How Prop Betting Multiplies the House Edge

A couple of weeks ago I wrote Part 1 of a two-part series on online gambling titled Sports Betting: How the House Always Wins

I don’t gamble and never have but have friends who do. For them it’s a form of entertainment – no different than the hobbies I spend my money and time on. In this post I take a look at prop betting.

Prop bets let you wager on specific events within a game rather than the final score. Will Patrick Mahomes throw over 2.5 touchdowns? Will the first play be a run or pass? Will the game go to overtime? You bet yes or no on each proposition.

Books set a line for each prop. Player props dominate the market. Over/under on passing yards, rushing yards, receptions, touchdowns. Team props include first score type, total penalties, time of possession. Game props cover coin toss results, length of national anthem, halftime show events.

The standard vig applies: risk $110 to win $100. But prop betting multiplies the house edge through three ways that don't exist in traditional spread betting.

Take a typical player prop: Travis Kelce over/under 64.5 receiving yards. The book offers both sides at -110. Say the book's data shows Kelce has a 52% chance of going over. Fair pricing would charge less than -110 on the over side. Instead, both sides cost -110. The book wins twice: once by treating a 52-48 split like it's 50-50, and again by charging the standard vig on top.

Run the numbers: 100 bettors risk $110 on over, 100 bettors risk $110 on under. Total wagered is $22,000. Kelce finishes with 68 yards. The book pays $21,000 to over bettors. Profit is $1,000. That's the baseline 4.55% edge. But the book already knew Kelce was more likely to go over. The real edge jumps to 6% or 8% because they priced it wrong on purpose.

Information gaps widen this advantage. Books track snap counts, matchup data, and injury reports you don't see. They price props based on information you can't access. Fewer bettors can spot bad lines.

Correlation multiplies losses. Mahomes passing yards and Chiefs total points move together. Books let you parlay both and pay like they're unrelated. They're not. Each linked prop increases the edge.

Volume drives profit. A single NFL game offers 200+ prop bets versus 10 to 15 traditional bets. More bets mean more vig collected. Books don't balance action on props. They set wide margins and accept the risk because the edge covers losses.

Books win 55% to 60% on props versus 52% to 53% on spreads. You still need 52.38% accuracy to break even. Props make that harder while feeling easier. Books promote props because the math works better for them.

The Internet in Seven Stages: Where We've Been and Where We're Going

The internet started as a research project connecting computers in the 1970s. Today it connects 5 billion people, countless devices, and increasingly intelligent systems. Mallik Tatipamula and Vint Cerf (the co-inventor of the internet) recently mapped its evolution into seven distinct phases in IEEE Spectrum, each building on the previous ones.

Phase 1: The Original Internet The foundation began with computer networks using common protocols. Early applications like email and file transfer proved that standardized connectivity could democratize information access. The World Wide Web in the early 1990s added URLs, HTTP, and browsers, transforming a research tool into a global platform.

Phase 2: Mobile Internet Smartphones in the early to mid-2000s made connectivity portable. The internet moved from desktops into pockets, enabling social networks, mobile payments, ridesharing, and on-demand services. The app economy emerged, putting services at our fingertips constantly.

Phase 3: Internet of Things Sensors, appliances, vehicles, and city infrastructure joined the network. The IoT created a nervous system linking physical and digital worlds, enabling smart farming, remote healthcare monitoring, and optimized manufacturing.

Phase 4: Internet of AI Agents (Current) We're now entering Phase 4, where AI agents can perceive, reason, act, and collaborate. Digital agents like coding copilots and workflow orchestrators operate in software. Physical agents like autonomous vehicles and industrial robots function in both digital and physical environments. Value comes from networked intelligence, not isolated systems.

Phase 5: Internet of Senses (Future) Networks will transmit touch, taste, and smell alongside audio and video. Haptic wearables will let shoppers feel fabric texture online. Doctors will examine patients remotely using haptic gloves. Smart cities will sense traffic and crowd movement directly through their networks using ISAC (Integrated Sensing and Communications).

Phase 6: Ubiquitous Internet (Future) Terrestrial and non-terrestrial networks (cellular, Wi-Fi, satellites, high-altitude platforms) will merge into one unified global system. Connectivity will extend everywhere: remote villages, oceans, skies, orbit, and cislunar space.

