Claude

• Ported a structured AI workflow to thirteen research repos at once.
• Each project now has specialist review agents, quality gates, and session management for Claude Code.
• Added Claude activity logging alongside the existing Codex diary system.
• The nightly blog digest now groups work by which AI tool did it.

Codex

• Added a first deterministic repair for disconnected observed plans in a political simulation project.
• Focused that repair on the main Utah failure case rather than widening the automatic policy.
• Mapped the next hardening pass so the repair path is more reproducible and easier to validate.
• Consolidated a replication project and verified reproducibility.
• Published a teaching post about AI on the blog.

• Extraction of intelligence is the key thing: how to develop the instincts, infrastructure, and persistence to get really great things out of the model.
• Understanding how computers work, and having some familiarity with Git, makes it much easier to set up that infrastructure. It is less about comprehensive know-how, because you can ask the model how to do something, and more about having enough foundation to think creatively about what is possible.
• Teaching people what is possible is easier than teaching people how to do specific things.
• The extraction part is really fun.
• This is an incredible gift of intelligence and time. What will students, and professors, do with it? Can lectures become a lot more exciting? Can classes become more hands-on and more innovative?
• If a student uses a chatbot to one-shot an essay, or Claude Code or Codex to one-shot a problem set, that is tough, and most will do this. But if a student iterates and iterates until the final product is pristine, that is learning.
• The social elements of academia should be invested in even more. We are the primary consumers of our work and ideas, so we need more forums to share the new awesome things the models are helping us create.
• The bar has to get a lot higher. Who can make something great with the model? This clashes with Q-score incentives and grade inflation.
• Honors theses need to become amazing.
• Right now, someone with a lot of experience using the top models to build things can get dropped into almost any company and become a huge value add. This will not always be the case as labor skills converge and as models become more autonomous.
• Force students to write in class. Use handwritten exams. Require oral presentations. Make them say out loud what the model does and how it does it. Have them explain it to you and to their peers, even if the model taught them what to say. Out-of-class assignments are for the experience, not the assessment.
• Find hobbies and creative outputs where you write.
• But a lot of this will end up being on them. The ones who want to learn will learn a lot. Absolute Superman knows what's up.¹

• Pretty clear at this point that we do not have the answers yet.

1. This will not scale. ↩

Spencer's theory has the virtue of sounding plausible before it sounds profitable. Early in a season, sportsbooks and bettors are still leaning on priors: last year's record, offseason narratives, roster headlines, vague expectations of continuity or collapse. If the market has not learned enough yet, some teams priced as underdogs should really be closer to coin flips. And when underdogs win, they pay more. A small probability mistake can matter a lot.

That logic is clean. The question is whether it actually worked in real historical betting data once bookmaker hold, league differences, and ordinary variance are taken seriously.

So Codex built a public-data test.

The project assembled a game-level closing-moneyline archive for the NFL, NBA, NHL, and MLB from 2011 through 2021 using a documented public GitHub archive of sportsbook data. The final cleaned panel contains 53,453 games across 44 league-seasons. The goal was not to find the single best backtested rule after the fact. The goal was to ask a narrower question honestly: if you had simply bet early-season underdogs, would that have made money often enough to deserve belief as a real edge?

The bottom line is careful but fairly clear. Spencer's rule looks better than generic underdog betting, and it may contain a small early informational effect, especially in MLB and maybe NFL. But the observed data do not support a strong claim that early-season underdogs were a robust cross-league money machine.

If you want the replication materials, code, intermediate outputs, and notes, they live here: replication folder for this analysis. The main executable pipeline is src/run_analysis.py, with an R-side robustness check in src/robustness_meta_analysis.R.

The method, in plain English

Codex started with the cleanest common public historical source it could find across all four leagues: the flancast90/sportsbookreview-scraper archive on GitHub. That source is imperfect. The strongest cross-league public window it supports consistently is 2011 through 2021, and it does not provide a perfect official season-type flag across leagues. But it does give dates, final scores, and closing moneylines for both sides of each game, which is enough to run a serious first-pass historical test.

