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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 2 new columns ({'date', 'volume_usd'}) and 5 missing columns ({'poll_date', 'divergence_pp', 'poll_pct', 'pollster', 'polymarket_date'}).

This happened while the csv dataset builder was generating data using

hf://datasets/AFOS-Analytics1/mexico-2024-electoral-divergence/data/mexico-market-odds-timeseries.csv (at revision 12188533c0dfc67b994be3745019de7341c392fe), ['hf://datasets/AFOS-Analytics1/mexico-2024-electoral-divergence@12188533c0dfc67b994be3745019de7341c392fe/data/mexico-divergence-timeseries.csv', 'hf://datasets/AFOS-Analytics1/mexico-2024-electoral-divergence@12188533c0dfc67b994be3745019de7341c392fe/data/mexico-market-odds-timeseries.csv', 'hf://datasets/AFOS-Analytics1/mexico-2024-electoral-divergence@12188533c0dfc67b994be3745019de7341c392fe/data/mexico-structural-context.csv', 'hf://datasets/AFOS-Analytics1/mexico-2024-electoral-divergence@12188533c0dfc67b994be3745019de7341c392fe/news/mexico-2024-press-coverage.csv', 'hf://datasets/AFOS-Analytics1/mexico-2024-electoral-divergence@12188533c0dfc67b994be3745019de7341c392fe/polls/mexico-polls.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              date: string
              candidate: string
              polymarket_pct: double
              volume_usd: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 751
              to
              {'poll_date': Value('string'), 'pollster': Value('string'), 'candidate': Value('string'), 'poll_pct': Value('float64'), 'polymarket_pct': Value('float64'), 'polymarket_date': Value('string'), 'divergence_pp': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 2 new columns ({'date', 'volume_usd'}) and 5 missing columns ({'poll_date', 'divergence_pp', 'poll_pct', 'pollster', 'polymarket_date'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/AFOS-Analytics1/mexico-2024-electoral-divergence/data/mexico-market-odds-timeseries.csv (at revision 12188533c0dfc67b994be3745019de7341c392fe), ['hf://datasets/AFOS-Analytics1/mexico-2024-electoral-divergence@12188533c0dfc67b994be3745019de7341c392fe/data/mexico-divergence-timeseries.csv', 'hf://datasets/AFOS-Analytics1/mexico-2024-electoral-divergence@12188533c0dfc67b994be3745019de7341c392fe/data/mexico-market-odds-timeseries.csv', 'hf://datasets/AFOS-Analytics1/mexico-2024-electoral-divergence@12188533c0dfc67b994be3745019de7341c392fe/data/mexico-structural-context.csv', 'hf://datasets/AFOS-Analytics1/mexico-2024-electoral-divergence@12188533c0dfc67b994be3745019de7341c392fe/news/mexico-2024-press-coverage.csv', 'hf://datasets/AFOS-Analytics1/mexico-2024-electoral-divergence@12188533c0dfc67b994be3745019de7341c392fe/polls/mexico-polls.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

