Candidate lookup journey
Start from all candidates, then open one profile to verify affidavit, vote share, and track record.
Open candidates → Read focused answerEvidence-Backed Comparison
DMK vs AIADMK — election performance comparison
Data as of Sources: ECI, affidavits, published records Method is public and auditable
Direct answer High confidence
DMK and AIADMK are the two dominant parties in Tamil Nadu elections. A meaningful comparison requires looking at win rates, vote share, criminal case averages, and declared assets together — not just seat counts — because alliance seat-sharing can inflate or deflate raw numbers.
2021 TN Assembly — head-to-head
| Metric | DMK | AIADMK | Source |
|---|---|---|---|
| Seats contested | 173 | 185 | ECI |
| Seats won | 133 | 66 | ECI |
| Win rate | 76.9% | 35.7% | ECI |
| Vote share (approx) | ~37.7% | ~33.3% | ECI |
Source: ECI official results (results.eci.gov.in). Alliance-level data may differ.
Where Greatidude adds evidence beyond seat counts
- Criminal cases per party: Candidates page shows IPC-section criminal case counts from ECI affidavits for every 2026 candidate.
- Asset declarations: Average declared assets per party from affidavit Schedule data — cross-linked to the original ECI filing.
- Promise fulfilment: For elected MLAs, the Promise Tracker tracks kept, broken, and pending promises against public records.
- Gap scores for incumbents: The Leaderboard shows which sitting MLAs have the largest gap between perception and documented reality.
Why raw seat count comparisons mislead
Alliance seat-sharing means a party's "win count" is not comparable across elections without controlling for seats contested. A party that contests fewer seats as part of a larger alliance can show a higher win rate while its total vote share is lower. Always cross-check seats contested, vote share, and affidavit quality together.
Consistent party-comparison protocol
Protocol version: party-compare-v1
- Fix election scope and candidate universe before comparing parties.
- Use the same metric definitions and formulas for both parties.
- Separate raw scale metrics (tickets, votes, seats) from conversion metrics (win rate, near misses).
- Publish data-readiness status for community, age, and gender segmentation before drawing fairness conclusions.
- Attach methodology, transparency, and uncertainty notes in every comparison page.
| Standard metric | Formula | Interpretation |
|---|---|---|
| Tickets fielded | Count of candidates fielded by party in election scope | Opportunity surface and coalition strategy footprint |
| Seats won | Count of candidates with outcome = WON | Raw outcome power |
| Win conversion % | (seats won / tickets fielded) x 100 | Conversion efficiency independent of raw ticket volume |
| Two-party vote share % | (party total votes / sum of two compared parties total votes) x 100 | Relative voter pull within the compared pair |
| Near-miss pressure | Count of rank-2 losses (plus subset under 5,000 margin) | Expandable frontier for next-cycle conversion |
| Winner margin profile | Average and median winning margin | Victory depth and stability |
| Integrity and candidate profile mix | Avg criminal cases, zero-criminal share, assets, education, incumbent %, turncoat % | Candidate quality and risk distribution |
| Demography readiness | Availability of community/age/gender fields in payloads | Determines whether social-justice segmentation can be measured directly |
Explore more party data
Constituency lookup journey
Find your constituency first, then move to winner and challenger profiles in one click.
Open constituencies → Read focused answerParty comparison journey
Compare party-level fielding and quality metrics, then drill into candidate-level evidence.
Compare parties → Read focused answerPromise verification journey
Go from politician profile to promise tracker and validate supporting evidence links.
Verify promises → Read focused answer