US troops deployed in Iran

v1 (2026-04-26) current

The starting probabilities of this market were calculated from the historical record alone: similar U.S. coercive episodes, including many cases where the United States applied force or moved major assets but never ended up putting U.S. troops on the ground in the target country.

History-only starting point

The April 2026 prior is intentionally based only on historical escalation patterns at roughly this level of confrontation. It does not use current official statements, current deployments, or other case-specific March 2026 information as model inputs. The starting point therefore reflects the historical mix of no-entry, raid, foothold, and campaign outcomes in similar episodes rather than any claim about what current policymakers privately intend.

Reference classes and partial pooling

The model uses four monthly states: no entry, raid, limited foothold, and major campaign. Instead of forcing one smooth curve, it partially pools several kinds of historical evidence so that no-entry months remain common while larger tails are still possible.

Reference layer	Raw case-months	Overlap-adjusted effective count	Role in the fit
Coercive no-ground-entry	7	7.0	Preserves real mass on all-no months.
Limited entry	5	4.5	Shapes the 25 to 2,000 troop range.
Large entry	6	5.5	Shapes only the upper tail above roughly 2,000 troops.

Some examples sit on the boundary between a foothold and a campaign. To avoid double counting, each operation-month gets total weight 1.0 and shares that weight across its tags. That overlap discount stops the same case from pulling both the middle and upper tail too hard.

The initial April blend assigns 65% weight to coercive no-ground-entry, 25% to limited entry, and 10% to large entry. These weights reflect the historical base rate: the large majority of high-intensity U.S. confrontations — including direct naval strikes and major air campaigns — end without U.S. troops entering the target country. The 25% limited-entry weight preserves meaningful raid-to-foothold probability, while the 10% large-entry weight reflects that regime-change-scale campaigns remain uncommon even from high-pressure starting points. Within each reference class, the per-class state probabilities (for example, a coercive-no-ground-entry episode has a 97% base rate of no ground entry; a large-entry-type episode has a 55% base rate) are analyst calibrations drawn from the broader historical record and are not derived mechanically from the listed reference cases. The listed cases instead inform the emission distributions — what a raid, foothold, or campaign looks like in terms of troop counts.

Note on Kosovo (1999): the NATO air campaign is coded as coercive no-ground-entry for the pre-armistice phase. NATO KFOR ground forces subsequently entered Kosovo territory after the armistice; the relevant analogue for this market is the coercive phase, in which airpower achieved its political objective without a prior ground commitment.

Most influential examples

Date	Example	Estimated peak troops	Scenario type	Why it matters	Source
1987-10	Tanker War reflagging and escort posture	0	No ground entry	Direct naval confrontation pressure around Iran without putting U.S. troops on the ground.	Office of the Historian: The Tanker War
1988-04	Operation Praying Mantis	0	No ground entry	Large U.S. strike operation against Iranian targets without putting U.S. troops on the ground.	Britannica: Operation Praying Mantis
1986-04	Operation El Dorado Canyon	0	No ground entry	Punitive U.S. air strikes on Libya without putting U.S. troops on the ground in the target country.	Britannica: Operation El Dorado Canyon
2011-03	Libya 2011 NATO air campaign	0	No ground entry	NATO air campaign achieved regime change in Libya without U.S. ground troops entering the country; demonstrates that coercive airpower can topple a government without a ground foothold.	Britannica: 2011 Libyan civil war
2020-01	Soleimani killing and U.S.–Iran stand-off	0	No ground entry	Direct U.S. strike killing Iran's top military commander; Iran responded with ballistic missile strikes on U.S. bases in Iraq; no U.S. troops entered Iran despite the most direct military confrontation since 1988. The most recent Iran-specific coercive episode.	DoD: statement on death of Qasem Soleimani
1980-04	Operation Eagle Claw	132	Raid-scale attempted entry	Failed rescue mission into Iran; useful low-end Iran-specific raid analogue.	Britannica: Operation Eagle Claw
2011-05	Abbottabad raid	79	Raid-scale entry	Politically constrained sovereign-territory raid with dozens of U.S. personnel.	White House archive: Bin Laden remarks
2019-10	Barisha raid against al-Baghdadi	70	Raid-scale entry	Another tightly bounded hostile-territory raid with a small force package.	White House archive: al-Baghdadi statement
2001-11	Opening U.S. foothold in Afghanistan	1,300	Limited foothold	Small but persistent opening-month ground footprint before later expansion.	Britannica: Afghanistan War
1983-10	Grenada opening week	7,300	Small invasion / overlap case	Overlap case: larger than a foothold, smaller than Iraq-scale campaigns.	Britannica: Grenada invasion
1991-02	Desert Storm ground offensive into Iraq/Kuwait	270,000	Major campaign	Upper-tail analogue for a very large U.S. ground campaign in the region.	Britannica: Persian Gulf War
2003-03	Iraq invasion opening month	148,000	Major campaign	Large ground invasion used to shape the upper campaign tail rather than the median month.	Britannica: Iraq War
1952-04	Korean War peak deployment	326,000	Major campaign	Peak U.S. deployment in a sustained ground war against a populous Asian adversary; upper-tail anchor calibrating the major-campaign distribution for a large-country opponent with a capable military.	Britannica: Korean War
1969-04	Vietnam War peak deployment	543,000	Major campaign	Largest sustained U.S. ground commitment in a hostile country; establishes the plausible ceiling for a large-scale campaign against a populous, militarily capable adversary. Iran (~85 million people, significant conventional and irregular forces) is closer in scale to Vietnam-era North/South Vietnam than to Iraq 2003.	Britannica: Vietnam War

