Mozart Data

Teoscar Hernandez won the Major League Baseball (MLB) #HomerunDerby. Going into the final round Bobby Witt Jr. had hit 37 homeruns and Hernandez had hit 35. If that's the true base rate (with say, the same denominator of pitches), and they'd see about 33 more pitches (27 plus the bonus format), I was interested in the likelihood Witt would win. ChatGPT can simulate (assuming a binomial distribution) and arrive at 52.3% (so very close) for Witt Jr. This is an example of a problem where #AI excels (and is an amazing tool to use). I think it's also good to know the bias of the underlying assumptions. Witt and Hernandez were likely tired, which would probably drive the result closer to 50/50. As a casual observer, there seems to be serial correlation (being in the zone / momentum / hot hand), which would also drive the result closer to 50/50. There is probably some mean reversion, which would also drive the result closer to 50/50. There might be a different base rate than I instructed it suggested by batting history outside of the derby, initial (or update) betting odds, or other observables (like barrel rate), which could go in the other direction. Given how incredibly close the odds are (and most biases would suggest that a 52% favorite might be too high an estimate), it's not at all a surprise Hernandez won.

Our Co-Founder and CEO Peter Fishman shares thoughts on being a technical founder learning the GTM side of the house.

Our Co-Founder and CEO Peter Fishman sat down with Prateek Mathur to chat startup world and data. If you're a founder or on the team at an early stage company, check this conversation out and hear their thoughts on topics like: ✅ Mental health and work-life balance ✅ Creating value in partnerships ✅ Acquisition strategies, content, and partnerships ✅ Leveraging your network to grow ✅ Common startup mistakes

🚀 Make Your Team Truly Data-Driven! 🚀 Want to turn insights into action? Book a demo with us and let’s make it happen! https://lnkd.in/gCTqDMtD

Mario Mendoza batted .215 in his career, not .200. In baseball, the term "Mendoza Line" was coined by Mendoza's Seattle Mariners teammates, Tom Paciorek and Bruce Bochte, who used it humorously to describe the threshold of ineffectiveness for hitters. Mendoza was a skilled defensive shortstop but his batting average often dipped below or crossed above .200 (this means a 20% chance an at-bat would result in a hit -- it is typical to round to the nearest thousandths and then the decimal is ignored, so fans would say batting 200). A big part of data is the marketing of it internally. Paciorek and Bochte (and later George Brett) succeeded in having data make a point. People think about 200. It's well measured and quickly updated, easily understood and observed, and it's a round number that makes a point. It serves as a good heuristic, but not a perfect one. Take Joey Gallo of the Washington Nationals, a sub 200 hitter with nearly an 800 OPS for his career (in his 10th MLB season and a 2x All-Star). Though imprecise -- both the perception of Mendoza's exact statistics and the infallibility of the metric -- it is a widespread understanding of "MVH," minimum viable hitting. The consumability of a metric matters.

For one sentence of an article I was writing, I wanted to give a concrete quantitative example that involved solving a #QuadraticEquation. As a teenager, I was good at that. In this case, I wanted to solve for what is the indifference probability of success for a 2 point try down 8 points (post TD) given (the simplification of) a 100% PAT and 50/50 OT. Which is P(S) + (1-P(S))*P(S)*0.5 = 0.5. I was not at at all going to factor or #CompleteTheSquare or plug in the #QuadraticFormula (which I can hardly remember). I simply copied the exact equation I typed above into #ChatGPT (without a prompt). It walked me through the steps, and quickly told me (see below) the answer. Of course, I had to ask it to "round the answer to the nearest hundredth" to make the post more readable. If it's a 38% chance of a 2-point conversion, "the analytics" say to go for it after scoring a TD when previously down 14. But more importantly, I'm shocked at how resistant I was to using a skill I once had. It wouldn't even cross my mind to check it or do it by hand, and I'm not sure how much longer I'll be even able to.

Joining James on today’s episode of Tech Salescraft is Peter Fishman, Co-Founder & CEO of Mozart Data. 🎙️ In this episode, Peter shares his journey from a failed academic to a data-centric role in tech companies. He elaborates on the process of identifying a critical need in other businesses and how this insight led to the founding of Mozart Data. Peter explains the importance of understanding customer problems and delivering value quickly. He also touches on the challenges and strategies of early-stage sales, including hiring their first Account Executive earlier than usual and the benefits of founder-led sales. 📈 Join us as Peter provides a behind-the-scenes glimpse into his entrepreneurial journey, including the pivotal moments and strategic decisions that shaped Mozart Data's growth and success. You can watch Tech Salescraft now on YouTube, or listen across all major podcast streaming platforms: https://lnkd.in/epwq2NgC #SalesLeadership #DrivingGrowth #TechSalescraft #TechSalesPodcast

Stop doing analysis for analysis' sake. Our Co-Founder and CEO Peter Fishman wrote about the time he spent working on analytics with the Philadelphia Eagles in 2008. While insights are interesting, their value is only theoretical until analysis changes decisions and is applied to action. Analysis must be novel, consumable, and wanted to impact decisions.

I've interviewed for data roles at 4/5 of #FAANG (+ Microsoft, so maybe 5/6, the only one I haven't done is Apple). Like Sahil Gaba, I've gotten some offers and some rejections (at various stages). I've even had the experience of getting an offer, followed by turning it down, and then later being rejected when re-applying. It is absolutely the case that a large component of interviewing is random, though it's hard not to internalize. As a hiring manager, I've made plenty of type I (made a bad hire) and type II (missed a great one) errors. I often say if you're absolutely perfect for a job, you are still more likely than not to be rejected. I love Sahil's point... "If you toss a coin and call heads, your probability of winning is 50%. If you toss a coin 7 times and keep calling heads, your probability of winning is about 99%." Because I did 5, maybe it's only 97%.

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