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REVIEW 4 major objections 5 minor 1 cited by

Are Betting Markets Better than Polling in Predicting Political Elections?

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper claims that Polymarket's daily betting prices predicted the 2024 U.S. presidential election outcome better than traditional polling, nationally and in most swing states.

desk verdict A well-written case study undone by comparing raw polling averages to market win probabilities on the same axis; the claimed Polymarket superiority is largely an artifact. read the letter →

arxiv 2507.08921 v1 pith:XZRDHLNH submitted 2025-07-11 stat.OT

Laurie E. Cutting , Sarah S. Hughes-Berheim , Paul M. Johnson , Hiba Baroud , Brett Goldstein This is my paper
classification stat.OT
keywords predictionmarketsPolymarketelectionforecastingpollingaccuracyBayesianstructuraltimeserieswisdomofcrowds2024presidentialswingstates
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Political elections are hard to forecast because polls ask whom people intend to vote for, not whom they expect to win. This paper argues that Polymarket, the largest cryptocurrency-based betting market, captured the latter and, in the 2024 presidential election, did a better job than traditional polling. The authors compare a daily Trump win probability from Polymarket contract prices with the aggregated FiveThirtyEight polling average, first visually and then with Bayesian structural time series forecasts. The market favored Trump at nearly every time point, reacted sharply to the assassination attempt, Harris's entry, and the debates, and by mid-October had forecast intervals that stayed above the 50 percent line. Polling averages never put Trump ahead and on Election Day still pointed to Harris, so the paper concludes that market-based forecasts are a viable, possibly superior, complement to polls.

What carries the argument

The analysis runs through a date-aligned pair of daily time series: Polymarket's closing price on the Trump full-ballot contract, treated as an implied win probability, and the average of all available polls from FiveThirtyEight, treated the same way. The predictive engine is a Bayesian structural time series model with a local-level state equation, fitted with spike-and-slab priors on regressors, which identifies Pennsylvania and Michigan as the state markets driving the national market and produces rolling forecasts with 95 percent predictive intervals. The model's fitted variance terms let the paper describe the market series as a random walk and the polling series as noise around a fixed mean, which is the formal reason the market forecasts are wider but more accurate.

What would settle it

Re-run the comparison on the 2016 or 2020 elections using the same daily market and polling series and the same BSTS forecasting protocol: if the market's mid-October lead over polling does not reproduce in either election, or disappears once both sources are converted to a common outcome space, the 2024 result alone is too weak to carry the paper's general conclusion.

Watch

Extended reading notes

Core claim

The central claim is that Polymarket was superior to polling in predicting the 2024 presidential election, both nationally and in five of the seven swing states, with Michigan and Wisconsin too close to call in either source. The market's daily contract price, read as the probability Donald Trump would win the presidency, called the winner early and stayed on the right side of the 50 percent decision boundary from mid-October onward. The polling average, built from preference questions, not only failed to call the result but got the direction wrong on Election Day. The paper presents this as the first systematic comparison of Polymarket data against traditional polls, and reads the result as support for the idea that a crowd wagering money can aggregate expectations more accurately than a crowd answering survey questions.

Load-bearing premise

The comparison assumes that a poll's vote-preference average can be plotted on the same 'Trump win probability' axis as a Polymarket contract that actually pays out on who wins the Electoral College, and if those quantities measure different things, the market's apparent advantage is an artifact.

Editorial extensions

If this is right

  • Campaigns and media could treat daily market prices as an early-warning signal for where resources should go, since the market saw Georgia and North Carolina as unwinnable for Harris months before the polls did.
  • Forecasters could use market data to measure how events move the race hour by hour, a granularity most polls cannot offer.
  • If markets are adopted as forecasting tools, regulators and platforms would need identity checks and anti-manipulation rules, because a single large bettor or wash trades can shift prices.
  • The 2024 election becomes a test case for wisdom-of-crowds theory, turning a stochastic market into a calibration point for collective judgment.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The strongest hidden comparison would be to map poll vote shares into win probabilities with an electoral-college model; on that common scale, the market's edge may shrink, and the paper does not run that test.
  • Michigan and Wisconsin, where the market added no clear signal, suggest the market's value may be limited to races with an identifiable structural lean, not genuinely tied states.
  • Adding a direct 'who do you think will win' question to standard polls could isolate whether the market's advantage is expectations rather than money, without relying on a crypto platform that was legally restricted inside the United States.
  • A single election is a sample of one; before recommending betting markets, the same protocol should be run across many political and non-political binary events to see if the superiority generalizes.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper compares daily Polymarket contract prices with daily polling averages from FiveThirtyEight for the 2024 U.S. presidential election, at the national level and in seven swing states, using descriptive time-series plots and Bayesian Structural Time Series (BSTS) models. The authors conclude that Polymarket was superior to polling in predicting the outcome, particularly in swing states, and suggest that betting markets could be used to predict elections and other events. The manuscript includes model fits, variable importance analysis, rolling forecasts, and an extensive limitations discussion, and states that code and data are available in supplementary materials.

