DOCSLIB.ORG

Real World Performance Tasks

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Real World Performance Tasks Los Angeles Lakers Real World Performance Tasks Real World Real Life, Real Data, Real-Time - These activities put students into real life scenarios where they use real-time, real data to solve proBlems. In the NLSN series, we use data from NBA.com and update our data regularly. Note - some data has been rounded or simplified in order to adjust the math to the appropriate level. Engaging Relevant – Students today are very familiar with professional Basketball, making these activities very relevant to their everyday lives. To pique their interest further, try asking the Your Challenge question to the class first. Authentic Tasks - Through these activity sheets students learn how to project a player’s efficiency rating and are prompted to form opinions and ideas about how they would solve real life proBlems. A glossary is included to help them with the unfamiliar terms used. Student Choice - Each set of activity sheets is available in multiple versions where students will do the same activities using data for different teams (e.g. Oklahoma City Thunder, Golden State Warriors, and Chicago Bulls). You or your students can pick the team that most interests them. Modular Principal Activity - The activity sheets always start with repeated practice of a core skill matched to a common core standard, as set out in the Teacher Guide. This principal activity (or Level 1 as it is labeled to students) can Be used in isolation. This should generally take around 10-15 minutes. Step Up Activity - For the Level 2 questions, students are required to integrate a different skill or set of skills with increasing complexity. The additional skills used to answer these questions are set out in the Teacher Guide. This should generally take around 20-30 minutes. Challenge - This is designed to require critical thinking skills and stretch students to reason with math and data to come to conclusions. They are matched up with one of the Common Core Standards for Mathematical Practice. These activities work well with students in pairs or small groups where they can discuss the math. Cross-Curricular Activity - Every activity sheet also includes a finale that you can use to extend the math lesson into another suBject (usually ELA). These could Be assigned in a second lesson or for homework. Customizable All of the activity sheets are provided in Word so that they can Be differentiated to add, remove, or edit questions or even add space for students to show their work. Suggested customizations for each activity sheet are given in the Teacher Guide. Community We would love for you and your students to tell us about your experience. Join the conversation on Twitter starting your tweet with @nextlesson and using #NLSN. Updated June 2015 © NextLesson 2015 Linear Functions Teacher Guide Sound Bite for Students: “In the real world, we use statistics to make projections into the future.” Skills Practiced: Principal Activity (Level 1): - Construct linear functions - Evaluate and solve linear functions Step Up Activity (Level 2): - Construct linear functions - Evaluate and solve linear functions - Assess data for correlation Common Core Math Standards Addressed: Interpret differences in shape, center, and spread in the context of the data HSS-ID.A.3 sets, accounting for possiBle effects of extreme data points (outliers). Represent data on two quantitative variables on a scatter plot, and descriBe Principal HSS-ID.B.6 how the variables are related. Activity: Fit a function to the data; use functions fitted to data to solve proBlems in the HSS-ID.B.6a context of the data. Use given functions or choose a function suggested By the context. Emphasize linear, quadratic, and exponential models. Fit a function to the data; use functions fitted to data to solve proBlems in the HSS-ID.B.6a context of the data. Use given functions or choose a function suggested By Step-Up the context. Emphasize linear, quadratic, and exponential models. Activity: Interpret the slope (rate of change) and the intercept (constant term) of a HSS-ID.C.7 linear model in the context of the data. Differentiation Tips: You can edit any of the activity sheets to: - Alter the tasks (e.g. add or remove players from the assignment, only introduce one new piece of data in the challenge) - Cue for differentiation purposes (e.g. can provide instruction for using TI calculators or provide labeled coordinate grids for scatterplots) - Utilize TI graphing technology Due to school paper restriction, the