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Completed Fights Intelligence Archive

Looking for completed event breakdowns? Browse 328 retrospective fight analyses across all 26 tracked events.

View Completed Archive (328 Fights) →
101

MMA Predictive Intelligence Library

Explore 101 comprehensive guides on quantitative modeling, statistical handicapping, and betting value.

Explore 101 Guides →
Master Fight Hub • 65 Saved Previews

UFC Fight Preview Library: 65 Saved Analyses

Browse 64 event matchup previews and one archived matchup proposal, with scheduled events and saved forecasts listed separately.

Date: Upcoming Season Calendar • Official UFCStats Fight Analysis • Pre-Fight Model Forecast •
Tags: #UFC#MMA#FightPredictions#UpcomingFights#SportsAnalytics

Welcome to the comprehensive BoutPredict upcoming fights intelligence hub. This catalog houses 64 event matchup previews plus one archived matchup proposal. Event status follows UTC broadcast times, and past forecasts remain available in the archive below.

Select any matchup below to explore the Tale of the Tape and predictive breakdown.

39 previews across 6 scheduled events25 archived event previews1 archived matchup proposal

Scheduled Events

UFC Fight Night: Allen vs. Duncan

Upcoming • October 10, 2026

UFC Fight Night: Buckley vs. Malott

Upcoming • October 17, 2026

UFC 333: Volkanovski vs. Evloev

Upcoming • October 24, 2026

UFC Fight Night: Moicano vs. Nolan

Upcoming • October 31, 2026

UFC Fight Night: Bonfim vs. Brady

Upcoming • November 07, 2026

UFC 334: Gane vs. Hokit

Upcoming • November 14, 2026

Archived Pre-Fight Previews

Saved forecasts from past event dates. These previews are separate from the completed-fight result archive.

UFC Fight Night: Rosas Jr. vs. Barcelos

Archived preview • September 26, 2026

UFC 332: Silva vs. Wang

Archived preview • October 03, 2026

Archived Matchup Proposals

Saved analysis of a replaced matchup; this proposal is excluded from scheduled event counts.

Arnold Allen vs Aaron Pico

Archived UFC 333 proposal. The current card pairs Aaron Pico with Losene Keita.

Read saved proposal →

How Our Predictive Engine Forecasts Upcoming Fight Cards

Developing automated algorithmic models for mixed martial arts requires isolating actionable technical signals from promotional noise. When my cofounder and I designed the BoutPredict neural architecture, our core breakthrough came from treating mixed martial arts not as a series of isolated moments, but as a continuous-state transition process.

Our predictive model isolates four specific analytical variables across every card on the calendar:

01. Positional Transition Efficiency

Measuring the exact speed and probability with which a fighter reverses bottom position, re-establishes underhooks, or secures back control.

02. Net Strike Differential At Range

Isolating landed strikes versus absorbed damage per minute specifically during open-mat boxing exchanges, discounting inactive clinch stalls.

03. Pace Decay Modeling

Simulating cardiovascular degradation across fifteen or twenty-five scheduled minutes under heavy lactic accumulation.

04. Monte Carlo Simulation Engine

Running 25,000 algorithmic simulations of every matchup to generate precise win probabilities, decision variances, and finish distributions.

My cofounder and I continuously audit training camp updates, weigh-in weight cut margins, and late replacement data to ensure our predictive forecasts remain the sharpest in combat sports.

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In case we are meeting for the first time, come over here, it'll be worth the roller coaster of articles that are gonna come up in the next few weeks.

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