poll_date
string
pollster
string
candidate
string
poll_pct
float64
polymarket_pct
float64
polymarket_date
string
divergence_pp
float64
2024-01-22
Enkoll
Sheinbaum
54
92
2024-01-22
38
2024-01-22
Enkoll
Gálvez
27
8
2024-01-22
-19
2024-01-22
Enkoll
Máynez
3
0.7
2024-01-22
-2.3
2024-01-30
Áltica
Sheinbaum
50
85.5
2024-01-30
35.5
2024-01-30
Áltica
Gálvez
35
15.5
2024-01-30
-19.5
2024-01-30
Áltica
Máynez
5
0.6
2024-01-30
-4.4
2024-02-05
LaEncuesta.mx
Sheinbaum
50.8
86.5
2024-02-05
35.7
2024-02-05
LaEncuesta.mx
Gálvez
39.9
15.5
2024-02-05
-24.4
2024-02-05
LaEncuesta.mx
Máynez
4.2
0.6
2024-02-05
-3.6
2024-02-14
Áltica
Sheinbaum
51
91
2024-02-14
40
2024-02-14
Áltica
Gálvez
35
11
2024-02-14
-24
2024-02-14
Áltica
Máynez
4
0.5
2024-02-14
-3.5
2024-02-19
LaEncuesta.mx
Sheinbaum
50.4
94.5
2024-02-19
44.1
2024-02-19
LaEncuesta.mx
Gálvez
40.9
3.4
2024-02-19
-37.5
2024-02-19
LaEncuesta.mx
Máynez
3.9
0.5
2024-02-19
-3.4
2024-02-19
De las Heras Demotecnia
Sheinbaum
67
94.5
2024-02-19
27.5
2024-02-19
De las Heras Demotecnia
Gálvez
15
3.4
2024-02-19
-11.6
2024-02-19
De las Heras Demotecnia
Máynez
2
0.5
2024-02-19
-1.5
2024-02-20
Mitofsky
Sheinbaum
51.6
92.5
2024-02-20
40.9
2024-02-20
Mitofsky
Gálvez
27.8
7.2
2024-02-20
-20.6
2024-02-20
Mitofsky
Máynez
5.1
0.5
2024-02-20
-4.6
2024-02-25
El Financiero
Sheinbaum
50
92.5
2024-02-25
42.5
2024-02-25
El Financiero
Gálvez
33
6.7
2024-02-25
-26.3
2024-02-25
El Financiero
Máynez
8
0.5
2024-02-25
-7.5
2024-02-28
MetricsMX
Sheinbaum
61
92.5
2024-02-28
31.5
2024-02-28
MetricsMX
Gálvez
20
6.9
2024-02-28
-13.1
2024-02-28
MetricsMX
Máynez
4.4
0.6
2024-02-28
-3.8
2024-02-28
Enkoll
Sheinbaum
53
92.5
2024-02-28
39.5
2024-02-28
Enkoll
Gálvez
29
6.9
2024-02-28
-22.1
2024-02-28
Enkoll
Máynez
3
0.6
2024-02-28
-2.4
2024-03-14
LaEncuesta.mx
Sheinbaum
49.4
92.5
2024-03-14
43.1
2024-03-14
LaEncuesta.mx
Gálvez
41.6
6.8
2024-03-14
-34.8
2024-03-14
LaEncuesta.mx
Máynez
5
0.5
2024-03-14
-4.5
2024-03-14
Áltica
Sheinbaum
51
92.5
2024-03-14
41.5
2024-03-14
Áltica
Gálvez
37
6.8
2024-03-14
-30.2
2024-03-14
Áltica
Máynez
6
0.5
2024-03-14
-5.5
2024-03-17
De las Heras Demotecnia
Sheinbaum
63
92.5
2024-03-17
29.5
2024-03-17
De las Heras Demotecnia
Gálvez
15
6.8
2024-03-17
-8.2
2024-03-17
De las Heras Demotecnia
Máynez
2
0.4
2024-03-17
-1.6
2024-03-17
Mitofsky
Sheinbaum
50.5
92.5
2024-03-17
42
2024-03-17
Mitofsky
Gálvez
28.8
6.8
2024-03-17
-22
2024-03-17
Mitofsky
Máynez
4.8
0.4
2024-03-17
-4.4
2024-03-24
MEBA
Sheinbaum
53.5
92.5
2024-03-24
39
2024-03-24
MEBA
Gálvez
28.9
6.8
2024-03-24
-22.1
2024-03-24
MEBA
Máynez
6
0.5
2024-03-24
-5.5
2024-03-25
MetricsMX
Sheinbaum
57.1
92.5
2024-03-25
35.4
2024-03-25
MetricsMX
Gálvez
23.7
6.8
2024-03-25
-16.9
2024-03-25
MetricsMX
Máynez
4.4
0.5
2024-03-25
-3.9
2024-03-31
Áltica
Sheinbaum