spacing provided is only for answers. However, you could modify the spacing to add room for work if desired. Students should be encouraged to show their work where possible. Updates: At NextLesson we strive to engage students with data that is real and real-time. This lesson uses data as of June 2015. Please come back for the most recent updates. Updated June 2015 © NextLesson 2015 Los Angeles Lakers Name: ________________________________ You are the Statistician for the Los Angeles Lakers and you are investigating the effectiveness and value of players on your roster. Your Challenge: Who are the most effective players on the Los Angeles Lakers? LEVEL 1 To analyze your roster after the 2014-2015 season, you are considering the new Player Efficiency Rating (PER) statistic that measures a player’s value to his team and how it may project for the next season. The table below shows the last three seasons of player efficiency ratings for some of the players on the Los Angeles Lakers. The average NBA player has a PER score of 15. Player 2012-13 PER 2013-14 PER 2014-15 PER Kobe Bryant 23.0 10.7 17.6 Carlos Boozer 17.1 14.4 16.4 Ronnie Price 6.8 7.6 10.2 Nick Young 13.3 16.0 14.2 Wayne Ellington 13.9 12.2 11.6 Jordan Hill 18.5 19.3 16.2 Ed Davis 17.2 15.9 20.0 Jeremy Lin 14.9 14.3 15.6 1 Updated June 2015 © NextLesson 2015 Los Angeles Lakers 1. Model the stats of each of player to produce a line of best fit and interpret its rate of change. Player Line of Best Fit Interpretation of Rate of Change Kobe Bryant Carlos Boozer Ronnie Price Nick Young Wayne Ellington Jordan Hill Ed Davis Jeremy Lin 2. Use the line of best fit to predict their player efficiency rating for the next season. Player Line of Best Fit Rate of Change 2015-16 Prediction Kobe Bryant Carlos Boozer Ronnie Price Nick Young Wayne Ellington Jordan Hill Ed Davis Jeremy Lin 3. Based on the projections, which players have the most significant expected increase and decrease? 4. Based on the projections, which three players are projected to have the highest player efficiency ratings in the 2015-2016 season? Is this a change from the current year? 5. If an average NBA player has a player efficiency rating of 15, how many of the Los Angeles Lakers players in the table are expected to be better than average next year? 2 Updated June 2015 © NextLesson 2015 Los Angeles Lakers LEVEL 2 You want to know if the average number of minutes played per game is related to a player’s player efficiency rating. You hope to observe a positive correlation between player efficiency rating and their minutes. Average 2014-15 Player Player Minutes Played Efficiency Rating Kobe Bryant 34.5 17.6 Carlos Boozer 23.8 16.4 Ronnie Price 22.8 10.2 Nick Young 23.8 14.2 Wayne Ellington 25.8 11.6 Jordan Hill 26.8 16.2 Ed Davis 23.3 20.0 Jeremy Lin 25.8 15.6 6. Construct a scatterplot to display the relationship and determine a line of best fit. 7. Interpret the rate of change. What does it mean in the context of the problem? 8. If a player was playing 25 minutes a game, what would you expect his player efficiency rating to be based on the models? 9. If a player was playing 5 minutes a game, what would you expect his player efficiency rating to be based on the models? 10. If a player had a player efficiency rating of 18, how many minutes would you expect him to play? 11. If a player had a player efficiency rating of 6, how many minutes would you expect him to play? 12. Assess the decisions of the team’s coaches in this situation. Did they use players to give their team the best chance of winning? 3 Updated June 2015 © NextLesson 2015 Los Angeles Lakers Challenge To finalize your report you wanted to include two more variables into your analysis – a player’s salary and his +/- rating. The +/- rating is determined by how many more or less points a player’s team scored when they were on the court. 2014-15 Player Average 2014-15 Player Efficiency +/- Rating Minutes Played Salary ($) Rating Kobe Bryant 34.5 17.6 -11.8 23,500,000 Carlos Boozer 23.8 16.4 -9.0 3,250,000 Ronnie Price 22.8 10.2 -3.2 1,316,809 Nick Young 23.8 14.2 -2.8 4,944,420 Wayne Ellington 25.8 11.6 -3.6 923,780 Jordan Hill 26.8 16.2 -10.6 9,000,000 Ed Davis 23.3 20.0 -7.2 981,084 Jeremy Lin 25.8 15.6 -6.5 8,374,646 1. Select a relationship between two variables that you haven’t yet explored. Using the methods from your earlier research, determine a linear model and interpret the results. 2. Based on your research, which three players on the team do you believe bring the most value to the roster? Use evidence from your research to support your opinion.
