Data in Sports Analytics

Last quarter, we spent $1,404,619 on AI tokens - an all-time high - and the ROI wasn’t what we expected… Most of the ROI didn’t come from “flashy AI”, it came from boring AI doing boring work at scale. Here’s where our spend went and what actually moved the needle: 1. Telling reps who to call today (and why) We’re using AI to sift through millions of signals and tell reps who to talk to today and why. The signals that we’ve found matter: Job changes (new decision makers = new opportunities), buying committee changes and intent signals (active web research and pricing page visits). The big ROI driver is helping our customers with daily prioritization so they don’t have to go fishing for actionable info. At ZoomInfo, We’ve seen a 25-33% increase in meeting quality and opp creation when AEs are sourcing using our AI tools. Win rates also jump from 16-20% to 30%. 2. Writing outreach that doesn’t sound automated We’re moving from “20 segments of 1,000” to 20,000 segments of 1. Not “VP IT at enterprise insurance” messaging… but John at State Farm, who we talked to last year, who competes with three of our customers, with context pulled in automatically. Customer ROI here ultimately comes from better response rates and higher close rates by being more relevant. Buyers care when you show you care. 3. Turning sales calls into usable data Every sales call (ours and customers) is recorded using @Chorus and becomes structured data: objection patterns, competitor mentions, deal risk, coaching moments. We’ve found the benefits of this are huge - 25-30% faster ramp time for new reps, and 10-15% larger deal sizes through better discovery and value articulation. The average rep sells more like the best rep. 4. Speeding up low-value engineering work Every engineer at Zoominfo has Intellij and VS Code w/ Cline. AI handles the unglamorous stuff: Boilerplate code, refactors, test coverage. We’ve seen ~25–30% faster execution on these routine tasks, which frees senior engineers to focus on system design and real product innovation. Our biggest lesson so far has been that if your data foundation is garbage, AI just helps you move faster in the wrong direction. You won’t get AI “working” until you have contextual customer/prospect data centralized, and you can actually build on top of it. We’re still early and we’re trying a lot of things but these have been the highest ROI drivers by a mile. If you’re testing AI in your GTM stack, drop a comment with what’s actually working for you - I’m all ears.

Here’s my major prediction for the professional sports industry next year.   By the end of 2026, artificial intelligence will no longer be a fringe experiment in sports – it will be a foundational layer powering the industry’s growth, on and off the field. Any organization still relying on gut feel, spreadsheets, and siloed data will be structurally behind in both revenue and relevance.   It’s not just about performance. The integration of AI is reshaping every part of the sports business — from fan engagement and ticketing to media, commercial operations and player health. This is key to unlocking a new era of scalable value creation, sustaining the growth we’ve seen in recent decades.   AI is already bending the curve, and the growth potential looks a lot like a hockey stick:   💲 Spend is exploding: The global “AI in sports” market, estimated at nearly $9B in 2024, is forecast to reach $28B by 2030, a 21%+ CAGR. That’s not a side bet; it’s a signal of where leaders and operators see future value.   ⚕️ Performance & health are moving first: Teams working with specialized platforms have reported material outcomes. One AI system forecasts ~75% of potential athlete injury risks inside a seven-day window. Another is helping Major League Soccer teams cut total injuries by ~28% and reduce the salary paid to unavailable players by ~30% (equating to millions of dollars a season). Those are direct P&L and asset-protection gains, not just “innovation theatre”.   📣 Fan experience is being rewired in real time: The NBA’s work with Microsoft and AWS, for example, is pushing AI into games broadcasts: instant narrative-building, multilingual recaps, “Inside the Game” analytics feeds, and new experiences across apps, social media and even inside the stadium/arena. Formula 1 is also turning 1.1 million data points per second per car into predictive race insights and storytelling for a global audience.   By 2026, the true outliers won’t be the AI pioneers, they’ll be the organizations that failed to adapt. Here’s what’s becoming table stakes:   – A robust AI layer across ticketing, pricing, media, sponsorship, and performance – A single, integrated data spine replacing fragmented systems – The skills, talent, and culture to deploy AI tools with the same fluency as playbooks and scouting reports   The road to AI-based optimization won’t be clean. There will be bad models, governance clashes, and cultural pushbacks. But positive transformation never happens in straight lines. It requires bold experimentation. The difference now is that AI’s upside can be quantified in revenue growth, commercial yield and fan lifetime value.   As AI capabilities are adapted across the sports value chain, the industry’s ability to continue growing its overall value could accelerate dramatically.   #BigIdeas2026 – here on LinkedIn.