50
94.5
2024-03-31
44.5
2024-03-31
Áltica
Gálvez
38
5.4
2024-03-31
-32.6
2024-03-31
Áltica
Máynez
6
0.5
2024-03-31
-5.5
2024-04-01
El Financiero
Sheinbaum
51
94.5
2024-04-01
43.5
2024-04-01
El Financiero
Gálvez
34
5.4
2024-04-01
-28.6
2024-04-01
El Financiero
Máynez
7
0.5
2024-04-01
-6.5
2024-04-05
LaEncuesta.mx
Sheinbaum
50.7
94.5
2024-04-05
43.8
2024-04-05
LaEncuesta.mx
Gálvez
40.9
5.3
2024-04-05
-35.6
2024-04-05
LaEncuesta.mx
Máynez
4.6
0.4
2024-04-05
-4.2
2024-04-13
Mitofsky
Sheinbaum
51.4
93.5
2024-04-13
42.1
2024-04-13
Mitofsky
Gálvez
26.7
4.2
2024-04-13
-22.5
2024-04-13
Mitofsky
Máynez
9.3
2
2024-04-13
-7.3
2024-04-14
C&E Mexico
Sheinbaum
59
93.5
2024-04-14
34.5
2024-04-14
C&E Mexico
Gálvez
33
4.2
2024-04-14
-28.8
2024-04-14
C&E Mexico
Máynez
8
2
2024-04-14
-6
2024-04-14
TResearch
Sheinbaum
54.3
93.5
2024-04-14
39.2
2024-04-14
TResearch
Gálvez
30.2
4.2
2024-04-14
-26
2024-04-14
TResearch
Máynez
4
2
2024-04-14
-2
2024-04-14
GEA-ISA
Sheinbaum
49
93.5
2024-04-14
44.5
2024-04-14
GEA-ISA
Gálvez
34
4.2
2024-04-14
-29.8
2024-04-14
GEA-ISA
Máynez
6
2
2024-04-14
-4
2024-04-14
Áltica
Sheinbaum
51
93.5
2024-04-14
42.5
2024-04-14
Áltica
Gálvez
39
4.2
2024-04-14
-34.8
2024-04-14
Áltica
Máynez
5
2
2024-04-14
-3
2024-04-14
Enkoll
Sheinbaum
54
93.5
2024-04-14
39.5
2024-04-14
Enkoll
Gálvez
30
4.2
2024-04-14
-25.8
2024-04-14
Enkoll
Máynez
7
2
2024-04-14
-5
2024-04-16
LaEncuesta.mx
Sheinbaum
49.7
93.5
2024-04-16
43.8
2024-04-16
LaEncuesta.mx
Gálvez
40.8
4.2
2024-04-16
-36.6
2024-04-16
LaEncuesta.mx
Máynez
6.3
2
2024-04-16
-4.3
2024-04-21
TResearch
Sheinbaum
53.8
94.5
2024-04-21
40.7
2024-04-21
TResearch
Gálvez
30.1
3.7
2024-04-21
-26.4
2024-04-21
TResearch
Máynez
4.4
1.4
2024-04-21
-3
2024-04-21
MEBA
Sheinbaum
51.2
94.5
2024-04-21
43.3
2024-04-21
MEBA
Gálvez
24.6
3.7
2024-04-21
-20.9
2024-04-21
MEBA
Máynez
10.7
1.4
2024-04-21
-9.3
2024-04-24
El Financiero
Sheinbaum
49
94.5
2024-04-24
45.5
2024-04-24
El Financiero
Gálvez
32
4.1
2024-04-24
-27.9
2024-04-24
El Financiero
Máynez
8
1.4
2024-04-24
-6.6
2024-04-26
MetricsMX
Sheinbaum
54.4
93.5
2024-04-26
39.1
2024-04-26
MetricsMX
Gálvez
21.3
4.4
2024-04-26
-16.9
2024-04-26
MetricsMX
Máynez
12.8
1.4
2024-04-26
-11.4
2024-04-28
TResearch
Sheinbaum
53
93.5
2024-04-28
40.5
2024-04-28
TResearch
Gálvez
28.5
4.4
2024-04-28
-24.1
2024-04-28
TResearch
Máynez
7.3
1.4
2024-04-28
-5.9
2024-04-30
C&E Mexico
Sheinbaum
55
93.5
2024-04-30
38.5
2024-04-30
C&E Mexico
Gálvez
34
4.3
2024-04-30
-29.7
2024-04-30
C&E Mexico
Máynez
11
1.4
2024-04-30
-9.6
2024-04-30
LaEncuesta.mx
Sheinbaum
48.9
93.5
2024-04-30
44.6
2024-04-30
LaEncuesta.mx
Gálvez
40.4
4.3
2024-04-30
-36.1
2024-04-30
LaEncuesta.mx
Máynez
6.9
1.4
2024-04-30
-5.5
2024-05-05
TResearch
Sheinbaum
52.4
95.5
2024-05-05
43.1
End of preview.

