Is each market a conversion play or a switcher fight, and how much runway is left?
Of 52 jurisdictions, 8 are conversion markets (penetration under 50%, sell against Original Medicare) and 44 are switcher markets (sell against incumbents). Texas tops the runway score at 100, with 2523K people still in Original Medicare. The score is a MedicareInsights index (gap × velocity × market size), never citable as CMS data.
Runway score by state: top 12
Score = penetration gap × velocity × Original Medicare base, normalized to the 95th-percentile market (not the single top one, so one outlier can't set the scale); the top few markets clamp to 100 and rank order holds below. Velocity for states is the enrollment-weighted average of their tracked counties; states with no tracked county inherit the tracked-set median (+0.9 pts) as a proxy, flagged in the table.
Full state table: class, score, and inputs
| Rank | State | Class | Score | Penetration | Gap (pts) | Velocity (pts YoY) | OM base (K) |
|---|---|---|---|---|---|---|---|
| 1 | Texas | Switcher | 100 | 54.9% | 27.2 | +0.9 | 2,523 |
| 2 | California | Switcher | 100 | 59.4% | 22.7 | +0.8 | 2,817 |
| 3 | New York | Switcher | 100 | 55.1% | 27 | +0.9 | 1,776 |
| 4 | Florida | Switcher | 100 | 64.7% | 17.4 | +1.2 | 1,855 |
| 5 | Pennsylvania | Switcher | 77.3 | 57.8% | 24.3 | +0.9 | 1,369 |
| 6 | Illinois | Switcher | 68.4 | 55% | 27.1 | +0.9 | 1,087 |
| 7 | Ohio | Switcher | 66.3 | 58.6% | 23.5 | +0.9 | 1,215 |
| 8 | Michigan | Switcher | 56.2 | 57.4% | 24.7 | +0.9 | 979 |
| 9 | North Carolina | Switcher | 54.5 | 57.9% | 24.2 | +0.9 | 970 |
| 10 | Washington | Switcher | 44.6 | 56.1% | 26 | +0.9* | 738 |
| 11 | New Jersey | Switcher | 42 | 57.4% | 24.7 | +0.9* | 732 |
| 12 | Massachusetts | Switcher | 40.8 | 55.4% | 26.7 | +0.9* | 657 |
| 13 | Georgia | Switcher | 40.2 | 59.5% | 22.6 | +0.9 | 766 |
| 14 | Virginia | Switcher | 39.8 | 58.3% | 23.8 | +0.9* | 719 |
| 15 | Wisconsin | Switcher | 39 | 54.3% | 27.8 | +0.9* | 603 |
| 16 | Minnesota | Switcher | 36.6 | 54.5% | 27.6 | +0.9* | 570 |
| 17 | Oregon | Switcher | 33.6 | 54.4% | 27.7 | +0.9* | 522 |
| 18 | Missouri | Switcher | 33.3 | 57.9% | 24.2 | +0.9* | 592 |
| 19 | Tennessee | Switcher | 32.1 | 60.5% | 21.6 | +0.9* | 640 |
| 20 | Arizona | Switcher | 28.3 | 63.4% | 18.7 | +0.9 | 651 |
| 21 | Indiana | Switcher | 27.7 | 60.3% | 21.8 | +0.9* | 546 |
| 22 | Oklahoma | Switcher | 27.1 | 51.8% | 30.3 | +0.9* | 385 |
| 23 | South Carolina | Switcher | 26.9 | 59.6% | 22.5 | +0.9* | 514 |
