About the creator
Michael Taricone, creator and developer of Was It Luck?
I built Was It Luck? to test a familiar fantasy football suspicion with data: was my record really being damaged by bad luck, or did the weekly results simply feel unfair? The site grew from that question into a transparent, Sleeper-first set of league analytics.
Why the project exists
Was It Luck? began as a personal attempt to examine suspected bad luck in a fantasy league. A standings table could show my wins and losses, but it could not show how the same weekly score would have performed against every other manager or how much opponent timing affected the outcome. I started calculating those comparisons to test the claim rather than relying on the strongest memory from a frustrating Sunday.
I now design and develop the website, its calculations and the written explanations around them. The aim is not to manufacture a single “manager score.” It is to make useful questions—about weekly performance, schedules, rivalries and scoring formats—specific enough that another person can understand what was measured.
A Sleeper-first, public-data method
The public analyzer starts with data available through Sleeper: league settings, rosters, users, weekly matchup scores, bracket data and links between renewed seasons where Sleeper supplies them. Was It Luck? keeps Sleeper as the primary provider because that public data supports the deepest and most reproducible version of the product.
Derived metrics are built on top of those records. Effective wins compare a team's weekly score with the rest of its league. Calendar Swap keeps observed scores fixed and exchanges two teams' opponent slots. League-history views follow linked seasons and aggregate the available records. The guide library states the formula, assumptions and a worked example for each major concept.
How calculations are checked
I first separate fields reported by Sleeper from values calculated by Was It Luck? Official wins, losses, settings and matchup points remain the baseline. A derived metric is then checked with small cases whose expected result can be calculated by hand—for example, counting how many of nine opponents a score would beat before normalizing the weekly value.
During changes, I compare repeatable calculation paths with small, known examples. Finally, the displayed table is read against the underlying league data and the written methodology. These checks reduce avoidable errors; they do not make incomplete provider data or every commissioner-specific rule knowable.
Limits I want readers to see
Fantasy outcomes combine managerial decisions with injuries, player variance, waiver activity, trades and opponent timing. A descriptive metric can isolate one part of that story, but it cannot prove that a person is skilled or unskilled. Small samples deserve especially cautious language.
Historical results also depend on what Sleeper exposes. A missing link between seasons can shorten league history. Changed owners or reassigned roster slots can complicate identity. Custom tiebreakers, manually adjusted outcomes and unusual brackets may not be fully reproduced. Each guide calls out the limitations relevant to its own calculation instead of hiding them in one general disclaimer.
Independence and privacy
Was It Luck? is an independent project. It is not affiliated with, endorsed by or sponsored by Sleeper. References to Sleeper identify the provider and the source structure used by the analyzer, not a commercial relationship.
Public league analysis uses public provider data. Signed-in features are optional and support saved leagues and account-level views. The current information-handling details, including analytics and account data, are set out in the Privacy Policy rather than implied by this profile.
Corrections and contact
If a formula, explanation or displayed result appears wrong, please use the contact form. A useful report includes the league season, the feature involved, the result shown and the result you expected—without sharing passwords or private credentials. I review reproducible cases against the source data and calculation, then update the implementation or wording where a correction is needed.
Methodology pages show a reviewed date so readers can tell when the explanation was last checked against the product. Questions and feature suggestions are welcome through the same contact page.
See the method applied to a Sleeper league
Enter a public Sleeper username, select a league and compare the official season with the analytical views described in the guides.