Briann January 5–8 • Guard Spokane, Wash
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Play by Play JPN 87 Vs 71 FRA FIRST QUARTER
Saitama Super Arena Basketball さいたまスーパーアリーナ バスケットボール / Basketball Super Arena de Saitama Women 女子 / Femmes FRI 6 AUG 2021 Semifinal Start Time: 20:00 準決勝 / Demi-finale Play by Play プレーバイプレー / Actions de jeux Game 48 JPN 87 vs 71 FRA (14-22, 27-12, 27-16, 19-21) Game Duration: 1:31 Q1 Q2 Q3 Q4 Scoring by 5 min intervals: JPN 9 14 28 41 56 68 78 87 FRA 11 22 27 34 44 50 57 71 Quarter Starters: FIRST QUARTER JPN 8 TAKADA M 13 MACHIDA R 27 HAYASHI S 52 MIYAZAWA Y 88 AKAHO H FRA 5 MIYEM E 7 GRUDA S 10 MICHEL S 15 WILLIAMS G 39 DUCHET A Game JPN - Japan Score Diff. FRA - France Time 10:00 8 TAKADA M Jump ball lost 7 GRUDA S Jump ball won 15 WILLIAMS G 2PtsFG inside paint, Driving Layup made (2 9:41 0-2 2 Pts) 8 TAKADA M 2PtsFG inside paint, Layup made (2 Pts), 13 9:19 2-2 0 MACHIDA R Assist (1) 9:00 52 MIYAZAWA Y Defensive rebound (1) 10 MICHEL S 2PtsFG inside paint, Driving Layup missed 52 MIYAZAWA Y 2PtsFG inside paint, Layup made (2 Pts), 13 8:40 4-2 2 MACHIDA R Assist (2) 8:40 10 MICHEL S Personal foul, 1 free throw awarded (P1,T1) 8:40 52 MIYAZAWA Y Foul drawn 8:40 52 MIYAZAWA Y Free Throw made 1 of 1 (3 Pts) 5-2 3 8:28 52 MIYAZAWA Y Defensive rebound (2) 10 MICHEL S 2PtsFG inside paint, Driving Layup missed 8:11 52 MIYAZAWA Y 3PtsFG missed 15 WILLIAMS G Defensive rebound (1) 8:03 5-4 1 15 WILLIAMS G 2PtsFG fast break, Driving Layup made (4 Pts) 88 AKAHO H 2PtsFG inside paint, Layup made (2 Pts), 13 7:53 7-4 3 MACHIDA R Assist (3) 7:36 39 DUCHET A 2PtsFG outside paint, Pullup Jump Shot missed 7:34 Defensive Team rebound (1) 7:14 13 MACHIDA -
Coaches Handbook
City of Buckeye COMMUNITY SERVICES DEPARTMENT -Recreation Division- COACHES HANDBOOK Important dates Opening day: Saturday, June 16th Picture day: Tuesday, June 19th and Thursday, June 21st Last day: Saturday, July 28th Peter Piper pizza party nights: TBD Community Services Department’s Vision and Mission Statement Our Vision “Buckeye Is An Active, Engaged and Vibrant Community.” Our Mission We are dedicated to enriching quality of life, managing natural resources and creating memorable experiences for all generations. .We do this by: Developing quality parks, diverse programs and sustainable practices. Promoting volunteerism and lifelong learning. Cultivating community events, tourism and economic development. Preserving cultural, natural and historic resources. Offering programs that inspire personal growth, healthy lifestyles and sense of community. Dear Coach: Thank you for volunteering to coach with the City of Buckeye Youth Sports Program. The role of a youth sports coach can be very rewarding, but can be challenging at times as well. We have included helpful information in this handbook to assist in making this an enjoyable season for you and your team. Our youth sports philosophy is to provide our youth with a positive athletic experience in a safe environment where fun, skill development, teamwork, and sportsmanship lay its foundation. In addition, our youth sports programs is designed to encourage maximum participation by all team members; their development is far more important than the outcome of the game. Please be sure to remember you are dealing with children, in a child’s game, where the best motivation of all is enthusiasm, positive reinforcement and team success. If the experience is fun for you, it will also be fun for the kids on your team as well as their parents. -
Evaluating Lineups and Complementary Play Styles in the NBA