Load more

Recommended publications
  • 2008-09 Review
    2008-09 REVIEW 2008-09 SEASON RESULTS Overall Home Away Neutral All Games 26-9 18-1 6-4 2-4 Pac-10 Conference 14-4 8-1 6-3 0-0 Non-Conference 12-5 10-0 0-1 2-4 Date Opponent W / L Score Attend High Scorer High Rebounder HUSKY ATHLETICS 11/15/08 at Portland L 74-80 2617 (30)Brockman, Jon (14)Brockman, Jon 11/18/08 # CLEVELAND STATE W 78-63 7316 (23)Brockman, Jon (13)Brockman, Jon 11/20/08 # FLORIDA INT'L W 74-51 7532 (21)Dentmon, Justin (7)Brockman, Jon (7)Holiday, Justin 11/24/08 ^ vs Kansas L 54-73 14720 (17)Thomas, Isaiah (18)Brockman, Jon 11/25/08 ^ vs Florida L 84-86 16988 (22)Brockman, Jon (11)Brockman, Jon 11/29/08 PACIFIC W 72-54 7527 (16)Pondexter, Quincy (12)Pondexter, Quincy 12/04/08 + OKLAHOMA STATE W 83-65 7789 (18)Thomas, Isaiah (11)Brockman, Jon 12/06/08 TEXAS SOUTHERN W 88-52 7241 (18)Bryan-Amaning, Matt (11)Brockman, Jon 12/14/08 PORTLAND STATE W 84-83 7280 (23)Bryan-Amaning, Matt (12)Bryan-Amaning, Matt 12/20/08 EASTERN WASHINGTON W 83-50 7401 (17)Brockman, Jon (7)Bryan-Amaning, Matt 12/28/08 MONTANA W 75-53 9045 (13)Brockman, Jon (15)Bryan-Amaning, Matt THIS IS HUSKY BASKETBALL (13)Thomas, Isaiah 12/30/08 MORGAN STATE W 81-67 8260 (27)Thomas, Isaiah (6)Pondexter, Quincy 01/03/09 * at Washington State W 68-48 8107 (19)Thomas, Isaiah (7)Pondexter, Quincy 01/08/09 * STANFORD W 84-83 9291 (19)Brockman, Jon (18)Brockman, Jon 01/10/09 * CALIFORNIA L 3ot 85-88 9946 (24)Dentmon, Justin (18)Brockman, Jon 01/15/09 * at Oregon W 84-67 8237 (23)Thomas, Isaiah (10)Brockman, Jon OUTLOOK 01/17/09 * at Oregon State W 85-59 6648 (16)Brockman,
    [Show full text]
  • Phoenix Suns' Troy Daniels Talks About the Art of Shooting LOS ANGELES – Suns Guard Troy Daniels Can Shoot the Ball
    Volume 2, Issue 2 December 21, 2017 GREAT AFTICLE ON SHOOTING BY ONE OF THE BEST IN THE NBA. HE POINTS OUT HOW NONE OF THE BEST IN THE LEAGUE SHOOT THE SAME WAY AND THIS IS SOMETHING WE STRESS AT CAMP & THE ACADEMY! ITS ABOUT GETTING UP COUNTLESS REPS!!! Phoenix Suns' Troy Daniels talks about the art of shooting LOS ANGELES – Suns guard Troy Daniels can shoot the ball. Daniels ranks 12th in the NBA in 3-point shooting percentage (44.1) and for his ca- reer is a 41.3 percent shooter from 3-point range. This season, among bench players, Special points of interest: only Cleveland’s Kyle Korver and Miami’s Wayne Ellington have made more 3s 2018 Summer Camp Dates than Daniels (67). being released soon!!! That singular talent is why the Suns acquired Daniels from the Memphis Grizzlies in Will worked with 20 groups late September. this fall! Daniels talked to azcentral sports Wednesday about the art of shooting: Academy has boys & girls Q: Who taught you how to shoot? from 42 different high school programs! A: I’d say my father and my mom. In our backyard, I used to shoot from Academy will be on Sunday below my chin as a little kid. Say, 5 evenings during the winter of 6 years old. I tried to master a form Clinic in Oshkosh on March when I first started out, and I air- 21st balled every time. But I ended up mastering it as time went on and just fell in love with shooting.
