Quantifying and predicting success in show business

Individual and team performance in cricket

Advancements in technology have recently allowed us to collect and analyse large-scale fine-grained data about human performance, drastically changing the way we approach sports. Here, we provide the first comprehensive analysis of individual and team performance in One-Day International cricket, one of the most popular sports in the world. We investigate temporal patterns of individual success by quantifying the location of the best performance of a player and find that they can happen at any time in their career, surrounded by a burst of comparable top performances. Our analysis shows that long-term performance can be predicted from early observations and that temporary exclusions of players from teams are often due to declining performances but are also associated with strong comebacks. By computing the duration of streaks of winning performances compared to random expectations, we demonstrate that teams win and lose matches consecutively. We define the contributions of specialists such as openers, all-rounders and wicket-keepers and show that a balanced performance from multiple individuals is required to ensure team success. Finally, we measure how transitioning to captaincy in the team improves the performance of batsmen, but not that of bowlers. Our work emphasizes how individual endeavours and team dynamics interconnect and influence collective outcomes in sports.

Quantifying hierarchy and prestige in US ballet academies as social predictors of career success

In the recent decade, we have seen major progress in quantifying the behaviors and the impact of scientists, resulting in a quantitative toolset capable of monitoring and predicting the career patterns of the profession. It is unclear, however, if this toolset applies to other creative domains beyond the sciences. In particular, while performance in the arts has long been difficult to quantify objectively, research suggests that professional networks and prestige of affiliations play a similar role to those observed in science, hence they can reveal patterns underlying successful careers. To test this hypothesis, here we focus on ballet, as it allows us to investigate in a quantitative fashion the interplay of individual performance, institutional prestige, and network effects. We analyze data on competition outcomes from 6363 ballet students affiliated with 1603 schools in the United States, who participated in the Youth America Grand Prix (YAGP) between 2000 and 2021. Through multiple logit models and matching experiments, we provide evidence that schools' strategic network position bridging between communities captures social prestige and predicts the placement of students into jobs in ballet companies. This work reveals the importance of institutional prestige on career success in ballet and showcases the potential of network science approaches to provide quantitative viewpoints for the professional development of careers beyond science.

Seller-buyer networks in NFT art are driven by preferential ties

Non-Fungible Tokens (NFTs) have recently surged to mainstream attention by allowing the exchange of digital assets via blockchains. NFTs have also been adopted by artists to sell digital art. One of the promises of NFTs is broadening participation to the art market, a traditionally closed and opaque system, to sustain a wider and more diverse set of artists and collectors. A key sign of this effect would be the disappearance or at least reduction in importance of seller-buyer preferential ties, whereby the success of an artist is strongly dependent on the patronage of a single collector. We investigate NFT art seller-buyer networks considering several galleries and a large set of nearly 40,000 sales for over 230 M USD in total volume. We find that NFT art is a highly concentrated market driven by few successful sellers and even fewer systematic buyers. High concentration is present in both the number of sales and, even more strongly, in their priced volume. Furthermore, we show that, while a broader-participation market was present in the early phase of NFT art adoption, preferential ties have dominated during market growth, peak and recent decline. We consistently find that the top buyer accounts on average for over 80% of buys for a given seller. Similar trends apply to buyers and their top seller. We conclude that NFT art constitutes, at the present, a highly concentrated market driven by preferential seller-buyer ties.

Analyzing and predicting success of professional musicians

The emergence of streaming services, e.g., Spotify, has changed the way people listen to music and the way professional musicians achieve fame and success. Classical music has been the backbone of Western media for a long time, but Spotify has introduced the public to a much wider variety of music, also opening a new venue for professional musicians to gain exposure. In this paper, we use open-source data from Spotify and Musicbrainz databases to construct collaboration-based and genre-based networks. We call genres defined in these databases primary genres. Our goal is to find the correlation between various features of each professional musician, the current stage of their career, and the level of their success in the music field. We build regression models using XGBoost to first analyze correlation between features provided by Spotify. We then analyze the correlation between the digital music world of Spotify and the more traditional world of Billboard charts. We find that within certain bounds, machine learning techniques such as decision tree classifiers and Q-based models perform quite well on predicting success of professional musicians from the data on their early careers. We also find features that are highly predictive of their success. The most prominent among them are the musicians' collaboration counts and the span of their career. Our findings also show that classical musicians are still very centrally placed in the general, genre-agnostic network of musicians. Using these models and success metrics, aspiring professional musicians can check if their chances for career success could be improved by increasing their specific success measures in both Spotify and Billboard charts.

