Articles by Alex
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Identiverse 2023: Open Standards for the Intelligent Trust Fabric
Identiverse 2023: Open Standards for the Intelligent Trust Fabric
Identiverse is one of my favorite events of the year. Where else can you spend hours talking about identity standards…
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Volleyball, Identiverse, and Open Identity Standards
Volleyball, Identiverse, and Open Identity Standards
Howdy folks! This week I begin a sabbatical from Microsoft so I can coach women’s volleyball at the University of…
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Come join us! We're hiring!
Come join us! We're hiring!
Howdy folks, Are interested in an opportunity to have a global impact, to make the world more secure from cyber attacks…
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12K followers
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Alex Simons shared thisI have some pretty exciting news to share (at least it's exciting for me!). I have accepted an offer to join the University of Texas Women's Volleyball Program as a Performance Analyst for the 2026 season! I am thrilled, a little stunned, and very much looking forward to getting going. I'm excited to work with (and learn from) Jerritt, David, Ben, Reilly, and the rest of the staff as we try to help the team win a national championship. Hook’em Horns!
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Alex Simons shared thisHowdy LinkedIn folks, One of the things I learned working in high tech, and which I now use every day coaching volleyball, is that the best metrics share a specific combination of traits: They're simple enough that everyone can understand them, they make it easy for everyone on the team to understand how they can help move the metric, and they are well aligned with achieving the outcome you care about. Getting all three of these things right is a LOT harder than you would think! My latest post is about a new metric I've been using with my volleyball teams that achieves all three: Good Attacks Per Possession. The core concept is so simple - when the ball comes over to our side of the net, how often do we turn it into a real attack? It's super easy to track on a whiteboard, easy to explain to a team, and yet it correlates with winning at r = 0.775 across 349 NCAA DI Women's programs and holds up even among the very best teams. The lesson I keep relearning in analytics: the simple metrics people will actually use beats the sophisticated one they won't! https://lnkd.in/gEjd_QD8Good Attacks Per Possession: One Simple Metric Your Whole Team Can Rally AroundGood Attacks Per Possession: One Simple Metric Your Whole Team Can Rally Around
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Alex Simons shared thisHowdy folks! My newest post is up and it's my favorite one so far because it is an optimization problem disguised as a volleyball question. When you're trying to maximize expected outcomes under uncertainty, the size of your variability rate matters just as much as where you aim. I built a model using 682,000 data points that finds the mathematically optimal target for each team — and discovered that almost every team in the dataset was aiming at the wrong spot. The practical rule of thumb that fell out of the model is clean enough to use without any math at all. That's always a super satisfying outcome! https://lnkd.in/gaGEifHcThe 3% Rule: A Data-Driven Approach to Setting Your Passing TargetThe 3% Rule: A Data-Driven Approach to Setting Your Passing Target
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Alex Simons shared thisHowdy folks, Part 8 of my NCAA volleyball analytics series is up, and I just keep learning more with each deep dive! Across 342 NCAA Women's DI programs, the correlation between how often a team's setter sets in transition and their set win% is essentially zero (r = 0.08, p = 0.15). Winning teams don't have their setter touching more transition balls. They just know how to convert better when someone else is setting. That's a meaningful distinction for how you think about building a program. You can't recruit or scheme your way to your setter touching every ball. Your opponent has too much say in that. What you can do is build a roster and a practice culture where your libero and your non-setters can deliver a hittable ball under pressure and your attackers know what to do with it. The efficiency numbers make the stakes clear. Setter sets produce 17.7% attack efficiency across DI. Libero sets produce 9.9%. Non-setter sets produce 3.1%. That's a 14.7-point gap from top to bottom. Whether you follow college volleyball or not, I think the underlying principle translates pretty broadly: if you can't prevent the situation from happening, you better invest in preparing for it! All the details are in my blog post -> https://lnkd.in/g9xfDGmMYour Libero Sets 17% of Your Transition Balls (How's That Working for You?)Your Libero Sets 17% of Your Transition Balls (How's That Working for You?)
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Alex Simons shared thisHowdy LinkedIn Compatriots! Part 6 of my NCAA volleyball analytics series just dropped, and I'm pretty excited about this one. I pulled every transition Dig-Set-Attack sequence from the 2025 NCAA season — 521,139 of them — and mapped attack efficiency by set location and attack type. The results confirm a lot of what great coaches already know intuitively. But a few findings genuinely surprised me. The single most effective transition attack in Power 4 play isn't the most common one. It's a gap attack and most teams significantly underuse it. The teams that have figured this out are running it at 34% attack efficiency. The teams that haven't are leaving real points on the table. I also found that slides in transition hit at 32% efficiency across all of DI — almost identical to middle quick attacks — yet teams run them at a fraction of the frequency of other middle attacks. The data doesn't have a great explanation for why. My suspicion is that it's a training and development habit that starts at the club level. Worth thinking about for those of us who coach younger athletes. There's also a set of individual player leaderboards in the post with some names I'd never heard of before who are doing something really well that nobody seems to be talking about. If you're interested in sports analytics, data-driven coaching, or just love volleyball, I think you'll enjoy it. 👉 https://lnkd.in/g5BHNByE
