What the average tells you
The arithmetic average uses every observation and is sensitive to unusually large winning or losing years. It is useful for measuring the mean historical outcome.
What the median tells you
The median is the middle observation after sorting outcomes. It is less sensitive to outliers and can show what a more typical season looked like.
Why the difference matters
If average performance is strongly positive but the median is near zero, a few large winners may be driving the result.
Measure the pattern instead of assuming it
Choose the exact spread, define the dates and compare multiple history windows with the individual seasons behind the average.
Analyze Seasonal BehaviorUse individual years as the final check
Neither statistic replaces the underlying data. Review the full year-by-year distribution, especially the worst seasons and the size of outliers.
Other useful statistics
- Win rate.
- Standard deviation or dispersion.
- Best and worst historical move.
- Maximum drawdown.
- Recent-versus-long-term comparison.
A simple average-versus-median example
Consider five historical moves of +2, +3, +4, +5 and +26 cents. The average is +8 cents while the median is +4. One large year makes the average look much stronger than most actual observations.
Now consider -20, +2, +3, +4 and +5. Four of five years are positive, but the average is negative because one large loss dominates.
What disagreement can mean
| Pattern | Possible interpretation |
|---|---|
| Average far above median | Large positive outliers |
| Average far below median | Large negative outliers |
| Average near median | More balanced distribution |
| High win rate, weak average | Losses may be larger than wins |
Why the full distribution matters
Seasonal samples are often small. Fifteen historical seasons means fifteen observations, so each outlier can materially change the headline statistic. Review individual years, worst moves and drawdowns.
How to use both statistics
Use the average for the mean outcome and the median for the middle observation, then compare both with win rate and worst-case history. The difference between them is itself useful information.
Average, median and win rate can disagree at the same time
Consider a sample where 10 of 15 years are positive, giving a 67% win rate. If the five losing years are much larger than the ten winners, the average can still be negative. Conversely, a few exceptionally strong winners can produce a positive average even when most years are flat.
That is why no single statistic should be promoted as “the answer.” The average describes magnitude, the median describes the middle observation, and win rate describes frequency. The worst year and drawdown describe historical downside. Together they provide a more complete picture.
When these measures disagree, investigate the distribution rather than choosing whichever metric makes the pattern look strongest.
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Frequently asked questions
What is futures seasonality?
Futures seasonality is the study of recurring historical behavior at similar times of year. It describes past patterns and does not guarantee future performance.
Why use dated contracts for seasonal spreads?
Calendar spreads depend on exact delivery months. Using dated contracts preserves the specific relationship being tested instead of blending different expirations.
Is the longest lookback always best?
No. Longer history adds sample size, while shorter windows may better reflect recent structural changes. Comparing several windows is usually more informative.
Should I trust the seasonal average?
Only after checking the individual years, losing seasons, dispersion and whether a few outliers dominate the average.
Research seasonal spreads with transparent history
Compare exact delivery months, recurring windows and every historical season behind the pattern.
Open Futures Spread AnalyzerFutures trading involves substantial risk. Historical seasonality is descriptive and does not guarantee future results. This material is for education and research only and is not investment advice.