AB 2.8 Literal Equations.Notebook September 17, 2013

Total Page：16

File Type：pdf, Size：1020Kb

AB 2.8 Literal Equations.notebook September 17, 2013 "Do Now" Classify each number (more than one answer may apply) 1 AB 2.8 Literal Equations.notebook September 17, 2013 Team Hoyt http://www.teamhoyt.com/about/index.html 2 AB 2.8 Literal Equations.notebook September 17, 2013 3 AB 2.8 Literal Equations.notebook September 17, 2013 4 AB 2.8 Literal Equations.notebook September 17, 2013 Unit 2: Solving Equations Name Date 2.8 Literal Equations Block 1. Team Hoyt finished a marathon in d = rt 2 hours and 40 mins (or about 2.6 hours). A marathon is about 26.2 miles. What was their average speed? Use the formula d = rt and round to the nearest tenth. 5 AB 2.8 Literal Equations.notebook September 17, 2013 2. Team Hoyt's average speed during a half- d = rt marathon was about 10.1 miles per hour. A half-marathon is about 13.1 miles. How long did it take them to finish the race? Use the formula d = rt and round to the nearest tenth. 6 AB 2.8 Literal Equations.notebook September 17, 2013 To find a baseball pitcher's 3. earned run average (ERA), you can use the formula Ei = 9r, where E represents ERA, i represents # innings pitched, and r represents # earned runs allowed. Solve the equation for E. What is a pitcher's ERA if he allows 5 earned runs in 18 innings pitched? 7 AB 2.8 Literal Equations.notebook September 17, 2013 On May 14, 1898, a severe 4. hailstorm hit Kansas City. The largest hailstones were 9.5 inches in circumference. Windows were broken in nearly every house in the area. The formula for the circumference of a circle is C = 2πr, where C represents circumference and r represents radius. a. Solve the formula for r. b. Find the radius of one of the largest hailstones that fell on Kansas City in 1898. 8 AB 2.8 Literal Equations.notebook September 17, 2013 5. The formula for a Fahrenheit temperature in terms of degrees Celsius is a) Solve for C. b) 32 F is equal to how many degrees Celsius? 9 AB 2.8 Literal Equations.notebook September 17, 2013 Whiteboards 10 AB 2.8 Literal Equations.notebook September 17, 2013 Whiteboards 11 AB 2.8 Literal Equations.notebook September 17, 2013 A= r2 Solve for for b for h 12 AB 2.8 Literal Equations.notebook September 17, 2013 Homework Change: #8, 10, 11, 15 13.

Copy Link

Recommended publications

NCAA Division I Baseball Records
Division I Baseball Records Individual Records .................................................................. 2 Individual Leaders .................................................................. 4 Annual Individual Champions .......................................... 14 Team Records ........................................................................... 22 Team Leaders ............................................................................ 24 Annual Team Champions .................................................... 32 All-Time Winningest Teams ................................................ 38 Collegiate Baseball Division I Final Polls ....................... 42 Baseball America Division I Final Polls ........................... 45 USA Today Baseball Weekly/ESPN/ American Baseball Coaches Association Division I Final Polls ............................................................ 46 National Collegiate Baseball Writers Association Division I Final Polls ............................................................ 48 Statistical Trends ...................................................................... 49 No-Hitters and Perfect Games by Year .......................... 50 2 NCAA BASEBALL DIVISION I RECORDS THROUGH 2011 Official NCAA Division I baseball records began Season Career with the 1957 season and are based on informa- 39—Jason Krizan, Dallas Baptist, 2011 (62 games) 346—Jeff Ledbetter, Florida St., 1979-82 (262 games) tion submitted to the NCAA statistics service by Career RUNS BATTED IN PER GAME institutions
[Show full text]
Sabermetrics: the Past, the Present, and the Future
Sabermetrics: The Past, the Present, and the Future Jim Albert February 12, 2010 Abstract This article provides an overview of sabermetrics, the science of learn- ing about baseball through objective evidence. Statistics and baseball have always had a strong kinship, as many famous players are known by their famous statistical accomplishments such as Joe Dimaggio’s 56-game hitting streak and Ted Williams’ .406 batting average in the 1941 baseball season. We give an overview of how one measures performance in batting, pitching, and fielding. In baseball, the traditional measures are batting av- erage, slugging percentage, and on-base percentage, but modern measures such as OPS (on-base percentage plus slugging percentage) are better in predicting the number of runs a team will score in a game. Pitching is a harder aspect of performance to measure, since traditional measures such as winning percentage and earned run average are confounded by the abilities of the pitcher teammates. Modern measures of pitching such as DIPS (defense independent pitching statistics) are helpful in isolating the contributions of a pitcher that do not involve his teammates. It is also challenging to measure the quality of a player’s fielding ability, since the standard measure of fielding, the fielding percentage, is not helpful in understanding the range of a player in moving towards a batted ball. New measures of fielding have been developed that are useful in measuring a player’s fielding range. Major League Baseball is measuring the game in new ways, and sabermetrics is using this new data to find better mea- sures of player performance.