Significant hot hand effect in the game of cricket

We investigate the predictability and persistence of individual and team performance (hot-hand effect) by analyzing the complete recorded history of international cricket. We introduce an original temporal representation of performance streaks, which is suitable to be modelled as a self-exciting point process. We confirm the presence of predictability and hot-hands across the individual performance and the absence of the same in team performance and game outcome. Thus, Cricket is a game of skill for individuals and a game of chance for the teams. Our study contributes to recent historiographical debates concerning the presence of persistence in individual and collective productivity and success. The introduction of several metrics and methods can be useful to test and exploit clustering of performance in the study of human behavior and design of algorithms for predicting success.

Nutritional Analysis of Foods and Beverages Posted in Social Media Accounts of Highly Followed Celebrities

IMPORTANCE

Celebrity social media posts engage millions of young followers daily, but the nutritional quality of foods and beverages in such posts, sponsored and unsponsored, is unknown.

OBJECTIVE

To quantify the nutritional quality of foods and beverages depicted in social media accounts of highly followed celebrities and assess whether nutritional quality is associated with post sponsorship, celebrity profession or gender, and followers' likes and comments.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study analyzed the content of food- and beverage-containing posts from Instagram (a photo- and video-sharing social media platform) accounts of 181 highly followed athletes, actors, actresses, television personalities, and music artists. Data were collected from May 2019 to March 2020.

MAIN OUTCOMES AND MEASURES

The nutritional quality of foods and beverages posted in celebrity social media accounts was rated using the Nutrient Profile Index (NPI) based on the sugar, sodium, energy, saturated fat, fiber, protein, and fruit and/or vegetable content per 100-g sample (a score of 0 indicated least healthy and 100, healthiest); foods with scores less than 64 and beverages with scores less than 70 were rated as "less healthy." Secondary outcomes were whether the nutritional quality of foods and beverages in social media posts was associated with post sponsorship, celebrity profession or gender, and followers' likes and comments. Mixed-effects regression models were used to estimate how outcomes differed across fixed effects.

RESULTS

The sample included social media accounts of 181 celebrities (66 actors, actresses, and television personalities [36.5%]; 64 music artists [35.4%]; and 51 athletes [28.2%]). A total of 102 celebrities (56.4%) were male, and the median age was 32 years (range, 17-73 years). Among 3065 social media posts containing 5180 total foods and beverages (2467 foods [47.6%]; 2713 beverages [52.4%]), snacks and sweets (920 [37.3%] of the foods) and alcoholic beverages (1375 [50.7%] of the beverages) were most common. Overall, 158 celebrity social media accounts (87.3%) earned a less healthy overall food nutrition score and 162 (89.5%) earned a less healthy overall beverage nutrition score, which would be unhealthy enough to fail legal youth advertising limits in the UK. For foods, social media posts with healthier nutrition scores were associated with significantly fewer likes (b, -0.003; 95% CI, -0.006 to 0.000; P = .04) and comments (b, -0.006; 95% CI, -0.009 to -0.003; P < .001) from followers. For beverages, nutrition scores were not significantly associated with likes (b, -0.010; 95% CI, -0.025 to 0.005; P = .18) or comments (b, -0.003; 95% CI, -0.022 to 0.016; P = .73). Only 147 food- or beverage-containing posts (4.8%) were sponsored by food- or beverage-relevant companies. Beverages in sponsored posts contained more than twice as much alcohol as those in nonsponsored posts (10.8 g [95% CI, 9.3 g to 12.3 g] per 100 g of beverage vs 5.3 g [95% CI, 4.7 g to 5.9 g] per 100 g of beverage).

