This project seeks to answer that question. Using the dropdown menu above, you can check out how all our major forecasts, going back to 2008, fared. (We’ll add new forecasts once they can be evaluated.) These tools reveal where our forecasts need some tweaking. But we also think they show that FiveThirtyEight’s models have performed strongly. All of our forecasts have proved to be more valuable than an unskilled guess, and things we say will happen only rarely … tend to happen only rarely.

Forecasts have always been a core part of FiveThirtyEight’s mission. They force us (and you) to think about the world probabilistically, rather than in absolutes. And making predictions, whether we’re modeling a candidate’s chance of being elected or a team’s odds of making the playoffs, improves our understanding of the world by testing our knowledge of how it works — what makes a team or a candidate win.

How do you judge a forecast?

There are many ways to judge a forecast. Here, we’re looking at two main things: the calibration of a forecast — that is, whether events that we said would happen 30 percent of the time actually happened about 30 percent of the time — and how our forecast compared with an unskilled estimate that relies solely on historical averages. We can answer those questions using calibration plots and skill scores, respectively. To show you how they work, we’ll use our MLB game predictions, which span the last three seasons.

Calibration plots

Calibration plots compare what we predicted with what actually happened — in this case, every MLB team’s chance of winning each game on the day it was played and the actual outcome of each of those games. Let’s start by looking at only games from September 2018 (so that there aren’t thousands of dots on the chart below). Every matchup gets two dots, one for the team that won and another for the team that lost.

Actual win percentage

Looking at the chart, you might think we were pretty lousy at picking winners. Our forecast gives most teams close to a 50 percent chance of winning and seems to be wrong almost as often as it is right. We’re not trying to pick winners, though; we’re trying to model the games, which means including in our predictions all of the randomness inherent in baseball. And baseball games are among the most random events we forecast — even the best teams lose about a third of their matchups every season.

Indeed, single predictions are hard to judge on their own. So let’s group every MLB game prediction (not just those from September 2018) into bins — for example, we’ll throw every prediction that gave a team between a 37.5 percent and 42.5 percent chance of winning into the same “40 percent” group — and then plot the averages of each bin’s forecasted chances of winning against their actual win percentage. If our forecast is well-calibrated — that is, if events happened roughly as often as we predicted over the long run — then all the bins on the calibration plot will be close to the 45-degree line; if our forecast was poorly calibrated, the bins will be further away.

MLB games, 2016-18

The plot of our MLB game predictions shows that our estimates were very well-calibrated. But it also shows that we rarely went out on a limb and gave any team a high chance of winning. Our second tool, skill scores, lets us evaluate our forecasts even further, combining accuracy and an appetite for risk into a single number.

Skill scores

Brier skill scores tell us how much more valuable our forecasts are than an unskilled estimate, one that is informed by historical averages — e.g., a guess that every baseball game is roughly 50-50. Here’s how our MLB games forecast compares with all our other forecasts, based on their Brier skill scores.

Sports forecasts Politics

You can see that all our forecasts performed better than an unskilled forecast. Our MLB games forecast, however, has a lower skill score than all of our other forecasts. That’s primarily because there’s a lot of uncertainty in baseball, so finding an edge over the unskilled estimate — which is essentially a coin flip in this case — is difficult. Other arenas lend themselves to more confident predictions. Compared with MLB games, U.S. House elections are easier to predict, in part because there’s less randomness involved and we have a better sense of what affects outcomes — for example, incumbents almost always keep their seats. So our forecasts of those elections have higher certainty that a candidate will win, and they perform far better than an unskilled estimate that assumes each candidate has an equal shot.