[Show full text]
FROM BULLDOGS to SUN DEVILS the EARLY YEARS ASU BASEBALL 1907-1958 Year ...Record
THE TRADITION CONTINUES ASUBASEBALL 2005 2005 SUN DEVIL BASEBALL 2 There comes a time in a little boy’s life when baseball is introduced to him. Thus begins the long journey for those meant to play the game at a higher level, for those who love the game so much they strive to be a part of its history. Sun Devil Baseball! NCAA NATIONAL CHAMPIONS: 1965, 1967, 1969, 1977, 1981 2005 SUN DEVIL BASEBALL 3 ASU AND THE GOLDEN SPIKES AWARD > For the past 26 years, USA Baseball has honored the top amateur baseball player in the country with the Golden Spikes Award. (See winners box.) The award is presented each year to the player who exhibits exceptional athletic ability and exemplary sportsmanship. Past winners of this prestigious award include current Major League Baseball stars J. D. Drew, Pat Burrell, Jason Varitek, Jason Jennings and Mark Prior. > Arizona State’s Bob Horner won the inaugural award in 1978 after hitting .412 with 20 doubles and 25 RBI. Oddibe McDowell (1984) and Mike Kelly (1991) also won the award. > Dustin Pedroia was named one of five finalists for the 2004 Golden Spikes Award. He became the seventh all-time final- ist from ASU, including Horner (1978), McDowell (1984), Kelly (1990), Kelly (1991), Paul Lo Duca (1993) and Jacob Cruz (1994). ODDIBE MCDOWELL > With three Golden Spikes winners, ASU ranks tied for first with Florida State and Cal State Fullerton as the schools with the most players to have earned college baseball’s top honor. BOB HORNER GOLDEN SPIKES AWARD WINNERS 2004 Jered Weaver Long Beach State 2003 Rickie Weeks Southern 2002 Khalil Greene Clemson 2001 Mark Prior Southern California 2000 Kip Bouknight South Carolina 1999 Jason Jennings Baylor 1998 Pat Burrell Miami 1997 J.D.
[Show full text]
An Offensive Earned-Run Average for Baseball
OPERATIONS RESEARCH, Vol. 25, No. 5, September-October 1077 An Offensive Earned-Run Average for Baseball THOMAS M. COVER Stanfortl University, Stanford, Californiu CARROLL W. KEILERS Probe fiystenzs, Sunnyvale, California (Received October 1976; accepted March 1977) This paper studies a baseball statistic that plays the role of an offen- sive earned-run average (OERA). The OERA of an individual is simply the number of earned runs per game that he would score if he batted in all nine positions in the line-up. Evaluation can be performed by hand by scoring the sequence of times at bat of a given batter. This statistic has the obvious natural interpretation and tends to evaluate strictly personal rather than team achievement. Some theoretical properties of this statistic are developed, and we give our answer to the question, "Who is the greatest hitter in baseball his- tory?" UPPOSE THAT we are following the history of a certain batter and want some index of his offensive effectiveness. We could, for example, keep track of a running average of the proportion of times he hit safely. This, of course, is the batting average. A more refined estimate ~vouldb e a running average of the total number of bases pcr official time at bat (the slugging average). We might then notice that both averages omit mention of ~valks.P erhaps what is needed is a spectrum of the running average of walks, singles, doublcs, triples, and homcruns per official time at bat. But how are we to convert this six-dimensional variable into a direct comparison of batters? Let us consider another statistic.