Significance. If the comparison were valid, the paper would be a useful contribution to the ongoing debate on prediction markets versus traditional polls, adding a large-market, state-level case study with Bayesian uncertainty quantification. The authors also deserve credit for acknowledging market-manipulation concerns and for making code and data available. However, the central comparison is compromised by the fact that the two series measure different quantities, and the evaluative claims rest on informal visual criteria and a single election; in its current form the paper does not establish that betting markets are better predictors.

major comments (4)
  1. [2.1, Figures 1–2, Figure 4] The polling data plotted as 'Trump Win Probability' are raw or averaged two-candidate vote shares (preference estimates), not probabilities of winning the Electoral College. A 45% vote share does not correspond to a 45% win probability; the mapping depends on the distribution of polling error and the correlation of state errors. Because the BSTS forecasting model in Equations (5)–(6) is applied directly to this vote-share series, the claim in Section 3 that 'almost all polling predictions favor Harris' is an artifact of comparing a vote-share quantity to a market-implied win probability, not evidence of inferior predictive skill. A valid comparison requires converting poll margins into win probabilities through a state-level forecast model with an explicit error structure before scoring.
  2. [3, Section 4.2, Figure 4] The conclusion that Polymarket was 'superior' is based on visual inspection of whether posterior predictive intervals fall above 50% at selected horizons, not on any proper scoring rule (e.g., Brier score, logarithmic score, calibration test). With one election, seven states, and eight evaluation time points, no adjustment for multiple comparisons or selection is made, and no statistical significance test is reported. The paper should compare the two forecasts using a pre-specified proper scoring rule and an appropriate test of equal predictive ability, rather than interpreting plotted intervals after the outcome is known.
  3. [4.1, 4.3, 4.4] The authors acknowledge that a single trader placed large pro-Trump bets across many accounts and that Polymarket was subject to wash-trading accusations during the exact period in which the market separated from the polls (October 2024). Given that a prediction-market price can be moved by capital as well as information, the headline claim of superiority requires robustness analysis: the comparison should at least be repeated excluding or downweighting the potentially manipulated post-October period, or with an adjustment for the large trader's positions.
  4. [2.4, Figure 3b] The BSTS variable-importance analysis for the national market uses state-level Polymarket prices as regressors for the national Polymarket price. This is a within-market decomposition, not an independent validation of market predictive power, and it does not address whether market prices added information beyond polls. The discussion should not present this analysis as evidence of superiority over polling.
minor comments (5)
  1. [1.2, References] The text contains several typos: 'Hilary Clinton' should be 'Hillary Clinton', the reference 'Fried and and Harris' has a duplicated 'and', and 'real-word data' in Section 4.4 should be 'real-world data'.
  2. [2.4, Results] Several equations and inline symbols are garbled: 'If 20, then t is constant' appears to have missing symbols for the variance parameters, and the Results section contains 'corroborate this notion (2)' and '(0 and ¿0)' where the estimated variance components are intended; these should be typeset correctly.
  3. [Figures 1–4] The legends in Figures 1–4 include 'Standard Deviation' without explaining that it refers to the standard deviation of the polling average on that day; the captions should state this explicitly.
  4. [Discussion and Figure 4] The manuscript states 'election day on November 4th, 2024' and Figure 4 labels 'November 04, 2024'; the 2024 U.S. presidential election was held on November 5, 2024, so the date is incorrect in both places.
  5. [Appendix A] The list of pollsters is extensive, but no information is given about the aggregation method, weighting, or inclusion criteria for the FiveThirtyEight polling averages; a brief description would help readers assess the comparability of the polling series.

Circularity Check

1 steps flagged · score 6.0 of 10

The comparison of polling to Polymarket is partly self-definitional: a vote-share poll average is relabeled as 'Trump Win Probability' and then forecast, so the conclusion that polls 'incorrectly predict Harris' is built into the axis rather than derived from evidence.

  1. self definitional [Section 2.1 (Data), Section 2.5 (Forecasting and Dynamic Comparison), Figure 4, Section 4.2 (Predictive Results)]
    "This study analyzed daily data on the probability of Donald Trump winning the 2024 U.S. presidential election using two primary sources: betting market data from Polymarket and public opinion polling data from multiple established polling aggregators. ... BSTS models were also trained on national and state level data up to various intermediate time points prior to the election and then projected forward to election day. ..."

    The 'polling data' series is a daily mean of vote-intention polls, a vote-share estimate, not a win probability. Equations (5)-(6) fit a local-level BSTS model to that mean and extrapolate it; the forecast is an extrapolation of the vote-share series itself. Figure 4 labels that forecast 'Trump Win Probability' and the paper treats its position relative to 0.50 as evidence that 'the mean of the polling data never showed Trump as the winner' and that 'polling data incorrectly predicts Harris as the winner.' Since a two-party vote share below 50% does not equal a below-50% win probability, the conclusion follows by construction from the label/axis, not from any estimated relationship between poll margins and outcomes.

