Citizen forecasting 2020 : a state-by-state experiment

The leading approaches to scientific election forecasting in the United States consist of structural models, prediction markets and opinion polling. With respect to the last, by far the dominant mode relies on vote intention polling, e.g., “If the election were held tomorrow, who would you vote for?” However, there exists an abiding opinion polling strategy that shows a good deal of promise—citizen forecasting. That is, rather than query on vote intention, query on vote expectation, e.g., “Who do you think will win the upcoming election?” This approach has been pursued most extensively in the United Kingdom (Murr 2016) and the United States (LewisBeck and Tien 1999). Recent performance evaluations have shown that in the United Kingdom vote expectations clearly offer more predictive accuracy than vote intentions (Murr et al. forthcoming) and that in the United States vote expectations appear to be superior to an array of rival forecasting tools (Graefe 2014). However, the timing of the data collection has forced most of the studies using citizen forecasts to forecast elections ex post, i.e., after they occurred. Indeed, to date, there are only two ex ante citizen forecasting papers to have appeared before a national election (Lewis-Beck and Stegmaier 2011; Murr 2016). Both these efforts forecasted British General Elections, with Murr (2016) relatively most accurate among 12 academic forecasts (Fisher and Lewis-Beck 2016). With respect to the United States, the case at hand, none of the work has been ex ante and all studies have focused on the national level, with the exception of a lone study carried out at the state level (Murr, 2015). The latter point seems critical, since the final selection of the president takes place in the Electoral College. The citizen forecasting research here stands unique, being ex ante and focusing on the states. Utilizing survey questions on Amazon.com’s Mechanical Turk (MTurk), administered in July, we render forecasts for the November 2020 presidential contest. This experiment, which has been conducted before-the-fact and looks at the states, provides a strong test of the quality of citizen forecasting in this American election

Asking people in each state who they think will win suggests that the presidential election may be very close.

In new survey research, Andreas E. Murr and Michael S. Lewis-Beck asked people in each of the 50 states and Washington DC who they thought would win their state in the presidential election. Adding up their raw data, their survey suggests that President Trump will win re-election next week with 320 electoral votes to 218 for former Vice President, Democrat Joe Biden

Vote expectations versus vote intentions : rival forecasting strategies

Are ordinary citizens better at predicting election results than conventional voter intention polls? We address this question by comparing eight forecasting models for British general elections: one based on voters’ expectations of who will win and seven based on who voters themselves intend to vote for (including “uniform national swing model” and “cube rule” models). The data come from ComRes and Gallup polls as well as the Essex Continuous Monitoring Surveys, 1950 – 2017, yielding 449 months with both expectation and intention polls. The large sample size allows us to compare the models’ prediction accuracy not just in the months prior to the election, but over the years leading up to it. In predicting both the winning party and parties’ seat shares, we find that vote expectations outperform vote intent ions models. Vote expectations thus appear an excellent tool for predicting the winning party and its seat share

Using citizen forecasts we predict that with 362 electoral votes, Hillary Clinton will be the next president

Who will be the next US President? Some commentators have argued that voter intention polls are flawed because it is difficult to know who will actually turn out to vote. To get around this problem, Andreas Murr, Mary Stegmaier, and Michael S. Lewis-Beck use citizen forecasts, a “who do you think will win” survey question, to predict the election result

Citizen forecasting 2019: a big win for the Conservatives

The recent failures of voter intention polls to predict UK election results has led to public scepticism about the usefulness of polls. Andreas Murr, Mary Stegmaier, and Michael S. Lewis-Beck deploy an alternative approach, which focuses on which party opinion poll respondents expect to win the election (rather than just on their voting intentions). This ‘voter expectations’ model predicts a solid Johnson majority, with the Conservatives gaining 360 seats, and Labour only 190

Citizen forecasts of the 2021 German election

There are various scientific approaches to election forecasting: poll aggregation, structural models, electronic markets, and citizen forecasting. With respect to the German case, the first two approaches—polls and models—perhaps have been the most popular. However, relatively little work has been done deploying citizen forecasting (CF), the approach described in this article. In principle, CF differs considerably from other methods and appears, on its face, quite simple. Before an election, citizens are asked in a national survey who they think will win. As the percentage of expectations for party X increases, the likelihood of an X win is judged to be higher. The method has been applied regularly with success in other established democracies, such as the United Kingdom and the United States

Citizen forecasting suggests Macron will win a comfortable victory over Marine Le Pen

With the second round of the French presidential election just days away, what can election forecasts tell us about the likely result? Drawing on a citizen forecasting model, Andreas Murr, Yannick Dufresne, Justin Savoie, Bruno Jérôme and Michael S. Lewis-Beck write that Emmanuel Macron looks set to win a comfortable victory over Marine Le Pen

Biological Maturity Status in Elite Youth Soccer Players: A Comparison of Pragmatic Diagnostics With Magnetic Resonance Imaging