    [Show full text]
  • Miami Heat at Cleveland Cavaliers
    Miami Heat at Cleveland Cavaliers A review of FG% by distance, opponent FG% by distance, % of shots taken by distance, rebounds, assists, turnovers, points per game, steals, and blocks. Projected Starters Position Cavs Heat PG Kyrie Irving Goran Dragic SG Deandre Liggins Rodney McGruder SF LeBron James Wayne Ellington PF Kevin Love Josh McRoberts C Tristan Thompson Hassan Whiteside Team Stats (Per Game) Offensive Distance Stats Distance Cavs Team Heat Team Difference Stat Cavs Heat Average Average Range FG% FG% (Advantage) Offensive Rebounds 10.1 11.5 10.3 0-3 feet 62.7% 58.7% 62.1% 4% (Cavs) Defensive Rebounds 34.4 32.9 33.8 3-10 feet 32.8% 37.6% 40.1% 4.8% (Heat) Assists 22.6 20.0 22.1 10-16 feet 45.4% 34.2% 40.3% 11.2% (Cavs) Turnovers 13.8 12.9 14.2 16 feet - 3PT 38.2% 37.5% 39.1% 0.7% (Cavs) FG% 45.7% 43.1% 44.9% 3PT 40.1% 34.1% 35.1% 6% (Cavs) 3PT% 40.1% 34.1% 35.1% Points 111.1 97.3 104.1 Possessions 99.7 96.4 99.0 Defensive Distance Stats Cavs Heat Distance Difference Opponent Opponent Average Range (Advantage) Team Stats (Per 100 Possessions) FG% FG% Stat Cavs Heat Average 0-3 feet 63.5% 56.1% 62.2% 7.4% (Heat) ORTG (Points scored) 111.0 100.4 104.2 3-10 feet 33.5% 41.9% 40.5% 8.4% (Cavs) DRTG (Points allowed) 105.2 103.0 104.2 10-16 feet 41.8% 43.3% 40.6% 1.5% (Cavs) 16 feet - 3PT 42.2% 39.3% 39.5% 2.9% (Heat) 3PT 35.5% 35.2% 35.3% 0.3% (Heat) Best and Worst Ranges Compared to Average Percent of Shots Taken • The Cavs are shooting best in the 10-16 feet range and Distance Cavs % of Heat % of defending best in the 3-10 feet range,
    [Show full text]
  • General Assembly of North Carolina Session 2009 Ratified Bill Resolution 2009-31 House Joint Resolution 1517 a Joint Resolution
    GENERAL ASSEMBLY OF NORTH CAROLINA SESSION 2009 RATIFIED BILL RESOLUTION 2009-31 HOUSE JOINT RESOLUTION 1517 A JOINT RESOLUTION RECOGNIZING THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL MEN'S BASKETBALL TEAM FOR AN OUTSTANDING SEASON CULMINATING IN THE 2009 NCAA DIVISION I CHAMPIONSHIP. Whereas, on April 6, 2009, the University of North Carolina at Chapel Hill men's basketball team won the 2009 National Collegiate Athletic Association (NCAA) Division I Championship by defeating Michigan State by a score of 89-72, the largest margin in a title game in 17 years; and Whereas, on the road to the final championship game, the Tar Heels defeated each of its opponents by 12 points or more, including the Radford Highlanders (101-58), LSU Tigers (84-70), Gonzaga Bulldogs (98-77), Oklahoma Sooners (72-60), and the Villanova Wildcats (83-69); and Whereas, the 2009 championship marks the fifth Division I NCAA championship title and sixth overall championship title for the men's basketball program at UNC; and Whereas, in NCAA tournament play, UNC has been selected as a No. 1 seed 13 times, appeared in 41 tournaments, and made 18 Final Four appearances, which is a NCAA record; and Whereas, the Tar Heels began their 2008-2009 season as a unanimous No. 1 pick and finished the season with a record of 34-4, adding to the basketball program's record of 20-win seasons 51 times and 30-win seasons 10 times; and Whereas, the Tar Heels were crowned the 2009 Atlantic Coast Conference (ACC) regular season champions, improving the program's ACC record to 27 regular