full rationale

The paper contains no load-bearing self-citations: its methods cite standard BSTS references (Brodersen et al., Scott and Varian) and its data are external, so self-citation patterns do not arise. The central problem is pattern 1 (self-definitional construction). The polling series is built from mean vote-intention estimates, yet it is analyzed and displayed as 'Trump Win Probability'; the BSTS forecast of that series is then read against a 50 percent decision boundary to declare that polling 'predicted Harris.' That declaration is an artifact of labeling a vote-share forecast as a win-probability forecast. The BSTS models themselves do produce genuine out-of-sample extrapolations, so the paper is not wholly tautological; however, the headline comparative conclusion depends on the relabeling step rather than on an independent conversion of poll margins into election win probabilities. This makes the central comparative claim partially circular, warranting a score of 6 rather than a higher score. The retrospective use of the known 2024 outcome to judge forecast accuracy is normal forecast evaluation, not circularity per se; the circularity is specifically the unacknowledged scale change imposed on the polling input.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The analysis rests on treating market prices and polling averages as comparable probability statements and on using a single known election as the evaluation. No new entities are invented. The BSTS models introduce estimated variance parameters, but these are not reported in detail.

free parameters (3)
  • BSTS local-level observation variance (sigma^2) = not reported
    Estimated for Polymarket and polling series; controls predictive interval width but numerical estimates are not reported in the paper.
  • BSTS local-level state variance (tau^2) = not reported
    Estimated separately for each series; the paper states it is positive for Polymarket and near zero for polling but gives no values.
  • Spike-and-slab prior hyperparameters = not reported
    Settings for the variable-selection prior in Section 2.4 are not specified; they affect which state predictors are included.
assumptions (5)
  • domain assumption Polymarket daily closing price equals an implied probability of Trump winning.
    Section 2.1 treats closing prices as probabilities without adjusting for market microstructure, fees, or manipulation.
  • domain assumption Polling averages can be directly compared with win probabilities.
    Figures 1 and 2 label the FiveThirtyEight polling average as 'Trump Win Probability', but poll averages are vote-intention shares, not Electoral College win probabilities.
  • domain assumption The 2024 election outcome is the sole correct ground truth for evaluating forecasting accuracy.
    The entire evaluation is retrospective with one binary outcome, which cannot establish calibration or statistical significance.
  • domain assumption The BSTS local-level model is appropriate for both series and extrapolation to Election Day is valid.
    Section 2.5 assumes random-walk extrapolation is a sensible forecast; this is especially strong for intermittent polling data.
  • domain assumption Wisdom of Crowds explains the market result.
    Sections 4.3 and 4.4 invoke Wisdom of Crowds, but the paper provides no direct evidence about bettor independence, diversity, or aggregation properties.

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Cite this review

Pith. "Pith review of Are Betting Markets Better than Polling in Predicting Political Elections?." pith.science (2026). https://pith.science/paper/XZRDHLNH

@misc{pith2026250708921,
  author       = {Pith},
  title        = {Pith review of: Are Betting Markets Better than Polling in Predicting Political Elections?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XZRDHLNH}},
  note         = {Machine review of arXiv:2507.08921}
}
read the original abstract

Political elections are one of the most significant aspects of what constitutes the fabric of the United States. In recent history, typical polling estimates have largely lacked precision in predicting election outcomes, which has not only caused uncertainty for American voters, but has also impacted campaign strategies, spending, and fundraising efforts. One intriguing aspect of traditional polling is the types of questions that are asked -- the questions largely focus on asking individuals who they intend to vote for. However, they don't always probe who voters think will win -- regardless of who they want to win. In contrast, online betting markets allow individuals to wager money on who they expect to win, which may capture who individuals think will win in an especially salient manner. The current study used both descriptive and predictive analytics to determine whether data from Polymarket, the world's largest online betting market, provided insights that differed from traditional presidential polling. Overall, findings suggest that Polymarket was superior to polling in predicting the outcome of the 2024 presidential election, particularly in swing states. Results are in alignment with research on ''Wisdom of Crowds'' theory, which suggests a large group of people are often accurate in predicting outcomes, even if they are not necessarily experts or closely aligned with the issue at hand. Overall, our results suggest that betting markets, such as Polymarket, could be employed to predict presidential elections and/or other real-world events. However, future investigations are needed to fully unpack and understand the current study's intriguing results, including alignment with Wisdom of Crowds theory and portability to other events.

Figures

Figures reproduced from arXiv: 2507.08921 by the authors.

Figure 1
Figure 1. Polymarket Versus Polling Data (National) – Trump was the winner of the general [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Polymarket Versus Polling Data (State) – Trump was the winner for each of these [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. BSTS Local-Level Model on National Polymarket Data [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Model Predictions for Polymarket and Polling Data [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]

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Forward citations

Cited by 1 Pith paper

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Reference graph

Works this paper leans on

81 extracted references · 76 canonical work pages · cited by 1 Pith paper

  1. [1]

    Polymarket 2024 us election state data

    Pedro Andrade. Polymarket 2024 us election state data. Dataset on Kaggle, 2024. URL https://www.kaggle.com/datasets/pbizil/polymarket-2024-us-election-state-data. Accessed 2025-07-07

    2024

  2. [2]

    A new paradigm for polling

    Michael A Bailey. A new paradigm for polling. Harvard Data Science Review, 5 0 (3), 2023. doi:10.1162/99608f92.9898eede

    doi:10.1162/99608f92.9898eede 2023

  3. [3]