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, most highly followed celebrity social media accounts depicted an unhealthy profile of foods and beverages, primarily in nonsponsored posts. These results suggest that influential depictions of unhealthy food and beverage consumption on social media may be a sociocultural problem that extends beyond advertisements and sponsorships, reinforcing unhealthy consumption norms.

Football is becoming more predictable; network analysis of 88 thousand matches in 11 major leagues

In recent years, excessive monetization of football and professionalism among the players have been argued to have affected the quality of the match in different ways. On the one hand, playing football has become a high-income profession and the players are highly motivated; on the other hand, stronger teams have higher incomes and therefore afford better players leading to an even stronger appearance in tournaments that can make the game more imbalanced and hence predictable. To quantify and document this observation, in this work, we take a minimalist network science approach to measure the predictability of football over 26 years in major European leagues. We show that over time, the games in major leagues have indeed become more predictable. We provide further support for this observation by showing that inequality between teams has increased and the home-field advantage has been vanishing ubiquitously. We do not include any direct analysis on the effects of monetization on football's predictability or therefore, lack of excitement; however, we propose several hypotheses which could be tested in future analyses.

Understanding the onset of hot streaks across artistic, cultural, and scientific careers

Across a range of creative domains, individual careers are characterized by hot streaks, which are bursts of high-impact works clustered together in close succession. Yet it remains unclear if there are any regularities underlying the beginning of hot streaks. Here, we analyze career histories of artists, film directors, and scientists, and develop deep learning and network science methods to build high-dimensional representations of their creative outputs. We find that across all three domains, individuals tend to explore diverse styles or topics before their hot streak, but become notably more focused after the hot streak begins. Crucially, hot streaks appear to be associated with neither exploration nor exploitation behavior in isolation, but a particular sequence of exploration followed by exploitation, where the transition from exploration to exploitation closely traces the onset of a hot streak. Overall, these results may have implications for identifying and nurturing talents across a wide range of creative domains.

Common Laws Driving the Success in Show Business

In this paper, we want to find out whether gender bias will affect the success and whether there are some common laws driving the success in show business. We design an experiment, set the gender and productivity of an actor or actress in a certain period as the independent variables, and introduce deep learning techniques to do the prediction of success, extract the latent features, and understand the data we use. Three models have been trained: the first one is trained by the data of an actor, the second one is trained by the data of an actress, and the third one is trained by the mixed data. Three benchmark models are constructed with the same conditions. The experiment results show that our models are more general and accurate than benchmarks. An interesting finding is that the models trained by the data of an actor/actress only achieve similar performance on the data of another gender without performance loss. It shows that the gender bias is weakly related to success. Through the visualization of the feature maps in the embedding space, we see that prediction models have learned some common laws although they are trained by different data. Using the above findings, a more general and accurate model to predict the success in show business can be built.

Impact of individual actions on the collective response of social systems

In a social system individual actions have the potential to trigger spontaneous collective reactions. The way and extent to which the activity (number of actions—A) of an individual causes or is connected to the response (number of reactions—R) of the system is still an open question. We measure the relationship between activity and response with the distribution of efficiency, a metric defined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\eta =R/A$$\end{document}η=R/A. Generalizing previous results, we show that the efficiency distribution presents a universal structure in three systems of different nature: Twitter, Wikipedia and the scientific citations network. To understand this phenomenon, we develop a theoretical framework composed of three minimal statistical models that contemplate different levels of dependence between A and R. The models not only are able to reproduce the empirical activity-response data but also can serve as baselines or null models for more elaborated and domain-specific approaches.

Brokering the core and the periphery: Creative success and collaboration networks in the film industry

In collaboration-based creative industries, such as film production, creators in the network core enjoy prestige and legitimacy that are key for creative success. However, core creators are challenged to maintain diverse access to new ideas or alternative views that often emerge from the network periphery. In this paper, we demonstrate that creators in the network core can increase the probability of their creative success by brokering peripheral collaborators to the core. The argument is tested on a dynamic collaboration network of movie creators constructed from a unique dataset of Hungarian feature films for the 1990-2009 period. We propose a new way to capture brokers' role in core/periphery networks and provide evidence that being in the core and at the same time bridging between the core and the periphery of the network significantly increases the likelihood of award winning.