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This article explores the dilemma of choosing talent using NBA data from 1987 to 2003. We find there is much uncertainty in selecting talent. If superstars are found, they are usually identified early. However, more false positives exist than correct decisions with high draft picks. Our results suggest the dilemma of choosing talent is not so much...
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... formula provides a measure of quality that is based upon performance in all aspects of the game. In Table 1, we report the mean, median, standard deviation, and highest level of the efficiency rating. We find in all cases the mean is higher than the median, suggesting a right skewed distribution of talent. ...
Citations
... While some demonstrated the strength of the draft process, 34,35 many authors questioned the ability of the draft to accurately identify "talent" and for those in decision-making positions to act in a rational and logical manner. 8,22,24,30,38 Particularly, the general findings indicate that first-round draftees do, in fact, go on to outperform their peers in the future. However, the large discrepancy in future performance in subsequent rounds, which form the majority of the draft, suggests decision makers' abilities to accurately find talent past the initial rounds is suboptimal. ...
In theory, professional sport ‘entry drafts’ are designed to promote parity by granting poorly performing teams with early selections and winning teams with later selections. While this process has intentions to ‘level the playing field’, mixed findings exist in the literature. The aim of this review is to identify and synthesize the literature examining the efficacy of the draft for professional, North American sport leagues. A systematic review of four databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐analyses) guidelines. Full‐text articles containing relevant data on the draft system for the four major professional North American sports were identified. Further restrictions were made to include articles focusing on a specific outcome regarding future success (i.e., whether the draft related to a measure of future performance). The search returned 10,962 records and after screening, 18 articles were synthesized. Of the articles examined, the measures of future success with relation to draft order were (a) career length and/or number of games played at the majors (n=8), (b) future performance statistics at the professional level (n=5), (c) change in winning percentage and/or number of wins produced (n=3), (d) financial compensation (n=1), and (e) a combination measures (a) to (d), (n=1). Most commonly, the first/early rounds most accurately predicted future measures of success (i.e., number of games played, singing bonuses and playing statistics) across sports. The middle and late rounds were less accurate, with the degree of accuracy increasing slightly in the last rounds. This review highlights several opportunities to better understand the draft process (e.g., potential improvements in middle round picks) and emphasizes the need for more research on analyzing and scrutinizing the draft.
... In fact, athletic measurement has been widely used with future sport performance success purposes [4]. However, research on National Basketball Association (NBA) and National Football League (NFL) players has shown that higher picks in the draft will not always have successful careers [5,6]. Moreover, research has pointed out that the best teams recruit young players according to their chronological age [7], meaning that more mature players have better opportunities of being scouted. ...
The NBA Draft Combine includes a series of standardized measurements and drills that provide NBA teams with an opportunity to evaluate players. The purpose of this research was to identify the Combine tests that explain draft position and future performance in the NBA rookie season. Variables were selected from the previous categories of anthropometric measurements and strength and agility tests. A regression analysis was carried out. Combine variables, anthropometric and agility/strength variables were analyzed to explore their effect on draft position. Moreover, correlation analyses were performed to identify relationships among: (i) Combine anthropometric and strength and agility measures and game performance through game related statistics; and (ii) the draft position and game performance using Pearson’s correlation coefficients. Results show that the Combine test does not predict draft position, with the exception of hand width and height in frontcourt players, and standard vertical jump and running vertical jump. Future performance indicators were explained by several Combine tests in all players.
... However, TID presents challenges due to a range of variables including non-linear development processes [2,3], non-genetic factors restraining performance (i.e., socio-economic) [4], subconscious biases towards specific skillsets [5] and unstable physical characteristics during maturation [6]. Thus, the unidimensional constructs often assessed in such combines may contribute to poor selection outcomes [7][8][9]. As such, it is important to establish the explanatory power of draft combine performance for predicting a player's long-term career success at the elite level. ...
... Forecasting career outcomes through draft combine performance has been undertaken across various sports (e.g. American football, basketball, ice hockey) with mixed results [8,[10][11][12][13][14]. Weak associations between draft combine testing results and career performance have been observed within the National Basketball Association (NBA), National Football League (NFL) and National Hockey League (NHL) [7][8][9][10][12][13][14][15][16]. Importantly, nuance exists within these findings, as certain combine performance variables are strongly associated to specific position types (e.g., running vertical jump and an NFL wide receiver career performance) [7,8,[13][14][15]. ...
