Basketball Reference Effective Field Goal Percentage
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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. -
1.3 Algebraic Expressions
1.3 Algebraic Expressions Modeling words with an Algebraic Expression: Example 1: Multiple Choice Which algebraic expression models the phrase "seven fewer than a number t"? A) -7t B) 7 - t C) t - 7 D) 7 + t Example 2: Multiple Choice Which algebraic expression models the phrase "two times the sum of a and b"? F) a + b G) 2a + b H) 2(a + b) I) a + 2b Modeling a Situation: Example 3: You start with $20 and save $6 each week. What algebraic expression models the total amount you save? Example 4: You had $150, but you are spending $2 each day. What algebraic expression models this situation? 1 Evaluating Algebraic Expressions: Example 5: What is the value of the expression for the given values of the variables? a. 7(a + 4) + 3b - 8 for a = -4 and b = 5 b. c. 2 Writing and Evaluating Algebraic Expressions: Example 6: In football, a touchdown (TD) is worth six points, and extra-point kick (EPK) one point, and a field goal (FG) three points. a. What algebraic expression models the total number of points that a football team scores in a game, assuming each scoring play is one of the three given types? Let t = the number of touchdowns Let k = the number of extra-point kicks Let f = the number of field goals b. Suppose a football team scores 3 touchdowns, 2 extra-point kicks, and 4 field goals. How many points did the team score? 3 Example 7: In basketball, teams can score by making two-point shots, three-point shots, and one-point free throws. -
© Clark Creative Education Casino Royale
© Clark Creative Education Casino Royale Dice, Playing Cards, Ideal Unit: Probability & Expected Value Time Range: 3-4 Days Supplies: Pencil & Paper Topics of Focus: - Expected Value - Probability & Compound Probability Driving Question “How does expected value influence carnival and casino games?” Culminating Experience Design your own game Common Core Alignment: o Understand that two events A and B are independent if the probability of A and B occurring S-CP.2 together is the product of their probabilities, and use this characterization to determine if they are independent. Construct and interpret two-way frequency tables of data when two categories are associated S-CP.4 with each object being classified. Use the two-way table as a sample space to decide if events are independent and to approximate conditional probabilities. Calculate the expected value of a random variable; interpret it as the mean of the probability S-MD.2 distribution. Develop a probability distribution for a random variable defined for a sample space in which S-MD.4 probabilities are assigned empirically; find the expected value. Weigh the possible outcomes of a decision by assigning probabilities to payoff values and finding S-MD.5 expected values. S-MD.5a Find the expected payoff for a game of chance. S-MD.5b Evaluate and compare strategies on the basis of expected values. Use probabilities to make fair decisions (e.g., drawing by lots, using a random number S-MD.6 generator). Analyze decisions and strategies using probability concepts (e.g., product testing, medical S-MD.7 testing, pulling a hockey goalie at the end of a game). -
The Evolution of Basketball Statistics Is Finally Here
ows ind Sta W tis l t Ready for a ic n i S g i o r f t O w a e r h e TURBOSTATS SOFTWARE T The Evolution of Basketball Statistics is Finally Here All New Advanced Metrics, Efficiencies and Four Factors Outstanding Live Scoring BoxScore & Play-by-Play Easy to Learn Automatically Tags Video Fast Substitutions The Worlds Most Color-Coded Shot Advanced Live Game Charts Display... Scoring Software Uncontested Shots Shots off Turnovers View Career Shooting% Second Chance Shots After Each Shot Shots in Transition Includes the Advanced Zones vs Man to Man Statistics Used by Top Last Second Shots Pro & College Teams Blocked Shots New NET Rating System Score Live or by Video Highlights the Most Sort Video Clips Efficient Players Create Highlight Films Includes the eBook Theory of Evolution Creates CyberLink Explaining How the New PowerDirector video Formulas Help You Win project files for DVDs* The Only Software that PowerDirector 