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Overview

Forecasting is the core activity on Metaculus. Forecasters make predictions about future events by submitting probability distributions that represent their beliefs about different outcomes.

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Making Predictions

Forecasts vary by question type, but all share common characteristics:
Submit a single probability between 0% and 100% representing your confidence the event will occur.Data Structure (from questions/models.py:639-642):
probability_yes: float = models.FloatField(null=True, blank=True)
Example: 75% probability that SpaceX launches Starship to orbit in 2025
Distribute probability across available options. Probabilities must sum to 100%.Data Structure (from questions/models.py:643-648):
probability_yes_per_category: list[float | None] = ArrayField(
    models.FloatField(null=True),
    null=True,
    blank=True,
)
Options not yet available at forecast time are stored as None.
Provide a cumulative distribution function (CDF) over the range of possible values.Data Structure (from questions/models.py:630-637):
continuous_cdf: list[float] = ArrayField(
    models.FloatField(),
    null=True,
    max_length=DEFAULT_INBOUND_OUTCOME_COUNT + 1,
    blank=True,
)
The CDF is evaluated at evenly-spaced quantiles from 0.0 to 1.0 (default: 201 points).

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Forecast Model

Each forecast is a snapshot of a user’s prediction at a specific time.

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Core Fields

From questions/models.py:609-663: 
class Forecast(models.Model):
    # Time range when this forecast is active
    start_time = models.DateTimeField(db_index=True)
    end_time = models.DateTimeField(null=True, db_index=True, blank=True)
    
    # Prediction data (one of these will be set based on question type)
    probability_yes: float           # Binary questions
    probability_yes_per_category: list[float | None]  # Multiple choice
    continuous_cdf: list[float]      # Numeric/Date/Discrete
    
    # Metadata
    author = models.ForeignKey(User, models.CASCADE)
    question = models.ForeignKey(Question, models.CASCADE)
    post = models.ForeignKey("posts.Post", models.CASCADE)
    source = models.CharField(max_length=30, choices=SourceChoices.choices)
    distribution_input = models.JSONField(null=True, blank=True)

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Forecast Sources

Forecasts can originate from different sources (from questions/models.py:664-669):
  • api: Made via the API
  • ui: Made through the web interface
  • automatic: Automatically assigned (e.g., when forecasts are split)

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Updating Forecasts

When you update your prediction, Metaculus creates a new forecast entry:
  1. The previous forecast’s end_time is set to the current timestamp
  2. A new forecast is created with start_time = current timestamp
  3. The new forecast has end_time = None (indicating it’s currently active)
This time-series approach allows Metaculus to track how predictions evolve and score forecasters based on their entire prediction history.

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Active Forecasts

A forecast is considered “active” when (from questions/models.py:574-601): 
def active(self):
    now = timezone.now()
    
    # Forecast timing conditions
    forecast_started = Q(start_time__lte=now)
    forecast_not_ended = Q(end_time__isnull=True) | Q(end_time__gt=now)
    
    # Question status conditions
    question_not_closed = Q(question__actual_close_time__isnull=True)
    question_still_accepting_forecasts = Q(question__scheduled_close_time__gt=now)
    question_opened = Q(question__open_time__lte=now)
    
    return self.filter(
        forecast_started
        & forecast_not_ended
        & question_not_closed
        & question_still_accepting_forecasts
        & question_opened
    )

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Forecast Aggregation

Metaculus combines individual forecasts into a Community Prediction (CP) that represents the consensus view.

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Aggregation Methods

From questions/types.py:18-22, Metaculus supports four aggregation methods:
Recent forecasts are weighted more heavily than older forecasts. This is the default method for most questions.Best for: Standard questions with extended forecasting periodsAlgorithm: Uses time-decaying weights to give recent predictions more influence

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Geometric Mean Aggregation

The core aggregation algorithm uses geometric mean (from scoring/score_math.py:28-53): 
def get_geometric_means(
    forecasts: Sequence[Forecast | AggregateForecast],
) -> list[AggregationEntry]:
    geometric_means = []
    timesteps: set[float] = set()
    
    # Collect all forecast start and end times
    for forecast in forecasts:
        timesteps.add(forecast.start_time.timestamp())
        if forecast.end_time:
            timesteps.add(forecast.end_time.timestamp())
    
    # Calculate geometric mean at each timestep
    for timestep in sorted(timesteps):
        prediction_values = [
            f.get_pmf()
            for f in forecasts
            if f.start_time.timestamp() <= timestep
            and (f.end_time is None or f.end_time.timestamp() > timestep)
        ]
        
        if not prediction_values:
            continue
        
        geometric_mean = gmean(prediction_values, axis=0)
        predictors = len(prediction_values)
        
        geometric_means.append(
            AggregationEntry(geometric_mean, predictors, timestep)
        )
    
    return geometric_means
Geometric mean is used instead of arithmetic mean because it better handles extreme probabilities and prevents a single forecaster from dominating the aggregate.

