Nba Research Papers

1. Gonzalez AM, Hoffman JR, Rogowski JP, Burgos W, Manalo E, Weise K, et al. Performance changes in NBA basketball players vary in starters vs. nonstarters over a competitive season. Journal of strength and conditioning research / National Strength & Conditioning Association. 2013;27(3):611–5. [PubMed]

2. Schelling X, Calleja-Gonzalez J, Torres-Ronda L, Terrados N. Using Testosterone and Cortisol as Biomarker for Training Individualization in Elite Basketball: A 4-Year Follow-up Study. Journal of strength and conditioning research / National Strength & Conditioning Association. 2015;29(2):368–78. [PubMed]

3. Gibson J. The ecological approach to visual perception Boston: Houghton Mifflin; 1979. 332 p.

4. Savelsbergh G, Davids K, van der Kamp J, Bennett SJ. Development of Movement Coordination in Children: Applications in the Field of Ergonomics, Health Sciences and Sport: Taylor & Francis; 2013.

5. Kauffman SA. The Origins of Order: Self Organization and Selection in Evolution: Oxford University Press; 1993.

6. Karipidis A, Fotinakis P, Taxildaris K, Fatouros J. Factors characterizing a successful performance in basketball. J Hum Movement Stud. 2001;41(5):385–97.

7. Malarranha J, Figueira B, Leite N, Sampaio J. Dynamic Modeling of Performance in Basketball. International Journal of Performance Analysis in Sport. 2013;13:377–86.

8. Sampaio J, Janeira M. Statistical analyses of basketball team performance: understanding teams’ wins and losses according to a different index of ball possessions. International Journal of Performance Analysis in Sport. 2003;3(1):40–9.

9. Kozar B, Vaughn RE, Whitfield KE, Lord RH, Dye B. Importance of Free-Throws at Various Stages of Basketball Games. Percept Motor Skill. 1994;78(1):243–8.

10. Ibanez SJ, Sampaio J, Feu S, Lorenzo A, Gomez MA, Ortega E. Basketball game-related statistics that discriminate between teams' season-long success. European journal of sport science. 2008;8(6):369–72.

11. Mikolajec K, Maszczyk A, Zajac T. Game Indicators Determining Sports Performance in the NBA. Journal of human kinetics. 2013;37:145–51. doi: 10.2478/hukin-2013-0035[PMC free article][PubMed]

12. MVP Nash Highlights All-NBA First Team 2006 [April 7, 2015]. Available from:

13. Maheswaran R, Chang Y-H, Henehan A, Danesis S. Deconstructing the Rebound with Optical Tracking Data. MIT Sloan Sports Analytics Conference 2012. 2012.

14. Goldsberry K, Weiss E. The Dwight Effect: A New Ensemble of Interior Defense Analytics for the NBA. MIT Sloan Sports Analytics Conference 2012. 2012.

15. Perše M, Kristan M, Kovačič S, Vučkovič G, Perš J. A trajectory-based analysis of coordinated team activity in a basketball game. Computer Vision and Image Understanding. 2009;113(5):612–21.

16. Buchheit M, Allen A, Poon TK, Modonutti M, Gregson W, Di Salvo V. Integrating different tracking systems in football: multiple camera semi-automatic system, local position measurement and GPS technologies. J Sport Sci. 2014;32(20):1844–57. [PubMed]

17. Ben Abdelkrim N, El Fazaa S, El Ati J. Time-motion analysis and physiological data of elite under-19-year-old basketball players during competition. British journal of sports medicine. 2007;41(2):69–75; discussion [PMC free article][PubMed]

18. Leite NM, Leser R, Goncalves B, Calleja-Gonzalez J, Baca A, Sampaio J. Effect of defensive pressure on movement behaviour during an under-18 basketball game. International journal of sports medicine. 2014;35(9):743–8. doi: 10.1055/s-0033-1363237[PubMed]

19. Sampaio J, Gonçalves B, Rentero L, Abrantes C, Leite N. Exploring how basketball players' tactical performances can be affected by activity workload. Sci Sport. 2014.

20. Aglioti SM, Cesari P, Romani M, Urgesi C. Action anticipation and motor resonance in elite basketball players. Nat Neurosci. 2008;11(9):1109–16. [PubMed]

21. Mangine GT, Hoffman JR, Wells AJ, Gonzalez AM, Rogowski JP, Townsend JR, et al. Visual Tracking Speed Is Related to Basketball-Specific Measures of Performance in NBA Players. Journal of strength and conditioning research / National Strength & Conditioning Association. 2014;28(9):2406–14. [PubMed]

22. Remmert H. Analysis of group-tactical offensive behavior in elite basketball on the basis of a process orientated model. Eur J Sport Sci. 2003;3(3):1–12.