Phase 7: Quantum Internet (Future) The final phase will use quantum entanglement and teleportation to create ultra-secure channels and connect distributed quantum processors. Quantum sensors will achieve unprecedented precision. The Quantum Internet won't replace classical networks but augment them.

Each phase extends connectivity's reach. The internet evolved from moving data packets to becoming the intelligent, resilient fabric supporting our digital future.

Read the full article at IEEE Spectrum: https://spectrum.ieee.org/history-of-internet-7-phases

Quantum Computers Just Got Much Closer to Breaking Your Passwords

On October 21, 2025, IonQ announced a significant achievement in quantum computing. They demonstrated their quantum computer can perform operations with 99.99% accuracy, which doesn't sound like much improvement over 99.9%, but it makes an enormous difference.

What is a Qubit?

Before diving into IonQ's achievement, you need to understand qubits (quantum bits). A regular computer bit is like a light switch: it's either off (0) or on (1). A qubit is fundamentally different.

A qubit can be 0, 1, or both simultaneously until you measure it. This property is called superposition. Think of a coin spinning in the air: it's neither heads nor tails until it lands. While spinning, it exists in both states at once.

Here's a practical example: If you have 3 regular bits, they can represent one number at a time (000, 001, 010, 011, 100, 101, 110, or 111). But 3 qubits can represent all eight of those numbers simultaneously. With 20 qubits, you can work with over a million values (2²⁰) at once. With 300 qubits, you could simultaneously process more values than there are atoms in the universe (2³⁰⁰).

This is why quantum computers can potentially break encryption: they can test millions of password combinations simultaneously instead of one at a time.

The qubits IonQ is talking about are made from trapped ions (charged atoms held in place by electromagnetic fields). When IonQ ran the basic two-qubit operation that creates entanglement and got it right more than 99.99% of the time, they were manipulating these trapped ions to perform calculations.

What Happened

Think of quantum computers as extremely precise machines that need to perform millions of calculations without making mistakes. IonQ's team ran the basic two-qubit operation that creates entanglement and got it right more than 99.99% of the time.

The bigger surprise: they did this without using the usual slow cooling process. Quantum computers typically need to be cooled to near absolute zero and kept incredibly still. IonQ deliberately heated the ions' motion and kept errors at or below 0.0005 per gate.This is like a high-wire artist performing perfectly even in windy conditions.

Why This Matters

Here's where the math gets interesting. If you need to run 1,000 operations:

·       At 99% accuracy, you'll get a completely error-free result about once in 23,000 tries

·       At 99.9% accuracy, you succeed about 37% of the time

·       At 99.99% accuracy, you succeed about 90% of the time

At 99.99% per gate, 1,000-gate circuits succeed error-free about 90.5% of the time. That extra decimal point transforms quantum computers from research toys into potentially useful tools.

The Speed Bonus

Because IonQ skipped the slow cooling step, their quantum computer runs much faster. Transport and cooling can dominate 98-99% of circuit time in these machines. Removing that bottleneck could make quantum computers 10 to 100 times faster at solving real problems.

What This Means for Encryption

Today's internet security relies on mathematical problems that regular computers can't solve in reasonable time. Quantum computers, once large enough, could break these codes.

Experts call this moment Q-Day: when quantum computers become powerful enough to crack today's encryption. For years, people assumed this was 15-20 years away. Recent estimates have shortened that to the early 2030s. IonQ's breakthrough suggests it could happen even sooner.

In May 2025, Craig Gidney at AI Google Quantum AI argued that less than 1 million noisy physical qubits could factor RSA-2048 in under a week. RSA-2048 is the encryption standard protecting most secure websites, banking transactions, and confidential communications.

IonQ's new accuracy level is 10 times better than what those estimates assumed. Better accuracy means you need fewer components to build a code-breaking quantum computer.

Timeline Estimates

Using conservative projections:

·       If quantum computing improves at 2.3 times per year (a reasonable middle estimate), we could see encryption-breaking quantum computers around 2029

·       If progress is slower (2 times per year), it pushes to 2033-2034

·       If companies like IonQ hit their aggressive goals (2.5 times per year improvement), it could arrive as early as 2027-2028

IonQ says they'll demonstrate a 256-qubit system in 2026 and aim for millions of qubits by 2030.