The influence of biological maturity status (BMS) on talent identification and development within elite youth soccer is critically debated. During adolescence, maturity-related performance differences within the same age group may cause greater chances of being selected for early maturing players. Therefore, coaches need to consider players' BMS. While standard methods for assessing BMS in adolescents are expensive and time-consuming imaging techniques (i.e., X-ray and MRI), there also exist more pragmatic procedures. This study aimed to evaluate commonly used methods to assess BMS within a highly selected sample of youth soccer players. A total of N = 63 elite male soccer players (U12 and U14) within the German Soccer Association's talent promotion program completed a test battery assessing BMS outcomes. Utilizing MRI diagnostics, players' skeletal age (SAMRI) was determined by radiologists and served as the reference method. Further commonly used methods included skeletal age measured by an ultrasound device (SAUS), the maturity offset (MOMIR), and the percentage of adult height (PAHKR). The relation of these alternative BMS outcomes to SAMRI was examined using different perspectives: performing bivariate correlation analyses (1), modeling BMS as a latent variable (BMSlat) based on the multiple alternative diagnostics (2), and investigating individual differences in agreement (3). (1) Correlations of SAMRI and the further BMS variables ranked from r = 0.80 to r = 0.84 for the total sample and were lower for U12 (0.56 ≤ r ≤ 0.66), and U14 (0.61 ≤ r ≤ 0.74) (2). The latent structural equation modeling (SEM) (R 2 = 51%) revealed a significant influence on BMSlat for MOMIR (β = 0.51, p <0.05). The additional contribution of PAHKR (β = 0.27, p = 0.06) and SAUS (β = -0.03, p = 0.90) was rather small (3). The investigation of individual differences between the reference method and alternative diagnostics indicated a significant bias for MOMIR (p <0.01). The results support the use of economical and time-efficient methods for assessing BMS within elite youth soccer. Bivariate correlation analyses as well as the multivariate latent variable approach highlight the measures' usefulness. However, the observed individual level differences for some of the utilized procedures led to the recommendation for practitioners to use at least two alternative assessment methods in order to receive more reliable information about players' BMS within the talent promotion process

Biological Maturity Status in Elite Youth Soccer Players: A Comparison of Pragmatic Diagnostics With Magnetic Resonance Imaging

The influence of biological maturity status (BMS) on talent identification and development within elite youth soccer is critically debated. During adolescence, maturity-related performance differences within the same age group may cause greater chances of being selected for early maturing players. Therefore, coaches need to consider players' BMS. While standard methods for assessing BMS in adolescents are expensive and time-consuming imaging techniques (i.e., X-ray and MRI), there also exist more pragmatic procedures. This study aimed to evaluate commonly used methods to assess BMS within a highly selected sample of youth soccer players. A total of N = 63 elite male soccer players (U12 and U14) within the German Soccer Association's talent promotion program completed a test battery assessing BMS outcomes. Utilizing MRI diagnostics, players' skeletal age (SAMRI) was determined by radiologists and served as the reference method. Further commonly used methods included skeletal age measured by an ultrasound device (SAUS), the maturity offset (MOMIR), and the percentage of adult height (PAHKR). The relation of these alternative BMS outcomes to SAMRI was examined using different perspectives: performing bivariate correlation analyses (1), modeling BMS as a latent variable (BMSlat) based on the multiple alternative diagnostics (2), and investigating individual differences in agreement (3). (1) Correlations of SAMRI and the further BMS variables ranked from r = 0.80 to r = 0.84 for the total sample and were lower for U12 (0.56 ≤ r ≤ 0.66), and U14 (0.61 ≤ r ≤ 0.74) (2). The latent structural equation modeling (SEM) (R 2 = 51%) revealed a significant influence on BMSlat for MOMIR (β = 0.51, p <0.05). The additional contribution of PAHKR (β = 0.27, p = 0.06) and SAUS (β = -0.03, p = 0.90) was rather small (3). The investigation of individual differences between the reference method and alternative diagnostics indicated a significant bias for MOMIR (p <0.01). The results support the use of economical and time-efficient methods for assessing BMS within elite youth soccer. Bivariate correlation analyses as well as the multivariate latent variable approach highlight the measures' usefulness. However, the observed individual level differences for some of the utilized procedures led to the recommendation for practitioners to use at least two alternative assessment methods in order to receive more reliable information about players' BMS within the talent promotion process

Applicability and usage of dose mapping/accumulation in radiotherapy

Dose mapping/accumulation (DMA) is a topic in radiotherapy (RT) for years, but has not yet found its widespread way into clinical RT routine. During the ESTRO Physics workshop 2021 on "commissioning and quality assurance of deformable image registration (DIR) for current and future RT applications", we built a working group on DMA from which we present the results of our discussions in this article. Our aim in this manuscript is to shed light on the current situation of DMA in RT and to highlight the issues that hinder consciously integrating it into clinical RT routine. As a first outcome of our discussions, we present a scheme where representative RT use cases are positioned, considering expected anatomical variations and the impact of dose mapping uncertainties on patient safety, which we have named the DMA landscape (DMAL). This tool is useful for future reference when DMA applications get closer to clinical day-to-day use. Secondly, we discussed current challenges, lightly touching on first-order effects (related to the impact of DIR uncertainties in dose mapping), and focusing in detail on second-order effects often dismissed in the current literature (as resampling and interpolation, quality assurance considerations, and radiobiological issues). Finally, we developed recommendations, and guidelines for vendors and users. Our main point include: Strive for context-driven DIR (by considering their impact on clinical decisions/judgements) rather than perfect DIR; be conscious of the limitations of the implemented DIR algorithm; and consider when dose mapping (with properly quantified uncertainties) is a better alternative than no mapping