23. Duarte A, Davids K, Chow J, Passos P, Raab M. The development of decision making skill in sport: An ecological dynamics perspective In: Duarte A, Hubert R, editors. Perspectives on Cognition and Action in Sport. United States of America: Nova Science Publishers, Inc., Suffolk; 2009. p. 157–69.

24. Pinder RA, Davids K, Renshaw I, Araujo D. Representative Learning Design and Functionality of Research and Practice in Sport. J Sport Exercise Psy. 2011;33(1):146–55. [PubMed]

25. Sampaio J, Janeira M, Ibanez S, Lorenzo A. Discriminant analysis of game-related statistics between basketball guards, forwards and centres in three professional leagues. European journal of sport science. 2006;6(3):173–8.

26. O'Donoghue P. Research Methods for Sports Performance Analysis. London: Routledge; 2010. 278 p.

27. Pedhazur E. Multiple Regression in Behavioral Research. Holt RW, editor. New York1982.

28. Norusis M. SPSS 13.0 Guide to Data Analysis. Upper Saddle-River, N.J.: Prentice Hall, Inc.; 2004.

29. Gold JI, Shadlen MN. The neural basis of decision making. Annu Rev Neurosci. 2007;30:535–74. [PubMed]

30. Davids K, Renshaw I, Glazier P. Movement models from sports reveal fundamental insights into coordination processes. Exerc Sport Sci Rev. 2005;33(1):36–42. [PubMed]

31. Vilar L, Araújo D, Davids K, Button C. The role of ecological dynamics in analysing performance in team sports. Sports Med. 2012;42(1):1–10. doi: 10.2165/11596520-000000000-00000[PubMed]

32. Esteves PT, Araújo D, Davids K, Vilar L, Travassos B, Esteves C. Interpersonal dynamics and relative positioning to scoring target of performers in 1 vs. 1 sub-phases of team sports. Journal of sports sciences. 2012;30(12):1285–93. doi: 10.1080/02640414.2012.707327[PubMed]

33. Headrick J, Davids K, Renshaw I, Araujo D, Passos P, Fernandes O. Proximity-to-goal as a constraint on patterns of behaviour in attacker-defender dyads in team games. Journal of sports sciences. 2012;30(3):247–53. doi: 10.1080/02640414.2011.640706[PubMed]

34. Correa UC, Vilar L, Davids K, Renshaw I. Informational constraints on the emergence of passing direction in the team sport of futsal. European journal of sport science. 2014;14(2):169–76. doi: 10.1080/17461391.2012.730063[PubMed]

35. Hüttermann S, Memmert D, Simons DJ. The size and shape of the attentional “spotlight” varies with differences in sports expertise. Journal of Experimental Psychology: Applied. 2014;20(2):147–57. doi: 10.1037/xap0000012[PubMed]

36. Memmert D, Furley P. "I spy with my little eye!": breadth of attention, inattentional blindness, and tactical decision making in team sports. Journal of sport & exercise psychology. 2007;29(3):365–81. [PubMed]

37. Messersmith LL, Corey SM. The Distance Traversed by a Basketball Player. Research Quarterly American Physical Education Association. 1931;2(2):57–60.

38. Weast JA, Shockley K, Riley MA. The influence of athletic experience and kinematic information on skill-relevant affordance perception. Q J Exp Psychol. 2011;64(4):689–706. [PubMed]

39. Davids K, Button C, Araujo D, Renshaw I, Hristovski R. Movement models from sports provide representative task constraints for studying adaptive behavior in human movement systems. Adaptive Behavior. 2006;14(1):73–95.

40. Yarrow K, Brown P, Krakauer JW. Inside the brain of an elite athlete: the neural processes that support high achievement in sports. Nat Rev Neurosci. 2009;10(8):585–96. doi: 10.1038/nrn2672[PubMed]

41. Gomez MA, Lorenzo A, Ibanez SJ, Sampaio J. Ball possession effectiveness in men's and women's elite basketball according to situational variables in different game periods. J Sports Sci. 2013;31(14):1578–87. doi: 10.1080/02640414.2013.792942[PubMed]

42. Simenz CJ, Dugan CA, Ebben WP. Strength and conditioning practices of National Basketball Association strength and conditioning coaches. Journal of strength and conditioning research / National Strength & Conditioning Association. 2005;19(3):495–504. [PubMed]

43. Esteves PT, de Oliveira RF, Araujo D. Posture-related affordances guide attacks in basketball. Psychol Sport Exerc. 2011;12(6):639–44.