| 24 | Colorado | Switcher | 26.1 | 57.8% | 24.3 | +0.9* | 462 |
| 25 | Iowa | Switcher | 25.5 | 50.3% | 31.8 | +0.9* | 345 |
| 26 | Maryland | Switcher | 23.5 | 60.4% | 21.7 | +0.9* | 466 |
| 27 | Alabama | Switcher | 22.5 | 58.5% | 23.6 | +0.9* | 411 |
| 28 | Kansas | Switcher | 20.7 | 51.3% | 30.8 | +0.9* | 289 |
| 29 | Louisiana | Switcher | 20.2 | 59.6% | 22.5 | +0.9* | 387 |
| 30 | Kentucky | Switcher | 19.4 | 61.4% | 20.7 | +0.9* | 404 |
| 31 | Hawaii | Conversion | 19.3 | 41.8% | 40.3 | +0.9* | 206 |
| 32 | Utah | Switcher | 19.2 | 53% | 29.1 | +0.9* | 284 |
| 33 | Maine | Conversion | 18.1 | 44.7% | 37.4 | +0.9* | 208 |
| 34 | Connecticut | Switcher | 18.1 | 56.6% | 25.5 | +0.9* | 305 |
| 35 | Arkansas | Switcher | 17.6 | 56.8% | 25.3 | +0.9* | 300 |
| 36 | Mississippi | Switcher | 16.5 | 58% | 24.1 | +0.9* | 295 |
| 37 | Nebraska | Switcher | 15.1 | 51.2% | 30.9 | +0.9* | 211 |
| 38 | Idaho | Conversion | 14.8 | 49.7% | 32.4 | +0.9* | 196 |
| 39 | Nevada | Switcher | 13.9 | 61% | 21.1 | +0.9* | 283 |
| 40 | New Hampshire | Switcher | 12.5 | 50.3% | 31.8 | +0.9* | 169 |
| 41 | Vermont | Conversion | 11.7 | 36.8% | 45.3 | +0.9* | 111 |
| 42 | West Virginia | Switcher | 11.7 | 56.2% | 25.9 | +0.9* | 194 |
| 43 | Montana | Conversion | 11.5 | 48.5% | 33.6 | +0.9* | 147 |
| 44 | New Mexico | Switcher | 9.6 | 60.6% | 21.5 | +0.9* | 192 |
| 45 | Wyoming | Conversion | 9.1 | 43.2% | 38.9 | +0.9* | 101 |
| 46 | Alaska | Conversion | 8.2 | 31% | 51.1 | +0.9* | 69 |
| 47 | South Dakota | Switcher | 8.1 | 50.9% | 31.2 | +0.9* | 112 |
| 48 | Rhode Island | Switcher | 7 | 54.7% | 27.4 | +0.9* | 110 |
| 49 | North Dakota | Switcher | 5.8 | 54.5% | 27.6 | +0.9* | 90 |
| 50 | District of Columbia | Conversion | 4.7 | 49% | 33.1 | +0.9* | 61 |
| 51 | Delaware | Switcher | 4.4 | 60% | 22.1 | +0.9* | 86 |
| 52 | Puerto Rico | Switcher | 0 | 82.1% | 0 | +0.9* | 159 |
* velocity is the tracked-set median proxy, not measured for this state. Penetration uses the CMS monthly-file basis (MA + other over all beneficiaries). The highlighted input in each row is that state's dominant score driver — the factor sitting at the highest percentile within the field — since the index is multiplicative and no single factor is additive. It answers "why is this score high?" at a glance.
Tracked counties, runway-ranked
County velocity is measured, not proxied, so county scores are the firmer read. Coverage is the 76-county tracked set until the full CPSC pipeline lands.