    Results from a dozen years of election futures markets research

    Joyce Berg, Robert Forsythe, Forrest Nelson, and Thomas Rietz. Results from a dozen years of election futures markets research. In Charles R. Plott and Vernon L. Smith, editors, Handbook of Experimental Economics Results, volume 1, pages 742--751. Elsevier, 2008. doi:10.1016/S1574-0722(07)00080-7

    work page doi:10.1016/s1574-0722(07)00080-7 2008

  4. [4]

    Us traders flock to an election-betting site they're banned from

    Lydia Beyoud and Sridhar Natarajan. Us traders flock to an election-betting site they're banned from. Bloomberg.com, August 2024. URL https://www.bloomberg.com/news/articles/2024-08-01/polymarket-rides-election-betting-frenzy-that-defies-a-us-ban

    work page 2024

  5. [5]

    2025 cryptocurrency adoption and consumer sentiment report

    Tom Blackstone. 2025 cryptocurrency adoption and consumer sentiment report. Security.org, January 2025. URL https://www.security.org/digital-security/cryptocurrency-annual-consumer-report/

    work page 2025

  6. [6]

    Brodersen, Florian Gallusser, Jim Koehler, Nicolas Remy, and Steven L

    Kay H. Brodersen, Florian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. Inferring causal impact using bayesian structural time-series models. The Annals of Applied Statistics, 9: 0 247--274, 2015. doi:10.1214/14-AOAS788

    doi:10.1214/14-aoas788 2015

  7. [7]

    David V. Budescu. Confidence in aggregation of opinions from multiple sources. In Klaus Fiedler and Peter Juslin, editors, Information Sampling and Adaptive Cognition, pages 327--352. Cambridge University Press, Cambridge, 2005. ISBN 978-0-521-53933-3. doi:10.1017/CBO9780511614576.014. URL https://www.cambridge.org/core/books/information-sampling-and-adap...

    work page doi:10.1017/cbo9780511614576.014 2005

  8. [8]

    Caldeira

    Gregory A. Caldeira. Expert judgment versus statistical models: Explanation versus prediction. Perspectives on Politics, 2 0 (4): 0 777--780, December 2004. ISSN 1541-0986, 1537-5927. doi:10.1017/S1537592704040526. URL https://www.cambridge.org/core/journals/perspectives-on-politics/article/expert-judgment-versus-statistical-models-explanation-versus-pred...

    work page doi:10.1017/s1537592704040526 2004

Show all 81 references
  1. [9]

    Joseph Campbell

    W. Joseph Campbell. Misfires and surprises: Polling embarrassments in recent u.s. presidential elections. American Behavioral Scientist, August 2022. ISSN 0002-7642. doi:10.1177/00027642221118901. URL https://doi.org/10.1177/00027642221118901

    2022 doi

  2. [10]

    What Killed Intrade? The New Yorker, March 2013

    John Cassidy. What Killed Intrade? The New Yorker, March 2013. ISSN 0028-792X. URL https://www.newyorker.com/news/john-cassidy/what-killed-intrade

    2013

  3. [11]

    Cftc orders event-based binary options markets operator to pay \ 1.4 million penalty

    CFTC . Cftc orders event-based binary options markets operator to pay \ 1.4 million penalty. Technical Report 8478-22, Commodity Futures Trading Commission, Washington, D.C., January 2022. URL https://www.cftc.gov/PressRoom/PressReleases/8478-22

    2022

  4. [12]

    Polling Paradox: What Actually Shapes the Numbers? Good Authority, October 2024

    Josh Clinton. Polling Paradox: What Actually Shapes the Numbers? Good Authority, October 2024. URL https://goodauthority.org/news/election-poll-vote2024-data-pollster-choices-weighting/

    2024

  5. [13]

    A lot of state poll results show ties

    Josh Clinton and John Lapinski. A lot of state poll results show ties. so are they tied because of voters --- or pollsters? NBC News, October 2024. URL https://www.nbcnews.com/politics/2024-election/state-poll-results-show-ties-are-tied-voters-pollsters-rcna177703

    2024

  6. [14]

    Aapor task force on 2020 pre-election polls: Performance of the polls in the democratic primaries

    Josh Clinton, Jennifer Agiesta, Megan Brenan, Camille Burge, Marjorie Connelly, Ariel Edwards-Levy, Bernard Fraga, Emily Guskin, Sunshine Hillygus, Chris Jackson, Jeff Jones, Scott Keeter, Kabir Khanna, John Lapinski, Lydia Saad, Daron Shaw, Andrew Smith, David Wilson, and Chr...

