Prediction of Efficient Player Combinations Among Members of the South Dakota State University Women's Basketball Squad
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Offensive and Defensive Plus–Minus Player Ratings for Soccer
applied sciences Article Offensive and Defensive Plus–Minus Player Ratings for Soccer Lars Magnus Hvattum Faculty of Logistics, Molde University College, 6410 Molde, Norway; [email protected] Received: 15 September 2020; Accepted: 16 October 2020; Published: 20 October 2020 Abstract: Rating systems play an important part in professional sports, for example, as a source of entertainment for fans, by influencing decisions regarding tournament seedings, by acting as qualification criteria, or as decision support for bookmakers and gamblers. Creating good ratings at a team level is challenging, but even more so is the task of creating ratings for individual players of a team. This paper considers a plus–minus rating for individual players in soccer, where a mathematical model is used to distribute credit for the performance of a team as a whole onto the individual players appearing for the team. The main aim of the work is to examine whether the individual ratings obtained can be split into offensive and defensive contributions, thereby addressing the lack of defensive metrics for soccer players. As a result, insights are gained into how elements such as the effect of player age, the effect of player dismissals, and the home field advantage can be broken down into offensive and defensive consequences. Keywords: association football; linear regression; regularization; ranking 1. Introduction Soccer has become a large global business, and significant amounts of capital are at stake when the competitions at the highest level are played. While soccer is a team sport, the attention of media and fans is often directed towards individual players. An understanding of the game therefore also involves an ability to dissect the contributions of individual players to the team as a whole. -
Difference-Based Analysis of the Impact of Observed Game Parameters on the Final Score at the FIBA Eurobasket Women 2019
Original Article Difference-based analysis of the impact of observed game parameters on the final score at the FIBA Eurobasket Women 2019 SLOBODAN SIMOVIĆ1 , JASMIN KOMIĆ2, BOJAN GUZINA1, ZORAN PAJIĆ3, TAMARA KARALIĆ1, GORAN PAŠIĆ1 1Faculty of Physical Education and Sport, University of Banja Luka, Bosnia and Herzegovina 2Faculty of Economy, University of Banja Luka, Bosnia and Herzegovina 3Faculty of Physical Education and Sport, University of Belgrade, Serbia ABSTRACT Evaluation in women's basketball is keeping up with developments in evaluation in men’s basketball, and although the number of studies in women's basketball has seen a positive trend in the past decade, it is still at a low level. This paper observed 38 games and sixteen variables of standard efficiency during the FIBA EuroBasket Women 2019. Two regression models were obtained, a set of relative percentage and relative rating variables, which are used in the NBA league, where the dependent variable was the number of points scored. The obtained results show that in the first model, the difference between winning and losing teams was made by three variables: true shooting percentage, turnover percentage of inefficiency and efficiency percentage of defensive rebounds, which explain 97.3%, while for the second model, the distinguishing variables was offensive efficiency, explaining for 96.1% of the observed phenomenon. There is a continuity of the obtained results with the previous championship, played in 2017. Of all the technical elements of basketball, it is still the shots made, assists and defensive rebounds that have the most significant impact on the final score in European women’s basketball. -
Co-Ed Micro Hoopers
