Previous research has explored ‘community size effects’ in a multitude of sporting and regional contexts and has shown that athletes are more likely to originate from small-medium population size categories, and less likely to originate from very small or large ones. However, it is not clear whether the production of athletes is homogenous within population size categories. Place of birth data were collected for all Canadian born hockey players drafted into the National Hockey League (NHL) from 2000–2014 from British Columbia (N = 192), Alberta (N = 218), Saskatchewan and Manitoba (N = 216), Ontario (N = 561), Quebec (N = 241), and the Atlantic Provinces (N = 74). To explore variations in the production of draftees within population size categories, proportions of productive cities, population mean (μ), population standard deviation (σ), as well as minimum/maximum values of the number of draftees were calculated for the different categories (<2,500; 2,500–4,999; 5,000–9,999; 10,000–29,999; 30,000–99,999; 100,000–249,999; 250,000–499,999; 500,000–999,999; >1,000,000). In addition, the number of draftees produced per 1,000 residents (i.e., yield) was calculated for each city within all categories. Results showed substantial intra-categorical variability in NHL talent development; moreover, heterogeneity in draftee production existed in various degrees across provincial regions of Canada. Intra-categorical variability suggests that a single homogenous community size effect may not exist for Canadian NHL draftees, and that future research may benefit from exploring other environmental constraints on athlete development such as income, population density, and proximity to local sport clubs.

Athlete selection is fundamental in elite sport, occurring regularly throughout an athlete's development. Research in this area reveals the accuracy of these decisions is questionable in even the most elite sport environments and athletes are increasingly disputing these decisions as unfair and punitive. As a countermeasure to these dispute and arbitration practices, many elite sport systems have created policies where coaches must outline and stand behind the criteria used for their selection decisions. Selection criteria policies have the potential to help encourage fair selection practices by holding selectors accountable to their selection criteria, but their implementation also has the potential to wrongfully nudge selectors toward developing more defendable, but less-accurate selection practices. The paper concludes with 10 suggestions to help support practitioners when implementing selection criteria.

Inconsistencies in community size effects found between and within countries (Baker et al Eur J Sport Sci. 2009;9:329-339; Bruner et al J Sports Sci. 2011;29:1337-1344; Wattie et al J Sports Sci. 2018;36:436-444) suggest population size may not be an accurate predictor of athlete development and that other proxies of early environmental characteristics are needed. Researchers have begun to explore the influence of population density and proximity to local sport clubs on athlete development in European countries; however, similar analysis remains to be conducted in Canadian ice hockey. The current study focused on National Hockey League (NHL) draftees and explored whether population density and proximity to Canadian Hockey League teams were associated with the number of draftees produced. Linear regression analyses showed a significant positive relationship between population density and the development of draftees in all provincial regions; however, a significant negative relationship between proximity to CHL teams and NHL draftee development was observed in four out of six provincial regions (British Columbia, Ontario, Quebec, and the Atlantic Provinces). Moreover, population density appeared to be a better predictor of NHL talent development than proximity to CHL teams. Future research may benefit from exploring the effects of these two variables within population size categories, as well as between different regions within provinces.

In 2017, Sports Illustrated (SI) made headlines when their remarkable prediction from 2014 that the Houston Astros (a team in one of the lowest Major League Baseball divisional rankings) would win the World Series, came true. The less-publicised story was that in 2017, SI predicted the Los Angeles Dodgers to win the Major League Baseball (MLB) title. Assessing the forecasting accuracy of experts is critical as it explores the difficulty and limitations of forecasts and can help illuminate how predictions may shape sociocultural notions of sport in society. To thoroughly investigate SI’s forecasting record, predictions were collected from the four major North American sporting leagues (the National Football League, National Basketball Association, Major League Baseball, and National Hockey League) over the last 30 years (1988–2018). Kruskal–Wallis H Tests and Mann–Whitney U Tests were used to evaluate the absolute and relative accuracy of predictions. Results indicated that SI had the greatest predictive accuracy in the National Basketball Association and was significantly more likely to predict divisional winners compared to conference and league champions. Future work in this area may seek to examine multiple media outlets to gain a more comprehensive perspective on forecasting accuracy in sport.

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 of measures (a) to (d), (n = 1). Most commonly, the first/early rounds most accurately predicted future measures of success (ie, number of games played, signing 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.