    2020

  7. [15]

    Polymarket plans u.s

    Jacqueline Corba. Polymarket plans u.s. return after signaling trump election win, founder says. CNBC, November 2024. URL https://www.cnbc.com/2024/11/07/polymarket-plans-to-return-to-the-us-founder-says-.html

    2024

  8. [16]

    Paul S. P. Cowpertwait and Andrew V. Metcalfe. Introductory Time Series with R. Springer, 2009. doi:10.1007/978-0-387-88698-5. URL https://doi.org/10.1007/978-0-387-88698-5

    2009 doi

  9. [17]

    The Politics of Resentment: Rural Consciousness in Wisconsin and the Rise of Scott Walker

    Katherine J Cramer. The Politics of Resentment: Rural Consciousness in Wisconsin and the Rise of Scott Walker. The University of Chicago Press, March 2016

    2016

  10. [18]

    A. M. Crossley. Straw polls in 1936. Public Opinion Quarterly, 1 0 (1): 0 24--35, January 1937. ISSN 0033-362X. doi:10.1086/265035. URL https://doi.org/10.1086/265035

    1936 doi

  11. [19]

    Davis-Stober , David V

    Clintin P. Davis-Stober , David V. Budescu, Jason Dana, and Stephen B. Broomell. When Is a Crowd Wise? Decision, 1 0 (2): 0 79--101, 2014. ISSN 2325-9973. doi:10.1037/dec0000004

    2014 doi

  12. [20]

    Republicans were into crypto long before trump

    Daniel de Vis \'e . Republicans were into crypto long before trump. here's why. USA Today, January 2025. URL https://www.usatoday.com/story/money/2025/01/07/republicans-liked-crypto-before-trump-did/77398253007/

    2025

  13. [21]

    First votes in 'digest's' 1936 poll

    Literary Digest. First votes in 'digest's' 1936 poll. Literary Digest, 122: 0 7--8, 1936

    1936

  14. [22]

    Political poll news site 538 to close amid larger shuttering across abc and disney

    Marina Dunbar. Political poll news site 538 to close amid larger shuttering across abc and disney. The Guardian, 2025-03-05. URL https://www.theguardian.com/us-news/2025/mar/05/abc-news-538-shut-down

    2025

  15. [23]

    October national poll: Biden with five-point lead one week out

    Emerson College Polling . October national poll: Biden with five-point lead one week out. News Nation, October 2020. URL https://emersonpolling.reportablenews.com/pr/october-national-poll-biden-with-five-point-lead-one-week-out

    2020

  16. [24]

    Erikson and Christopher Wlezien

    Robert S. Erikson and Christopher Wlezien. Are Political Markets Really Superior to Polls as Election Predictors? Public Opinion Quarterly, 72 0 (2): 0 190--215, January 2008. ISSN 0033-362X. doi:10.1093/poq/nfn010. URL https://doi.org/10.1093/poq/nfn010

    2008 doi

  17. [25]

    and Harris

    Amy Fried and Douglas B. and Harris . Governing with the polls. The Historian, 72 0 (2): 0 321--353, June 2010. ISSN 0018-2370. doi:10.1111/j.1540-6563.2010.00264.x. URL https://doi.org/10.1111/j.1540-6563.2010.00264.x

    2010

  18. [26]

    G. H. Gallup. The Gallup Poll: Public Opinion, 1935--1971. Rowman & Littlefield Publishers, 1972

    1935

  19. [27]

    Public Opinion and Polling around the World: A Historical Encyclopedia, volume 2

    John Gray Geer, editor. Public Opinion and Polling around the World: A Historical Encyclopedia, volume 2. ABC-CLIO, Santa Barbara, Calif, 2004. ISBN 978-1-57607-911-9

    2004

  20. [28]

    Getting it right: The art and science of competent polling

    Martin Goldfarb. Getting it right: The art and science of competent polling. Policy Magazine, 2013

    2013

  21. [29]

    How Do People in the U.S

    John Gramlich. How Do People in the U.S. Take Pew Research Center Surveys, Anyway? Pew Research Center, June 2024. URL https://www.pewresearch.org/short-reads/2024/06/26/how-do-people-in-the-us-take-pew-research-center-surveys-anyway/

    2024

  22. [30]

    Economic impact of united states presidential election years: A comparative analysis, September 2024

    Shaan Guru. Economic impact of united states presidential election years: A comparative analysis, September 2024. URL https://papers.ssrn.com/abstract=4956582

    2024

  23. [31]

    Biases of Online Political Polls: Who Participates? Socius, 4: 0 2378023118791080, January 2018

    Eszter Hargittai and Gokce Karaoglu. Biases of Online Political Polls: Who Participates? Socius, 4: 0 2378023118791080, January 2018. ISSN 2378-0231. doi:10.1177/2378023118791080. URL https://doi.org/10.1177/2378023118791080

    2018 doi

  24. [32]

    Sunshine Hillygus

    D. Sunshine Hillygus. The evolution of election polling in the united states. Public Opinion Quarterly, 75 0 (5): 0 962--981, December 2011. ISSN 0033-362X. doi:10.1093/poq/nfr054. URL https://doi.org/10.1093/poq/nfr054

    2011 doi

  25. [33]

    Sunshine Hillygus and Todd G

    D. Sunshine Hillygus and Todd G. Shields. The Persuadable Voter : Wedge Issues in Presidential Campaigns. Princeton University Press, 2014. URL https://www.torrossa.com/en/resources/an/5579435

    2014

  26. [34]

    Elections insights: Why iowa markets are beating polls in 2024 predictions

    Jeff Hulett. Elections insights: Why iowa markets are beating polls in 2024 predictions. The Curiosity Vine, October 2024. URL https://www.thecuriosityvine.com/post/the-iowa-markets-know-more-about-2024-than-the-polls-here-s-why

