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We’re starting off with the binary matter of whether the U.S. will experience 2 consecutive quarters of negative real economic growth in 2025, but will add some more doodads (e.g. mean quarterly and full-year growth forecasts) as additional markets come online and achieve meaningful volume.

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Fow now, we’ve archived their historical data, poll links, etc. and are looking into interesting ways we might combine polling and prediction market aggregates or simply offer up some of what went missing when ABC shut off the lights.

If you’re among those mourning the shuttering, please let us know what you’d most like to see resurrected.

You weren’t sure, but one thing you knew was it wasn’t gonna be Kalshi.

And that was probably a reasonable guess, given they’d been absolutely curbstomped by Polymarket, volume-wise. (We don’t yet have Betfair volume, but will update with it if/when we get it.)

A glance back at the conference championships in on January 26 hinted otherwise though.

The price action during the afternoon matchup between the Eagles and the Commanders was pretty similar across the three platforms. A little chunkier and spastic on Kalshi (a function of lower volumes and whole penny pricing), but no obvious leaders/laggards.

Notably, though, Kalshi diverged from the other two after the final whistle, fading the ultimately victorious Eagles a bit as the Chiefs-Bills game got underway.

But it was during that second conference champsionship that we saw some separation first emerge. That was also a much closer contest, with the favorite switching 9 times during the game, according to ESPN Analytics.

Here, when it mattered more (and there were multiple opportunities to quickly double or triple up), Kalshi began to shine. Its pricing was still the chunkiest and the most spastic (again, lower volumes and the inexpiable whole penny pricing), but if you discarded the spikes and watched for sustained moves, you’d usually anticipate a Betfair move coming a couple minutes later, with a corresponding Polymarket move a couple minutes after that.

Simply shifting Betfair’s pricing ahead by 2 minutes and Polymarket’s by 4 minutes syncs things up rather nicely. The prices don’t always exactly agree, but Kalshi’s leadership is pretty well zeroed out.

Before we move onto the Big Game itself, here’s a quick peek at the market action over the two weeks following the conference championships. All tell a similar story (the slight underdog Eagles gradually shortening their odds), but the whole penny chunkiness is on full and grotesque display.

The Su&er B*owl itself, as you’ll recall, was a pretty one-sided affair, with the Eagles flying to an easy victory that was all but assured by halftime.

Still, the first three scoring drives gave traders a few discrete opportunities to display their shifting expectations. And once again, despite its relatively miniscule volume, Kalshi led both Betfair and Polymarket on each significant move, consistently by roughly the same 2 and 4 minutes, respectively.

Make the same adjustment, and everything lines up neatly.

What’s the upshot? Kalshi, despite 1) being the newest entrant into sports futures among the three, 2) sticking with the insufferable whole penny pricing, 3) suffering the occasional price spasms, and 4) sporting mugh lighter trading volume, appears to have had the sharpest (or at least most swifly reactive) in-game pricing.

Whether Betfair and Polymarket traders were simply tailing Kalshi prices or were independently (but a bit more slowly) arriving at the same directional adjustments is less clear, but also perhaps less relevant. Either way, you likely could have made a small but consistent profit on the highly liquid, tight-spread Polymarket contracts by keeping an eye on Kalshi, ignoring the spikes, and chasing the sustained moves.

If you actively traded any of the three platofrms during the game, what was your strategy? Did you attempt to exploit the price lag or other cross-market inefficiences? Did you use any non-prediction market inputs (ESPN, sportsbooks, etc.) to guide you?

The Chiefs were the slight favorites ahead of kick-off, but things quickly turned the other way.

We tracked the pricing minute-by-minute for weeks leading up to and during the game.

Which market do you suppose had the sharpest, most responsive, least exploitable pricing leading up to and during the game, and which do you suppose was the fishiest?

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TCI is derived from dozens of prediction market prices on legislative outcomes, staffing decisions, executive actions, and more exotic things like impeachment and removal from office.

A detailed methodological discussion is available below, but suffice it to say a higher number suggests more chaos, less stability and less visibility into short/medium-term policy changes.

More significant than TCI’s absolute level are its direction and rate of change over time.

Discussion

Each component in the TCI is classified as chaotic, neutral, or antichaotic.

• A chaotic component implies more policy uncertainty as its odds of occurrence approach 100%.