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Aggregate Forecast Model

Aggregated forecasts are stored separately from individual forecasts (from questions/models.py:760-833): 
class AggregateForecast(models.Model):
    question = models.ForeignKey(Question, models.CASCADE)
    method = models.CharField(max_length=200, choices=AggregationMethod.choices)
    
    # Time range for this aggregate
    start_time = models.DateTimeField(db_index=True)
    end_time = models.DateTimeField(null=True, db_index=True)
    
    # Aggregated prediction values
    forecast_values: list[float | None] = ArrayField(
        models.FloatField(null=True),
        max_length=DEFAULT_INBOUND_OUTCOME_COUNT + 1
    )
    
    # Metadata
    forecaster_count: int | None = models.IntegerField(null=True)
    
    # Uncertainty quantification (for continuous distributions)
    interval_lower_bounds = ArrayField(models.FloatField(), null=True)
    centers = ArrayField(models.FloatField(), null=True)
    interval_upper_bounds = ArrayField(models.FloatField(), null=True)
    means = ArrayField(models.FloatField(), null=True)
    histogram = ArrayField(models.FloatField(), null=True, size=100)

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Forecaster Count

The forecaster_count field tracks how many unique forecasters contributed to the aggregate at each timestep. This is important for:
  • Determining minimum participation thresholds
  • Calculating peer scores (which require at least 2 forecasters)
  • Displaying confidence in the community prediction

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Forecast Constraints

Metaculus enforces several constraints on forecasts:

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Time Constraints

From questions/models.py:691-697: 
constraints = [
    # end_time must be after start_time
    models.CheckConstraint(
        check=Q(end_time__isnull=True) | Q(end_time__gt=F("start_time")),
        name="end_time_after_start_time",
    ),
]

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Question Period Filter

Forecasts are filtered to only count those made during the question’s active period (from questions/models.py:539-562): 
def filter_within_question_period(self):
    return self.filter(
        # Has no end time or an end time after question open time
        (Q(end_time__isnull=True) | Q(end_time__gt=F("question__open_time")))
        # AND has a start time earlier than the questions close time
        & (
            (Q(question__actual_close_time__isnull=False)
             & Q(start_time__lt=F("question__actual_close_time")))
            | (Q(question__actual_close_time__isnull=True)
               & Q(start_time__lt=F("question__scheduled_close_time")))
        ),
    )

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Bot Forecasts

Metaculus includes bot forecasters that provide baseline predictions.

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Bot Types

From the user model:
  • is_bot: Marks automated forecasters
  • is_primary_bot: Distinguishes official Metaculus bots from third-party bots

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Including Bots in Aggregates

Questions can control whether bots are included (from questions/models.py:89): 
include_bots_in_aggregates = models.BooleanField(default=False)
Bot forecasts can be excluded from queries using (from questions/models.py:569-572): 
def exclude_non_primary_bots(self):
    return self.filter(
        Q(author__is_bot=False) | Q(author__is_primary_bot=True),
    )

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Distribution Input

The distribution_input field stores the raw input format provided by the user (from questions/models.py:681-684): 
distribution_input = models.JSONField(null=True, blank=True)
This preserves the original format (e.g., parameters of a normal distribution) even though the forecast is stored as a CDF.

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Best Practices

Update Regularly

Frequent updates as you gather new information lead to better scores

Calibrate Carefully

Avoid overconfidence - extreme predictions (0%, 100%) are rarely justified

Consider Base Rates

Start with historical frequencies before adjusting for specifics

Document Reasoning

Comment on your forecasts to track your thinking and help others

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API Reference

Forecasts API

Explore the full Forecasts API documentation

Questions

Understand question types and structure

Scoring

Learn how forecasts are evaluated

Leaderboards

Track your performance against others

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