Understanding 2020 Candidate Rankings: Methods, Sources, and How to Interpret Them

Candidate rankings in an election year like 2020 reflect aggregated judgments from news organizations, policy institutes, advocacy groups, and analytical platforms about how candidates compare on issues, electability, qualifications, and fitness for office. This explainer describes how these rankings are constructed, what different methodologies emphasize, and how readers can interpret them with appropriate context. The focus is on evergreen principles for understanding scorecards and rankings, enabling more informed, responsible use rather than treating any single list as a definitive verdict.

What 2020 Candidate Rankings Typically Measure

Most 2020 candidate rankings emphasize a small set of high-value dimensions that map onto public decision-making. These commonly include issue positions and voting records, coalition-building and legislative effectiveness, electoral competitiveness and fundraising strength, governance experience and institutional knowledge, and communication clarity and consistency. Together, these factors help readers see how candidates stack up on substance and execution, although the weight given to each dimension varies widely across producers. Understanding what a list emphasizes is as important as the ranking order itself.

Common Methodologies Behind Candidate Rankings

Methodologies shape how rankings are produced and what they signal. Typical approaches include structured scorecards with weighted criteria, head-to-head comparative judgments, multi-criteria decision analysis such as ELECTRE or TOPSIS, qualitative expert consensus combined with quantitative indicators, and data-driven models that incorporate polling, fundraising, and voting data. Transparent methodologies disclose scoring rubrics, source selection, normalization steps, and uncertainty ranges. Readers should check whether a producer documents its process, discloses data sources, and explains how ties or close calls are handled.

Scorecard Frameworks

Scorecard frameworks assign measurable values to specific criteria, then aggregate them into an overall index or grade. Key design choices include criterion selection, normalization across metrics, and weighting of importance. Best-practice scorecards document each choice, provide sensitivity analyses, and show how results change when weights or inputs are adjusted. When used appropriately, scorecards make assumptions explicit and support reasoned comparison.

Expert Consensus and Qualitative Judgment

Many rankings rely on qualitative judgment from subject-matter experts, journalists, and practitioners who assess candidates against domain-specific standards. These processes often include structured deliberation, blind review where appropriate, and clear conflict-of-interest policies. The strength of this approach lies in contextual nuance; the risk is inconsistency unless protocols define what evidence matters and how disagreements are resolved. Look for evidence of calibration exercises and transparency about whose judgments are included and why.

Interpreting and Using Rankings Responsibly

Rankings compress complex information into a compact form, which makes them useful for orientation but incomplete for decision-making. Responsible interpretation involves checking the date of publication, confirming whether criteria have changed from earlier cycles, and comparing multiple perspectives rather than relying on a single list. It is important to separate signal from noise, recognize when differences between candidates are marginal, and acknowledge areas where data are missing or contested. Rankings are best used as one input alongside direct review of candidate materials, debates, and primary sources.

Typical Data Sources and Evidence Types

Reliable rankings draw on diverse, verifiable inputs. These may include legislative voting records and sponsorship patterns, public statements and debate transcripts, campaign finance reports and fundraising totals, endorsements and coalition affiliations, and policy analysis from nonpartisan research organizations. When possible, rankings should cite datasets, document coding rules, and note any exclusions or limitations. Cross-checking claims against original sources—such as official vote databases or candidate filings—helps readers assess credibility and avoid misinterpretation.

Practical Guidance for Evaluating Rankings

When reviewing any 2020 candidate ranking, focus on clarity, methodological detail, and independence. Ask who produced the list, what incentives they face, which criteria are included and how they are weighted, how data were collected and cleaned, and how uncertainties are represented. Favor producers that allow you to inspect underlying evidence, adjust assumptions, and reproduce results. Remember that every ranking reflects choices; the goal is to make those choices informed and explicit rather than hidden or arbitrary.

Key Attributes of Methodologically Sound 2020 Candidate Rankings