The strategy definitions were fixed early in the workflow before reading the output tables. Codex compared several natural versions of "early season":

1. First 2 calendar weeks.
2. First 3 calendar weeks.
3. First 5 games played by the betting team.
4. First 10 percent of the team-season proxy.

The main strategy was as transparent as possible:

• bet $100 on every qualifying underdog
• settle the bet at the closing moneyline
• compare the realized profit with sensible alternatives

Those alternatives mattered. Codex did not just report one ROI number and declare victory or failure. It compared Spencer's idea with:

• all underdogs over the likely-regular-season proxy
• favorites in the same early windows
• random teams in the same early windows
• later-season underdogs
• a timing-placebo benchmark that permutes when underdog bets occur within season

It also did the more important thing that betting arguments often skip: check calibration. Underdogs can sometimes look good in realized ROI just because payouts are asymmetric. The sharper question is whether they actually won more often than the no-vig market implied.

The headline result

The strongest naive pooled rule in this run was "bet every underdog in the first 3 calendar weeks." That rule still did not deliver a clean positive result.

• ROI: -0.4%
• bets: 5,901
• 95% bootstrap interval: [-4.0%, 3.4%]

That is the kind of estimate that should slow down anyone who wants a triumphalist answer. It is close to breakeven. The interval still includes modest gains, but it also includes ordinary losses. There is no disciplined way to call that a proven positive edge.

The pooled ROI figure below shows the main windows side by side.

Better than generic underdog betting, but still not a gold mine

Where Spencer's idea does look interesting is in relative rather than absolute terms.

Compared with the early 3-week underdog rule:

• all underdogs over the likely-regular-season proxy lost 1.8%
• early favorites lost 4.1%
• a random team in the same early window also did worse on average
• permuted underdog timing also looked worse than the actual early window

That pattern is consistent with a small early informational story. Early underdogs seem to be less overpriced than underdogs in general. But that is not the same thing as showing a durable positive betting edge after vig.

So the right reading is not "this made money." It is closer to: "this lost less than the obvious alternatives, and maybe for a real reason."

The league split

The four-league breakdown is where the story gets more interesting and more dangerous.

Early 3-week underdog ROI by league:

• MLB: +3.9%
• NFL: +1.8%
• NBA: -0.4%
• NHL: -8.2%

On a casual read, that looks like a simple story: maybe the edge is in MLB and NFL, maybe not in NBA, definitely not in NHL. But the project did not stop at casual reads. Codex fit a partial-pooling summary that shrinks league-specific results back toward the pooled mean, because these league-by-league splits are noisy and should not be treated as four isolated truths.

That shrinkage matters. It means the right interpretation is:

• MLB is the strongest positive hint.
• NFL is also directionally positive, but on a much smaller sample.
• NBA is basically flat.
• NHL is the clearest negative case.

The forest plot below shows the raw league estimates and the shrunk versions together.

This is also where a broader domain story starts to wobble. If the mechanism were something simple like "short seasons create more mispricing" or "continuity helps market learning," the league pattern would likely line up more cleanly than this. It does not. That is another reason to stay modest.

The key evidence is calibration, not just ROI

The most useful part of the analysis is probably the calibration check.

For pooled early 3-week underdogs:

• realized win rate: 40.45%
• no-vig implied win probability: 39.70%
• calibration gap: +0.75 percentage points

That is a favorable sign. Early underdogs did win slightly more often than the de-vigged market implied. But the gap is small.

The calibration plot below makes the point visually.

League-level calibration gaps follow the same basic pattern as the ROI splits:

• MLB: +2.14 points
• NFL: +2.69 points
• NBA: +0.65 points
• NHL: -2.15 points

This is why the most defensible conclusion is not "the strategy failed completely." It is "the market may have been mildly too low on some early underdogs, but not by enough to create a strong cross-league historical profit claim."

The one refined variant that looks tempting

There was one result in the package that could easily tempt someone into overclaiming: bigger underdogs in the first 3 weeks.