    [Show full text]
  • 2018-19 Phoenix Suns Media Guide 2018-19 Suns Schedule
    2018-19 PHOENIX SUNS MEDIA GUIDE 2018-19 SUNS SCHEDULE OCTOBER 2018 JANUARY 2019 SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT 1 SAC 2 3 NZB 4 5 POR 6 1 2 PHI 3 4 LAC 5 7:00 PM 7:00 PM 7:00 PM 7:00 PM 7:00 PM PRESEASON PRESEASON PRESEASON 7 8 GSW 9 10 POR 11 12 13 6 CHA 7 8 SAC 9 DAL 10 11 12 DEN 7:00 PM 7:00 PM 6:00 PM 7:00 PM 6:30 PM 7:00 PM PRESEASON PRESEASON 14 15 16 17 DAL 18 19 20 DEN 13 14 15 IND 16 17 TOR 18 19 CHA 7:30 PM 6:00 PM 5:00 PM 5:30 PM 3:00 PM ESPN 21 22 GSW 23 24 LAL 25 26 27 MEM 20 MIN 21 22 MIN 23 24 POR 25 DEN 26 7:30 PM 7:00 PM 5:00 PM 5:00 PM 7:00 PM 7:00 PM 7:00 PM 28 OKC 29 30 31 SAS 27 LAL 28 29 SAS 30 31 4:00 PM 7:30 PM 7:00 PM 5:00 PM 7:30 PM 6:30 PM ESPN FSAZ 3:00 PM 7:30 PM FSAZ FSAZ NOVEMBER 2018 FEBRUARY 2019 SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT 1 2 TOR 3 1 2 ATL 7:00 PM 7:00 PM 4 MEM 5 6 BKN 7 8 BOS 9 10 NOP 3 4 HOU 5 6 UTA 7 8 GSW 9 6:00 PM 7:00 PM 7:00 PM 5:00 PM 7:00 PM 7:00 PM 7:00 PM 11 12 OKC 13 14 SAS 15 16 17 OKC 10 SAC 11 12 13 LAC 14 15 16 6:00 PM 7:00 PM 7:00 PM 4:00 PM 8:30 PM 18 19 PHI 20 21 CHI 22 23 MIL 24 17 18 19 20 21 CLE 22 23 ATL 5:00 PM 6:00 PM 6:30 PM 5:00 PM 5:00 PM 25 DET 26 27 IND 28 LAC 29 30 ORL 24 25 MIA 26 27 28 2:00 PM 7:00 PM 8:30 PM 7:00 PM 5:30 PM DECEMBER 2018 MARCH 2019 SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT 1 1 2 NOP LAL 7:00 PM 7:00 PM 2 LAL 3 4 SAC 5 6 POR 7 MIA 8 3 4 MIL 5 6 NYK 7 8 9 POR 1:30 PM 7:00 PM 8:00 PM 7:00 PM 7:00 PM 7:00 PM 8:00 PM 9 10 LAC 11 SAS 12 13 DAL 14 15 MIN 10 GSW 11 12 13 UTA 14 15 HOU 16 NOP 7:00
    [Show full text]
  • Numbers Game -- the Washington Times the Washington Times
    Numbers game -- The Washington Times The Washington Times www.washingtontimes.com Numbers game By Patrick Hruby THE WASHINGTON TIMES Published April 13, 2004 Everyone else has it wrong. The fans. The press. Even the league. They're blinded by box scores. Hamstrung by hype. Of this and more, Wayne Winston is certain. A single mouse click tells him so. "Nobody should be talking about LeBron James and Carmelo Anthony," he says. "They should be talking about Dwyane Wade. It's a crime." For Winston, Wade's superiority is not a matter of opinion. It's a fact, cold and hard, like an icicle. You can argue politics, and you can argue the best "Godfather" flick (well, excluding part III). But when it comes to the NBA Rookie of the Year race, you can't argue the data. At least not with Winston, a former "Jeopardy" champ who's good with math the way Eric Clapton is good with chords. "James rates as an average NBA player," says Winston, a professor of decision sciences at Indiana University. "That's good since very few rookies rate that high. But Wade's a real impact player for Miami. He ranks 21st best in the league in terms of changing the chances of your team winning a game." Like any MIT graduate worth his sodium chloride, Winston has the numbers to prove his point. More than 5,000 pages' worth, to be exact. Only you won't find his statistics in a newspaper. Together with fellow sports math guru Jeff Sagarin -- the brain behind USA Today's computer rankings -- Winston has created Winval, a sophisticated program that rates and ranks the value of every NBA player from Tariq Abdul-Wahad to Lorenzen Wright.