Evaluating Lineups and Complementary Play Styles in the NBA The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:38811515 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Contents 1 Introduction 1 2 Data 13 3 Methods 20 3.1 Model Setup ................................. 21 3.2 Building Player Proles Representative of Play Style . 24 3.3 Finding Latent Features via Dimensionality Reduction . 30 3.4 Predicting Point Diferential Based on Lineup Composition . 32 3.5 Model Selection ............................... 34 4 Results 36 4.1 Exploring the Data: Cluster Analysis .................... 36 4.2 Cross-Validation Results .......................... 42 4.3 Comparison to Baseline Model ....................... 44 4.4 Player Ratings ................................ 46 4.5 Lineup Ratings ............................... 51 4.6 Matchups Between Starting Lineups .................... 54 5 Conclusion 58 Appendix A Code 62 References 65 iv Acknowledgments As I complete this thesis, I cannot imagine having completed it without the guidance of my thesis advisor, Kevin Rader; I am very lucky to have had a thesis advisor who is as interested and knowledgable in the eld of sports analytics as he is. Additionally, I sincerely thank my family, friends, and roommates, whose love and support throughout my thesis- writing experience have kept me going. v Analytics don’t work at all. It’s just some crap that people who were really smart made up to try to get in the game because they had no talent. -
The Unseen Play the Game to Win 03/22/2017
The Unseen Play the Game to Win 03/22/2017 Play the Game to Win What Rick Barry and the Atlanta Falcons can teach us about risk management “Something about the crowd transforms the way you think” – Malcolm Gladwell - Revisionist History With 4:45 remaining in Super Bowl LI, Matt Ryan, the Atlanta Falcons quarterback, threw a pass to Julio Jones who made an amazing catch. The play did not stand out because of the way the ball was thrown or the agility that Jones employed to make the catch, but due to the fact that the catch eas- ily put the Falcons in field goal range very late in the game. That reception should have been the play of the game, but it was not. Instead, Tom Brady walked off the field with the MVP trophy and the Patriots celebrated yet another Super Bowl victory. NBA basketball hall of famer Rick Barry shot close to 90% from the free throw line. What made him memorable was not just his free throw percentage or his hard fought play, but the way he shot the ball underhanded, “granny-style”, when taking free throws. Every basketball player, coach and fan clearly understands that the goal of a basketball game is to score the most points and win. Rick Bar- ry, however, was one of the very few that understood it does not matter how you win but most im- portantly if you win. The Atlanta Falcons crucial mistake and Rick Barry’s “granny” shooting style offer stark illustrations about how human beings guard their egos and at times do imprudent things in order to be viewed favorably by their peers and the public. -
PJ Savoy Complete
PJ SAVOY 6-4/210 GUARD LAS VEGAS, NEVADA CHAPARRAL HIGH SCHOOL (CHANCELLOR DAVIS) LAS VEGAS HIGH SCHOOL (JASON WILSON) SHERIDAN COLLEGE (MATT HAMMER) FLORIDA STATE UNIVERSITY (LEONARD HAMILTON) PJ Savoy’s Career Statistics Year G-GS FG-A PCT. 3FG-3FGA PCT. FT-FTA PCT. PTS.-AVG. OR DR TR-AVG. PF-D AST TO BLK STL MIN 2016-17 28-0 47-114 .412 40-100 .400 21-30 .700 155-5.5 4 19 23-0.8 15-0 7 9 1 10 228-8.1 2017-18 27-4 58-158 .367 50-135 .370 16-22 .727 182-6.7 5 33 38-1.4 28-0 15 17 1 6 355-13.1 2018-19 37-18 68-187 .364 52-158 .329 32-39 .821 220-5.9 7 37 44-1.2 44-0 18 30 4 17 542-14.6 Totals 92-22 173-459 .381 142-393 .366 69-91 .749 557-6.03 16 89 105-1.1 87-0 40 56 6 33 1125-25.2 PJ Savoy’s Conference Statistics Year G-GS FG-A PCT. 3FG-3FGA PCT. FT-FTA PCT. PTS.-AVG. OR DR TR-AVG. PF-D AST TO BLK STL MIN 2016-17 17-0 27-63 .429 21-54 .389 10-15 .667 85-5.0 4 15 19-1.1 6-0 2 5 1 6 279-15.5 2017-18 11-1 20-56 .357 18-51 .353 8-11 .727 66-6.0 2 7 9-0.8 11-0 7 7 0 1 147-13.4 2018-19 18-5 30-85 .353 24-74 .324 13-15 .867 97-5.4 3 14 17-0.9 21-0 5 11 1 9 218-12.1 Totals 46-6 77-204 .378 63-179 .352 31-41 .756 248-5.4 9 36 45-1 38-0 14 23 2 16 644-14.0 PJ Savoy’s NCAA Tournament Statistics Year G-GS FG-A PCT. -