Fitted state distributions

Positive-entry states are modeled on log1p(troops) with heavy tails. The median and 90th-percentile troop counts below are the fitted state distributions before the monthly state probabilities are mixed together.

State	Median troops	90th percentile troops
No entry	0	0
Raid	120	285
Limited foothold	1,235	14,632
Major campaign	67,358	618,637

Each state's emission distribution is partially pooled across adjacent tiers to prevent overconfident estimates from small samples. The raid distribution borrows 20% of its log-mean from the limited-entry tier; the limited-foothold distribution draws 60% from limited-entry cases, 25% from large-entry cases, and 15% from raid cases; the major-campaign distribution borrows 10% of its log-mean from the derived foothold. Log-sigma values are floored at 0.40 (raid), 0.70 (foothold), and 0.85 (campaign) to maintain realistic tail width given the limited case counts. The Korea and Vietnam cases are included in the large-entry tier specifically because Iran's population (~85 million) and military depth are closer in scale to those theatres than to Iraq 2003 or Panama, so those upper-tail cases are relevant calibration points for what a major campaign could require.

Monthly state probabilities

The April prior is a history-only blend of the three reference layers above. Later months use a persistent transition model: once an entry state is reached it tends to persist rather than snapping back immediately to no-entry. Escalation transition rates are also scaled by a multiplier that starts at 1.00 for the May step and decays month-by-month to 0.72 by December (schedule: 1.00, 0.97, 0.93, 0.88, 0.84, 0.80, 0.76, 0.72). This captures the empirical pattern that confrontations either escalate quickly or settle into a stable standoff; a situation that has not escalated by mid-year is historically less likely to generate fresh escalation than one that is just beginning. The practical effect is that the probability of a new low-level incursion (such as a raid) falls as the year progresses, while the major-campaign probability continues rising because it benefits from state persistence once escalation is entered. The market is history-only: it does not encode any claim about current U.S. government intentions, ongoing negotiations, or current force posture.

Month	No entry	Raid	Limited foothold	Major campaign
Apr 2026	86.6%	6.3%	4.3%	2.9%
May 2026	88.3%	4.0%	4.1%	3.7%
Jun 2026	88.7%	3.3%	3.7%	4.3%
Jul 2026	88.8%	3.0%	3.4%	4.7%
Aug 2026	88.9%	2.9%	3.3%	4.9%
Sep 2026	89.0%	2.8%	3.2%	5.1%
Oct 2026	89.1%	2.7%	3.1%	5.1%
Nov 2026	89.3%	2.6%	3.0%	5.1%
Dec 2026	89.5%	2.5%	2.9%	5.1%

Selected threshold probabilities

These are the starting probabilities for a few representative thresholds from the full ladder. Because the lowest listed threshold is 25 troops, a no-entry month can still resolve as all no.

Month	≥25	≥100	≥1,000	≥5,000	≥20,000
Apr 2026	13.3%	10.6%	5.2%	3.7%	2.5%
May 2026	11.6%	9.8%	5.9%	4.4%	3.1%
Jun 2026	11.2%	9.6%	6.3%	4.9%	3.5%
Jul 2026	11.1%	9.6%	6.5%	5.2%	3.8%
Aug 2026	11.0%	9.6%	6.7%	5.4%	4.0%
Sep 2026	10.9%	9.6%	6.7%	5.5%	4.1%
Oct 2026	10.8%	9.5%	6.8%	5.5%	4.1%
Nov 2026	10.6%	9.4%	6.7%	5.5%	4.1%
Dec 2026	10.4%	9.3%	6.6%	5.4%	4.1%

Resolution approach

This market is intentionally built for partial observability. We do not expect every month's exact troop count to be provable. Instead, resolution is threshold-by-threshold. If the best-supported interval for a month is, for example, 120 to 140 troops, thresholds at or below 100 resolve Yes, thresholds at or above 150 resolve No, and thresholds inside the interval remain unresolved. If the best-supported interval is 2,800 to 4,200 troops, the same logic applies at the higher thresholds. The point is to resolve as many rows as can reasonably be supported without pretending to know more than the evidence shows.

Generation script

The Python script used to produce this market's starting probabilities is available for download: generate_market.py