    [Show full text]
  • Rockets in the Playoffs
    Rockets in the Playoffs 33 Years, Won 153, Lost 157 (.494) — Series: 60, Won 29, Lost 31 Home: 98-58 (.628), Road: 55-99 (.357) Opponent W-L Home Road Series Opponent W-L Home Road Series Atlanta 2-6 2-2 0-4 0-2 Oklahoma City 17-25 12-9 5-16 2-6 Years Played: 1969, 1979 Years Played: 1982, 1987, 1989, 1993, 1996, 1997, Last Meeting: April 13, 1979, at Atlanta 2013, 2017 (Hawks 100-91, Series: Atlanta 2-0) Last Meeting: April 25, 2017, at Toyota Center (Rockets 105-99, Series: Houston 4-1) Boston 5-16 4-6 1-10 0-4 Years Played: 1975, 1980, 1981, 1986 Orlando 4-0 2-0 2-0 1-0 Last Meeting: June 8, 1986, at Boston Year Played: 1995 (Celtics 114-97, Series: Boston 4-2) Last Meeting: June 14, 1995, at The Summit (Rockets 113-101, Series: Houston 4-0) Dallas 8-8 4-4 4-4 1-2 Years Played: 1988, 2005, 2015 Philadelphia 2-4 1-2 1-2 0-1 Last Meeting: Apr. 28, 2015, at Toyota Center Year Played: 1977 (Rockets 103-94, Series: Rockets 4-1) Last Meeting: May 17, 1977, at The Summit (76ers 112-109, Series: Philadelphia 4-2) Denver 4-2 3-0 1-2 1-0 Year Played: 1986 Phoenix 8-6 4-3 4-3 2-0 Last Meeting: May 8, 1986, at Denver Years Played: 1994, 1995 (Rockets 126-122, 2OT, Series: Houston 4-2) Last Meeting: May 20, 1995, at Phoenix (Rockets 115-114, Series: Houston 4-3) Golden State 7-16 6-5 1-10 0-3 Year Played: 2015, 2016, 2018, 2019 Portland 12-8 8-2 4-6 3-1 Last Meeting: May 10, 2019, at Toyota Center Years Played: 1987, 1994, 2009, 2014 (Warriors 118-113), Series: Warriors 4-2) Last Meeting: May 2, 2014, at Portland (Blazers 99-98, Series: Houston 4-2) L.A.