• A neutral component implies more uncertainty as odds approach 50%.

• An antichaotic component implies more uncertainty as odds approach 0%.

Classification does not suggest a normative evaluation of an outcome as socially desirable or undesirable, economically beneficial or harmful, or otherwise good or bad.

A chaotic component is simply one that, if its underlying event were to occur, would represent significant, perhaps even unprecedented political upheaval, such that it would necessarily usher in multiple significant follow-on policy uncertainties (e.g. eliminating a federal department or repealing landmark legislation).

A neutral component is one tied to an outcome that different groups might favor or disfavor, but whose outcome - if relatively assured, whether affirmatively or negatively - is well precedented, well understood, and/or can be readily quantified in terms of its economic or political impact (e.g. tax cuts, narrow regulatory reforms). Hence uncertainty is minimized when the odds approach either 100% or 0%.

An antichaotic component may simply be the mirror image of what would be a chaotic component (if the prediction market question had been written in the negative) or it may be tied to a multi-outcome market that “should” resolve to a telegraphed preferred outcome, such as an announced nominee being the candidate ultimately confirmed to that office. In the case of multi-outcome markets, the highest-priced outcome is used in constructing the index.

Each underlying outcome is assigned a subjective relative weight (detailed below) to reflect the fact that not all chaos is equally utter. Because prices are drawn from multiple real-money prediction markets, when two or more markets are trading the same or a sufficiently similar outcome, that outcome’s weight is split across the multiple equivalent markets.

Construction Methodology

Prices are collected once per minute and represent the midpoint of the bid/ask spread published at that time by the respective platforms.

TCI = 100 * sum of Weighted Chaos Scores (WCS) for each market.
WCS = Unweighted Chaos Score (UCS) * Weight / sum of Weights
UCS = Price (for Chaotic components), 1 - Price (Antichaotic), or -2 * |Price - 0.5| + 1 (Neutral).

We rebalance the index from time to time and for various reasons, including the launch of new markets and resolution of existing ones. Whenever we rebalance, we apply a normalization factor sufficient to cause the immediately pre-rebalancing and immediately post-rebalancing index levels to equal one another. That normalization factor is applied to each calculation until the subsequent rebalancing, when the normalization factor is recalculated.

TCI Components (last adjusted Nov. 27, 2024)

This is our 4th forecaster match-up. See the original post in the series for more context on what’s going on here.

Previous match-ups:

• Nate Silver vs. Lichtman’s 13 Keys

• 538 vs. DDHQ

• Manifold vs. Metaculus

The Rules

1. Forecasters start with $10,000.

2. Once a day, the forecasters get to buy or sell/short Trump shares, depending on whether their own odds are higher or lower than the Polymarket price.

3. The size of each trade is driven by the size of their bankroll and how certain they are of the mispricing - the bigger the mismatch, the bigger the trade.

4. We mark the holdings to market each day, so unrealized gains are reflected in daily account value.

5. A total of 48 trades are made, from September 19 through November 5. Any realized gains booked along the way are theirs too keep. After the election, all Trump shares pay $1 and Harris shares become worthless.

Harry Crane vs. Super Model

TL;DR Super Model’s own multi-market-derived forecast did loads better than any of the forecasters we’ve scored thus far, but that’s a low bar. That’s evidenced by the fact that we still would’ve lost money (-15%) using it to trade on Polymarket, since their Trump price was consistently (if only slightly) higher than ours, meaning we would’ve consistently been making small pro-Harris bets.

Crane’s forecast, on the other hand, blows everyone out of the water, returning +85% over the 7 weeks of simluated trading (the only profitable forecast we’ve encountered so far). If you haven’t seen it, check out Crane’s final forecast, which had Trump at 66.5% to win and explains how his methodology differs from legacy forecasters.

Below, we dive into the daily performance metrics - how the respective forecasters’ odds differed from market prices each day, how big a daily bet they place on Trump or Harris as a result, and how their portfolio value grew/shrank over time.

Previously…

This is our 3rd forecaster match-up. See the original post in the series for more context on what’s going on here.

Previous match-ups:

• Nate Silver vs. Lichtman’s 13 Keys

• 538 vs. DDHQ

The Rules

1. Forecasters start with $10,000.

2. Once a day, the forecasters get to buy or sell/short Trump shares, depending on whether their own odds are higher or lower than the Polymarket price.