The Hoopers Basketball League follows OSAA High School rules unless stated. No Jewelry Permitted For the safety of all players, NCPRD encourages not wearing any jewelry. The removal of jewelry includes but is not limited to watches, rings, nose rings and facial piercings. If an earring cannot be removed, it must be taped. No hoop earring of any size is permitted. Only One Coach Standing Only one coach on each team (game manager) is allowed to stand. The other should remain seated. This helps both the ref and players better determine who to listen to and reduces the intensity of the game. The officials can also instruct the standing coach to sit. Page 1 of 9 REV 10/19 Kindergarten Coed League follows OSAA High School rules unless stated. **Kindergarten first two weeks are practices. Each week after, games immediately follow shortened practices to stay within 1 hour time-frame each Sunday. Basket height will be placed at 8 feet. 25.5” basketball will be used. No timeouts Game scores will NOT be kept. There will be neither scorebooks nor scorekeepers. Each player will have equal playing time with the exception of an injury/illness. o Refer to Equal Play Time Chart GAME MANAGEMENT Games will be eight (8) 4-minute segments with Running Clock. 2-3 minute break between the 4th and 5th segment (i.e “halftime”). HOME TEAM provides volunteer timekeeper. One coach from each team will serve as the referees. PLAY Games will start with a coin toss then will play alternating possession (no jump balls). Teams will play 4-on-4, on the full court. -
Analysis of Different Types of Turnovers Between Winning and Losing Performances in Men’S NCAA Basketball
한국컴퓨터정보학회논문지 Journal of The Korea Society of Computer and Information Vol. 25 No. 7, pp. 135-142, July 2020 JKSCI https://doi.org/10.9708/jksci.2020.25.07.135 Analysis of different types of turnovers between winning and losing performances in men’s NCAA basketball 1)Doryung Han*, Mark Hawkins**, HyongJun Choi*** *Honorary principal professor, Major of Security secretary Studies Continuing Education Center, Kyonggi University, Seoul, Korea **Head coach, Performance Analysis of Sport, University of Wales, UK ***Associate Professor, Dept. of Physical Education (Performance Analysis in Sport), Dankook University, Yongin, Korea [Abstract] Basketball is a highly complex sport, analyses offensive and defensive rebounds, free throw percentages, minutes played and an efficiency rating. These statistics can have a large bearing and provide a lot of pressure on players as their every move can be analysed. Performance analysis in sport is a vital way of being able to track a team or individuals performance and more commonly used resource for player and team development. Discovering information such as this proves the importance of these types of analysis as with post competition video analysis a coach can reach a far more accurate analysis of the game leading to the ability to coach and correct the exact requirements of the team instead of their perceptions. A significant difference was found between winning and losing performances for different types of turnovers supporting current research that states that turnovers are not a valid predictor of match outcomes and that there is no specific type of turnover which can predict the outcome of a match as briefly mentioned in Curz and Tavares (1998). -
Other Basketball Leagues
OTHER BASKETBALL LEAGUES {Appendix 2.1, to Sports Facility Reports, Volume 13} Research completed as of August 1, 2012 AMERICAN BASKETBALL ASSOCIATION (ABA) LEAGUE UPDATE: For the 2011-12 season, the following teams are no longer members of the ABA: Atlanta Experience, Chi-Town Bulldogs, Columbus Riverballers, East Kentucky Energy, Eastonville Aces, Flint Fire, Hartland Heat, Indiana Diesels, Lake Michigan Admirals, Lansing Law, Louisiana United, Midwest Flames Peoria, Mobile Bat Hurricanes, Norfolk Sharks, North Texas Fresh, Northwestern Indiana Magical Stars, Nova Wonders, Orlando Kings, Panama City Dream, Rochester Razorsharks, Savannah Storm, St. Louis Pioneers, Syracuse Shockwave. Team: ABA-Canada Revolution Principal Owner: LTD Sports Inc. Team Website Arena: Home games will be hosted throughout Ontario, Canada. Team: Aberdeen Attack Principal Owner: Marcus Robinson, Hub City Sports LLC Team Website: N/A Arena: TBA © Copyright 2012, National Sports Law Institute of Marquette University Law School Page 1 Team: Alaska 49ers Principal Owner: Robert Harris Team Website Arena: Begich Middle School UPDATE: Due to the success of the Alaska Quake in the 2011-12 season, the ABA announced plans to add another team in Alaska. The Alaska 49ers will be added to the ABA as an expansion team for the 2012-13 season. The 49ers will compete in the Pacific Northwest Division. Team: Alaska Quake Principal Owner: Shana Harris and Carol Taylor Team Website Arena: Begich Middle School Team: Albany Shockwave Principal Owner: Christopher Pike Team Website Arena: Albany Civic Center Facility Website UPDATE: The Albany Shockwave will be added to the ABA as an expansion team for the 2012- 13 season. -
