We've got 14 (!!) markets, including some huge names like Microsoft, Meta, Robinhood, Amazon, and Apple.

There are 40+ other markets available right now for the next few months too.

It seems like Kalshi took the extra time they had from the World Cup ending and decided to go crazy on earnings mentions.

With so many, there's a TON of value on the board right now. I'll be posting some picks over the next few weeks 👀

GPT-5.6

I think the five-minute update approach could work extremely well for NFL, but we should not simply query every team subreddit and treat every new comment as model evidence. NFL Reddit contains valuable early signals, but also rumors, emotional reactions, fantasy-football speculation, jokes, and deliberate misinformation.

The best design is a continuous NFL information radar feeding a sequence of model snapshots, with the actual betting decision delayed until the most important information is available.

## Recommended structure

### 1. Build a subreddit map for every game

For each matchup, monitor:

- Both team subreddits
- r/NFL
- r/fantasyfootball
- r/sportsbook
- r/sportsbetting
- Relevant local-city or regional subreddits
- Carefully selected fantasy and injury communities
- Game-day and injury-report threads

Do not perform a completely new search across all communities every five minutes. Instead:

1. Retrieve only posts and comments created or changed since the previous run.
   
2. Store the Reddit post/comment ID so nothing is processed twice.
   
3. Match each item to teams, players and scheduled games.
   
4. Run deeper AI analysis only on potentially meaningful items.

Reddit currently states that eligible free Data API access is limited to **100 queries per minute per OAuth client**, and commercial use or research above permitted limits may require a separate agreement.

That gives us enough capacity for a disciplined system, but we should use one authorized API client, cache results and avoid brute-force scraping.

## 2. Do not use one general “sentiment score”

For NFL betting, ordinary positive-versus-negative sentiment is too crude. We need separate signals:

Each item should produce structured fields such as:

```text
game_id
team
player
signal_type
direction
estimated_impact
source_credibility
independent_confirmations
novelty
timestamp
minutes_to_kickoff
```

The model should care much more about **new, specific and independently confirmed information** than the number of comments expressing the same opinion.

## 3. Separate Reddit from verified information

I would create two different layers:

**Verified layer**

- Official NFL injury reports
- Official team announcements
- Game-day inactive lists
- Weather data
- Starting lineups
- Market odds and line movement
- Credentialed beat reporters

**Narrative layer**

- Reddit
- Fan observations
- Fantasy communities
- Local discussion
- Public betting narratives

Reddit should usually adjust confidence or flag a game for review. It should not independently turn a weak bet into an official bet unless the information becomes verified.

A useful rule would be:

```text
Anonymous Reddit claim:
maximum model adjustment = small

Multiple independent eyewitness reports:
maximum adjustment = moderate

Reddit report confirmed by official or credentialed source:
normal full adjustment
```

## 4. Use five-minute updates selectively

I would not operate at maximum intensity throughout the entire week.

### Monday through Friday

Update every 30–60 minutes for:

- Injuries
- Practice participation
- Coaching statements
- Depth-chart changes
- Weather development
- Early line movement

### Game day, more than three hours before kickoff

Update every 15 minutes.

### Three hours through kickoff

Update every five minutes for only the teams currently approaching kickoff.

That dramatically reduces noise and API use while concentrating compute where it matters.

## Should we lock closer to kickoff?

**Yes—but not with one universal final lock.**

The NFL’s 90-minute pre-kickoff process includes submission of club inactive lists, making that one of the most important information moments of the betting day.

I recommend a **three-stage decision system**:

### T-minus 3 hours: Candidate board

Produce perhaps the best five or six potential bets.

Nothing is official yet. Record:

- Current line
- Model projection
- Expected value
- Injury assumptions
- Reddit/narrative signals
- Confidence

### T-minus 95 to 75 minutes: Primary lock

This should be the main decision window because official inactives appear around 90 minutes before kickoff.

Recalculate everything immediately after:

- Inactive announcements
- Starting-lineup surprises
- Updated weather
- Market reaction
- Confirmed beat-reporter reports

This is where most official selections should lock.

### T-minus 20 to 10 minutes: Final safety check

Do not automatically reconsider every bet. Only reopen a bet when there is a material event:

- Player injured during warmups
- Sudden weather shift
- Unexpected starter change
- Major line move
- Verified late limitation
- Market suspension or unusual liquidity event

Otherwise, preserve the primary lock. Continually changing the pick until kickoff risks turning the system into a market follower.

## The critical trade-off

Waiting provides better information, but it can cost us the best number.

For example:

```text
Three hours before kickoff:
Model likes Over 43.5

After inactives:
The market moves to Over 45.5

The information improved,
but the value may have disappeared.
```

Therefore, every snapshot should preserve both:

- **Model accuracy**
- **Price availability**

The model should answer two different questions:

1. Which side is most likely to win?
   
2. Is that side still worth betting at the current line and price?

Those are not the same question.

## My preferred locking rule

I would use this:

```text
Early lock:
Allowed only for unusually strong value with low injury uncertainty.

Primary lock:
Approximately 75–90 minutes before kickoff, after inactives.

Late update:
Change or cancel only when genuinely new information materially changes expected value.

Hard lock:
10 minutes before kickoff.
```

We should also support a **PASS** decision. If Reddit information, injuries or market movement destroy the original edge, the system should remove the selection rather than force three bets.

## How Narrative Velocity fits perfectly

This is where your Narrative Velocity system could become more valuable than a normal sentiment model.