    [Show full text]
  • Rosters Set for 2014-15 Nba Regular Season
    ROSTERS SET FOR 2014-15 NBA REGULAR SEASON NEW YORK, Oct. 27, 2014 – Following are the opening day rosters for Kia NBA Tip-Off ‘14. The season begins Tuesday with three games: ATLANTA BOSTON BROOKLYN CHARLOTTE CHICAGO Pero Antic Brandon Bass Alan Anderson Bismack Biyombo Cameron Bairstow Kent Bazemore Avery Bradley Bojan Bogdanovic PJ Hairston Aaron Brooks DeMarre Carroll Jeff Green Kevin Garnett Gerald Henderson Mike Dunleavy Al Horford Kelly Olynyk Jorge Gutierrez Al Jefferson Pau Gasol John Jenkins Phil Pressey Jarrett Jack Michael Kidd-Gilchrist Taj Gibson Shelvin Mack Rajon Rondo Joe Johnson Jason Maxiell Kirk Hinrich Paul Millsap Marcus Smart Jerome Jordan Gary Neal Doug McDermott Mike Muscala Jared Sullinger Sergey Karasev Jannero Pargo Nikola Mirotic Adreian Payne Marcus Thornton Andrei Kirilenko Brian Roberts Nazr Mohammed Dennis Schroder Evan Turner Brook Lopez Lance Stephenson E'Twaun Moore Mike Scott Gerald Wallace Mason Plumlee Kemba Walker Joakim Noah Thabo Sefolosha James Young Mirza Teletovic Marvin Williams Derrick Rose Jeff Teague Tyler Zeller Deron Williams Cody Zeller Tony Snell INACTIVE LIST Elton Brand Vitor Faverani Markel Brown Jeffery Taylor Jimmy Butler Kyle Korver Dwight Powell Cory Jefferson Noah Vonleh CLEVELAND DALLAS DENVER DETROIT GOLDEN STATE Matthew Dellavedova Al-Farouq Aminu Arron Afflalo Joel Anthony Leandro Barbosa Joe Harris Tyson Chandler Darrell Arthur D.J. Augustin Harrison Barnes Brendan Haywood Jae Crowder Wilson Chandler Caron Butler Andrew Bogut Kentavious Caldwell- Kyrie Irving Monta Ellis
    [Show full text]
  • NBA Court Realty
    NBA Court Realty Dan Cervone, Luke Bornn and Kirk Goldsberry New York Unversity, Simon Fraser University, and University of Texas (Austin) 1 Intro: the Basketball Court is a Real Estate Market Continuously throughout every basketball possession, players control different regions of the basket- ball court. Some regions are more valuable than others, and players’ control (or lack thereof) of valu- able court space dictates the low and strategy of a basketball possession for both the offense and defense. As professionals engaged in high-stakes competition, we assume players are rational actors, and the exchanges they make to acquire new court space represent a winning strategy for their team. This simple assumption allows us to infer the value (price) of court real estate based on player and ball movement alone. In this paper, we provide a deinition of court space ownership, and infer the value of court space using SportVu player location data. Spatial tracking data has enabled a recent paradigm shift in bas- ketball analytics [4, 7, 1, 3]; we add to this growing literature by focusing on space, itself, as the object of investigation. By modeling court space and court ownership, we reveal different valuable regions of the court among the NBA’s players and teams, and insightful new metrics for both offense and de- fense. For instance, we can measure ballcarriers’ off-ball impact on offense by calculating the value of the space freed up for their teammates to control. For analyzing defense, we can quantify how ef- fectively different teams (and players) contain the offense (and particular players) within low-value regions of the court.
    [Show full text]
  • Mean, Median, Mode (Part 1) Extend Your Learning
    Mean, Median, Mode (Part 1) Extend Your Learning The 2008 U.S. Olympic Men’s Basketball team won the gold medal in Beijing. The team consisted of the following 12 men. U.S.A. Team Roster Height in Height in Weight in Name Number Position Centimeters Feet and Inches Pounds Carlos Boozer 4 F 206 cm 6'9" 266 lbs. Jason Kidd 5 G 194 cm 6'4" 210 lbs. LeBron James 6 F 203 cm 6'8" 250 lbs. Deron Williams 7 G 191 cm 6'3" 205 lbs. Michael Redd 8 G 199 cm 6'6" 215 lbs. Dwyane Wade 9 SG 193 cm 6'4" 216 lbs. Kobe Bryant 10 G 199 cm 6'6" 205 lbs. Dwight Howard 11 C 211 cm 6'11" 265 lbs. Chris Bosh 12 P/C 208 cm 6'10" 230 lbs. Chris Paul 13 PG 183 cm 6'0" 175 lbs. Tayshaun Prince 14 F 206 cm 6'9" 215 lbs. Carmelo Anthony 15 SF 203 cm 6'8" 230 lbs. Source: http://www.usabasketball.com/news.php?news_page=2008msntroster 1. Determine the mean height of the players in centimeters. 2. Determine the mean height of the players in feet and inches. 3. Which unit of measurement was easier to work with when determining the mean height? Explain your reasoning. 2010 1 Mean, Median, Mode (Part 1) Extend Your Learning 4. Determine the median height of the players in centimeters. 5. Determine the median height of the players in feet and inches. 6. One website showing height data reports the average height of the players is 200 cm and 6'7".