International Journal of Computer Science in Sport a Comprehensive
International Journal of Computer Science in Sport Volume 18, Issue 1, 2019 Journal homepage: http://iacss.org/index.php?id=30 DOI: 10.2478/ijcss-2019-0001 A comprehensive review of plus-minus ratings for evaluating individual players in team sports Lars Magnus Hvattum Faculty of Logistics, Molde University College, Molde, Norway Abstract The increasing availability of data from sports events has led to many new directions of research, and sports analytics can play a role in making better decisions both within a club and at the level of an individual player. The ability to objectively evaluate individual players in team sports is one aspect that may enable better decision making, but such evaluations are not straightforward to obtain. One class of ratings for individual players in team sports, known as plus-minus ratings, attempt to distribute credit for the performance of a team onto the players of that team. Such ratings have a long history, going back at least to the 1950s, but in recent years research on advanced versions of plus-minus ratings has increased noticeably. This paper presents a comprehensive review of contributions to plus- minus ratings in later years, pointing out some key developments and showing the richness of the mathematical models developed. One conclusion is that the literature on plus-minus ratings is quite fragmented, but that awareness of past contributions to the field should allow researchers to focus on some of the many open research questions related to the evaluation of individual players in team sports. KEYWORDS: RATING SYSTEM, RANKING, REGRESSION, REGULARIZATION IJCSS – Volume 18/2019/Issue 1 www.iacss.org Introduction Rating systems, both official and unofficial ones, exist for many different sports. -
Our Shining Moment: Hierarchical Clustering to Determine NCAA Tournament Seeding
Trunzo Scholz 1 Dan Trunzo and Libby Scholz MCS 100 June 4, 2016 Our Shining Moment: Hierarchical Clustering to Determine NCAA Tournament Seeding This project tries to correctly predict the NCAA Tournament Selection Committee’s bracket arrangement. We look both to correctly predict the 36 at large teams included in the tournament, and to seed teams in a similar way to the selection committee. The NCAA Tournament Selection Committee currently operates in secrecy, and the selection process is not transparent, either for teams or fans. If we successfully replicate the committee’s selections, our variable choices shed light on the “black box” of selection. As basketball fans ourselves, we hope to better understand which teams are in, which are out, and why Existing Selection Process 32 teams automatically qualify for the March NCAA tournament by winning their conference championship tournament. The other 36 “at large” teams are selected by the ten person committee through multiple voting rounds, where a team needs progressively fewer votes from the committee to make the tournament. Once the teams are selected, a similar process follows for seeding, where the selection committee ranks the best eight teams remaining, then the best six, and then the best four as more teams are placed in the bracket. Once all 68 teams are ranked, they are then placed in a bracket. At this point, the actual rank order may be violated to avoid “teams from the same conference… meet[ing] prior to the regional final if they played each other three or more times during the regular season and conference tournament,” or prior to the semifinals if they played at least twice. -
Official Basketball Statistics Rules Basic Interpretations
Official Basketball Statistics Rules With Approved Rulings and Interpretations (Throughout this manual, Team A players have last names starting with “A” the shooter tries to control and shoot the ball in the and Team B players have last names starting with “B.”) same motion with not enough time to get into a nor- mal shooting position (squared up to the basket). Article 2. A field goal made (FGM) is credited to a play- Basic Interpretations er any time a FGA by the player results in the goal being (Indicated as “B.I.” references throughout manual.) counted or results in an awarded score of two (or three) points except when the field goal is the result of a defen- sive player tipping the ball in the offensive basket. 