An Electoral Vote Forecast Formula: Simulation or Meta-analysis Not Required

An Electoral Vote Forecast Formula: Simulation or Meta-analysis Not Required

Oct.31, 2011

It’s very surprising that election forecasting blogs and academics who use the latest state polls as input to their models don’t apply basic probability, statistics and simulation concepts in forecasting the electoral vote and corresponding win probabilities.

A meta-analysis or simulation is not required to calculate the expected electoral vote. Of course the individual state projections will depend on the forecasting method used. But the projection method is not the main issue here; it’s how the associated win probabilities are used to calculate the expected EV, win probability and frequency distribution.

Calculating the expected electoral vote is a three-step process:

1. Project the 2-party vote share V(i) for each state(i) as the sum of the poll share PS(i) and the undecided voter allocation UVA(i):

V (i) = PS(i) + UVA(i), i-1,51

2. Calculate the probability P(i) of winning state (i) given the margin of error (95% confidence):

P (i) = NORMDIST (V(i), 0.5, MoE/1.96, true) , i=1,51

3. Calculate the total expected electoral vote EV as the sum:

EV = ∑ P(i) * EV(i), i = 1,51

The 2004 Election Model allocated 75% of the undecided vote to Kerry and projected that he would have 337 electoral votes (99% win probability) with a 51.8% two-party vote share. The unadjusted, pristine state exit poll aggregate provided by exit pollsters Edison-Mitofsky 3 months after the election indicated that Kerry won 52.0% of the vote with an identical 337 electoral votes.

The challenger is expected to win the majority (60-90% UVA) of the undecided vote, depending on incumbent job performance. Gallup allocated 90% of undecided voters to Kerry in their final projection, pollsters Zogby and Harris: 75-80%. The National Exit Poll indicated that 65% of undecided voters broke for Kerry. Bush had a 48% approval rating on Election Day 2004.

After calculating the individual state probabilities, we can calculate the EV win probability. The best, most straightforward method is Monte Carlo simulation. This technique is widely used in many different applications when an analytical solution is prohibitive and is perfectly suited for calculating the EV win probability. The Election Model uses a 5000 election trial simulation. The win probability is the total number of winning election trials/5000.

The average electoral vote is calculated for the 5000 election trials. Of course, the average will only be an approximation to the theoretical value based on the summation formula. But the Law of Large Numbers (LLN) applies: the EV average and median are usually within one or two electoral votes of the theoretical mean. The close match between the Monte Carlo EV simulation average, median and theoretical expected mean is proof that 5000 election trials are more than sufficient.

Princeton Professor Wang’s EV estimator is an unnecessarily complex method and overkill for calculating the expected Electoral Vote. His Meta-analysis projected that Kerry would win 311 electoral votes and had a 98% win probability. But he was wrong to suggest that Bush won the undecided vote as an explanation for why his forecast was “wrong”. Just like AAPOR, the media pundits and political scientists, he never considered that Election Fraud was the cause. But overwhelming statistical and other documented evidence indicates that the election was stolen, just like it was in 2000.

The 2008 Election Model includes a sensitivity (risk) analysis of five Obama undecided voter (UVA) scenario assumptions ranging from 40-80%, with 60% as the base case. This enables one to view the effects of the UVA factor variable on the expected electoral vote and win probability. Electoral vote forecasting models which do not provide a risk factor sensitivity analysis are incomplete.