3. The size of each trade is driven by the size of their bankroll and how certain they are of the mispricing - the bigger the mismatch, the bigger the trade.

4. We mark the holdings to market each day, so unrealized gains are reflected in daily account value.

5. A total of 48 trades are made, from September 19 through November 5. Any realized gains booked along the way are theirs too keep. After the election, all Trump shares pay $1 and Harris shares become worthless.

Manifold Markets vs. Metaculus

TL;DR Ignominy on both sides, once again, though far less so at . By only losing 38.85%, they’ve not only easily dispatched Metaculus, but they’ve ousted previous record-holder DDHQ’s -38.88% (by a margin of just $2.49). As has been the case with every simulation thus far (with the exception of Nate Silver), both forecast’s Trump odds always lagged/tied Polymarket’s price. So there are once again zero long Trump trades, despite both contenders having Trump at or above 50% for 2 consecutive weeks.

Below, we dive into the daily performance metrics - how the respective forecasters’ odds differed from market prices each day, how big a daily bet they place on Trump or Harris as a result, and how their portfolio value grew/shrank over time.

Previously…

This is our 2nd forecaster match-up. See the original post in the series for more context on what’s going on here and the results of Nate Silver vs. Lichtman’s 13 Keys.

The Rules

1. Forecasters start with $10,000.

2. Once a day, the forecasters get to buy or sell/short Trump shares, depending on whether their own odds are higher or lower than the Polymarket price.

3. The size of each trade is driven by the size of their bankroll and how certain they are of the mispricing - the bigger the mismatch, the bigger the trade.

4. We mark the holdings to market each day, so unrealized gains are reflected in daily account value.

5. A total of 48 trades are made, from September 19 through November 5. Any realized gains booked along the way are theirs too keep. After the election, all Trump shares pay $1 and Harris shares become worthless.

ABC News’ 538 vs. Decision Desk HQ

TL;DR Another double atrocity. DDHQ “wins” by losing a little less than 538. 538 came close to saving itself a few bucks in the final days, until it herded back to a last-minute Harris win. DDHQ managed to finish with Trump favored, but remained so far under the Polymarket price that it always guided toward buying Harris on all but one day (when it made no trade). Still, DDHQ’s somewhat shallower bath makes them our best performer to date!

Below, we dive into the daily performance metrics - how the respective forecasters’ odds differed from market prices each day, how big a daily bet they place on Trump or Harris as a result, and how their portfolio value grew/shrank over time.

Let us know if there’s a particular head-to-head you’d like to see. Surely at least a few (one?) will turn a profit…

Previously…

There’s been a bit of image rehab and goalpost nudging among the legacy forecasting set since election day. From ex post pivoting from models to modals, to blaming distortive (but apparently unforeseeable) “misogyny, racism, xenophobia, [and] antisemitism.”

Whatever tapestry of unmodelable forces conspired to cause a few of the most celebrated centralized soothsayers to misfire this cycle, it’s worth taking a moment to dispassionately assess which were better (or less bad) than others.

After all, the one thing black box forecasters can agree on is that they’re sharper oddsmakers than the dumb-money-soaked, inumerate echo chambers known as prediction markets.

So we’ve set aside a cool $10,000 in pretend, retroactive cash for each major forecaster to use trading its own predictions against ’s presidential winner market (the most liquid real-money prediction market with the narrowest spreads) to see who can rack up the biggest gains.

Scroll to the bottom for detailed notes on our simulation methodology, but here’s the short version…

The Rules

1. Forecasters start with $10,000.

2. Once a day, the forecasters get to buy or sell/short Trump shares, depending on whether their own odds are higher or lower than the Polymarket price.

3. The size of each trade is driven by the size of their bankroll and how certain they are of the mispricing - the bigger the mismatch, the bigger the trade.

4. We mark the holdings to market each day, so unrealized gains are reflected in daily account value.

5. A total of 48 trades are made, from September 19 through November 5. Any realized gains booked along the way are theirs too keep. After the election, all Trump shares pay $1 and Harris shares become worthless.

We’ll do a handful of these one-on-one comparisons, based on thematic or simply amusing matchups.

First up…

Nate Silver’s Silver Bulletin vs. Allan Lichtman’s 13 Keys To the White House.