• big_dog_3w: ROI +4.2%

This is exactly the sort of result that deserves suspicion rather than celebration. It appears after testing multiple plausible windows and variants. Its confidence interval is still wide. NHL moves sharply against it. The whole thing is vulnerable to the usual p-hacking problem of selecting the tail bucket that happens to look best.

So Codex treated that result as exploratory. Interesting, yes. Bankable, no.

Kelly sizing does not rescue the story

Codex also ran a leave-one-season-out half-Kelly exercise using out-of-sample predicted win probabilities. If there were a meaningful exploitable edge, a more disciplined stake size should help.

It barely did:

• half-Kelly, 2 weeks: +0.1%
• half-Kelly, 3 weeks: +0.2%

That is basically breakeven. It reinforces the broader conclusion: whatever signal may exist here is small.

What the simulation adds

Because the underlying theory is about learning, Codex added a stylized market-learning simulation with:

• preseason strength priors
• slow or fast market updating
• a possible public premium on favorites

The simulation shows that early underdogs can become profitable in a world where the market updates slowly and favorites are slightly overpriced. That makes Spencer's idea coherent in theory.

But the simulation is a mechanism illustration, not calibrated proof. The historical data are directionally compatible with a small version of that story, not enough to claim that the simulation quantitatively explains observed betting outcomes.

What this does and does not show

It does not show that betting early underdogs was a reliable profit strategy across the major U.S. leagues.

It does show three more limited things:

1. Early underdogs performed better than generic underdog betting.
2. They may have benefited from a small early informational effect in some leagues.
3. That effect was not large or stable enough to produce a convincing historical edge once vig and uncertainty are taken seriously.

That is a more interesting answer than either extreme. The strategy was not obviously silly. It just was not a clean money printer.

The takeaway

If you want the shortest version:

• Spencer's idea was plausible.
• The data weakly support a mild early-season underpricing story.
• The strongest hints are in MLB and maybe NFL.
• The pooled evidence does not support a robust cross-league claim that early-season underdog betting made money.

That is where I would land after looking at the tables, the calibration evidence, the benchmarks, the shrinkage, the Kelly exercise, and the adversarial review: probably no durable all-league edge, but a real enough hint to make the theory worth taking seriously as a research question.

• Completed a multi-league test of an early-season underdog betting idea.
• Finished the Oscar portion of a film-podcast analysis.
• Organized and reviewed two replication repos for accepted papers.
• Automated validation of ballot referenda data.
• Hardened timeout recovery in an elections and redistricting workflow.
• Checked the remaining backlog in that same elections workflow.
• Tightened diagnostics and startup-plan handling in a political simulation project.
• Cleaned up footnotes, spacing, and privacy-sensitive figures on the coyotes blog post.

From A Man in Love:

When I discuss such topics with Geir, with whom I talk on the telephone for an hour every day, he is wont to quote Sven Stolpe, who has written somewhere about Bergman claiming that he would have been Bergman irrespective of where he had grown up, implying, in other words, that you are who you are whatever your surroundings. What shapes you is the way you are towards your family rather than the family itself. When I was growing up I was taught to look for the explanation of all human qualities, actions, and phenomena in the environment in which they originated. Biological or genetic determinants, the givens, that is, barely existed as an option, and when they did they were viewed with suspicion. Such an attitude can at first sight appear humanistic, inasmuch as it is intimately bound up with the notion that all people are equal, but upon closer examination it could just as well be an expression of a mechanistic attitude to man, who, born, empty, allows his life to be shaped by his surroundings.

Perhaps Bergman (harder to pin Knausgaard down) was overstating, but one lesson from these childhood mover papers is that the experience of society makes people more similar.¹ Those estimators measure common effects of place that make people experiencing the same environment more similar. In a segregated society, variation in treatment emerges. But what are the common experiences of society, generationally, nationally, or globally, for which we can't approximate the counterfactual?

1. I heard Chuck Klosterman say this in an interview years ago. I now wonder if he was thinking about this Knausgaard passage. ↩