County runway table (top 20)
| Rank | County | Class | Score | Penetration | Velocity | OM base (K) | Carriers |
|---|---|---|---|---|---|---|---|
| 1 | Cook, IL | Switcher | 100 | 51.4% | +0.87 | 562 | 6 |
| 2 | Los Angeles, CA | Switcher | 100 | 64.9% | +0.93 | 846 | 7 |
| 3 | Maricopa, AZ | Switcher | 100 | 59.4% | +0.87 | 482 | 6 |
| 4 | Harris, TX | Switcher | 100 | 57.8% | +0.87 | 430 | 6 |
| 5 | Dallas, TX | Switcher | 100 | 55.4% | +0.87 | 348 | 5 |
| 6 | Travis, TX | Conversion | 83.6 | 48.4% | +0.87 | 212 | 4 |
| 7 | Franklin, OH | Switcher | 80.7 | 54.8% | +0.87 | 272 | 5 |
| 8 | DuPage, IL | Conversion | 75.1 | 48.6% | +0.87 | 192 | 4 |
| 9 | Wake, NC | Switcher | 72 | 50.4% | +0.87 | 198 | 4 |
| 10 | Tarrant, TX | Switcher | 71.9 | 53.2% | +0.87 | 224 | 5 |
| 11 | Cuyahoga, OH | Switcher | 70.7 | 57.2% | +0.87 | 272 | 5 |
| 12 | Suffolk, NY | Switcher | 69.5 | 55.4% | +0.87 | 242 | 4 |
| 13 | Kings, NY | Switcher | 69.3 | 58.4% | +0.87 | 287 | 6 |
| 14 | Wayne, MI | Switcher | 66.3 | 57.4% | +0.87 | 258 | 5 |
| 15 | Mecklenburg, NC | Switcher | 66.1 | 52.8% | +0.87 | 202 | 5 |
| 16 | Allegheny, PA | Switcher | 65.7 | 58.4% | +0.87 | 272 | 6 |
| 17 | Fulton, GA | Switcher | 64.8 | 54.2% | +0.87 | 212 | 5 |
| 18 | Oakland, MI | Switcher | 64.2 | 53.4% | +0.87 | 202 | 4 |
| 19 | Gwinnett, GA | Conversion | 63.2 | 49.6% | +0.87 | 168 | 3 |
| 20 | San Diego, CA | Switcher | 61.8 | 61.2% | +0.8 | 338 | 5 |
So what / Now what
The same quota needs a different sales motion depending on the market class. Conversion markets still have a large Original Medicare base ageing in: the pitch is MA versus FFS (a MOOP cap, extra benefits, one card) and the competition is inertia. Switcher markets are saturated: growth only comes from incumbent members, so the pitch is comparative (benefits, network, premium shock) and the battlecards page is the weapon. The runway score ranks where winnable members remain once both class and momentum are priced in.
- Territory design: assign conversion-market reps an ageing-in and FFS-conversion motion; assign switcher-market reps a competitive-displacement motion armed with the carrier battlecards.
- Prioritize by score, not penetration alone: a large low-velocity market can hold more winnable members than a small hot one, and the score prices that in.
- Download the CSV to segment territories in the CRM; the runway_class column is the motion flag, the score column is the priority order.
- Treat the score as directional. It is a MedicareInsights index built on CMS enrollment data, not a CMS figure, and the formula is documented in the provenance panel below.
Provenance — Growth runway index by state and county
- Source file
- MedicareInsights index computed from CMS Medicare Monthly Enrollment and CPSC county extracts
- Vintage
- Plan year 2026 (Feb 2026 enrollment)
- Last updated
- July 17, 2026
- Refresh cadence
- Recomputed on every enrollment ingest
- Method
- Runway score = penetration gap x velocity x market size, normalized so the top geography scores 100. Penetration gap is the distance to the highest penetration observed in the tracked set (revealed attainable ceiling). Velocity is the YoY penetration point change: actual for tracked counties, enrollment-weighted tracked-county average for their states, and the tracked-set median as a proxy for states with no tracked county. Market size is the Original Medicare base in millions. Geographies under 50% penetration are classed as conversion markets (sell against FFS); 50% and above are switcher markets (sell against incumbents), matching the state-page framing.
- Known limitations
- This is a MedicareInsights index, not CMS data, and is never citable as CMS data. Velocity for states without tracked counties inherits the tracked-set median, a rough proxy. Use for territory prioritization, not filings.