To be a great sales manager, you have to be a great coach. But coaching often slips through the cracks — especially when there are big deals to close. Now, AI is making it possible for every manager to not just coach more, but coach better. When I was in sales, I saw many new managers fall into the same trap: putting on their superhero cape to rescue deals instead of coaching their reps through them. I was guilty, too! We all knew coaching was important — but we had no time and no tools to scale. With AI, sales managers can now deliver highly targeted coaching at scale. It’s now possible to analyze multiple call transcripts in minutes and pull in unstructured data to understand what happens between calls. You can: 1. Review each rep’s recent calls, emails, and conversations to get a complete picture of how they’re selling — and give them targeted recommendations for improvement. 2. Analyze calls from a specific segment and compare what’s working in closed-won versus closed-lost deals to pinpoint the messaging and strategies that perform best. 3. Generate summaries of how top performers handle objections, communicate ROI, and build a business case — and share those insights with new reps as they ramp. Many HubSpot customers (and our own sales managers) are already using these insights to send regular, personalized coaching to reps — and improve the productivity of their teams. Being a sales manager used to feel like you’re a “super rep” — jumping between calls, rescuing deals, trying to fit in some coaching along the way. Now, it feels like you’re a “super coach” — spotting trends, sharing insights, and helping your whole team scale their impact. Exciting times!

AI won't make you a good manager. But AI will help you make good on your good intentions. And that's what makes a good manager great. Here's the uncomfortable truth: Most managers genuinely care about their people. - They want to coach them. - They want to develop them. - They want to catch problems early. But they're overwhelmed. They're working from: - scattered notes  - stale reviews  - gut feelings They manage by reaction, not intention. The gap is timely information.  And the time to make sense of it. That's where AI changes everything. Here's what an AI Chief of Staff does for people leaders: 1. It knows your people deeply Combines resumes, assessments, and feedback into one clear picture. Strengths, gaps, motivations, all in one profile. 2. It connects profiles to expectations Links each person's capabilities to their role requirements. Preempt blind spots before they become problems. 3. It builds targeted development plans Focuses on 2-3 capabilities that matter most. Tracks progress with real milestones, not assumptions. 4. It spots patterns you'd otherwise miss Analyzes weekly updates, meeting notes, and KPIs. Surfaces early warnings before performance shifts become crises. 5. It prepares you to coach with precision Suggests coaching topics tailored to each person. Makes every 1-on-1 more intentional and impactful. The AI advantage: It never forgets context. It spots patterns across months of data. It turns your good intentions into consistent action. [Use my 7 CoS starter prompts from the carousel below] Most managers react to problems after they happen. Great managers predict and prevent them. Better information leads to better decisions. Better decisions lead to better teams. 📕 Save this for access to the prompts later. ♻️ Share to help other managers lead with more intention. 🔔 Follow Dave Kline for more AI-powered management strategies.

Most coaches & consultants don’t have a time problem. They have a systems problem. AI doesn’t fix chaos. It scales whatever system you already have. Here are 5 AI tools that actually plug into your daily workflow (with real use-cases): 1. ChatGPT: Use it to think, not just write. Daily integration: Pre-call: Generate 5 sharp questions based on client background Post-call: Convert notes into insights and next steps Sales: Practice objection handling before discovery calls Example: “Here are my client notes → identify blind spots and suggest 3 tough questions for next session.” 2. Notion AI :Your second brain for client delivery. How to use: Create client dashboards with auto summaries Maintain SOPs for your programs Turn session transcripts into insights + next steps Example: Upload session notes → “Summarize key breakthroughs + assign action items” Your client gets clarity instantly. 3. Descript: Content creation without the headache. How to use: Edit podcasts/videos by editing text Remove filler words automatically Repurpose long-form content into shorts Example: Record a 20-min coaching insight → Cut it into 5 LinkedIn videos + 10 reels in under an hour. 4. Otter.ai.: Never miss what your client actually said. Daily integration: Record and transcribe coaching calls Highlight key patterns across sessions Build a repository of client insights over time Example: Spot recurring phrases like “I feel stuck” and use that language in your next session to go deeper. 5. Make: Where everything connects. Daily integration: Auto-send session summaries after calls Connect forms to CRM, email, and task managers Build end-to-end onboarding flows Example: Client fills a form, gets a calendar link, books a call, receives a prep doc, and you get a summary. All automated. Here’s the shift most people miss: Don’t ask, “Which AI tool should I use?” Ask, “Which part of my workflow is still manual?” That’s where AI fits. Because the goal isn’t to use more tools. It’s to free up more thinking time. What’s one task in your workflow you’d love to automate right now?