44. Apostolidis N, Nassis GP, Bolatoglou T, Geladas ND. Physiological and technical characteristics of elite young basketball players. J Sport Med Phys Fit. 2004;44(2):157–63. [PubMed]

45. Davids K, Glazier P, Araujo D, Bartlett R. Movement systems as dynamical systems—The functional role of variability and its implications for sports medicine. Sports Med. 2003;33(4):245–60. [PubMed]

MUCH TO THE chagrin of some (we're looking at you, Terry Collins), analytics have become a vital part of the day-to-day conversation in sports. And behold: The next wave of advanced studies for your consumption! For the supporters and doubters alike, we've summarized (full versions: here) the eight finalists for the MIT Sloan Sports Analytics Conference's research paper of the year. The winner will be announced at this year's event on March 11 and 12 in Boston.

Editor's Picks


What you need to know: Here's the worst nightmare for the aforementioned Mets manager: lots of acronyms. PECOTA and ZiPS are popular systems for projection, but what if those could be improved? Introducing Arsenal/Zone Rating, a predicting tool for pitchers that relies on PitchF/X data. Speed and movement of pitches, along with location, are analyzed closely as a predictor of future performance. If a left-hander puts his slider in the right spot more often than not or if a hard thrower can sink it consistently, that's going to mean success.

The research says ... "The final model, which we call arsenal-zone-combined rating, is a good pitching performance predictor by itself. It has comparable result with mainstream projection systems, and it has distinct advantage in picking out the breakout and breakdown pitchers than those mainstream systems."

The big number: 95 -- that's the minimum number of innings needed in the previous season to provide enough data for a proper projection.


What you need to know: This research examines NBA players and how they play with each other. First, a baseline for the probability of certain game outcomes (shooting, rebounding and turning the ball over) is set, and the likelihood of certain players performing those tasks is determined. Indeed, it turns out that the Pelicans' supporting cast is better when you sub Anthony Davis in for a replacement player, and the Spurs, not surprisingly, have constructed a lineup that fits well together. But, in general, NBA teams -- ahem, Knicks -- aren't adept at identifying those less obvious team builders. The biggest difference-maker last season? (That is, the largest difference between a lineup's expected points on offense and those given up on defense.) Marc Gasol. Who says the big man is dead?

The research says ... "NBA teams are not doing a good job of identifying the value a player brings to a team through his complementarities with existing players and are largely paying players based on their individual offensive contributions."

The big number: 8.2 -- the increase last season in the difference between expected offensive points and defensive points allowed, per 100 possessions, with Gasol in the Grizzlies' lineup. Gasol was well ahead of runners-up LaMarcus Aldridge and Tyson Chandler, who landed at 7.2.


What you need to know: Of course, success on the soccer field is predicated on the ability to control the ball on offense and take it away on defense. Sounds easy, right? But some teams, and players, are better at doing so in different areas of the field. And now we know exactly where those are. According to this study, we now have data, or "team-specific cartography," that will provide better understanding of where on the field teams struggle controlling the ball. Coaches, you're welcome: You can now adjust your team's play where it is weakest and attack where the opponent struggles. Sun Tzu would be proud.

The research says ... "Over the past three seasons, teams that have had the same manager (such as Arsenal, Liverpool and West Ham United) had a consistent value for their disruption and control coefficients. On the other hand, teams that have gone through several managers (such as Aston Villa, Manchester United and Southampton) have seen dramatic shifts."

The big number: 649 -- the number of shots it took for Manchester City to score 83 goals in 2014-15. That's frightening efficiency. The key was an ability to control the ball in the field's final third, which research revealed Man City did better than any club that season.

RECOGNIZING AND ANALYZING BALL-SCREEN DEFENSE IN THE NBA -- by Avery McIntyre, Joel Brooks, John Guttag and Jenna Wiens

What you need to know: Update the scouting reports! NBA defenses, do not go under a screen against ... Michael Carter-Williams? Using data from multiple seasons, this paper categorizes the defense of the NBA's most popular offensive action in four ways: over, under, switch and trap. For example, teams defend quick guards like Kyrie Irving and Jrue Holiday by going over the screen more than 75 percent of the time, even though each scores about 0.8 points per possession in those situations. Kevin Durant, Mo Williams and LeBron James are successful against the switch. And Carter-Williams nets more than 0.8 PPP -- more than Russell Westbrook, John Wall and Mike Conley -- when defenders duck under screens, for the highest rate in the league.