AFOS · Mexico 2024 Electoral Divergence

AFOS · Mexico 2024 Electoral Divergence Dataset

🌐 English · Português · Español

Open dataset cross-referencing opinion polls × prediction markets for Mexico's 2024 presidential election (single round, 2 June 2024), in the same spirit as the AFOS Brazil 2026 dataset: sources reported side by side with explicit divergence, never blended into a single average.

Maintained by AFOS Analytics. No personal data, only public electoral information. Candidate-level: the polls measure candidate vote share, while the market prices the probability of winning the presidency. These are two different quantities, and the gap between them is the signal.


Press coverage layer

The qualitative third axis of the AFOS cross (market x polls x press) is now a structured file: news/mexico-2024-press-coverage.csv, 9 dated headlines from 5 national outlets across the cycle (polls, election, result, analysis), in ES. Headlines and links only (outlets retain copyright); dates are publication/coverage dates, best-effort. It complements the quantitative market-vs-poll divergence; it is not sentiment-scored.


English

Sheinbaum won, and the market had called it from January

Claudia Sheinbaum won the 2024 presidential election with about 59.8% of the vote, the largest vote count in Mexican history, and became the country's first woman president. The signal showed early: from January the market already gave Sheinbaum about 90% probability of winning, while the polls measured her vote share around 50% (the Oraculus aggregate closed near 54%). The market treated the race as decided long before election day, and the actual result ran even higher than the polls suggested. Where some polls still showed a contest, the market read the outcome.

Official result: Claudia Sheinbaum (Morena) 59.8% · Xóchitl Gálvez (PAN-PRI-PRD) 27.5% · Jorge Álvarez Máynez (Movimiento Ciudadano) 10.3%.

Polymarket probability of winning the presidency over the 2024 campaign

Market probability of winning versus poll vote share on the eve of the vote

Contents (start with the polls):

Path Rows Content
polls/mexico-polls.csv 147 Candidate vote-share polling, long format (one row per candidate × poll), 3 candidates, 49 polls, Jan to May 2024.
polls/mexico-polls.json n/a Full structured polls (pollster, fieldwork, sample, per-candidate results).
data/mexico-market-odds-timeseries.csv 552 Daily Polymarket probability of winning the presidency per candidate (4 series, Jan to Jun 2024) from the "Mexico Presidential Election Winner" market (total volume US$ 2.08M).
data/mexico-divergence-timeseries.csv 135 Market × poll divergence per candidate, each poll's vote share joined to that candidate's market odds on its date.
data/mexico-poly-raw.json n/a Raw Polymarket payload, kept for provenance.

Market fetched from Polymarket's gamma-api plus clob via a US-resolving function. Single-round presidential election. Post-election resolution points were dropped, so the series ends on election day.

⚖️ Notable divergences (why divergence beats the average)

The market prices who wins the presidency, the polls measure vote share. In 2024 the market was confident and early, and the result proved it right.

