The ten March 15 primaries (5 Democratic and 5 Republican) went just as expected, if you were following the prediction markets. On March 14 the market-based predictions for the Republicans were: Donald Trump 86% for Florida, Trump 73% for IL, Trump 54% for Missouri, Trump 99% for North Carolina, and John Kasich 71% for Ohio. At the same time the market-based predictions for the Democrats were: Hillary Clinton 93% for Florida, Clinton 54% for Illinois, Bernie Sanders 68% for Missouri, Clinton 90% for North Carolina, and Clinton 66% for Ohio. Depending on the final outcome in Missouri, 9 or 10 of these predictions pointed to the eventual winner. But, there was uncertainty in the some of the elections, they were not all 100%, and the elections eventually breaking in a certain way did make difference.

The likelihood of Republican nomination moved meaningfully towards Trump. Trump started the day at 74% and ended the day at 80%. Kasich, with his big win in Ohio, started the day at 11% and ended the day at 8%. Ted Cruz was down from 14% to 11%. Marco Rubio, highly likely to lose Florida, was essentially already discounted going into the day.


Note: Aggregation on PredictWise, data includes Betfair, Hypermind, PredictIt, and assorted Bookies

Why did Kasich drop, despite his big win in Ohio; the probability of a brokered convention, defined as the convention going to the second ballot, settled to around 36%, down from 42% the day before. Kasich and Cruz need a second ballot to win the nomination, thus their successes are hollow if they cannot block Trump from reaching the majority of delegates before the convention. And, while Kasich’s win in Ohio blocked some delegates from Trump, it also ensured a three-way race for the rest of the primary contests. Trump benefits from Cruz and Kasich splitting any anti-Trump votes.

Trump is the only candidate with a non-negligible probability of winning the Republican nomination on the first ballot, thus (with a 36% likelihood of a second ballot) Trump is 64% likely to win the nomination on the first ballot. Trump is also 16%, Cruz 11%, and Kasich 9% to win at a brokered convention. The remaining 1% goes to unity candidate that did not run this year, Paul Ryan. Should Trump fail to win the nomination outright, he is about 40-45% to win the nomination at the convention.

The Democratic nomination moved much more on the strength of Clinton’s strong victories over Sanders. She started the day at 90% to win the nomination and finished the evening at 96% to win the nomination. This puts her back to where she was prior to Sanders’ surprise upset over Clinton in Michigan. Clinton has been in control of this nomination from the beginning; the lowest point for her, since Joe Biden announced he would not run in October, was 81% just after the New Hampshire primary.


Note: Aggregation on PredictWise, data includes Betfair, Hypermind, PredictIt, and assorted Bookies

There is one more probability that matters, in many ways it is the only probability that matters: the likelihood of the eventual Democratic nominee beating the eventual Republican nominee in the general election. After Trump and Clinton’s strong showing on March 15 this probability climbed to 72%. By comparison my fundamental model, joint work with Patrick Hummel, (using presidential approval, economic indicators, incumbency, and past election results) has the generic Democratic candidate at 48% versus the generic Republican candidate. The actual likely Democratic nominee is outperforming the generic Democratic nominee by 24 percentage points. As another point of reference, Barack Obama running for reelection was at 60% at this time in 2012. 72% is a very high probability on March 16 of an election year.


Note: Aggregation on PredictWise, data includes Betfair, Hypermind, PredictIt, and assorted Bookies

Method Note: There is a three step process for aggregating the prediction market data on PredictWise. Step 1: construct prices from the back/sell, lay/bid, and last transaction odd/price in the order book. I always take the average of the highest price traders are willing to buy a marginal share and the lowest price people are willing to sell a marginal share, unless the differential is too large or does not exist. Step 2: correct for historical bias and increased uncertainty in constructed prices near $0 or $1. I raise all of the constructed prices to a pre-set value depending on the domain. Step 3: I normalize the probabilities to equal 100% for any mutually exclusive set of outcomes.