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Alex Simons shared thisI spent 34 years at Microsoft building and shipping products. One thing I learned: the skills that make you successful in the regular season (regular parts of the cycle) don't always translate to the playoffs (the end-game). I've been retired for almost 3 months now, and I'm diving deep into a second career as a volleyball coach and analyst. This week I published what might be my favorite piece of analysis yet — a deep dive into all 63 matches of the 2025 NCAA Women's Volleyball Championship, comparing what predicts winning during the regular season versus what predicts winning when everything is on the line. The findings map surprisingly well onto competitive dynamics I saw repeatedly in the tech industry: In the regular season, efficiency metrics dominate — teams that minimize errors and play consistent, low-variance volleyball win more. Sound familiar? In a stable, competitive market, operational excellence and consistency are often enough. In the tournament, the emphasis shifts completely. Error rate stops distinguishing winners from losers. The teams that go deep are the ones that take aggressive, fast swings and finish — even if it means accepting more risk. When the stakes are highest and every opponent is dangerous, playing it safe is its own kind of risk. Nebraska had the best numbers of any team in the entire field. They went home in the Elite 8. The team that won it all got there by elevating exactly the right skills at exactly the right moment. I've seen that movie before. Haven't you? https://lnkd.in/gPcTZ7mz
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Alex Simons shared thisOne of the most satisfying moments in data analysis is when the data tells you something that contradicts conventional wisdom. I just published Part 4 of my NCAA volleyball analytics series, and the serving result is a good example: across 66 Power 4 teams, serving toughness correlates with winning at r=0.17 — not significant in a single conference. Meanwhile, a blocking metric I constructed (stuffs + positive touches as a share of total opponent attacks) hits r=0.67 across the same dataset and is significant in 8 of 15 conference groupings. Same sport. Same teams. Wildly different predictive power. What I find most interesting analytically isn't the headline number — it's the conference-level pattern. Serving toughness ranges from r=0.90 in a 6-team mid-major to essentially zero in the SEC. Blocking stays positive and consistent everywhere. That kind of result tells you something about what creates competitive differentiation at different talent levels, and I think it has parallels well beyond volleyball. If you're interested in sports analytics, applied statistics, or just enjoy seeing how data challenges assumptions, the full post is here: 👉 https://lnkd.in/giDFWNx3
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Alex Simons shared thisMost volleyball metrics measure a single action. Kill%. Pass%. Attack Efficiency%. But some of the most useful metrics measure a chain — three consecutive actions that all have to work together. First Ball Side-Out% (FBSO%) is one of them: a pass good enough to set, a set good enough to attack, and an attack that produces a kill. Break any link and it doesn't count. Across 66 Power 4 teams and 349 DI teams, FBSO% turns out to be the 6th strongest predictor of winning I've found (r = 0.81) — stronger than MTRP%, Good Pass%, and every individual resilience metric I track. But the number I find most useful isn't FBSO% itself. It's FBSO Share: what percentage of your total side-out points come from that first ball? The Power 4 average is ~50%. Teams above ~55% tend to be first-ball dependent — excellent when the chain works, vulnerable in extended rallies. Teams below ~48% may have room to improve their first-ball offense even if their transition game is carrying them. Texas A&M's FBSO Share? 56.4% — the highest in Power 4. When you're also passing at 59.2% (2nd in Power 4) and serving as aggressively as they do, that first-ball dominance is a feature, not a vulnerability. Wisconsin vs. Penn State is the contrast that really jumped out. Almost identical FBSO%. Very different FBSO Share. And a 5+ point gap in Side-Out% that tells you exactly where the difference is coming from. Full analysis (with charts and the full DI dataset) is out here: #volleyball #sportsanalytics #ncaavolleyball
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Alex Simons shared thisAfter my first blog post, Serve Error% was the topic that generated the most debate. So I decided to dig in. I started with a hypothesis from David Hunt at Texas about why the SEC looked different from the other Power 4 conferences. That led me to a fascinating story about Kentucky and Texas A&M pulling the data in opposite directions. And then I ran the analysis across all 349 NCAA DI teams and 5,100+ matches. The result? Serve Error% ranks 31st out of 36 metrics in predicting winning. Meanwhile, Creighton rode an 11.2% error rate all the way to the Elite 8.Micro post — Do Service Errors really hurt your chances of winning?Micro post — Do Service Errors really hurt your chances of winning?
Alex Simons replied to a comment
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Grace Stephens thank you! That’s very kind of you ❤️
Alex Simons replied to a comment
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Rob Hoffman thank you! Ben is pretty awesome. Already enjoying working with him!
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Alex Simons reacted on thisBig move in the Microsoft Security world, you will see device security and data security come closer as one cohesive outcome. You don't try to control the devices of your work force just for the sheer pleasure of wiping your users' baby photos... You do it to protect the data on it.Alex Simons reacted on thisThree decades into this journey at Microsoft, I'm taking on a chapter I'm especially energized about: an expanded role as CVP of Management & Data Security, now leading both Intune and Purview. As AI accelerates the volume, velocity, and value of data flowing through every organization, the world needs security that's built for how data actually moves. That's the opportunity in front of us, and it's what makes this next chapter so exciting. A few things I'm focused on with the Purview team: - Securing the AI transformation. - Making Purview the trusted foundation for data security and compliance as enterprises scale AI. - Securing with AI. Driving smarter classification at scale. Readiness for AI. Delivering a simpler, stronger experience. To the customers, partners, and CISOs I've worked with through Intune, I'm looking forward to deepening that partnership in this expanded scope. And to those I haven't met yet, I'm excited to learn from you and earn your trust. A huge thanks to the incredible teams across Intune and Purview. The work ahead is meaningful, and I couldn't ask for a better group of people to take it on with. Excited for what’s ahead! #Management #DataSecurity #Purview #Intune #Microsoft
Experience & Education
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SUDDEN IMPACT VOLLEYBALL CLUB
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Volunteer Experience
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Volleyball Coach
Sudden Impact Volleyball Club
- Present 14 years 10 months
Children
Head coach for U17 Storm
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Board Member
ID2020
- 3 years 1 month
Poverty Alleviation
Member of Board of Directors + Finance Subcommittee Member
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Board Member
Sudden Impact Volleyball
- Present 6 years 7 months
Children
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