[Show full text]
Improving the FIP Model
Project Number: MQP-SDO-204 Improving the FIP Model A Major Qualifying Project Report Submitted to The Faculty of Worcester Polytechnic Institute In partial fulfillment of the requirements for the Degree of Bachelor of Science by Joseph Flanagan April 2014 Approved: Professor Sarah Olson Abstract The goal of this project is to improve the Fielding Independent Pitching (FIP) model for evaluating Major League Baseball starting pitchers. FIP attempts to separate a pitcher's controllable performance from random variation and the performance of his defense. Data from the 2002-2013 seasons will be analyzed and the results will be incorporated into a new metric. The new proposed model will be called jFIP. jFIP adds popups and hit by pitch to the fielding independent stats and also includes adjustments for a pitcher's defense and his efficiency in completing innings. Initial results suggest that the new metric is better than FIP at predicting pitcher ERA. Executive Summary Fielding Independent Pitching (FIP) is a metric created to measure pitcher performance. FIP can trace its roots back to research done by Voros McCracken in pursuit of winning his fantasy baseball league. McCracken discovered that there was little difference in the abilities of pitchers to prevent balls in play from becoming hits. Since individual pitchers can have greatly varying levels of effectiveness, this led him to wonder what pitchers did have control over. He found three that stood apart from the rest: strikeouts, walks, and home runs. Because these events involve only the batter and the pitcher, they are referred to as “fielding independent." FIP takes only strikeouts, walks, home runs, and innings pitched as inputs and it is scaled to earned run average (ERA) to allow for easier and more useful comparisons, as ERA has traditionally been one of the most important statistics for evaluating pitchers.
[Show full text]
How to Do Stats
EXPLANATION OF STATS IN SCORE BOOK FIELDING STATISTICS COLUMNS DO - Defensive Outs The number of put outs the team participated in while each player was in the line-up. Defensive outs are used in National Championships as a qualification rule. PO - Put out (10.09) A putout shall be credited to each fielder who (1) Catches a fly ball or a line drive, whether fair or foul. (2) Catches a thrown ball, which puts out a batter or a runner. (3) Tags a runner when the runner is off the base to which he is legally entitled. A – Assist (10.10) Any fielder who throws or deflects a battered or thrown ball in such a way that a putout results or would have except for a subsequent error, will be credited with an Assist. E – Error (10.12) An error is scored against any fielder who by any misplay (fumble, muff or wild throw) prolongs the life of the batter or runner or enables a runner to advance. BATTING STATISTICS COLUMNS PA - Plate Appearance Every time the batter completes his time at bat he is credited with a PA. Note: if the third out is made in the field he does not get a PA but is first to bat in the next innings. AB - At Bat (10.02(a)(1)) When a batter has reached 1st base without the aid of an ‘unofficial time at bat’. i.e. do not include Base on Balls, Hit by a Pitched Ball, Sacrifice flies/Bunts and Catches Interference. R – Runs (2.66) every time the runner crosses home plate scoring a run.
[Show full text]
2012 Holy Cross Baseball Yearbook Is Published by Commitment to the Last Principle Assures That the College Secretary:
2 22012012 HOOLYLY CRROSSOSS BAASEBALLSEBALL AT A GLLANCEANCE HOLY CROSS QUICK FACTS COACHING STAFF MISSION STATMENT Location: . .Worcester, MA 01610 Head Coach:. Greg DiCenzo (St. Lawrence, 1998) COLLEGE OF THE HOLY CROSS Founded: . 1843 Career Record / Years: . 93-104-1 / Four Years Enrollment: . 2,862 Record at Holy Cross / Years: . 93-104-1 / Four Years DEPARTMENT OF ATHLETICS Color: . Royal Purple Assistant Coach / Recruiting Coordinator: The Mission of the Athletic Department of the College Nickname: . Crusaders . .Jeff Kane (Clemson, 2001) of the Holy Cross is to promote the intellectual, physical, Affi liations: . NCAA Division I, Patriot League Assistant Coach: and moral development of students. Through Division I President: . Rev. Philip L. Boroughs, S.J. Ron Rakowski (San Francisco State, 2002) athletic participation, our young men and women student- Director of Admissions: . Ann McDermott Assistant Coach:. Jeff Miller (Holy Cross, 2000) athletes learn a self-discipline that has both present and Offi ce Phone: . (508) 793-2443 Baseball Offi ce Phone:. (508) 793-2753 long-term effects; the interplay of individual and team effort; Director of Financial Aid: . Lynne M. Myers E-Mail Address: . [email protected] pride and self esteem in both victory and defeat; a skillful Offi ce Phone: . (508) 793-2265 Mailing Address: . .Greg DiCenzo management of time; personal endurance and courage; and Director of Athletics: . .Richard M. Regan, Jr. Head Baseball Coach the complex relationships between friendship, leadership, Associate Director of Athletics:. Bill Bellerose College of the Holy Cross and service. Our athletics program, in the words of the Associate Director of Athletics:. Ann Zelesky One College Street College Mission Statement, calls for “a community marked Associate Director of Athletics:.