12/2010 Tracks Actual Rebound % Optional Player Photos Statistics for Individual Customizable Display Plays and Options Shows Four Factors, Event List, Player Team Stats by Point Guard Simulated image on the Statistics or Scouting Samsung ATIV SmartPC. Actual Screen Size is 11.5 Per Minute Statistics for Visit Samsung.com for tablet All Categories pricing and availability Imports Game Data from * PowerDirector sold separately NCAA BoxScores (Websites HTML or PDF) Runs Standalone on all Windows Laptops, Tablets and UltraBooks. Also tracks ... XP, Vista, 7 plus Windows 8 Pro Effective Field Goal%, True Shooting%, Turnover%, Offensive Rebound%, Individual Possessions, Broadcasts data to iPads and Offensive Efficiency, Time in Game Phones with a low cost app +/- Five Player Combos, Score on the Only Tablets Designed for Data Entry Defensive Points Given Up and more.. -
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. -
Rules of the Game January 2015
3x3 Official Rules of the Game January 2015 The Official FIBA Basketball Rules of the Game are valid for all game situations not specifically mentioned in the 3x3 Rules of the Game herein. Art. 1 Court and Ball The game will be played on a 3x3 basketball court with 1 basket. A regular 3x3 court playing surface is 15m (width) x 11m (length). The court shall have a regular basketball playing court sized zone, including a free throw line (5.80m), a two point line (6.75m) and a “no-charge semi-circle” area underneath the one basket. Half a traditional basketball court may be used. The official 3x3 ball shall be used in all categories. Note: at grassroots level, 3x3 can be played anywhere; court markings – if any are used – shall be adapted to the available space Art. 2 Teams Each team shall consist of 4 players (3 players on the court and 1 substitute). Art. 3 Game Officials The game officials shall consist of 1 or 2 referees and time/score keepers. Art. 4 Beginning of the Game 4.1. Both teams shall warm-up simultaneously prior to the game. 4.2. A coin flip shall determine which team gets the first possession. The team that wins the coin flip can either choose to benefit from the ball possession at the beginning of the game or at the beginning of a potential overtime. 4.3. The game must start with three players on the court. Note: articles 4.3 and 6.4 apply to FIBA 3x3 Official Competitions* only (not mandatory for grassroots events). -
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. -
Competition Wrongs Abstract
NICOLAS CORNELL Competition Wrongs abstract. In both philosophical and legal circles, it is typically assumed that wrongs depend upon having one’s rights violated. But within any market-based economy, market participants may be wronged by the conduct of other actors in the marketplace. Due to my illicit business tactics, you may lose profits, customers, employees, reputation, access to capital, or any number of other sources of value. This Article argues that such competition wrongs are an example of wrongs that arise without an underlying right, contrary to the typical philosophical and legal assumption. The Article thus draws upon various forms of business law to illustrate what is a conceptual point: that we can and do wrong one another in ways that do not involve violating our private entitlements but rather violating only public norms. author. Assistant Professor, University of Michigan Law School. This Article has benefitted from comments and suggestions from many others. Specifically, I would like to thank Mitch Ber- man, Dan Crane, Ryan Doerfler, Chris Essert, Rich Friedman, John Goldberg, Don Herzog, Wa- heed Hussain, Julian Jonker, Greg Keating, Greg Klass, George Letsas, Gabe Mendlow, Sanjukta Paul, Tony Reeves, Arthur Ripstein, Amy Sepinwall, Henry Smith, Sabine Tsuruda, David Wad- dilove, and Gary Watson. I am also grateful to audiences at Binghamton, Bowdoin, Harvard, IN- SEED, Michigan, Oxford, Penn, and Virginia, as well as the Bechtel Workshop in Moral and Po- litical Philosophy, the Legal Philosophy Workshop, and the North American Workshop on Private Law Theory. 2030 thecompetition claims of wrongs official reason article contents introduction 2032 i. -
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. -
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.