Probable Starting Lineups This Game by the Numbers
Louisville Basketball Quick Facts Location Louisville, Ky. 40292 Founded / Enrollment 1798 / 22,000 Nickname/Colors Cardinals / Red and Black Sports Information University of Louisville Louisville, KY 40292 www.UofLSports.com Conference BIG EAST Phone: (502) 852-6581 Fax: (502) 852-7401 email: [email protected] Home Court KFC Yum! Center (22,000) President Dr. James Ramsey Louisville Cardinals vs. Notre Dame Fighting Irish Vice President for Athletics Tom Jurich Head Coach Rick Pitino (UMass '74) U of L Record 238-91 (10th yr.) PROBABLE STARTING LINEUPS Overall Record 590-215 (25th yr.) Louisville (18-5, 7-3) Ht. Wt. Yr. PPG RPG Hometown Asst. Coaches Steve Masiello,Tim Fuller, Mark Lieberman F 5 Chris SMITH 6-2 200 Jr. 9.8 4.5 Millstone, N.J. Dir. of Basketball Operations Ralph Willard F 44 Stephan VAN TREESE 6-9 220 So. 3.5 3.9 Indianapolis, Ind. All-Time Record 1,625-849 (97 yrs.) C 23 Terrence JENNINGS 6-9 220 Jr. 9.3 5.4 Sacramento, Calif. All-Time NCAA Tournament Record 60-38 G 2 Preston KNOWLES 6-1 190 Sr. 14.9 3.7 Winchester, Ky. (36 Appearances, Eight Final Fours, G 3 Peyton SIVA 5-11 180 So. 10.7 2.9 Seattle, Wash. Two NCAA Championships - 1980, 1986) Important Phone Numbers Notre Dame (19-4, 8-3) Ht. Wt. Yr. PPG RPG Hometown Athletic Office (502) 852-5732 F 1 Tyrone NASH 6-8 232 Sr. 9.7 5.8 Queens, N.Y. Basketball Office (502) 852-6651 F 21 Tim ABROMAITIS 6-8 235 Sr. -
PHOENIX MERCURY GAME NOTES #5 Phoenix Mercury (1-0) Vs
PHOENIX MERCURY GAME NOTES #5 Phoenix Mercury (1-0) vs. #4 Minnesota Lynx (0-0) Playoff Game 2 | Thursday, September 17, 2020 IMG Academy | Bradenton, Fla. | 7:00 p.m. ET TV: ESPN2 Sr. Manager, Basketball Communications: Bryce Marsee [email protected] | Cell: (765) 618-0897 | @brycemarsee TONIGHT'S PROBABLE MERCURY STARTERS (2020 PLAYOFF AVERAGES) No. Name PPG RPG APG Notes Aquired by the Mercury in a sign-and-trade with Dallas on Feb. 12, 2020...named Western Conference Player of the Week on 9/8 for week of 8/31-9/6...finished 4 Skylar Diggins-Smith 24.0 6.0 5.0 the season ranked 7th in scoring, 10th in assists and tied for 4th in three-point G | 5-9 | 145 | Notre Dame '13 field goals (46)...scored a postseason career-high and team-high 24 points on 9/15 vs. WAS...picked up her first playoffs win over Washington on 9/15 WNBA's all-time leader in postseason scoring and ranks 3rd in all-time assists in the playoffs...6 assists shy of passing Sue Bird for 2nd on WNBA's all-time playoffs as- 3 Diana Taurasi 23.0 4.0 6.0 sists list...ranked 5th in the league in scoring and 8th in assists...led the WNBA in 3-pt G | 6-0 | 163 | Connecticut '04 field goals (61) this season, the 11th time she's led the league in 3-pt field goals... holds a perfect 7-0 record in single elimination games in the playoffs since 2016 Started in 10 games for the Mercury this season..scored a career-high 24 points on 9/11 against Seattle in a career-high 35 mimutes...also posted a 2 Shatori Walker-Kimbrough 8.0 2.0 0.0 career-high 5 steals this season in the 8/14 game against Atlanta...scored G | 6-1 | 170 | Missouri '19 in double figures 5 of the final 8 games of the regular season...scored 8 points in Mercury's Round 1 win on 9/15 vs. -
USA Vs. Oregon State