[Show full text]
NFCA Home Plate: ATEC: Beyond the Basics of Scoring Fastpitch Softball
NFCA Home Plate: ATEC: Beyond the Basics of Scoring Fastpitch Softball by Jeri Findlay Published by National Fastpitch Coaches Association Copyright 1999. All Right Reserved Introduction Basic Guidelines and Scorer Responsibilities Proving A Box Score Percentages and Averages Cumulative Performance Records Called and Forfeited Games Offense: Statistics Offense: Hits Offense: Extra Base Hits Offense: Game Ending Hits Offense: Fielder's Choice Offense: Sacrifices Offense: Runs Batted In (RBI) Offense: Batting Out of Order Offense: Strikeouts Offense: Stolen Bases Offense: Caught Stealing (Unsuccessful Attempt) Defense: Statistics Defense: Errors Defense: Putouts Defense: Assists Defense: Double Play/Triple Play Defense: Throw Outs Pitching: Statistics Pitching: Earned Runs Pitching: Charging Runs Scored (When Relief Pitchers Are Used) Pitching: Strikeouts Pitching: Bases On Balls Pitching: Wild Pitches/Passed Balls Pitching: Winning and Losing Pitcher Pitching: Saves Scoring The Tie-Breaker Some images Copyright www.arttoday.com Web design by Ray Foster. Reproduction of material from any NFCA Home Plate pages without written permission is strictly prohibited. Copyright ©1999 National Fastpitch Coaches Association. NFCA, 409 Vandiver Drive, Suite 5-202, Columbia, MO 65202 TELEPHONE (573) 875-3033 | FAX (573) 875-2924 | EMAIL http://www.nfca.org/indexscoringfp.lasso [1/27/2002 2:21:41 AM] NFCA Homeplate: ATEC: Beyond The Basics of Scoring Fastpitch Softball TABLE OF CONTENTS Introduction Introduction Basic Guidelines and Scorer - - - - - - - - - - - - - - - - - - - - - - - Responsibilities Proving A Box Score Published by: National Softball Coaches Association Percentages and Averages Written by Jeri Findlay, Head Softball Coach, Ball State University Cumulative Performance Records Introduction Called and Forfeited Games Scoring in the game of fastpitch softball seems to be as diversified as the people Offense: Statistics playing it.
[Show full text]
Package 'Mlbstats'
Package ‘mlbstats’ March 16, 2018 Type Package Title Major League Baseball Player Statistics Calculator Version 0.1.0 Author Philip D. Waggoner <[email protected]> Maintainer Philip D. Waggoner <[email protected]> Description Computational functions for player metrics in major league baseball including bat- ting, pitching, fielding, base-running, and overall player statistics. This package is actively main- tained with new metrics being added as they are developed. License MIT + file LICENSE Encoding UTF-8 LazyData true RoxygenNote 6.0.1 NeedsCompilation no Repository CRAN Date/Publication 2018-03-16 09:15:57 UTC R topics documented: ab_hr . .2 aera .............................................3 ba ..............................................4 baa..............................................4 babip . .5 bb9 .............................................6 bb_k.............................................6 BsR .............................................7 dice .............................................7 EqA.............................................8 era..............................................9 erc..............................................9 fip.............................................. 10 fp .............................................. 11 1 2 ab_hr go_ao . 11 gpa.............................................. 12 h9.............................................. 13 iso.............................................. 13 k9.............................................. 14 k_bb............................................