What Organizations Should Do

You don't need to understand quantum physics to act on this news. Here's what matters:

Right Now (next 3 months):

1.    Create an inventory of where your organization uses encryption. This includes websites, VPNs, email systems, and file storage.

2.    Update purchasing requirements to specify "quantum-resistant" or "post-quantum" encryption for new systems. The U.S. government has already published approved algorithms (FIPS 203 and 204).

3.    Start small pilot projects. Test the new encryption methods on a few non-critical systems to learn how they work.

4.    Ask your software vendors about their plans for quantum-resistant encryption. Put it in writing that you expect them to support it.

Within a Year:

1.    Build tools that automatically track what encryption you're using across your organization. You can't protect what you can't see.

2.    Re-encrypt long-term sensitive data using quantum-resistant methods. Hackers might steal encrypted data now and decrypt it later when quantum computers arrive (called "harvest now, decrypt later").

3.    Make your systems flexible enough to swap encryption methods quickly. Don't hard-code cryptography into applications.

4.    Get written commitments from critical vendors about when they'll support quantum-resistant encryption.

Why Act Now

Some people might say, "Why worry if this is still years away?" Three reasons:

1.    Large organizations take years to update their security infrastructure. If Q-Day arrives in 2029, you need to start migrating now to finish in time.

2.    Sensitive data stolen today could be decrypted in 5 years. If your data needs to stay confidential beyond 2030, it's already at risk.

3.    Government regulations are coming. U.S. federal agencies must complete their transitions by 2030-2033. Private sector requirements will follow.

The Bottom Line

IonQ's achievement moves quantum computing from "someday" to "soon." They proved that quantum computers can be both more accurate and faster than previously demonstrated. This pulls the Q-Day timeline closer.

You don't need a PhD to respond appropriately. You need a plan, a timeline, and the commitment to execute. Treat this like Y2K: a known deadline requiring systematic preparation. The organizations that start now will be ready. Those that wait may find themselves scrambling when quantum computers arrive ahead of schedule.

The good news: unlike many cybersecurity threats, we know this one is coming, and we have the tools to prepare. The question is whether organizations will act with appropriate urgency.

Sports Betting: How the House Always Wins

 I've never been a gambler but have friends who do for fun. It is no different for them than me spending $100 on boat fuel to catch $50 worth of fish. They treat it as entertainment, fully 
expecting to lose their stake while enjoying the process. But for others it can cause real problems when it gets serious. People can lose control, damage their finances, and hurt the people around them. Recent accusations involving professional athletes and gambling have raised new concerns about the scope of the problem. To add, mobile apps have made sports gambling so easy - available 24/7 - increasing risk for impulsive bettors. 

Some Definitions First

A bettor is a person who places a wager on a sporting event. A sportsbook is a business, either a physical location or online platform, that accepts wagers on sporting events, sets odds, takes bets, and pays winners. Book is short for sportsbook. A parlay is a single bet linking two or more individual wagers where all selections must win for the parlay to pay out. It offers higher payout than individual bets but requires all picks to be correct. 

The house refers to the sportsbook itself, the entity running the betting operation and collecting the edge. The term comes from casino gambling where "the house" means the casino. Vig is short for vigorish, the commission or fee the sportsbook charges for accepting a bet. Handle is the total amount of money wagered with a sportsbook over a specific period. Action refers to total betting activity, and balanced action means roughly equal money on both sides of a bet.

The Vigorish Model

It’s all about the vig and here is how it works. Standard point spread bets require you to risk $110 to win $100, expressed as -110 in American odds. You must win 52.38% of your bets to break even. Win exactly 50% and you lose money steadily. Here's the math: win 1 bet and you gain $100, lose 1 bet and you lose $110, netting negative $10 after 2 bets at 50% win rate. To break even, you need to win 110 divided by 210, which equals 52.38%. The 10% commission creates an automatic house edge of 4.5% when action balances on both sides.

When a sportsbook takes $110,000 on Team A at -110 (better bets $110 to make $100 with a win) and $110,000 on Team B at -110, the total handle is $220,000. If Team A wins, those bettors receive $210,000, which is their $110,000 stake plus $100,000 profit. Team B bettors get nothing. The sportsbook collected $220,000 and pays out $210,000, keeping $10,000 regardless of outcome. That's a 4.5% margin on balanced action.