USA WOMEN’S NATIONAL TEAM • 2019 FALL TOUR USA vs. Oregon State NOV. 3, 2019 | GILL COLISEUM | 7 PM PST | PAC-12 NETWORKS PROBABLE STARTERS 2019-20 SCHEDULE/RESULTS (7-0) NO NAME PPG RPG APG CAPS 2019 FIBA AMERICUP (6-0) 5 Seimone Augustus 10.8 1.8 2.6 105 6 Sue Bird 10.1 1.7 7.1 140 9/22 USA 110, Paraguay 31 13 Sylvia Fowles 13.6 8.9 1.5 73 9/24 USA 88, Colombia 46 16 Nneka Ogwumike 16.1 8.8 1.8 48 9/25 USA 100, Argentina 50 12 Diana Taurasi 20.7 3.5 5.3 132 9/26 USA 89, Brazil 73 9/28 USA 78, Puerto Rico 54 9/29 USA 67, Canada 46 RESERVES 2019 FALL TOUR (1-0) NO NAME PPG RPG APG CAPS 23 Layshia Clarendon 4.8 1.8 2.2 21 11/2 USA 95, No. 3 Stanford 80 Pac-12 Networks 24 Napheesa Collier 13.1 6.6 2.6 40* 11/4 Oregon State (7/6)7 pm Pac-12 Networks 17 Skylar Diggins-Smith 17.9 3.3 6.2 38* 11/7 Texas A&M (6/7) 7 pm TBA 35 Allisha Gray 10.6 4.1 2.3 3 11/9 Oregon (1/1) 4 pm Pac-12 Networks 18 Chelsea Gray 14.5 3.8 5.9 0 2019 FIBA AMERICAS PRE-OLYMPIC 9 A’ja Wilson 16.5 6.4 1.8 39 QUALIFYING TOURNAMENT NOTES: 11/14 USA vs. Brazil Bahía Blanca, ARG • Stats listed for most athletes are from the 2019 WNBA 11/16 USA vs. -
FIBA Official Interpretations 2019, JAN 2019
2020 OFFICIAL BASKETBALL RULES OBRI – OFFICIAL INTERPRETATIONS Valid as of 1st January 2021 1 January 2021 version 2.0 Official Basketball Rules 2020 Official Interpretations Valid as of 1st January 2021 The colours demonstrate the content that was updated. (Yellow version) Page 2 of 112 OFFICIAL BASKETBALL RULES INTERPRETATIONS 1 January 2021 version 2.0 In case you find any inconsistency or error, please report the problem to: [email protected] 1 January 2021 version 2.0 OFFICIAL BASKETBALL RULES INTERPRETATIONS Page 3 of 112 TABLE OF CONTENTS Introduction . .......................................................................................................................................................... 5 Article 4 Teams ............................................................................................................................................... 6 Article 5 Players: Injury and assistance .................................................................................................... 7 Article 7 Head coach and first assistant coach: Duties and Powers ................................................. 10 Article 8 Playing time, tied score and overtime ...................................................................................... 12 Article 9 Beginning and end of a quarter, overtime or the game ........................................................ 14 Article 10 Status of the ball ......................................................................................................................... -
Ranking the Greatest NBA Players: an Analytics Analysis
1 Ranking the Greatest NBA Players: An Analytics Analysis An Honors Thesis by Jeremy Mertz Thesis Advisor Dr. Lawrence Judge Ball State University Muncie, Indiana July 2015 Expected Date of Graduation May 2015 1-' ,II L II/du, t,- i II/em' /.. 2 ?t; q ·7t./ 2 (11 S Ranking the Greatest NBA Players: An Analytics Analysis . Iv/If 7 Abstract The purpose of this investigation was to present a statistical model to help rank top National Basketball Association (NBA) players of all time. As the sport of basketball evolves, the debate on who is the greatest player of all-time in the NBA never seems to reach consensus. This ongoing debate can sometimes become emotional and personal, leading to arguments and in extreme cases resulting in violence and subsequent arrest. Creating a statistical model to rank players may also help coaches determine important variables for player development and aid in future approaches to the game via key data-driven performance indicators. However, computing this type of model is extremely difficult due to the many individual player statistics and achievements to consider, as well as the impact of changes to the game over time on individual player performance analysis. This study used linear regression to create an accurate model for the top 150 player rankings. The variables computed included: points per game, rebounds per game, assists per game, win shares per 48 minutes, and number ofNBA championships won. The results revealed that points per game, rebounds per game, assists per game, and NBA championships were all necessary for an accurate model and win shares per 48 minutes were not significant. -
