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| Analytics rankings of North American clubs in the MLB, NBA, NFL, and NHL by ESPN The Magazine in 2015.
Source publication
This paper describes developments in the player-side analytics in major team sports. We take a decision-making lens to the role of analytics in player decisions by general managers and coaches. We outline key accelerators and inhibitors to the wider adoption and acceptance of data analytics playing a greater role in the decisions of clubs.
Contexts in source publication
Context 1
... on their analysis, clubs were also placed into one of five categories: "All-In, " "Believers, " "OneFoot-In, " "Skeptics, " or "Non-Believers." A summary of this data is shown as Figure 4, which presents a summary of the clubs in each category by league, as well as the Top 10 and the Bottom 10. The ESPN study highlighted the vast differences across clubs in their perceived level of and commitment to analytics being an important part of player side decision making. ...
Context 2
... over media criticism can lead to some highly risk-averse coaches taking decisions that line up with "conventional wisdom" and not basing their decision with guidance from analytics because of fear it may have a bad outcome. Figure 4 highlights that even in 2015 a sizable number of teams were classified in the ESPN Analytics rankings as either Skeptical (27/122 = 22%) or NonBelievers (10/122 = 8%) despite much evidence that it was an important part of player-side decision making. ...
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Citations
... Data analytics in areas such as playing talent identification, in-game decision-making and health and fitness protocols have been differentially embraced and differentially executed by different GMs and coaches, which offers opportunities to examine the impact of this differential adoption on team performance as well as GM and coach longevity. Another area here is innovations in playing styles, such as the marked shift in the NBA to players who have high three-point shooting ability (see Foster et al., 2021). However, not all of the "so-called data analytic innovations" or "new playing strategies" result in their promised outcomes. ...
Purpose
This article seeks to enhance the understanding as to why head coaches and general managers (GMs) in the National Basketball Association (NBA) and the National Football League (NFL) exit from their positions.
Design/methodology/approach
Three hypotheses were investigated using a series of quantitative and qualitative data from the past 30 years. The samples analyzed are comprised of 891 GM and coach annual observations for the NBA clubs and 949 GM and coach observations for the NFL clubs. Analyses include a logit analysis for coach exit/retention, a logit analysis for GM exit/retention and textual analysis via topic modeling via latent Dirichlet allocation.
Findings
Results show a correlation between a coach exiting and a GM exiting simultaneously, thus amplifying the importance of these two roles in enhancing or destroying the success of a club and supporting the need for a deeper understanding of both roles, particularly the GM. The results further highlight cultural differences across clubs in terms of GM and coach turnover, a factor that often is heavily influenced by club ownership.
Originality/value
The results support the role of owners in exits, confirm the importance of winning in avoiding an exit, find a high level of interrelationship between GM and coach exits and show that past culture of firings influences future exit decisions.
... Data-driven decision-making (DDDM), or the process of using facts, metrics, or data, rather than observation or intuition alone, to guide strategic decisions (Provost & Fawcett, 2013), is not new in sport, particularly regarding athletic performance (e.g., Foster et al., 2021;Leung & Joseph, 2014). Recently, the growth of supporting tools (e.g., customer relationship management software, merchandise/team store software) has transformed the way sports are promoted and managed (Fried & Mumcu, 2016), especially in sport consumer behavior (Wedel & Kannan, 2016) and strategic communications (Wiesenberg et al., 2017). ...
... Multiple sports associations and companies have implemented data analytics for player selection and team training, including the US National Basketball Association (NBA), Spanish Football Federation, and German Football Association. [97][98][99] Today, most elite teams use player performance metrics along with GPS, semi-automatic cameras or radio-frequency systems, alone or in combination, to track training and match performance. ...
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decisions for the long and medium-term future.