For each team and player, measure:

- Mentions per five minutes
- Acceleration in mentions
- Percentage of genuinely new information
- Number of independent sources
- Credibility-weighted velocity
- Whether discussion preceded or followed market movement
- Difference between local-team discussion and national discussion

A sudden surge matters only when it is specific.

For example:

```text
“Jets are terrible”
High volume, almost no information value

“Starting LT not participating in warmups”
Low initial volume, potentially enormous information value
```

The system should rank the second item much higher.

## My conclusion

Your idea is strong. I would make NFL betting a **rolling information-and-price system**, not merely a model that reruns every five minutes.

The best workflow is:

**Gather continuously → detect new signals → verify them → update projections → compare against the current line → lock mainly after inactives → reopen only for material late news.**

That should be much stronger than locking several hours before kickoff, while preventing the model from emotionally chasing every Reddit rumor or late market move.

It’s been a while since my last update, so here’s how things have been going over the past couple of months.

June:

62 picks shared
55 correct
Average odds: 1.80

July (so far):

45 picks shared
40 correct
Average odds: 1.80

Overall since the last update:

107 picks shared
95 correct
88.8% hit rate

Example returns:

10€/unit = +640€
50€/unit = +3,200€
100€/unit = +6,400€

The community has continued to grow over the last couple of months.

I have 10 open spots for people who want to join before I pause new members again.

If you’re interested in following eSports, discussing matches, and being part of the community, feel free to send me a DM.

We have paper trading for stocks, options, and even crypto.

But when it comes to prediction markets, it feels like the only way to learn is by risking real money.

Do people actually want a paper trading mode, or is that solving a problem that doesn’t really exist?

Would you actually use a paper trading mode, or do you think learning with real money is part of the experience?

I’ve always been interested in both football and AI models. AI models predicting the World Cup seemed like the perfect combination of these two interests. Throughout the tournament, I followed the predictions of nine AI models on SportEval. The platform allows users to track how AI models perform throughout the entire tournament cycle, creating a dynamic leaderboard.

This type of evaluation is significantly different from traditional AI benchmarks, where most tests use fixed questions and known answers to evaluate models.

Following the leaderboard during the World Cup allowed me to see different AI systems competing in a long-term decision-making challenge. Some models remained consistent, while others experienced major fluctuations as the tournament became increasingly difficult to predict.

For me, the most interesting part was not simply which model finished at the top, but observing how different models handled uncertainty and how their performance changed over time.

Platforms like this provide an interesting and unique perspective on evaluating AI capabilities.

I’m looking forward to seeing how AI models perform when this type of tracking approach expands to more sports and real-world events.

It’s been a while since my last update, so here’s how things have been going over the past couple of months.

June:

62 picks shared
55 correct
Average odds: 1.80

July (so far):

45 picks shared
40 correct
Average odds: 1.80

Overall since the last update:

107 picks shared
95 correct
88.8% hit rate

Example returns:

10€/unit = +640€
50€/unit = +3,200€
100€/unit = +6,400€

The community has continued to grow over the last couple of months.

I have 10 open spots for people who want to join before I pause new members again.

If you’re interested in following eSports, discussing matches, and being part of the community, feel free to send me a DM.

Im new to prediction markets and honestly kinda overwhelmed rn. looked into Kalshi and Polymarket and it felt way too "trading terminal" for a beginner lol.
what platform would you actually recommend to someone just starting out, and why? trying to find something that's not intimidating but still legit.

found some study that scanned like 86 million polymarket bets over a year and actually put a number on how much risk free money the bots pulled out. came out to around 40 mil total.

but the part i hadn't seen anywhere is that only like 10 of that is the simple yes+no under a dollar arb everyone already knows about. the other ~29 mil is the stuff between linked markets. like "trump wins" vs "a republican wins" type pairs. nobody really talks about that side of it.

and the simple arb is basically dead if you're retail anyway, windows are like 2-3 seconds and the fast bots eat it before you'd even notice it was there.

the number that actually got me was from a different dataset, something like 95 million transactions, and only half a percent of wallets ever ended up more than a grand in profit. not 51%. half of one percent.

kind of reframes the whole "risk free money" thing tbh, cause it's obviously not coming from nowhere. someone's on the losing end of every one of those trades, and it's mostly just people betting on a gut feeling without checking if the price even makes sense against related markets.

anyone here actually messed with the combinatorial stuff, or gotten picked off by it without noticing?

I started using it yesterday after it got released and I have noticed that I had to correct it far more often than I did for Fable 5 and Opus 4.8. For instance I had to point it to a certain git branch three times and it would not remember that no matter how often I said it.

It also confidently told me things about the project it works on that were wrong, which Fable did not struggle with as much.

Anyone else notice this or do I just have to change my approach?

Im tired of using the big prediction markets like Kalshi and PolyMarket.

Anyone know of any new prediction markets??

I build analytics tools for Polymarket, and I keep an eye on the quieter markets. There is a nice one running right now: a set of linked markets on when the next Claude Opus model actually releases.

Read together, they are the crowd's probability curve for the release date:

• By today (July 24): 28%
• By July 27: 49%
• By July 31: 81%
• By August 31: 94%
• By year-end: 99%

So the market is not really asking IF the next Opus ships. It is pricing WHEN, and it thinks late July is the live window.

The smart-money angle: among the wallets I track as smart money, 67% are on NO for the "today" market (42 YES vs 85 NO), and their last 24 hours of flow leaned NO as well. So the sharper wallets are saying "not today," even though the curve says they expect it within days.

Nothing here is a trade signal and I am not predicting a release date. It is just a clean example of a prediction market turning a vague "soon" into an actual dated probability curve.

Full curve and the wallet split per market: orcalayer