Electoral Forecast Model

El-Sayed 54 in 100
Stevens 46 in 100
*Weighted average of letter-graded public polls and heavily weighted prediction market pseudo-polls.

[ Methodological Framework ]

The Weighted Pseudo-Poll Approach: The data in this primary is garbage and traditional polling is notoriously unreliable. Per instructions, our model keeps it super simple: we scrape available polls, assign them letter grades (A through C-), and then heavily weight the prediction market as a pseudo-poll. Why? Because insiders on betting markets are seeing non-public internal polls, allowing us to capture those shifts directly before they hit public view.

The Win Target (50%+1): Calculated by taking Expected Statewide Primary Turnout (~23% of registered Democrats across 83 counties), dividing it in half, and adding one vote: (1,250,000 × 0.50) + 1 = 625,001. (Step 4: profit).

Turnout Scenarios & Margins

Scenario Est. Ballots Cast Win Target Threshold Electorate Composition
LOW (18%) 1,000,000 500,001 High-Propensity Retirees / Oakland & Macomb Base
EXPECTED (23%) 1,250,000 625,001 Balanced Statewide Primary Base
HIGH (28%) 1,500,000 750,001 Working Families / Youth, College & Wayne/Washtenaw Surge

Capital Allocation per Voter

Candidate Model Win Prob. Campaign Disb. Independent Exp. Total Capital Est. $/Voter [6]
Dr. A. El-Sayed [2] 54% $6.2M $2.5M $8.7M $13.92
U.S. Rep. H. Stevens [2] 46% $5.8M $45.0M $50.8M $81.28
State Sen. M. McMorrowSuspended 7/5 [1] < 4% $3.1M $0.5M $3.6M --

Electoral Cartography: Statewide Precinct Topography

I-75 CORRIDOR WAY/WAS OAK/MAC KNT

Spatial Distribution of Propensity Cohorts

  • Wayne, Washtenaw & Ingham (WAY/WAS): Core progressive base anchored by Detroit, Ann Arbor, East Lansing, and Dearborn. High turnout among university students, healthcare workers, and voters under 50 [2].
  • Oakland & Macomb Counties (OAK/MAC): Strong suburban base for U.S. Rep. Haley Stevens, driven by suburban voters over 60, business endorsements, and heavy broadcast media spending [3].
  • Kent County & West Michigan (KNT): Highly elastic primary electorate in Grand Rapids, Muskegon, and Kalamazoo, responsive to healthcare debates and environmental protection messaging [4].

The Architecture of the Ground Game: Statewide Primary

I

n Michigan’s U.S. Senate Democratic Primary, voters face a high-stakes choice to fill the open seat created by retiring two-term Senator Gary Peters [1]. Following State Senator Mallory McMorrow’s campaign suspension on July 5 [1], the contest has sharpened into a head-to-head race between Dr. Abdul El-Sayed—a public health physician, former Detroit Health Director, and 2018 gubernatorial candidate—and U.S. Representative Haley Stevens of Oakland County.

Observe the financial data tables above. The primary has drawn over $50 million in outside independent expenditures from national super PACs supporting Stevens [3]. In contrast, El-Sayed’s campaign relies entirely on small-dollar donors, raising over $2.5 million from 38,000+ individual contributors since July 1 alone [3].

El-Sayed holds a strong lead in our weighted poll-averaging model, fueled by letter-graded public surveys and heavily weighted betting market signals that capture non-public internal polling data [2].

Representative Stevens, endorsed by retiring Senator Gary Peters and major trade/business PACs, maintains strong support among suburban voters over 60 across Oakland and Macomb counties [2].

At an estimated cost per target vote, pro-Stevens super PAC spending amortizes to nearly $81.28 per target vote, compared to El-Sayed’s grassroots rate of $13.92 per target vote [6].

With primary turnout expected at 1.25 million voters statewide [5], the win threshold sits at 625,001 votes. Turnout differentials between Wayne/Washtenaw college towns and Oakland County suburbs will prove decisive three days from now.