The sportsbook doesn't care which team wins. It wants equal money on both teams, collecting vig from losers while paying winners with other bettors' money. Oddsmakers adjust lines in real time to balance action, not predict outcomes. When too much money comes in on one side, the book faces risk. If $200,000 comes in on Team A and only $50,000 on Team B, the book loses $150,000 if Team A wins. They'll move the line from Patriots -3 (Patriots favored to win by 3 points) to Patriots -3.5 (Patriots favored to win by 3.5 points,) then to Patriots -4 (Patriots favored to win by 4 points) if needed, until action balances or they accept the risk.

Parlay Mathematics

Parlays are a whole other ballgame, appearing attractive to bettors because they offer higher payouts than single bets. A $100 two-team parlay pays $264 while two separate $100 bets only win $200 total. But the math reveals the trap. For a two-team parlay assuming two 50/50 bets, the true probability of winning is 25%. Fair payout at 3 to 1 would give you $300 profit on a $100 bet. Actual payout at 2.6 to 1 gives you $260 profit. The house keeps $40 per winning parlay, creating a 13.3% house advantage. Compare this to the 4.5% edge on straight bets.

A four-team parlay has true odds of 15 to 1, meaning a 6.25% chance of winning. Fair payout would be $1,500 profit on $100 bet. Typical payout is 12 to 1, giving you $1,200 profit. The house keeps $300 per winning parlay, a 20% house advantage. A ten-team parlay has true odds of 1,023 to 1, a 0.0977% chance. Fair payout would be $102,300 profit on $100 bet. Typical payout is 600 to 1, giving you $60,000 profit. The house keeps $42,300 per winning parlay, a 41.4% house advantage.

Most bettors don't calculate true odds. A 10-team parlay paying 600 to 1 sounds generous, but true odds are 1,023 to 1. You're getting 58.6% of fair value on a bet that already has a 0.0977% chance of winning.

Who Wins and Who Loses

Research on millions of bets shows professional bettors in the top 1% win 54-56% on straight bets with 2-5% ROI on total money wagered. Recreational bettors in the bottom 90% win 45-48% on straight bets with -8% to -12% ROI on total money wagered. At 45% win rate with standard -110 odds, you lose approximately 10% of total money wagered.

Over 100 bets at $110 each, you wager $11,000 total. With 45 wins, you profit $4,500. With 55 losses, you lose $6,050. Net result is negative $1,550, a 14.1% loss rate. Even at 48%, which is better than most recreational bettors, you still lose 8.4% of total wagered. You need 52.38% just to break even.

I’ll cover prop and exotics bets in my next post. These push margins higher.

Why I'm Bringing Oral Exams to Circuits 1

Almost all engineering students take an introductory electrical engineering course commonly referred to as "Circuits 1". It's a foundational requirement across disciplines, from mechanical to computer engineering. This coming spring 2026 semester, I'm planning something new: I'm adding oral exams to my Circuits 1 course. Why?

UC San Diego researchers found that engineering students who took oral exams scored 14% higher on subsequent written midterms compared to students who didn't take oral exams. That's not a marginal improvement. That's significant learning gains.

The motivation numbers are even more striking. 70% of the UCSD students reported that oral exams increased their motivation to learn, with first-generation students showing the strongest response at 78%. In a discipline where we hemorrhage students after the first circuit analysis course, motivation matters.

Here's what sold me: oral exams test conditional knowledge, not just procedural knowledge. You can memorize Kirchhoff's laws and plug numbers into equations. That gets you through a written exam. But can you explain why you chose mesh analysis over nodal analysis for a particular circuit? Can you justify your sign conventions when I change the problem slightly? That's where oral exams shine.

One student in the UC San Diego study captured it perfectly: written exams let you prepare through memorization and notes, but oral exams require deeper understanding because the instructor can ask follow-up questions. You can't fake your way through a conversation about why a capacitor blocks DC current while passing AC.

I know the objections. Oral exams don't scale. They're time-intensive. How do you maintain consistency across different examiners? These are valid concerns, and UC San Diego's team is working on standardized rubrics and TA training protocols to address them. For my Circuits 1 class of a dozen students, scalability isn't my main problem.

