4.1 What this knowledge adds to what we already know
Ankle sprain was the most frequently occurring orthopedic injury in the past [1,6], similar to the finding of this study (Table 1), which was obtained by the frequency count method. When SNA was applied, an identical result was obtained, similar to the result in Figure 2.
The curious reader may gain awareness of the difference between the traditional counting approach and SNA when referring to Table 1 and Figure 2. The themes shown in the latter might be clearer and more structural than the former.
Injuries and illnesses affect players in terms of their sport careers and quality of life after retirement . Retirement age depends on factors, such as health, skill, and love for career. All-star players show great charisma, which can prolong their NBA careers compared with the regular players.
Most large men, including Yao Ming and Amare Stoudemire, besides those who incurred injuries on their large bodies, can competitively play until their late 30’s. Recently, for example, Tim Duncan, who retired at the age of 40 years old after having declined from his prime playing days, contributed well to his teams and also won a championship at the age of 37. However, his body started to decline considerably, preventing him from competing against spry 20-year-old center players, such as DeMarcus Cousins (New Orleans Pelicans), Rudy Gobert (Utah Jazz), Karl-Anthony Towns (Minnesota Timberwolves), and Nikola Jokic (Denver Nuggets).
We removed 470 coded events from this study to obtain less relation to injuries and illnesses. Also, teams were resting players, and it was said to protect their stars healthy on the court or prepare for heading into playoffs. Coach Pop of Spurs rested his players the most in NBA. Teams may also rest players to increase their chances of finishing lower and having more balls in the lottery . Interestingly, the results of injuries/illness in the NBA might be contaminated if resting players were analyzed in this study.
4.2 What this study contributes to current knowledge
According to the total number of injury events of the traditional method, the Bucks, Timberwolves, and Lakers have had the greatest number of injuries, whereas the Thunder, Blazers, and Pacers showed the least . Kevin Love, Jason Smith, and Eric Gordon appeared on the list of players most frequently impacted by injuries .
If we consider the weights of the most games missed on injury types, including patellofemoral inflammation, lateral ankle sprain, knee sprain, and lumbar strain , the ranks of the most injured NBA teams and players will change. The determination of the most injured teams and players and the calculation of their metrics would be more complicated and difficult.
The total number of injury events (=sum(ci), where i ranges from 1 to n injury types) can be compared with the number of citations in scientometrics. Several studies applied the total number of citations (=sum(ci), where i ranges from 1 to n on articles) to determine individual research achievements (IRA). AIF, similar to the total counts of injuries divided by the number of injury conditions suffered by players, has been used on IRA ; author-level metrics (e.g., x, h, g, and Ag) [12,13] were proposed and applied in the literature [8,9,16-19] to remove largely redundant citations and publications . Accordingly, we attempted to apply scientometrics to evaluate the most injured NBA teams and players in Figures 3 and 4, respectively. Hopefully, more discussions on the issue of using scientometrics in assessing the extent of the impact of injuries are expected in the future.
We also applied the bootstrapping method to estimate standard errors in data. As of June 20, 2019, more than 227 articles were searched by the keyword “bootstrapping” in titles. The latest article  addressed the bootstrapping method that is suitable for use in time-to-event data, similar to the current study in Table 2, which compares the differences in retirement ages between groups (i.e., injury and non-injury).
4.3 Implications of the results and suggested actions
This study has several strengths. First, we applied SNA to objectively separate the clusters, which were rarely observed in previously published papers using SNA to evaluate injury/illness types in NBA. NBA all-star players and teams were shown on Google maps with dashboards, which can be manipulated by users to check the details on their own.
Second, the total number of injuries and illnesses for each type in Table 1 can be expressed by scientometrics, with h = 35, x = 38.99, g = 67, and Ag = 67.34 (see Additional file 1 for details about the calculations). Referring to the relative weights (RWs) on DRGs, the physician’s case mix index (CMI) is traditionally computed by the total RWs divided by the case number, similar to the AIF that was defined in a previous section. Whether scientometrics can be applied to evaluate individual CMI, as we did on injured NBA teams and players, requires discussions in the future.
Third, the comparison between groups using the bootstrapping method is suitable for a data distribution fee, different from t-test and analysis of variance that requires normally distributed data.
Furthermore, we presented the research results with dashboard-type visual representations on Google maps, which were seldom observed in previous studies. The application of this animated display allows readers to easily and quickly browse more information on the internet.
4.4 Limitations and suggestions
This study features several limitations. First, caution should be exercised when interpreting and generalizing findings beyond the period from 2010 to 2018 of research as the data were extracted from the transaction events of an NBA-related website .
Second, the most injured NBA players were less meaningful in comparison because the results depending on player career were included in the data collection period. For instance, the NBA playing career for Nate Robinson and Kevin Love in Figure 4 might be longer than that of the others.
Third, the suitability of applying scientometrics to evaluate NBA teams and players impacted by injuries and illness should be discussed and examined further in the future because no such kinds of metrics have been applied to NBA nor other relevant fields besides bibliometric field.
Finally, the results in Table 2 cannot be generalized to other professional sports, such as the Major League Baseball, the National Football League, and the National Hockey League. The approaches applied in this study are recommended for other relevant research in the future.