1. APPROVED RULING—Approved rulings (indicated as A.R.s) are designed to interpret the spirit of the applica- Related rules in the NCAA Men’s and Women’s Basketball tion of the Official Basketball Rules. A thorough under- Rules and Interpretations: standing of the rules is essential to understanding and (1) 4-33: Definition of “Goal” applying the statistics rules in this manual. (2) 4-49.2: Definition of “Penalty for Violation” (3) 4-69: Definition of “Try for Field Goal” and definition of 2. STATISTICIAN’S JOB—The statistician’s responsibility is “Act of Shooting” to judge only what has happened, not to speculate as (4) 4-73: Definition of “Violation” to what would have happened. The statistician should (5) 5-1: “Scoring” not decide who would have gotten the rebound if it had (6) 9-16: “Basket Interference and Goaltending” not been for the foul. -
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. -
The University of Texas at Austin • Division of Recreational Sports •
The University of Texas at Austin • Recreational Sports TOP TEN THINGS TO KNOW ABOUT INTRAMURAL BASKETBALL 1. A team must have 4 players present to start the game. For coed, there must be at least 2 females present as well to start the game (For example, a team cannot start with 3 males and 1 female). 2. Undershirt may be sleeveless, BUT must at least cover the entire width of where the jersey is on the shoulder. There should be no skin-to-jersey contact. 3. Games starting on time will begin with a jump ball, and thereafter the alternating possession rule shall apply. Any team not ready to play at the scheduled game time will be penalized 2 points for every minute late (i.e. If a team shows up 7 minutes late, they will start the game down 14-0). The team ready to play will be awarded the ball to start the game. If a team is not ready to play at 10 minutes past game time, a no show "win" will be awarded to the team ready to play with the score 10-0. 4. We operate with a running clock, and it does not stop for timeouts. It will only stop in the last minute of the second half. 5. You can make substitutions the entire first half. In the second half, you can’t make any substitutions between the 4-minute-mark and the 1-minute-mark. 6. There are no timeouts in either half after the 4-minute-mark. In the second half, teams can call timeouts again under 1 minute. -
Investigating the Game-Related Statistics and Tactical Profile in NCAA Division I Men’S Basketball Games
OriginalTechnical Paper and tactical demands in college basketball https://doi.org/10.5114/biolsport.2018.71602 Investigating the game-related statistics and tactical profile in NCAA division I men’s basketball games 1,2,3 1 2 2 AUTHORS: Daniele Conte , Antonio Tessitore , Aaron Gjullin , Dominik Mackinnon , Corrado Corresponding author: Lupo4, Terence Favero2 Daniele Conte Lithuanian Sports University Institute of Sport Science and 1 Department of Movement, Human and Health Sciences, University of Rome Foro Italico, Rome, Italy Innovations 2 Department of Biology, University of Portland, Portland, OR, USA Sporto g. 6 44221 Kaunas 3 Institute of Sport Science and Innovations, Lithuanian Sports University, Kaunas, Lithuania Lithuania 4 School of Exercise & Sport Sciences, SUISM,Department of Medical Sciences, University of Torino, Turin, Italy E-mail: [email protected] ABSTRACT: The aim of this study was to analyze the game-related statistics and tactical profile in winning and losing teams in NCAA division I men’s basketball games. Twenty NCAA division I men’s basketball close (score difference: 1-9 points) games were analyzed during the 2013/14 season. For each game, the game- related statistics were collected from the official teams’ box scores. Number of ball possessions, offensive and defensive ratings and the Four Factors (effective field goal percentage; offensive rebounding percentage, recovered balls per ball possession, free throw rate) were also calculated. The tactical parameters evaluated were: ball reversal, dribble in key area, post entry, on-ball screen, off-ball screen, and hand off. Differences between winning and losing teams were calculated using a magnitude-based approach. Winning teams showed a likely higher percentage of 3-point goals made, number of defensive rebounds and steals and a very likely higher number of free throws made and free throws attempted.