[1] Candidate Lineup & McMorrow Suspension: State Senator Mallory McMorrow suspended her U.S. Senate campaign on July 5, 2026, though her name remains on the printed ballot (AP News: McMorrow Suspends Campaign; Bridge Michigan: McMorrow Ends Senate Campaign; WDET 101.9 FM Coverage).

[2] Statewide Polling & Race Overview: Aggregated model combining Emerson College Polling and heavily weighted prediction market pseudo-polls (Emerson College Polling Statewide Report; Ballotpedia: MI US Senate Primary).

[3] Campaign Finance & Super PAC Disparities: Outside groups pouring over $45 million into broadcast advertising to boost Stevens (Bridge Michigan: Clash Over Campaign Cash; El-Sayed Campaign Statement on Super PAC Disparity).

[4] Progressive & Environmental Endorsements: Major endorsements from Bernie Sanders, Elizabeth Warren, AOC, and Clean Water Action (Clean Water Action Endorsement Statement; Abdul El-Sayed Endorsements Hub).

[5] Statewide Historical Turnout: According to historical voter turnout figures from the Michigan Secretary of State, non-presidential Democratic primary turnout in Michigan averages 1.1M to 1.3M voters (Ballotpedia Michigan Election Hub).

[6] Cost Per Voter Methodology: Calculated by dividing Total Estimated Capital (Campaign Expenditures + Independent Super PAC Spending) by the Expected Win Target of 625,001 votes (50% + 1 of expected 1.25M turnout).

EDITOR'S NOTE DISCLAIMER: THIS FORECAST MODEL RUNS ON A VERY BASIC R SCRIPT BUILT BY A GUY NAMED BOB WHO NOW SETS PRICES AT TARGET, CAUSE HE ENDED UP GETTING A PhD IN MATH AND SUCH. HE NEVER OFFICIALLY PUBLISHED THIS CAUSE HE'S PRETTY MUCH "SPY OR DIE" AND HIS WIFE YELLED AT HIM TO CLEAN THE BASEMENT. PROCEED WITH ANALYTICAL CAUTION. NOT PAID FOR BY ANYONE CAUSE THIS WEBSITE IS STILL SOMEHOW COASTING OF THOSE FREE SQUARESPACE CREDITS YOU HERE ABOUT ON THOSE COMMMERICALS. WE GOT THEM AND OUR POCKETS ARE GETTING HEAVY.
× # michigan_senate_forecast_v1.R # Author: Bob (Target pricing / math PhD) # Note: Adam said keep it super simple, no over engineering. # Scrape polls, letter grade them, and treat betting market as a heavy pseudo-poll. # (4) profit library(dplyr) # 1. Load Polls and assign letter grades polls <- data.frame( pollster = c("Emerson College / WLNS", "Internal Tracking"), elsayed_pct = c(54, 56), stevens_pct = c(39, 37), adam_grade = c("A", "B") ) grade_weights <- c("A" = 1.0, "B" = 0.75, "C-" = 0.5) polls$weight <- grade_weights[polls$adam_grade] # 2. Market Data -> Pseudo-Poll (Capturing non-public insider polls) market_poll <- data.frame( pollster = "Prediction_Market_Agg", elsayed_pct = 58.0, stevens_pct = 42.0, adam_grade = "MARKET", weight = 5.0 # Heavy weight multiplier per instructions ) all_data <- bind_rows(polls, market_poll) # 3. Weighted Average Calculation final_elsayed <- sum(all_data$elsayed_pct * all_data$weight) / sum(all_data$weight) final_stevens <- sum(all_data$stevens_pct * all_data$weight) / sum(all_data$weight) print(paste("Final El-Sayed Win Prob:", round(final_elsayed, 1))) print(paste("Final Stevens Win Prob:", round(final_stevens, 1))) # I have no more insight, the chart is self-explanatory. # I am a bot. ❄️