  • Sheinbaum: 90% in the market from day one, while polls measured ~50% of the vote. The two are different quantities, probability of winning versus vote share, so the ~40-point gap is not a polling error. What matters is that the market never wavered from a near-certain Sheinbaum win, and the result (59.8%) came in above the final polls, which had her closer to 50 to 54%. The polls understated the margin, the market did not.
  • Gálvez: some polls put her in the 33 to 40% range, the market never gave her more than ~11% to win. The market simply did not believe the contest was close. The result validated it: Gálvez finished at 27.5%, roughly 31 points behind.
  • Máynez: a steady distant third. Polls around 10%, market near 0% to win, result 10.3%. Vote share and "winning" were never the same question for him.

The reading: vote share tells you how Mexico voted, the market told you the presidency was settled months ahead. A blended poll average, especially one fed by the more competitive-looking polls, would have understated how decided the race already was.

Press and forecast layer

The poll and market series were cross-read against five free public sources, the same kind AFOS audits daily for Brazil: Oraculus (the leading Mexican poll-of-polls), the INE public registry of electoral polls (the official source that pollsters must file with), AS/COA's Mexico poll tracker (English-language), Milenio (which ran its own campaign tracking poll), and Animal Político (independent fact-checking).

Pollsters covered: El Financiero, Mitofsky, De las Heras, Enkoll, GEA-ISA, Reforma, AtlasIntel, Áltica, MEBA, LaEncuesta.mx, Massive Caller, and others.

Provenance and method: poll figures compiled deterministically (rowspan/colspan-aware HTML parser) from the public Wikipedia aggregation "Opinion polling for the 2024 Mexican general election." Poll-of-poll aggregators (Oraculus, Polls.mx) were excluded from the per-poll table. Market odds from the public Polymarket market. Nothing imputed or smoothed, missing values left blank.

License (dual): the data (CSV, JSON) are released under CC BY 4.0, the code (parse-mexico-wiki.mjs, build-mexico-market-divergence.mjs) under Apache 2.0 (see LICENSE and LICENSE-APACHE). Underlying poll numbers are facts released by the named pollsters, the Wikipedia aggregation is CC BY-SA. Please attribute AFOS Analytics and the original pollsters.

Cite: AFOS Analytics. Mexico 2024 Electoral Divergence Dataset. Hugging Face, 2026. CC BY 4.0. (see CITATION.cff)

Disclaimer: observational research. Not investment advice, not voting guidance.


Português

Dataset aberto cruzando pesquisas × mercados de previsão para a eleição presidencial do México de 2024 (turno único, 02/jun/2024). Nível candidato: as pesquisas medem voto por candidato, o mercado precifica a probabilidade de vencer a presidência. Duas grandezas diferentes, e a distância entre elas é o sinal.

Sheinbaum venceu, e o mercado já sabia desde janeiro

Claudia Sheinbaum venceu a eleição presidencial de 2024 com cerca de 59,8% dos votos, a maior votação da história do México, e tornou-se a primeira mulher presidente do país. O sinal apareceu cedo: desde janeiro o mercado já dava a Sheinbaum cerca de 90% de probabilidade de vencer, enquanto as pesquisas mediam seu voto em torno de 50%. O mercado tratou a eleição como decidida muito antes da urna, e o resultado real veio ainda mais alto que as pesquisas indicavam.

Resultado oficial: Claudia Sheinbaum (Morena) 59,8% · Xóchitl Gálvez (PAN-PRI-PRD) 27,5% · Jorge Álvarez Máynez (Movimiento Ciudadano) 10,3%.

  • polls/mexico-polls.csv: voto por candidato, formato largo, 3 candidatos, 49 pesquisas (jan a mai 2024).
  • data/mexico-market-odds-timeseries.csv / mexico-divergence-timeseries.csv: probabilidade Polymarket de vencer a presidência por candidato (volume total US$ 2,08 mi) e divergência mercado × pesquisa.