[Show full text]
1. Intro to Scorekeeping
1. Intro To Scorekeeping The following terms will be used on this site: Cell: The term cell refers to the square in which the player’s at-bat is recorded. In this illustration, the cell is the box where the diagram is drawn. Scorecard, Scorebook: Will be used interchangeably and refer to the sheet that records the player and scoring information during a baseball game. Scorekeeper: Refers to someone on a team that keeps the score for the purposes of the team. Official Scorer: The designated person whose scorekeeping is considered the official record of the game. The Official Scorer is not a member of either team. Baseball’s Defensive Positions To “keep score” of a baseball game it is essential to know the defensive positions and their shorthand representation. For example, the number “1” is used to refer to the pitcher (P). NOTE : In the younger levels of youth baseball leagues 10 defensive players are used. This 10 th position is know as the Short Center Fielder (SC) and is positioned between second base and the second baseman, on the beginning of the outfield grass. The Short Center Fielder bats and can be placed anywhere in the batting lineup. Defensive Positions, Numbers & Abbreviations Position Number Defensive Position Position Abbrev. 1 Pitcher P 2 Catcher C 3 First Baseman 1B 4 Second Baseman 2B 5 Third Baseman 3B 6 Short Stop SS 7 Left Fielder LF 8 Center Fielder CF 9 Right Fielder RF 10 Short Center Fielder SC The illustration below shows the defensive position for the defense. Notice the short center fielder is illustrated for those that are scoring youth league games.
[Show full text]
Framing the Game Through a Sabermetric Lens: Major League
FRAMING THE GAME THROUGH A SABERMETRIC LENS: MAJOR LEAGUE BASEBALL BROADCASTS AND THE DELINEATION OF TRADITIONAL AND NEW FACT METRICS by ZACHARY WILLIAM ARTH ANDREW C. BILLINGS, COMMITTEE CHAIR DARRIN J. GRIFFIN SCOTT PARROTT JAMES D. LEEPER KENON A. BROWN A DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the College of Communication and Information Sciences in the Graduate School of The University of Alabama TUSCALOOSA, ALABAMA 2019 Copyright Zachary William Arth 2019 ALL RIGHTS RESERVED i ABSTRACT This purpose of this dissertation was to first understand how Major League Baseball teams are portraying and discussing statistics within their local broadcasts. From there, the goal was to ascertain how teams differed in their portrayals, with the specific dichotomy of interest being between teams heavy in advanced statistics and those heavy in traditional statistics. With advanced baseball statistics still far from being universally accepted among baseball fans, the driving question was whether or not fans that faced greater exposure to advanced statistics would also be more knowledgeable and accepting of them. Thus, based on the results of the content analysis, fans of four of the most advanced teams and four of the most traditional teams were accessed through MLB team subreddits and surveyed. Results initially indicated that there was no difference between fans of teams with advanced versus traditional broadcasts. However, there were clear differences in knowledge based on other factors, such as whether fans had a new school or old school orientation, whether they were high in Schwabism and/or mavenism, and how highly identified they were with the team.
[Show full text]
Baseball with Popcorn, Statistics and SAS - What a Mix David Franklin, Theprogrammerscabin.Com, Litchfield, NH
NESUG 2011 Statistics & Analysis Baseball with Popcorn, Statistics and SAS - What A Mix David Franklin, TheProgrammersCabin.com, Litchfield, NH ABSTRACT The year is almost over. At the end of September another year of baseball will be over. Then the talk will be over who was the best player for the season – a look at the statistics will those who make this decision. Statistics in sports has been around since sports were first played, and where there is statistics, SAS® is not far behind. This paper looks at Baseball statistics, how they were developed, then look at the how some of the more common statistics are calculated. Finally, a program will be presented that was developed and used in local minor league to solve the riddle of who was the best pitcher and batter. INTRODUCTION Baseball statistics can be traced back to its roots in cricket. It was a person by the name of Henry Chadwick in the 19th century who first brought the science of numbers to the sport with his experience in cricket statistics. From the start statistics such as the batting average and earned run average have been prominent. However in recent years there have been a new slew of statistics computed that draw attention to almost every facet of baseball, whether it be batting, pitching or fielding. This paper will have a look at how a few of the more common statistics that are calculated, and then show an example based on the real world in a local minor league where the question was answered, who was the best pitcher and best batter for the season.
[Show full text]

AB ­ 2.8 Literal Equations.Notebook September 17, 2013

AB 2.8 Literal Equations.Notebook September 17, 2013