... American football, basketball, ice hockey) with mixed results [8,[10][11][12][13][14]. Weak associations between draft combine testing results and career performance have been observed within the National Basketball Association (NBA), National Football League (NFL) and National Hockey League (NHL) [7][8][9][10][12][13][14][15][16]. Importantly, nuance exists within these findings, as certain combine performance variables are strongly associated to specific position types (e.g., running vertical jump and an NFL wide receiver career performance) [7,8,[13][14][15]. Whilst combine performance may not relate to career performance outcomes, it has been shown to accurately explain draft selection order, their salary and signing bonus [10]. ...
Physical testing-based draft combines are undertaken across various sporting codes to inform talent selection. To determine the explanatory power of the Australian football league (AFL) draft combine, participants drafted between 1999–2016 (n = 1488) were assessed. Testing performance, draft selection order and playing position, AFL matches played, AFL player ranking points and AFL player rating points were collected as career outcomes. Boosted regression tree analysis revealed that position and draft selection order were the most explanatory variables of career outcomes. Linear modelling based on testing results is able to explain 4% of matches played and 3% of in-game performance measures. Each individual combine test explained <2% of the matches played outcome. Draft selection order demonstrated mixed results for career outcomes relative to playing position. For instance, key forwards and draft selection order were observed as a slight negative relationship using the AFL Player Ranking points career outcome measure. These findings indicate that the AFL draft combine is a poor measure for informing talent selection, thus providing minimal utility for the practices investigated in this study.
... He won five NBA championships, two NBA final MVPs, and four allstar NBA MVPs [13]. Several critics consider the black mamba to be the closest player to being as great as MJ [14]. On the other hand, LeBron Raymone James, also known as King James, was born in 1984. ...
Objectives: Michael Jordan, Kobe Bryant or LeBron James; each one of those top athletes is undoubtedly considered to be the best of his own basketball era. However, many professional athletes and sports critics still do consider Michael Jeffery Jordan to be the greatest basketball player of all time. The purpose of this study is to derive a statistical inference based on data derived from the surface web in relation to the most popular basketball player, the GOAT.
Materials and methods: This study is based on data crunching of; Google Trends database (1) and the surface web (2), literature databases (3), grey literature (4), sports websites (5), in addition to media networks (5). An internet snapshot was taken for the trends database with a subsequent retrospective analysis of data retrieved from the past five years (2012-2017).
Results: There was sharp (acute and intermittent) rise in the attentiveness of web users towards each of the three players. However, those were more noticeable for LeBron James. These were correlated with milestone events for each athlete career including; Kobe Bryant final NBA game, and LeBron opting out from his contract with Miami Heat. There was a significant difference in between LeBron’s popularity over both Jordan and Bryant (p-value<0.001). Furthermore, geo-mapping of data revealed that the top countries of highest attentiveness were; the Philippines, Dominican Republic, US, Canada and Hong Kong.
Conclusion: For the past half-decade, it seems that the attentiveness of surface web users was more focused towards LeBron James than to either of Kobe Bryant or Michaela Jordan. Accordingly, LeBron is the GOAT since 2012. However, the data analysis did not go back in time prior to 2012, in which it is expected that the relative popularity of the three competitive players might be different.
... Despite a number of performance-related measures which tried to measure the "talent" factor identified by Rosen (1981) as the key element in order to explain the disproportionate salaries of superstar players (see for example Brown, Spiro & Keenan, 1991;Burdekin & Idson, 1991;Groothuis, Hill & Perri, 2009;Hausman & Leonard, 1997;Scott, Long & Somppi, 1985), the lack of an unambiguous performance indicator and the variety of roles in soccer rendered this approach to identify superstars extremely difficult. We therefore followed Hakes and Turner (2009) example and defined as superstars the players belonging to the fifth quintile in salary distribution. ...