⚖️ Divergências em destaque

  • Sheinbaum: 90% no mercado desde o primeiro dia, enquanto a pesquisa media ~50% do voto. São grandezas diferentes (probabilidade de vencer × voto), então o gap de ~40 pontos não é erro de pesquisa. O que importa: o mercado nunca abandonou a vitória quase certa de Sheinbaum, e o resultado (59,8%) veio acima das pesquisas finais, que a colocavam mais perto de 50 a 54%. A pesquisa subestimou a margem, o mercado não.
  • Gálvez: algumas pesquisas a colocavam na faixa de 33 a 40%, o mercado nunca lhe deu mais que ~11% de vencer. O mercado simplesmente não acreditou que a disputa fosse apertada. O resultado validou: Gálvez terminou com 27,5%, cerca de 31 pontos atrás.
  • Máynez: um terceiro lugar distante e estável. Pesquisa em torno de 10%, mercado perto de 0% de vencer, resultado 10,3%.

A leitura: o voto diz como o México votou, o mercado disse que a presidência estava resolvida com meses de antecedência. Uma média de pesquisas teria subestimado o quanto a disputa já estava decidida.

Camada de imprensa: as séries foram cruzadas com cinco fontes públicas e gratuitas (Oraculus, o registro oficial de pesquisas do INE, o tracker da AS/COA, o Milenio e o Animal Político), o mesmo tipo de fonte que a AFOS audita diariamente no Brasil.


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Dataset abierto que cruza encuestas × mercados de predicción para la elección presidencial de México de 2024 (vuelta única, 02 jun 2024). Nivel candidato: las encuestas miden voto por candidato, el mercado valora la probabilidad de ganar la presidencia.

Ganó Sheinbaum, y el mercado ya lo sabía desde enero

Claudia Sheinbaum ganó la elección presidencial de 2024 con cerca del 59,8% de los votos, la mayor votación en la historia de México, y se convirtió en la primera mujer presidenta del país. La señal apareció temprano: desde enero el mercado ya daba a Sheinbaum cerca del 90% de probabilidad de ganar, mientras las encuestas medían su voto en torno al 50%. El mercado trató la elección como decidida mucho antes, y el resultado real fue aún más alto que lo que indicaban las encuestas.

Resultado oficial: Claudia Sheinbaum (Morena) 59,8% · Xóchitl Gálvez (PAN-PRI-PRD) 27,5% · Jorge Álvarez Máynez (Movimiento Ciudadano) 10,3%.

⚖️ Divergencias destacadas

  • Sheinbaum: 90% en el mercado desde el primer día, mientras la encuesta medía ~50% del voto. Son grandezas diferentes (probabilidad de ganar × voto), así que la brecha de ~40 puntos no es error de encuesta. El mercado nunca abandonó la victoria casi segura de Sheinbaum, y el resultado (59,8%) llegó por encima de las encuestas finales. La encuesta subestimó el margen, el mercado no.
  • Gálvez: algunas encuestas la ponían entre 33 y 40%, el mercado nunca le dio más de ~11% de ganar. El resultado lo validó: Gálvez terminó con 27,5%, unos 31 puntos atrás.
  • Máynez: un tercer lugar distante. Encuesta en torno al 10%, mercado cerca de 0% de ganar, resultado 10,3%.

Fuente: agregación pública de Wikipedia, Polymarket, cruzadas con Oraculus, el registro del INE, AS/COA, Milenio y Animal Político. Licencia (dual): datos CC BY 4.0, código Apache 2.0 (atribuir a AFOS Analytics y a las encuestadoras). Investigación observacional, no es asesoría de inversión.


Sources / Fontes / Fuentes: Pollsters (El Financiero, Mitofsky, Enkoll, …) · Oraculus · INE registro de encuestas · AS/COA poll tracker · Wikipedia aggregation · Polymarket. Column definitions in DATA_DICTIONARY.md.

Structural context (World Bank)

Beyond the divergence data, this dataset ships data/mexico-structural-context.csv: official, open World Bank indicators that frame the country — governance (Worldwide Governance Indicators, 0-100 scale) plus economy & education (World Development Indicators: population, GDP, GDP per capita, inflation, public education spending, expected years of schooling). These are annual structural indicators that contextualize the country; they do not predict the electoral outcome. Columns are documented in DATA_DICTIONARY.md.

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