    2024

  27. [35]

    Behind biden's 2020 victory

    Ruth Igielnik, Scott Keeter, and Hannah Hartig. Behind biden's 2020 victory. Pew Research Center, June 2021. URL https://www.pewresearch.org/politics/2021/06/30/behind-bidens-2020-victory/

    2020

  28. [36]

    Wash trades as a stock market manipulation tool

    Serkan Imisiker and Bedri Kamil Onur Tas. Wash trades as a stock market manipulation tool. Journal of Behavioral and Experimental Finance, 20: 0 92--98, December 2018. ISSN 2214-6350. doi:10.1016/j.jbef.2018.08.004. URL https://www.sciencedirect.com/science/article/pii/S221463...

    2018 doi

  29. [37]

    Veikko Isotalo, Petteri Saari, Maria Paasivaara, Anton Steineker, and Peter A. Gloor. Predicting 2016 us presidential election polls with online and media variables. In Matth \"a us P. Zylka, Hauke Fuehres, Andrea Fronzetti Colladon, and Peter A. Gloor, editors, Designing Netw...

    2016

  30. [38]

    Jacobs and Robert Y

    Lawrence R. Jacobs and Robert Y. Shapiro. Polling politics, media, and election campaigns. Public Opinion Quarterly, 69 0 (5): 0 635--641, January 2005. ISSN 0033-362X. doi:10.1093/poq/nfi068. URL https://doi.org/10.1093/poq/nfi068

    2005 doi

  31. [39]

    \ 16 billion will be spent in the 2024 election

    Jaclyn Jeffrey-Wilensky. \ 16 billion will be spent in the 2024 election. where's it all going? US News & World Report, November 2024. URL https://www.usnews.com/news/national-news/articles/2024-11-01/16-billion-will-be-spent-in-the-2024-election-wheres-it-all-going

    2024

  32. [40]

    Election polling errors across time and space

    Will Jennings and Christopher Wlezien. Election polling errors across time and space. Nature Human Behaviour, 2 0 (4): 0 276--283, April 2018. ISSN 2397-3374. doi:10.1038/s41562-018-0315-6. URL https://www.nature.com/articles/s41562-018-0315-6

    2018 doi

  33. [41]

    Response rates in telephone surveys have resumed their decline

    Courtney Kennedy and Hannah Hartig. Response rates in telephone surveys have resumed their decline. Pew Research Center, February 2019. URL https://www.pewresearch.org/short-reads/2019/02/27/response-rates-in-telephone-surveys-have-resumed-their-decline/

    2019

  34. [42]

    Key things to know about u.s

    Courtney Kennedy and Scott Keeter. Key things to know about u.s. election polling in 2024. Pew Research Center, August 2024. URL https://www.pewresearch.org/short-reads/2024/08/28/key-things-to-know-about-us-election-polling-in-2024/

    2024

  35. [43]

    An evaluation of the 2016 election polls in the united states

    Courtney Kennedy, Mark Blumenthal, Scott Clement, Joshua D Clinton, Claire Durand, Charles Franklin, Kyley McGeeney, Lee Miringoff, Kristen Olson, Douglas Rivers, Lydia Saad, G Evans Witt, and Christopher Wlezien. An evaluation of the 2016 election polls in the united states. ...

    2016 doi

  36. [44]

    How public polling has changed in the 21st century

    Courtney Kennedy, Dana Popky, and Scott Keeter. How public polling has changed in the 21st century. Pew Research Center, April 2023. URL https://www.pewresearch.org/methods/2023/04/19/how-public-polling-has-changed-in-the-21st-century/

    2023

  37. [45]

    How money affects elections

    Maggie Koerth. How money affects elections. FiveThirtyEight, September 2018. URL https://fivethirtyeight.com/features/money-and-elections-a-complicated-love-story/

    2018

  38. [46]

    Larrick, Albert E

    Richard P. Larrick, Albert E. Mannes, and Jack B. Soll. The social psychology of the wisdom of crowds. In Social Judgement and Decision Making, page 16. Psychology Press, 1 edition, 2012. URL https://www.taylorfrancis.com/chapters/edit/10.4324/9780203854150-17/social-psycholog...

    2012 doi

  39. [47]

    Lauderdale, Delia Bailey, Jack Blumenau, and Douglas Rivers

    Benjamin E. Lauderdale, Delia Bailey, Jack Blumenau, and Douglas Rivers. Model-based pre-election polling for national and sub-national outcomes in the us and uk. International Journal of Forecasting, 36 0 (2): 0 399--413, April 2020. ISSN 0169-2070. doi:10.1016/j.ijforecast.2...