The AI challenge is my main problem. Students can now generate circuit solutions, proofs, and explanations with Claude or Gemini. I'm not interested in playing whack-a-mole with AI detectors or creating ever-more-baroque written exams. I'd rather assess what matters: can you think like an engineer?

My plan is straightforward. Students will take traditional written exams for procedural competency. But they'll also sit for 15-minute oral exams where I'll give them a circuit problem and ask them to talk through their approach. I want to hear their reasoning before they touch a calculator. I want them to explain why they're applying superposition or why they chose a particular reference node.

This isn't about catching cheaters. It's about pushing students toward expert-level thinking. Experts spend more time on problem planning and strategy than beginners, who rush to plug in equations. Oral exams force that strategic thinking to the surface.

Will it work? I'll measure overall exam performance as best I can. I'll survey students about motivation and confidence. I'll track office hours and optional help session attendance. And I'll be honest about the results, positive or negative.

In an age where AI can solve circuit problems faster than any human, the skill that matters is knowing which problem to solve and why. Oral exams test that skill. Everything else is just calculations.

Massachusetts' Quantum Workforce Development and Good Socks

Yesterday I attended the 2025 Quantum Massachusetts conference in Boston. The event brought together faculty, researchers, engineers, investors, and officials to discuss the state's quantum ecosystem.  

To date Massachusetts has invested over $50 million in quantum computing through the Massachusetts Technology Collaborative, including $1 million to UMass Boston and Western New England University in 2022, $3.5 million to Northeastern University in 2022, $3.8 million to UMass Boston in 2025, $5 million for a Quantum Computing Complex at the Massachusetts Green High Performance Computing Center in Holyoke in 2024, and $40 million in an economic development bond bill. The Holyoke project, which combines state funding with $11 million from QuEra Computing for a total $16 million, makes Massachusetts the first state to fund a quantum computing complex. These investments focus on building critical infrastructure like dilution refrigerators and measurement facilities, support 14 research universities with 131 research groups, along with community colleges and have helped secure additional federal funding for quantum research centers.

 

Quantum computing is projected to become a $4 billion market by 2028 and (like any emerging technology field) faces a critical workforce shortage. Like any emerging technology field, it faces a critical workforce shortage. Companies need scientists, engineers, and technicians who understand quantum algorithms, hardware systems, and practical applications. Few educational programs prepare students for these roles.

The Socks

The conference bag included a pair of socks. During the two-hour drive back to Western Massachusetts, I thought about socks and workforce development. Both systems share key characteristics:

Scale and precision matter. Workforce development gains power through network effects when graduates enter industries and skills compound across sectors. Socks gain power when you find the complete set and can do laundry.

Both need precise initial states. Workforce programs need students with strong foundations and employers with clear skill requirements before education starts. Socks need to start as matched pairs.

Both solve problems classical approaches cannot touch. Workforce development tackles skill gaps, economic mobility, and industry transformation that traditional education models miss. Good socks prevent blisters and cold feet.

Both need sustained coherence. Workforce programs need isolation from funding cuts, political interference, and mission drift over multi-year grant cycles. Sock pairs need isolation from the dryer's singularity.

Both multiply value through parallel processing. Workforce initiatives train multiple cohorts simultaneously across regions while updating curriculum based on industry feedback. Sock drawers hold multiple pairs simultaneously until Monday morning proves otherwise.

Both work best when you design for the system you have. Effective workforce programs respect student constraints: work schedules, childcare, transportation, prior education gaps. Sock buyers respect that black, gray, and navy are three different colors at 6 AM.

Both transform outcomes when you invest in quality. STEM workforce development delivers scientists, engineers, and technicians who keep critical infrastructure running. Quality socks deliver people who arrive on time without foot pain.

Both depend on error correction. Effective workforce programs use wraparound services, mentoring, and iterative curriculum refinement. You buy socks in bulk packs of twelve.

Both face the measurement problem. Assessing workforce programs changes behavior and outcomes. Checking if socks match often reveals they don't.

Both eventually fail without maintenance. Workforce programs need ongoing industry partnerships, updated curriculum, and sustained funding. Socks need regular replacement. Nobody writes grants for socks.

The quantum workforce challenge is real. The socks metaphor works because both systems eventually fail when you ignore the fundamentals. The difference is that socks cost less to replace than a skilled STEM workforce.