Measuring Production and Predicting Outcomes in the National Basketball Association
Measuring Production and Predicting Outcomes in the National Basketball Association Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Michael Steven Milano, M.S. Graduate Program in Education The Ohio State University 2011 Dissertation Committee: Packianathan Chelladurai, Advisor Brian Turner Sarah Fields Stephen Cosslett Copyright by Michael Steven Milano 2011 Abstract Building on the research of Loeffelholz, Bednar and Bauer (2009), the current study analyzed the relationship between previously compiled team performance measures and the outcome of an “un-played” game. While past studies have relied solely on statistics traditionally found in a box score, this study included scheduling fatigue and team depth. Multiple models were constructed in which the performance statistics of the competing teams were operationalized in different ways. Absolute models consisted of performance measures as unmodified traditional box score statistics. Relative models defined performance measures as a series of ratios, which compared a team‟s statistics to its opponents‟ statistics. Possession models included possessions as an indicator of pace, and offensive rating and defensive rating as composite measures of efficiency. Play models were composed of offensive plays and defensive plays as measures of pace, and offensive points-per-play and defensive points-per-play as indicators of efficiency. Under each of the above general models, additional models were created to include streak variables, which averaged performance measures only over the previous five games, as well as logarithmic variables. Game outcomes were operationalized and analyzed in two distinct manners - score differential and game winner. -
Successful Shot Locations and Shot Types Used in NCAA Men's Division I Basketball"
Northern Michigan University NMU Commons All NMU Master's Theses Student Works 8-2019 SUCCESSFUL SHOT LOCATIONS AND SHOT TYPES USED IN NCAA MEN’S DIVISION I BASKETBALL Olivia D. Perrin Northern Michigan University, [email protected] Follow this and additional works at: https://commons.nmu.edu/theses Part of the Programming Languages and Compilers Commons, Sports Sciences Commons, and the Statistical Models Commons Recommended Citation Perrin, Olivia D., "SUCCESSFUL SHOT LOCATIONS AND SHOT TYPES USED IN NCAA MEN’S DIVISION I BASKETBALL" (2019). All NMU Master's Theses. 594. https://commons.nmu.edu/theses/594 This Open Access is brought to you for free and open access by the Student Works at NMU Commons. It has been accepted for inclusion in All NMU Master's Theses by an authorized administrator of NMU Commons. For more information, please contact [email protected],[email protected]. SUCCESSFUL SHOT LOCATIONS AND SHOT TYPES USED IN NCAA MEN’S DIVISION I BASKETBALL By Olivia D. Perrin THESIS Submitted to Northern Michigan University In partial fulfillment of the requirements For the degree of MASTER OF SCIENCE Office of Graduate Education and Research August 2019 SIGNATURE APPROVAL FORM SUCCESSFUL SHOT LOCATIONS AND SHOT TYPES USED IN NCAA MEN’S DIVISION I BASKETBALL This thesis by Olivia D. Perrin is recommended for approval by the student’s Thesis Committee and Associate Dean and Director of the School of Health & Human Performance and by the Dean of Graduate Education and Research. __________________________________________________________ Committee Chair: Randall L. Jensen Date __________________________________________________________ First Reader: Mitchell L. Stephenson Date __________________________________________________________ Second Reader: Randy R.