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Platform Engine

Recompete Prediction Engine — How Aliff Forecasts Contract Rebids

Forecasts when federal contracts will recompete by analyzing contract end dates, modification histories, option year patterns, and agency acquisition forecasts. Surfaces opportunities 6-24 months before competitors learn about them.

Overview

Federal recompetes are predictable in aggregate but require systematic monitoring to surface individually. The Recompete Prediction Engine continuously tracks contract period of performance end dates, option year exercise patterns, modification histories, and agency-specific recompete-vs-extend behavior to forecast which contracts will rebid and when. The output is a forward calendar of expected recompetes ranked by relevance to the client's NAICS and target agencies.

Why Recompete Prediction Matters

For established federal contractors, recompetes are the single largest source of capturable revenue — roughly $190B+ in annual federal services recompete volume per our analysis of FPDS data. Yet most BD teams treat recompetes opportunistically rather than as a calendar. The data from our State of GovCon Recompetes 2026 research shows that capture investments started 18-24 months ahead of recompete have substantially higher challenger win rates than capture work started inside 12 months.

The strategic question isn't whether recompetes are worth pursuing — they clearly are. The question is which specific recompetes to invest pursuit dollars in, and the answer requires advance visibility plus vulnerability assessment.

How the Engine Works

The Recompete Prediction Engine ingests data from federal procurement sources daily, applies pattern recognition against historical recompete cadence by agency-office, and produces a forward-looking calendar of expected recompete events. Each predicted recompete carries a probability score, an expected window (typically a 3-6 month range), and links to the underlying contract data.

Predictions are continuously re-evaluated as new data arrives. Contracts where the agency exercises option years on schedule get pushed forward; contracts where options are skipped or modifications spike get flagged as more imminent recompetes.

Data Sources

  • FPDS-NG — contract period of performance end dates, option year history, modification patterns
  • SAM.gov Contract Opportunities — pre-RFP solicitations and sources sought notices
  • USASpending.gov — obligation history and award trends
  • Agency-specific acquisition forecast pages
  • GAO bid protest decisions — protest patterns on prior recompetes
  • Public CPARS proxies

Outputs

  • Forward calendar of expected recompetes by NAICS and target agency
  • Probability score per predicted recompete event
  • Expected RFP window (typically 3-6 month range)
  • Direct links to underlying contract data for verification
  • Alerts when recompete probability or timing changes materially

Frequently Asked Questions

What is the Recompete Prediction Engine?

The Recompete Prediction Engine is one of four proprietary analytical engines in the Aliff Solutions platform. It forecasts when federal contracts are approaching rebid by analyzing contract end dates, option year patterns, modification histories, and agency acquisition forecasts. The output is a forward calendar of expected recompete opportunities ranked by relevance to the client's NAICS codes and target agencies.

How early does the engine identify upcoming recompetes?

The engine typically surfaces recompete opportunities 6-24 months before competitors learn about them. Visibility starts at award by tracking the contract's period of performance and option year structure; refinement happens as the contract progresses and modification, performance, and option exercise data accumulates.

What data sources power recompete prediction?

Primary sources include FPDS-NG for contract end dates and option history, SAM.gov for pre-RFP signals, USASpending for obligation trends, agency-specific acquisition forecast pages, GAO bid protest decisions, and public CPARS proxies. The engine pulls these daily and updates predictions as new data arrives.

How does the engine forecast contract value before the RFP is released?

The engine applies predictive valuation models to the historical award data — analyzing prior award values, modification adjustments, and inflation patterns. The forecast accounts for likely scope changes based on agency announcements and prior recompete patterns. The output supports bid/no-bid decisions before a draft RFP exists.

How does this engine connect to capture planning?

Predicted recompetes feed directly into Phase 01 (Strategic Planning) of the 9-Phase Win Loop. The forward calendar lets capture managers align customer relationship building, capability investments, teaming discussions, and pricing analysis to expected RFP windows 18-24 months ahead — when the work has the highest leverage.

What's the difference between recompete prediction and option year tracking?

Option year tracking just reports which contracts have options remaining. Recompete prediction is forward-looking — it evaluates the probability that each option will be exercised versus the contract going to recompete or bridge extension. Contracts with skipped or partially-exercised options have meaningfully higher recompete-loss probability and the engine adjusts predictions accordingly.

Can the engine predict bridge contracts?

Yes. Bridge contracts (short-term extensions while a recompete is in progress) follow recognizable patterns — typically issued 30-90 days before contract expiration when the recompete RFP has slipped. The engine flags candidates for bridge issuance and tracks the resulting recompete window, which is often 6-18 months further out than originally projected.

See Recompete Prediction Engine on a live opportunity

Schedule a 30-minute walkthrough. Bring an active pursuit and we'll run it through Recompete Prediction Engine live so you can see the methodology applied to your specific opportunity.