TL;DR They both get wrecked, but Lichtman way worse, who goes for broke - and quickly gets there.

Below, we dive into the daily performance metrics - how the respective forecasters’ odds differed from market prices each day, how big a daily bet they place on Trump or Harris as a result, and how their portfolio value grew/shrank over time.

Next match-up…

Fine print re simulation methodology: We’re using a modified version of fractional (5%) Kelly betting. (Any more aggressive than 5% and the forecaster burns through the cash too quickly and overly weights his early predictions.) The Kelly criterion is a formula that informs sizing of a series of binary bets in order to maximize long-term expected growth. With apologies to Dr. Kelly, it’s not entirely apt here, since these bets are not (even close to) independent events; in fact, they’re all the same event, just with 48 different entry points. But it still serves to prescribe a daily bet direction and size that corresponds to the degree of certainty the trader/forecaster has about the market’s mispricing.

For Nate Silver, since we have the time stamp for most of his model updates, whenever possible, we place the trade immediately after his daily update, such that any impact his model has on the market is (hopefully) not yet fully priced in. This also serves to simulate the pricing a relatively fleet-fingered user might be able to get if trading off Silver’s insights. Since Lichtman never adjusted his 100% probabilistic forecast, there was less risk of any interim pronouncements moving prices, so we made all of his trades simultaneous to Silver’s.

For the bankroll variable in the Kelly equation, we use the total account value (cash + mark-to-market value of the net Trump/Harris position). Using just the daily cash balance could lead to situations where a trader should be motivated by dramatically shifting odds/prices to take a large offsetting position (and can afford to do so, since it wouldn’t require additional cash), but is prescribed a much smaller offsetting position because their nominal cash value has become so small.

Bid/ask spreads are assumed to be $0.001 centered around the observed historical midpoints and we assume enough liquidity at the best price to accommodate whatever trade size the forecaster desires.

And with the ink not yet dry on the election results, 2024 is shaping up in similar fashion.

A series of increasingly controversial announcements have left the world (and traders) to wonder: 1) whether the new Senate majority headed by John Thune will manage to (or want to) get the nominees confirmed, and 2) whether Trump will resort to recess appointments for the least palateable picks.

Happily, between , Kalshi, and , we have dozens of active markets to look at. They don’t all measure the same outcomes, which actually gives us even more granular insights into how traders expect the cabinet confirmations (or lack thereof) to play out.

The more traditional picks (Rubio for State, Burgum fo Interior, Collins for VA, Zeldin for EPA) are all trading above 90%. Instead - for now - we’ll focus on… the other guys (and gals).

The four names Trump has announced (rather than simply floated) as winning his nomination, who face the rockiest path to actually serving are:

• Homeland Security: SD Governor Kristi Noem

• Defense: Fox News personality and Army National Guard officer Pete Hegseth

• Health and Human Services: Former presidential candidate and activist Robert Kennedy, Jr.

• Justice (Attorney General): Former Florida Congressman Matt Gaetz

We’ve identified all the active markets on those 3 platforms, applied our qualitative weights (based on volume, real-moneyness, tick size, fees, and market microstructure) and present you with the following aggregated market-implied fates of the Flagitious Four.

Of the bunch, Gaetz (on whom a House ethics report was set to drop just before his elegantly-time resignation) is clearly the most controversial, though Kennedy is perhaps not too far behind.

Traders, however, give Kennedy a much better shot at actually serving in the cabinet (82%) and find it more likely than not he’ll get there via a successful Senate vote (71%). Gaetz on the other hand, will likely never take the office to which Trump claims he intends to appoint him (42%) and is even less likely to get there with a Senate blessing (31%).

Hegseth is only slightly safer than Kennedy, with an 86% chance of serving (72% that he’s confirmed). Meanwhile, Noem (the erstwhile scandalousness of whose nomination seems suddenly quaint in light of who was to follow) finds herself sitting pretty at 95% to serve (90% to be confirmed), climbing steadily from the mid-80s in recent days, as the rogues gallery has grown more crowded.

We don’t have live-updating real-time tracking aggregates for cabinet markets yet, but those will be coming soon, at least for the HHS and AG markets (and whichever others may next cause Senatorial and journalistic jaws to drop).