    [Show full text]
  • The Effect Alternate Player Efficiency Rating Has on NBA Franchises Regarding Winning and Individual Value to an Organization
    St. John Fisher College Fisher Digital Publications Sport Management Undergraduate Sport Management Department Spring 2012 The Effect Alternate Player Efficiency Rating Has on NBA Franchises Regarding Winning and Individual Value to an Organization Anthony Van Curen St. John Fisher College Follow this and additional works at: https://fisherpub.sjfc.edu/sport_undergrad Part of the Sports Management Commons How has open access to Fisher Digital Publications benefited ou?y Recommended Citation Van Curen, Anthony, "The Effect Alternate Player Efficiency Rating Has on NBAr F anchises Regarding Winning and Individual Value to an Organization" (2012). Sport Management Undergraduate. Paper 35. Please note that the Recommended Citation provides general citation information and may not be appropriate for your discipline. To receive help in creating a citation based on your discipline, please visit http://libguides.sjfc.edu/citations. This document is posted at https://fisherpub.sjfc.edu/sport_undergrad/35 and is brought to you for free and open access by Fisher Digital Publications at St. John Fisher College. For more information, please contact [email protected]. The Effect Alternate Player Efficiency Rating Has on NBAr F anchises Regarding Winning and Individual Value to an Organization Abstract For NBA organizations, it can be argued that success is measured in terms of wins and championships. There are major emphases placed on the demand for “superstar” players and the ability to score. Both of which are assumed to be a player’s value to their respective organization. However, this study will attempt to show that scoring alone cannot measure success. The research uses statistics from the 2008-2011 seasons that can be used to measure success through aspects such as efficiency, productivity, value and wins a player contributes to their organization.
    [Show full text]
  • Player Benchmarking and Outcomes: a Behavioral Science Approach
    Player Benchmarking and Outcomes: A Behavioral Science Approach Ambra Mazzelli (Asia School of Business & MIT) Robert Nason (Concordia University) Track: Basketball Paper ID: 1548699 1. Introduction Benchmarking is important to monitor and provide feedback to players on their performance. (i.e. are they under or overperforming). Behavioral science research, especially that grounded in social psychology, indicates that performance feedback has a strong impact on human behavior including: individual motivation (Deci, 1972; DeNisi, Randolph, & Blencoe, 1982; Pavett, 1983), risk-taking (Kacperczyk et al., 2015; Krueger Jr & Dickson, 1994; March & Shapira, 1992), and performance (Sehunk, 1984; Smither, London, & Reilly, 2005). As a result, performance feedback that is provided to players has the potential to induce changes in player performance. For example, Hall of Famers Shaquille O'Neal and Charles Barkley recently gave voice to negative performance feedback regarding Philadelphia 76ers Center Joel Embiid by openly criticizing him on TNT’s Inside the NBA. Early in the 2019 season, Joel Embiid’s player efficiency rating (PER) currently ranks an impressive 11th in the league (24.73), but is under his own past performance (2018/2019 PER = 26.21)1. Shaq and Charles suggested that Embiid’s recent relative underperformance was due to a lack of motivation and made a point of comparing Embiid to centers on other teams with higher PERs, such as Giannis Antetokounmpo and Anthony Davis. In the next game following Shaq and Charles comments (December 12, 2019 vs. the Boston Celtics) Embiid posted his best game of the season with a season high 38 points and a plus minus of +21 (in a game that the Sixers only won by 6 points).
    [Show full text]