. Did you know that Cleveland had their own version of Moneyball before Moneyball even existed?”
I sat up straighter in the beanbag. “Wait, really?”
“Really! In the spring of 2000 – three full years before Michael Lewis published Moneyball – the Indians developed something called DiamondView.” She wrote the name on the board in large blue letters with a flourish.
“It was a proprietary database that integrated performance statistics, scouting reports, medical information, contracts, salary data, and player projections all into one system that got updated daily.” As she talked, she drew a diamond around the name and started adding arrows pointing to it from different sources of data: “Stats,” “Scouting,” “Medical,” “Contracts,” “Projections.”
She turned back to me, eyes bright with that classic Saber-enthusiasm. “The front office wanted to digitize all those massive binders of scouting reports and turn raw data into actionable intelligence.”
“That’s actually pretty cool,” I admitted.
“It was revolutionary!” She drew a star next to “DiamondView” on the board. “But here’s the key insight, Mario – this was the first wave of the analytics arms race. Teams like Cleveland and Oakland gained an advantage by having better data and better metrics. But once sabermetrics became public, that advantage disappeared… and the analyses had to evolve.”
“So if DiamondView and Moneyball were the first wave,” I said, “what’s happening now?”
Professor Saber’s eyes lit up even brighter… if that was possible. She grabbed an orange marker and started drawing furiously on the board.
“What’s happening now? Now it gets really interesting!” She drew a large box at the top labeled “PUBLIC DATA – Available to All 30 Teams” with arrows pointing down to all the team names below. “Today, every single MLB team has equal access to the same raw data. Statcast, biomechanics tracking, all of it available through platforms like BigQuery. The playing field is completely level when it comes to access.”
“So like we said, everyone’s back to square one,” I confirmed.
“Not quite!” she quickly responded. “Because the new arms race isn’t about having the data; it’s about what you build with it. Teams are developing proprietary algorithms and custom models that no other team has.”
She drew another layer below the team names. “The Red Sox have something called ‘Beacon’ – a proprietary baseball information system that lets them combine various data sources and create custom projections. Other teams are integrating biomechanical data with performance metrics in ways the public never sees. They’re building models that weight variables differently, ask questions no one else is asking, and find patterns in the noise.”
“Like what kind of questions?” I said, leaning forward in the beanbag.
“That’s the beautiful part – we don’t know!” She laughed in a pleased way before continuing. “By definition, the best work is invisible. If you can see it, it’s already too late: the advantage is gone. But we can see the results – teams like the Rays consistently outperforming their payroll, or the Dodgers turning a stable of high-value prospects into eventual All-Stars. It isn’t just advanced scouting… it’s advanced statistical analyses that outperform other teams’ analytical attempts.”
She stepped back from the board, surveying her colorful diagram. “It’s not about access to data anymore, Mario. It’s about the questions you’re asking and how teams uniquely use statistical models to answer those questions.”
She tapped the whiteboard with her marker for emphasis. “That’s where the new competitive advantage lies. And that’s the arms race happening right now as teams scramble to crunch the numbers and find those analytical advantages crucial to winning.”
I sank back into the beanbag, processing everything she’d just explained. The whiteboard behind her was a riot of colors – greens, blues, oranges – all mapping out the evolution of baseball analytics like some kind of beautiful mathematical tapestry.
“So basically,” I said slowly, “fans like me are arguing about which sabermetric is better… but that whole debate is…”
“Already obsolete!” Professor Saber finished, grinning. “Exactly! Those arguments are mere academic exercises now. The real game moved on years ago. While fans debate FanGraphs leaderboards, front offices are asking entirely different questions using entirely different tools.”
She set down her marker and leaned against her desk with that characteristic enthusiasm still radiating from her. I pulled myself up and out of the beanbag chair – which took considerably more effort than sitting down had – and looked at the colorful chaos on the whiteboard one more time with DiamondView in the center: the evolution from data scarcity to data abundance, and the invisible arms race taking place in analytics departments across baseball.
“Thanks, Professor,” I said. “This was exactly what I needed.”
As I turned to leave a picture caught my attention on Professor Saber’s desk. In it was Professor Saber standing next to another woman with orange hair equally as wild the professors’.
“That woman next to you sure looks a lot like Miss Frizzle from The Magic School Bus,” I commented.
The professor laughed joyfully, “Well she may be Miss Frizzle to you, but to me she’s just Aunt Valerie.”
“Wait, Miss Frizzle is your aunt?” I exclaimed?
“Well where do you think I got my hair from?” she replied playfully. “It runs in the family!”