    2020 doi

  40. [48]

    Money in Politics: How Does It Affect Election Outcomes? SAGE Open, 14 0 (4): 0 21582440241279659, October 2024

    Thanh Le, Ilke Onur, Rubayat Sarwar, and Erkan Yalcin. Money in Politics: How Does It Affect Election Outcomes? SAGE Open, 14 0 (4): 0 21582440241279659, October 2024. ISSN 2158-2440. doi:10.1177/21582440241279659. URL https://doi.org/10.1177/21582440241279659

    2024 doi

  41. [49]

    Donald trump and kamala harris spend \ 3.5bn in most expensive presidential election

    Sam Learner. Donald trump and kamala harris spend \ 3.5bn in most expensive presidential election. Financial Times, November 2024. URL https://www.ft.com/content/c3613e1b-c15d-47b8-a502-400c4114c09e

    2024

  42. [50]

    Analyzing the effect of regional modality in polling surveys: A case study of the 2020 u.s

    Eric Levy, ERIC Chiang, and Ting Levy. Analyzing the effect of regional modality in polling surveys: A case study of the 2020 u.s. presidential election results in florida. American Behavioral Scientist, page 00027642231166879, May 2023. ISSN 0002-7642. doi:10.1177/00027642231...

    2020 doi

  43. [51]

    The presidential election, us social policy, and whether canadians should care

    Ted Marmor. The presidential election, us social policy, and whether canadians should care. Policy Options, pages 37--41, 2004

    2004

  44. [52]

    Survey reveals 1 in 5 americans own crypto, with 76\ CryptoSlate, April 2025

    Gino Matos. Survey reveals 1 in 5 americans own crypto, with 76\ CryptoSlate, April 2025. URL https://cryptoslate.com/survey-reveals-1-in-5-americans-own-crypto-with-76-reporting-personal-benefits/

    2025

  45. [53]

    Us appeals court clears kalshi to restart elections betting

    Laura Matthews. Us appeals court clears kalshi to restart elections betting. Reuters, October 2024. URL https://www.reuters.com/legal/us-federal-court-upholds-ruling-letting-kalshiex-list-election-betting-contracts-2024-10-02/

    2024

  46. [54]

    Calling the 2024 presidential race state by state

    Nicole Meir. Calling the 2024 presidential race state by state. The Associated Press, November 2024. URL https://www.ap.org/the-definitive-source/behind-the-news/calling-the-2024-presidential-race-state-by-state/

    2024

  47. [55]

    Psychological strategies for winning a geopolitical forecasting tournament

    Barbara Mellers, Lyle Ungar, Jonathan Baron, Jaime Ramos, Burcu Gurcay, Katrina Fincher, Sydney E Scott, Don Moore, Pavel Atanasov, Samuel A Swift, Terry Murray, Eric Stone, and Phillip E Tetlock. Psychological strategies for winning a geopolitical forecasting tournament. Psyc...

    2014

  48. [56]

    Comparing two types of online survey samples

    Andrew Mercer and Arnold Lau. Comparing two types of online survey samples. Pew Research Center, September 2023. URL https://www.pewresearch.org/methods/2023/09/07/comparing-two-types-of-online-survey-samples/

    2023

  49. [57]

    Online opt-in polls can produce misleading results, especially for young people and hispanic adults

    Andrew Mercer, Courtney Kennedy, and Scott Keeter. Online opt-in polls can produce misleading results, especially for young people and hispanic adults. Pew Research Center, March 2024. URL https://www.pewresearch.org/short-reads/2024/03/05/online-opt-in-polls-can-produce-misle...

    2024

  50. [58]

    Granger Morgan

    M. Granger Morgan. Use (and abuse) of expert elicitation in support of decision making for public policy. Proceedings of the National Academy of Sciences, 111 0 (20): 0 7176--7184, May 2014. doi:10.1073/pnas.1319946111. URL https://www.pnas.org/doi/abs/10.1073/pnas.1319946111

    2014 doi

  51. [59]

    How prediction markets saw something the polls and pundits didn't cnn business

    Allison Morrow. How prediction markets saw something the polls and pundits didn't cnn business. CNN Business, November 2024. URL https://www.cnn.com/2024/11/08/business/polymarket-election-trump-nightcap

    2024

  52. [60]

    The polls and their context

    Roger Mortimore and Anthony Wells. The polls and their context. In Dominic Wring, Roger Mortimore, and Simon Atkinson, editors, Political Communication in Britain: Polling, Campaigning and Media in the 2015 General Election, pages 19--38. Springer International Publishing, Cham, 2017

    2015

  53. [61]

    How the u.s

    Erica Moser. How the u.s. presidential campaigns are targeting digital ads by zip code. Penn Today, October 2024. URL https://penntoday.upenn.edu/news/pores-andrew-arenge-how-us-presidential-campaigns-are-targeting-digital-ads-zip-code

    2024

  54. [62]

    Porter, and Paul Freedman

    Moeen Mostafavi, Maria Phillips, Yichen Jiang, Michael D. Porter, and Paul Freedman. A tale of two metrics: Polling and financial contributions as a measure of performance. In IEEE International Systems Conference, March 2021

    2021

  55. [63]

    Jd vance says cryptocurrency can help everyday americans

    Joe Murphy. Jd vance says cryptocurrency can help everyday americans. here's how many actually use it. NBC News, May 2025. URL https://www.nbcnews.com/data-graphics/crypto-how-many-americans-use-jd-vance-bitcoin-rcna209463

    2025

  56. [64]