We did an experiment: 36 leaders. Same data. Different use of AI. Completely different outcomes. Last week, I was a guest lecturer in Dr. H /Hila Lifshitz (Hán, X也)’s Artificial Intelligence for Managers program, where we conducted a live experiment with 36 senior leaders, including CEOs, VPs, and CIOs from manufacturing, healthcare, public sector, and tech. The goal: to explore how different ways of working with AI change the quality of decisions. Each group received the same set of startup pitch decks, but with different AI access: Group A used AI from the start. Group B used AI only in the last 15 minutes. Group C used no AI until the end. The results were eye-opening. Here’s what we learned fast: → Prompts = process Give AI a role (“Act as a seed-stage angel with 100 investments”), set step-by-step criteria (team → market → moat → risk), and finish with a devil’s advocate challenge. → Stay in control Use AI as analyst and coach, but you make the final call. → Match the mode to the moment: ↳ Sentry (guardrails first) for high-risk or regulated work ↳ Cyborg (human + AI intertwined) for complex decision-making ↳ Autopilot (delegate and verify) only for low-risk, repeatable tasks In the second part of the lecture, I shared a practical framework I call “your new AI toolbox”: 1. NotebookLM for board prep Upload materials, ask for blind spots and key questions. 2. AI Studio for difficult conversations Draft, role-play, and refine your language. 3. Gemini for talent Recruit your best advisory board with personalized outreach. 4. AI Studio Live Share your screen, co-prompt, and capture real-time decisions. 5. Feedback loop Let AI critique your work, then decide what to keep or drop. Why it matters: same people, same data. But leaders who know how to use AI thoughtfully make faster, clearer, and more confident decisions. If your team is navigating how to integrate AI into daily decisions, feel free to DM me and consult with me. ♻️ Repost if you found this helpful. 🔔 Follow me, Yuval Passov, for weekly insights on startup growth, founder wellness, and leadership in the age of AI.

From chatbots that personalize microlearning to systems that predict who’s likely to disengage, artificial intelligence (AI) is changing how we train and learn. AI opens new opportunities to improve on some of the challenges with traditional training models such as scalability, personalization and real-time feedback. Core AI applications in the L&D space can be broken down into four categories: Artificial Intelligence (AI) Platforms: These tools tailor difficulty, pacing and topics in real time. An AI-enhanced platform can tailor the content to the learner based on their performance trends. Natural Language Tools: These are used to summarize content, create quizzes and provide conversational coaching. These applications can reduce time spent on administrative tasks and increase the focus on building relationships and delivering value. Predictive Analytics: This category of tools help learning leaders identify skills gaps and forecast learner success. Virtual Coaches and Chatbots: These tools reinforce knowledge through spaced repetition and feedback loops. AI-Powered Learning: A Case Study Streamline Services is a fifth-generation plumbing, electrical and HVAC company that handles up to 200 calls a day and serves thousands of customers each month. The company is using AI to not only coach employees but also identify areas where the team needs skills development or training. Streamline adopted an AI-powered virtual ride along platform to help transform everyday customer interactions — both in the field and in the call center — into powerful, data-driven learning opportunities. Traditionally, managers and trainers could only coach based on a handful of ride alongs or recorded calls each month. With AI, every service visit and customer conversation has become searchable, analyzable and coachable. AI highlights key themes including customer concerns, missed opportunities and tone shifts, allowing trainers to see real patterns instead of isolated incidents. The training team and managers use this knowledge to design training and structure coaching for individual needs. Because AI is deepening Streamline’s understanding of customer needs, the L&D team can develop targeted training that improves customer service and empathy across the company. Streamline’s experience illustrates how AI is fundamentally changing the learning process — from reactive coaching based on limited observation to proactive, personalized development powered by real data. This case study showcases how technology can elevate human performance rather than replace it. AI offers the ability to provide more learning opportunities and personalized learning across roles and industries. L&D professionals need to embrace this change and evolve alongside the technology. The future of learning isn’t artificial — it’s intelligently human. #LearningandDevelopment #AI #FutureofLearning

𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗲𝗻𝗵𝗮𝗻𝗰𝗲𝗿 𝗶𝗻 𝘀𝗽𝗼𝗿𝘁𝘀 𝘄𝗼𝗻’𝘁 𝗯𝗲 𝗮 𝘀𝘂𝗽𝗽𝗹𝗲𝗺𝗲𝗻𝘁 — 𝗶𝘁’𝗹𝗹 𝗯𝗲 𝗱𝗮𝘁𝗮. With just a simple IMU (Inertial Measurement Unit) sensor, we can capture precise motion — acceleration, rotation, angle, and speed — of virtually any movement. Now, imagine applying that to cricket. Legends like 𝗞𝘂𝗺𝗮𝗿 𝗦𝗮𝗻𝗴𝗮𝗸𝗸𝗮𝗿𝗮 𝗼𝗿 𝗩𝗶𝗿𝗮𝘁 𝗞𝗼𝗵𝗹𝗶 built their success not only on skill, but on the 𝗽𝗲𝗿𝗳𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝗺𝗼𝘁𝗶𝗼𝗻 — 𝘁𝗵𝗲𝗶𝗿 𝘁𝗶𝗺𝗶𝗻𝗴, 𝗮𝗻𝗴𝗹𝗲𝘀, 𝗮𝗻𝗱 𝗯𝗼𝗱𝘆 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗮𝘁 𝘁𝗵𝗲 𝗰𝗿𝗲𝗮𝘀𝗲. What if we could record every movement behind those iconic shots — every six, every cover drive — using 𝗜𝗠𝗨 𝘀𝗲𝗻𝘀𝗼𝗿𝘀 𝗼𝗿 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝘃𝗶𝘀𝗶𝗼𝗻? We’d gain an exact digital signature of their batting: swing arcs, body rotation, weight transfer, and reaction times. Now take that data and compare it to an emerging player — 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 differences, improving precision, and accelerating learning. This isn’t just analysis; it’s a new form of 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. From cricket to football to athletics, 𝗺𝗼𝘁𝗶𝗼𝗻 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 will redefine how athletes train, recover, and evolve. Because the next frontier of performance enhancement won’t come from nutrition or supplements — it will come from how effectively we measure and interpret movement itself. 𝗗𝗮𝘁𝗮 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗱𝗿𝘂𝗴. How far away do you think we are from seeing this become the standard in professional sports? #Idea8 #SportsTech #DataAnalytics #PerformanceInnovation #FutureOfSports #MotionIntelligence

AI products are easy to demo, but much harder to turn into real business impact at scale. That’s why I’m excited to see the latest research in The Quarterly Journal of Economics (QJE) showcasing the tangible results AI is delivering in the real world. This study, originally conducted by the National Bureau of Economic Research (NBER), provides some of the strongest evidence yet on how AI-driven guidance can significantly improve business performance and ROI. At Cresta, we’ve seen this firsthand. This same research was featured in Harvard Business Review, where we partnered with a customer to study the impact that AI-powered real-time coaching had on their customer experience operations. What were the findings? A 13.8% increase in productivity, with the biggest gains seen by the least experienced agents—closing the performance gap by 60%. As AI continues to reshape how businesses operate, these findings reinforce a crucial point: AI can unlock new levels of efficiency and expertise across teams, and empower agents to perform at their best. If you’re curious to dive into the research, you can explore the QJE version here: https://lnkd.in/g-NSRssY Or read the licensed HBR version here: https://lnkd.in/gbjSt5NY Would love to hear your thoughts — how is AI driving measurable impact in your organization?

Sony's acquisition of STATSports is more than another tech buy. It’s a major piece of a much larger, and far more strategic, puzzle. Sony is methodically building a complete, end-to-end data ecosystem for global sports. The goal moves beyond mere participation; towards owning the operating system of athletic performance. Here’s how I think and feel the pieces fit together: Layer 1: Positional data (The "Where") This is handled by Sony's Hawk-Eye Innovations Ltd and KinaTrax acquisitions. Through optical tracking, they capture the external movements, knowing precisely where every player and the ball is at all times. Layer 2: Physiological data (The "How") This is the new, critical piece from STATSports. Their GPS wearables provide the internal data (heart rate, acceleration, deceleration, and overall physical load). It explains how an athlete is performing those movements. Layer 3: Monetization (The "Wow") Beyond Sports and Pulselive take this raw data and package it. Beyond Sports creates visualizations for broadcasts and tactical analysis, while Pulselive builds fan engagement platforms around it. Controlling this entire data value chain unlocks immense strategic value. This is almost a vertical integration play, following the data lifecycle. An end-to-end "Data Tollbooth" comes to life: Sony can now offer a fully integrated data package. Why would teams or leagues subscribe to multiple separate services when Sony can provide a single, more powerful, and likely more efficient solution? The betting and gaming goldmine perspective: The demand for granular, real-time data in live sports betting is enormous. Sony's proprietary data feeds will be invaluable. For their own PlayStation division, imagine a future FIFA or Madden where AI players' performance is driven by the real-time physiological data of their human counterparts. The future of athlete performance: By combining KinaTrax's biomechanical analysis with STATSports' internal data, Sony can enter the lucrative field of predictive analytics for player health. They can identify injury risks before they happen: a priceless tool for any club. With the STATSports acquisition, Sony solidifies a new, data-centric pillar for its business. They are strategically transitioning from a provider of sports technology to becoming the central data infrastructure of the entire sports industry. And this is a great challenge to integrate and consolidate into a full platform-play, as Rufus Hack, CEO of Hawk-Eye, Pulselive, Beyond Sports and KinaTrax stated: “This acquisition is a powerful step in our journey to build the ultimate sports data and analytics engine. This opens up a path for new applications in performance analysis, as well as officiating, and fan engagement, which enables us to deliver a more complete and valuable solution to our partners and the entire sports community.”