In recent years, analytics has started to revolutionize the game of basketball: Quantitative analyses of the game inform team strategy; management of player health and fitness; and how teams draft, sign, and trade players. In this review, we focus on methods for quantifying and characterizing basketball gameplay. At the team level, we discuss methods for characterizing team strategy and performance, while at the player level, we take a deep look into a myriad of tools for player evaluation. This includes metrics for overall player value, defensive ability, and shot modeling, and methods for understanding performance over multiple seasons via player production curves. We conclude with a discussion on the future of basketball analytics and, in particular, highlight the need for causal inference in sports. Expected final online publication date for the Annual Review of Statistics, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Those best-positioned to profit from the proliferation of artificial intelligence (AI) systems are those with the most economic power. Extant global inequality has motivated Western institutions to involve more diverse groups in the development and application of AI systems, including hiring foreign labour and establishing extra-national data centres and laboratories. However, given both the propensity of wealth to abet its own accumulation and the lack of contextual knowledge in top-down AI solutions, we argue that more focus should be placed on the redistribution of power, rather than just on including underrepresented groups. Unless more is done to ensure that opportunities to lead AI development are distributed justly, the future may hold only AI systems which are unsuited to their conditions of application, and exacerbate inequality.
Background All life on earth has adapted to the effects of changing seasons. The general and ESKD populations exhibit seasonal rhythms in physiology and outcomes. The ESKD population also shows secular trends over calendar time that can convolute the influences of seasonal variations. We conducted an analysis that simultaneously considered both seasonality and calendar time to isolate these trends for cardiovascular, nutrition, and inflammation markers. Methods We used data from adult patients on hemodialysis (HD) in the United States from 2010 through 2014. An additive model accounted for variations over both calendar time and time on dialysis. Calendar time trends were decomposed into seasonal and secular trends. Bootstrap procedures and likelihood ratio methods tested if seasonal and secular variations exist. Results We analyzed data from 354,176 patients on HD at 2436 clinics. Patients were 59615 years old, 57% were men, and 61% had diabetes. Isolated average secular trends showed decreases in pre-HD systolic BP (pre-SBP) of 2.6 mm Hg (95% CI, 2.4 to 2.8) and interdialytic weight gain (IDWG) of 0.35 kg (95% CI, 0.33 to 0.36) yet increases in post-HD weight of 2.76 kg (95% CI, 2.58 to 2.97). We found independent seasonal variations of 3.3 mm Hg (95% CI, 3.1 to 3.5) for pre-SBP, 0.19 kg (95% CI, 0.17 to 0.20) for IDWG, and 0.62 kg (95% CI, 0.46 to 0.79) for post-HD weight as well as 0.12 L (95% CI, 0.11 to 0.14) for ultrafiltration volume, 0.41 ml/kg per hour (95% CI, 0.37 to 0.45) for ultrafiltration rates, and 3.30 (95% CI, 2.90 to 3.77) hospital days per patient year, which were higher in winter versus summer. Conclusions Patients on HD show marked seasonal variability of key indicators. Secular trends indicate decreasing BP and IDWG and increasing post-HD weight. These methods will be of importance for independently determining seasonal and secular trends in future assessments of population health.
In recent years, analytics has started to revolutionize the game of basketball: quantitative analyses of the game inform team strategy, management of player health and fitness, and how teams draft, sign, and trade players. In this review, we focus on methods for quantifying and characterizing basketball gameplay. At the team level, we discuss methods for characterizing team strategy and performance, while at the player level, we take a deep look into a myriad of tools for player evaluation. This includes metrics for overall player value, defensive ability, and shot modeling, and methods for understanding performance over multiple seasons via player production curves. We conclude with a discussion on the future of basketball analytics, and in particular highlight the need for causal inference in sports.
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