Athletic career is defined as “multiyear sport activity, voluntarily chosen by the person and aimed at achieving his or her individual peak in athletic performance in one or several sport events” (Alfermann & Stambulova, 2007; p. 713). The career of athletes has received considerable attention by sport psychology and medicine (e.g. Stambulova, Alfermann, Statler & Côté, 2009; Wylleman, Alfermann & Lavallee, 2003; Willeman & Lavallee, 2003). On average, athletes start their career at the age of 7 to 10 years and sometimes even earlier, depending on the type of sport. After the age of 27 their sport-related performance starts progressively to decrease due to the ageing process (Stambulova, Stephan & Jäphag, 2007), and by their mid-thirties they retire. However, the trajectory described by this process poorly fits the career of some athletes who extended their career over the “regular” transition points indicated by the literature, at the same time continuing to compete at professional level and earning rich salaries. These athletes are characterized by being superstar players, such as talented performers who are in the highest percentiles of the salary distribution in their occupational market (Rosen, 1981). Therefore, even if they are entering the latter part of their career (when the ageing process decreases their athletic performances, thus reducing their job alternatives) such athletes have, possibly for the last time, good career opportunities, due to their individual characteristics (Forrier, Sels & Stynen, 2009). For instance, in the case of team sports, they may join a top team in order to achieve relevant sport-related results (e.g. win a national or international competition), or sign a contract with lower-tier teams, or teams competing in emerging leagues, in order to obtain more favourable per-year salary, to maximize their overall compensation, or to lengthen their athletic career. In all these cases, their career trajectory deviates from the “traditional” model, thus suggesting alternatives strategies to manage late career.
The aim of this study is to explore the job mobility of superstar soccer players who are entering the declining phase of their athletic career. Studying a sample of European soccer superstars, we will describe their last job transitions identifying a typology of individuals and their relative late career choices.
... Between 1997 and 2003, only 80% of first picks had at least one superstar season, compared to 40% of second picks, 30% of third picks and 20% of fifth picks (Groothuis, Hill & Perri, 2009). ...
... Groothuis et al. (2009) consider what happens to the probability of finding high-quality talent when the lower bound for high quality increases, talent is distributed continuously, and the signal a firm receives when it hires is the same as that used herein. They find that, the higher the level of talent desired, the smaller the probability one with a favourable signal exceeds the threshold for high talent. ...
Oyer (20074.
Oyer , P. 2007. Is there an insider advantage in getting tenure?. American Economic Review, 97: 501–5. [CrossRef], [Web of Science ®]View all references, 2008) considered the turnover of economics professors early in their careers. He found professors are more likely to move down from higher ranked schools than up from lower ranked schools. An asymmetric information model suggests this phenomenon is explained by imperfect screening at one's initial hiring. The smaller the fraction of more able individuals, and the more accurate the screening, the greater the chance downward movement exceeds upward movement.
... Sowell and Mounts (2005) investigated the overlap between age and ability, concluding that the interaction is " one of the most basic in all of economics…[i]t is at the foundation of all acts of production or utility creation. " In other studies, NBA labor market outcomes have specifically been investigated as a function of college production (Coates and Oguntimein, 2010), opportunity costs (McCann, 2004) and the problem of choosing talent in a hyper-competitive workplace (Groothuis et al., 2009). This paper adds to the literature by focusing on age of entry into the labor market as the variable of interest. ...
... with P denoting our career outcome dependent variables (MNT, PER, and A-S). Along with our variable of interest (AGE), our control variables are consistent with many of those used in the general economics literature pertaining to human capital accumulation (Rosen, 1983; Acemoglu, 1996) 13 and the relevant sports economic literature (Staw and Hoang, 1995; Eschker et al., 2004; Groothuis et al., 2009; Coates and Oguntimein, 2010; Berri et al., 2011). ...
We show that precocity, as measured by the age of entry into the elite-level professional basketball labor pool, often leads to better career outcomes. Our findings cast doubt on the on-court efficacy of the National Basketball Association's contentious age eligibility rule.
This article examines the persistent effects of early career contracts using the National Basketball Association (NBA)’s draft data over the period 1995–2019. We use regression discontinuity design (RDD) to compare the differences in career outcomes between the first-round picked rookies and the second-round picked ones. The empirical results suggest that draft rounds per se significantly influence a player’s career outcome in almost all indicators (i.e. career earnings, total points scored, and total years played). Explorations of the mechanisms suggest that differences in rookie contract length and sunk costs influence teams’ human capital investment in rookies.