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Where they stumbled a bit was in foreseeing that Republicans would keep their majority in the House of Representatives.

In the six weeks leading up to the election, the favorite flipped several times, with the GOP never getting above the mid-50s. In the end, they basically heralded a coin flip (not a terrible prediction, considering the final tally will once again be a closely divided chamber).

But clearly the markets covered themselves in less glory with their Congressional prognositcation than their Presidential, which outperformed (nearly) all pro forecasters.

So who did best and worst in what turned out to be the most challenging 2024 outcome to handicap?

To find the answer, we looked at minute-by-minute pricing of the GOP majority contract on , Betfair, Smarkets, Metaculus, Kalshi and our own Super Model aggregate, from the moment Kalshi began trading on October 2, through election night at midnight (when it was still not a done deal).

We compared three metrics:

• Percent of the time the price favored the correct outcome (above $0.50)

• Average price over those five weeks

• Average price over the final 24 hours

For each metric, the higher score the better. Weighing all three equally, we present you our official House majority forecast rankings:

1. Polymarket

2. Super Model

3. Kalshi

4. Betfair

5. Smarkets

6. Metaculus

We’ll use these and other predictive metrics collected this cycle to refine our weightings of the various markets for our aggregates going forward.

The idea was to bring you the most accurate and actionable intel from the world of prediction markets, based on a few broad assumptions:

1. If your goal is to accurately handicap future uncertainty, market signals are generally superior to any individual forecaster or model (or even aggregates thereof) because any useful signal they throw off will be incorporated into markets.

2. While markets are the best mechanisms we have for discounting not only future cash flows, but future uncertainties, they’re not perfect and they’re not created equal. So by understanding their relative strengths and weaknesses (margin of inefficiency, liquidity, fee-based distortions, and market microstructure), you can likely derive an even better market signal by aggregating and weighting them by such factors.

3. A small number of traders are consistently better at forecasting than all the others. That’s presumably always been true, but until recently, it was only valuable to them. With the rise of blockchain prediction markets like , we can scrutinize the historical performance (and current positioning) of every trader and seek to amplify their signal for even better, earlier insights.

The first two are the basis of our market aggregates, which we’ve used to show you how the big 2024 electoral races were playing out in the mind of traders across the prediction market landscape.

The third premise was the basis of SuperVision, we we debuted yesterday to track the most profitable Polymarket traders as they positioned themselves for election day and responded in real-time to early data.

The result was projecting the final EV count (and Trump’s battleground sweep) correctly at 8:43 pm, nearly 6 hours before the NYT Needle arrived at that projection and 9 hours before the AP called the race.

We had (might still be having) a few tech glitches and growing pains, but overall we’re very happy with our debut and hope you found it useful!

But this was all just a proof of concept. While media attention to such things is highly election-driven, preediction markets don’t go away after elections and neither will we.

In recent cycles, we’ve in fact seen huge new political and economic uncertainties unfold upon the inauguration of new administrations and new Congresses. Not only will this cycle follow suit, it looks to usher in potentially unprecedented uncertainty - from domestic economic and fiscal policy to existential geopolitical and technological risks.

So expect more market aggregates, more SuperVision dashboards around events big and small, and more prediction market analysis and commentary from our team.

Never before have prediction markets been more societally and economically valuable. And never before has the complex of such markets been so rich, diverse, and liquid.

This election cycle, the markets acquitted themselves well, at least vis a vis legacy forecasters and pollsters. But make no mistake - there will be biffs. Big biffs. Part of what we aim to do is sniff out those biffs so you can not only be prepared for unexpected outcomes, but potentially profit from occasional market mispricings.

As we said, markets not perfect. But they’re decent.

And we’ll continue to try to be slightly better.

The more they pile into one side of a trade, the more confidence we have that it’s even more underpriced, and we adjust the fair price accordingly.

This not only gives us an electoral projection that should be even better (and faster) than you get from public pricing on the most liquid, efficient markets; it also shows you where the most accurate forecasters are finding the best value throughout the day.

We’re offering EV toplines to all readers, but to access the interactive map, state-by-state mispricing, and the raw feed of trades by our cadre of Super Sharps, you need to upgrade to a paid subscription.

But good news! There’s a free 7-day trial, which will last you until well after we know the winner. (Gulp. Right?)

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