The research says ... "Interestingly, pairs that defend well with one scheme do not appear dominant across all schemes. ... Chris Paul and Blake Griffin, while effective at the switch [0.42 points allowed per possession in 2013-14], are one of the worst pairs when it comes to [going] over [the screen], averaging close to 1.2 points allowed."

The big number: 270,823 -- the number of ball screens that were analyzed for this research, using SportVu data from the past four seasons.


What you need to know: They've got to speed up the game! That's the refrain from some baseball fans that spend down time at games tapping smart phones, but advertisers might not agree. This research shows that corporate sponsorships -- those banks, airlines or hotels that are omnipresent during three-hour games -- do have an effect on consumer behavior. This information was gleaned from Internet browsing at MLB stadiums -- ad-monetized sites and apps -- and used the location data to provide context. It was determined that fans increasingly engage with team sponsors as the season moves along. So yes, the Yankees Museum Presented by Bank of America is a good business decision for both the team and the bank. But more importantly for sports teams, even outside MLB, this model can help them understand where car sponsors aren't as effective (hint: New York City) and what brand might be a good replacement.

The research says ... "A national hotel chain for the Chicago Cubs, a regional airline for the Colorado Rockies and a national bank for the New York Yankees were the three sponsorship brands studied for this experiment. ... The engagement rate was highest with the regional airline brand sponsoring the Colorado Rockies."

The big number: 70 -- the percentage of individual mobile devices that were seen multiple times at games during the 2015 season across the general MLB audience. This allowed the research to distinguish between casual and avid attendees.


What you need to know: Sports organizations increasingly lean on the numbers to project the success of players and lineups alike. But for most organizations, including behemoths like the IOC and FIFA, business predictions -- specifically dealing with sponsorships and the likelihood of a long-term revenue stream-- aren't as advanced. This research laments reliance on the renewal rate, a practice that's more old-school scout than new-school statistician. But fear not, dear reader: It also focuses on revenue probability that takes into account median lifetimes of sponsorships and when they're most likely to renew. The research -- analyzing 71 FIFA and Olympic sponsorships over the past three decades -- determined that these relationships are unlikely to last more than 12 years. And what does that mean for business models? Budget for these changes or risk a rude awakening.

The research says ... "For five sponsors (less than half of the current total of 120, a duration of one time interval would equate to $435 million in revenue for the IOC. These figures illustrate the implications of determining the most accurate method for computing the historical lifetime for global sponsorships ... ."

The big number: $87 million -- the value of one sponsor, over four years, to the TOP, which is the grouping of Olympic sponsors.


What you need to know: All tennis-playing styles are not created equal, or so says the emerging world of analytics within the sport. A match between Rafael Nadal and Novak Djokovic, for example, can hinge on the outcome of a few important points between two evenly matched stars. Within a rally, each player's probability of winning can change in real-time. The aim of the research: "Our task was to accurately estimate the probability that a player will win the point at any moment, given current and previous shots in the rally." Often, the outcomes are attributed to the players' shot patterns, as well as a confluence of other factors like surface and weather. This would give tennis fans, broadcasters and players an insight into what works and what doesn't in each matchup.

The research says ... "For the majority of [a rally between Nadal and Djokovic], it appears to be a stalemate, until the seventh stroke, where something happens allowing Djokovic to take control and eventually win the point. We call this change in winning probability the thin end of the wedge -- the winning player ekes out a small advantage, then systematically capitalizes on that advantage to secure the point."

The big number: 37,727 -- the number of shots that were included in this research, taken from three years of data from Hawk-Eye -- a ball-tracking technology used in tennis -- at the Australian Open.


What you need to know: Heat check! In basketball, the defense can adjust to a player on a roll. That ability to send multiple defenders toward a star (think: Spurs vs. LeBron) has led to the conclusion that hot hands do not exist, mostly supported by shooting stats that regress to the mean. Baseball, on the other hand, doesn't offer the luxury of a true team defense. As a result, this paper uses the walk -- baseball's defense mechanism against a hitter who appears unstoppable -- as a matter of context. It found baseball's hot hand (Daniel Murphy in the NLCS) is not a fallacy, based on the ebbs and flows of 10 different stats and opponents' responses to player success. Sure, pitching around a red-hot hitter is smart, but the research warns that opposing teams should make this determination on a large-enough sample size. In Murphy's case, the NLCS was good enough.

The research says ... "Strong recent performance relative to being in a neutral state corresponds to roughly a one quartile increase in the distribution of present performance. Thus, a 50th percentile hitter will hit like a 75th percentile hitter following strong recent performance."

The big number: 25 -- the number of at-bats that allow for a clear picture of the hot hand of a hitter. According to the research, opponents instead often "overreact" to a player's last five at-bats.

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