    What the 2024 polls got right --- and what they got wrong

    Mark Murray. What the 2024 polls got right --- and what they got wrong. NBC News, November 2024. URL https://www.nbcnews.com/politics/2024-election/2024-polls-got-right-got-wrong-trump-harris-rcna181105

    2024

  57. [65]

    Henrik Olsson, W. B. de Bruin , M. Galesic, and D. Prelec. Harvesting the wisdom of crowds for election predictions using the bayesian truth serum, 2019, December 27

    2019

  58. [66]

    A mystery \ 30 million wave of pro-trump bets has moved a popular prediction market, 2024

    Alexander Osipovich. A mystery \ 30 million wave of pro-trump bets has moved a popular prediction market, 2024. URL https://www.wsj.com/finance/betting-election-pro-trump-ad74aa71

    2024

  59. [67]

    Three things to look for in any election poll

    Kaleigh Rogers. Three things to look for in any election poll. The New York Times, September 2024. ISSN 0362-4331. URL https://www.nytimes.com/2024/09/28/us/elections/election-polls.html

    2024

  60. [68]

    Ban the polls: Uk election strategists slam `inaccurate' voter surveys

    Tim Ross. Ban the polls: Uk election strategists slam `inaccurate' voter surveys. POLITICO, November 2024. URL https://www.politico.eu/article/ban-polls-uk-election-strategists-slam-inaccurate-voter-surveys-morgan-mcsweeney-keir-starmer-isaac-levido/

    2024

  61. [69]

    Political bias in prediction markets: Evidence from the u.s

    Saman Saama. Political bias in prediction markets: Evidence from the u.s. 2024 presidential elections. Master's thesis, Aalto Universit School of Business, March 2025

    2024

  62. [70]

    Trump's polymarket odds spike as elon musk touts presidential betting predictor

    Derek Saul. Trump's polymarket odds spike as elon musk touts presidential betting predictor. Forbes, October 2024. URL https://www.forbes.com/sites/dereksaul/2024/10/07/trumps-election-odds-spike-on-polymarket-as-musk-touts-election-betting-site/

    2024

  63. [71]

    D. G. Schwartz. Roll the Bones: The History of Gambling, volume 494. Gotham Books, New York, 2006

    2006

  64. [72]

    Exclusive: Election betting site polymarket gives trump a 67\ Fortune Crypto, October 2024

    Leo Schwartz. Exclusive: Election betting site polymarket gives trump a 67\ Fortune Crypto, October 2024. URL https://fortune.com/crypto/2024/10/30/polymarket-trump-election-crypto-wash-trading-researchers/

    2024

  65. [73]

    Scott and Hal R

    Steven L. Scott and Hal R. Varian. Predicting the present with bayesian structural time series. International Journal of Mathematical Modelling and Numerical Optimisation, 2013

    2013

  66. [74]

    What's behind Trump's Surge in Prediction Markets? Silver Bulletin, October 2024

    Nate Silver. What's behind Trump's Surge in Prediction Markets? Silver Bulletin, October 2024. URL https://www.natesilver.net/p/whats-behind-trumps-surge-in-prediction

    2024

  67. [75]

    Final silver bulletin 2024 presidential election forecast

    Nate Silver and Eli McKown-Dawson . Final silver bulletin 2024 presidential election forecast. Silver Bulletin, November 2024. URL https://www.natesilver.net/p/nate-silver-2024-president-election-polls-model

    2024

  68. [76]

    Soll and Richard P

    Jack B. Soll and Richard P. Larrick. Strategies for revising judgment: How (and how well) people use others' opinions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35 0 (3): 0 780--805, 2009. ISSN 1939-1285. doi:10.1037/a0015145

    2009 doi

  69. [77]

    Polymarket nailed the 2024 election call---then came the fbi

    Janya Sundar. Polymarket nailed the 2024 election call---then came the fbi. Fast Company, November 2024. URL https://www.fastcompany.com/91229325/fbi-raid-polymarket-election-betting-market-manipulation-us

    2024

  70. [78]

    The Wisdom of Crowds: Why the Many Are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations

    James Surowiecki. The Wisdom of Crowds: Why the Many Are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations. Random House, New York, NY, 2004

    2004

  71. [79]

    Public opinion polling by newspapers in the presidential election campaign of 1824

    James W Tankard, Jr. Public opinion polling by newspapers in the presidential election campaign of 1824. Journalism Quarterly, 49 0 (2): 0 361--365, June 1972. ISSN 0022-5533. doi:10.1177/107769907204900219. URL https://doi.org/10.1177/107769907204900219

    1972 doi

  72. [80]

    Prediction markets

    Justin Wolfers and Eric Zitzewitz. Prediction markets. Journal of Economic Perspectives, 18 0 (2): 0 107--126, June 2004. ISSN 0895-3309. doi:10.1257/0895330041371321

    2004 doi

  73. [81]

    The us presidential election shows the limits of the `science' of polling - impact of social sciences

    Jeanna Sheehan Zaino. The us presidential election shows the limits of the `science' of polling - impact of social sciences. London School of Economics and Political Science Impact Blog, January 2025. URL https://blogs.lse.ac.uk/impactofsocialsciences/2025/01/16/the-us-preside...

    2025

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.