Mathletics : : How Gamblers, Managers, and Fans Use Mathematics in Sports, Second Edition / / Scott Nestler, Konstantinos Pelechrinis, Wayne L. Winston.
How to use math to improve performance and predict outcomes in professional sportsMathletics reveals the mathematical methods top coaches and managers use to evaluate players and improve team performance, and gives math enthusiasts the practical skills they need to enhance their understanding and en...
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Winston, Wayne L., author. aut http://id.loc.gov/vocabulary/relators/aut Mathletics : How Gamblers, Managers, and Fans Use Mathematics in Sports, Second Edition / Scott Nestler, Konstantinos Pelechrinis, Wayne L. Winston. Princeton, NJ : Princeton University Press, [2022] ©2019 1 online resource (608 p.) : 197 line illus. 55 tables. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Frontmatter -- Contents -- Preface -- Acknowledgments -- Abbreviations -- Part I. Baseball -- 1. Baseball’s Pythagorean Theorem -- 2. Who Had a Better Year: Mike Trout or Kris Bryant? -- 3. Evaluating Hitters by Linear Weights -- 4. Evaluating Hitters by Monte Carlo Simulation -- 5. Evaluating Baseball Pitchers, Forecasting Future Pitcher Performance, and an Introduction to Statcast -- 6. Baseball Decision Making -- 7. Evaluating Fielders -- 8. Win Probability Added (WPA) -- 9. Wins Above Replacement (WAR) and Player Salaries -- 10. Park Factors -- 11. Streakiness in Sports -- 12. The Platoon Effect -- 13. Was Tony Perez a Great Clutch Hitter? -- 14. Pitch Count, Pitcher Effectiveness, and PITCHf/x Data -- 15. Would Ted Williams Hit .406 today? -- 16. Was Joe DiMaggio’s 56-Game Hitting Streak the Greatest Sports Record of All Time? -- 17. Projecting Major League Performance -- Part II. Football -- 18. What Makes NFL Teams Win? -- 19. Who’s Better: Brady or Rodgers? -- 20. Football States and Values -- 21. Football Decision Making 101 -- 22. If Passing Is Better than Running, Why Don’t Teams Always Pass? -- 23. Should We Go for a One-Point or a Two-Point Conversion? -- 24. To Give Up the Ball Is Better than to Receive: The Case of College Football Overtime -- 25. Has the NFL Finally Gotten the OT Rules Right? -- 26. How Valuable Are NFL Draft Picks? -- 27. Player Tracking Data in the NFL -- Part III. Basketball -- 28. Basketball Statistics 101: The Four Factor Model -- 29. Linear Weights for Evaluating NBA Players -- 30. Adjusted +/− Player Ratings -- 31. ESPN RPM and FiveThirtyEight RAPTOR Ratings -- 32. NBA Lineup Analysis -- 33. Analyzing Team and Individual Matchups -- 34. NBA Salaries and the Value of a Draft Pick -- 35. Are NBA Officials Prejudiced? -- 36. Pick-n- Rolling to Win, the Death of Post Ups and Isos -- 37. SportVU, Second Spectrum, and the Spatial Basketball Data Revolution -- 38. In-Game Basketball Decision Making -- Part IV. Other Sports -- 39. Soccer Analytics -- 40. Hockey Analytics -- 41. Volleyball Analytics -- 42. Golf Analytics -- 43. Analytics and Cyber Athletes: The Era of e-Sports -- Part V. Sports Gambling -- 44. Sports Gambling 101 -- 45. Freakonomics Meets the Bookmaker -- 46. Rating Sports Teams -- 47. From Point Ratings to Probabilities -- 48. The NCAA Evaluation Tool (NET) -- 49. Optimal Money Management: The Kelley Growth Criterion -- 50. Calcuttas -- Part VI. Methods and Miscellaneous -- 51. How to Work with Data Sources: Collecting and Visualizing Data -- 52. Assessing Players with Limited Data: The Bayesian Approach -- 53. Finding Latent Patterns through Matrix Factorization -- 54. Network Analysis in Sports -- 55. Elo Ratings -- 56. Comparing Players from Different Eras -- 57. Does Fatigue Make Cowards of Us All? The Case of NBA Back-to- Back Games and NFL Bye Weeks -- 58. The College Football Playoff -- 59. Quantifying Sports Collapses -- 60. Daily Fantasy Sports -- Bibliography -- Index restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star How to use math to improve performance and predict outcomes in professional sportsMathletics reveals the mathematical methods top coaches and managers use to evaluate players and improve team performance, and gives math enthusiasts the practical skills they need to enhance their understanding and enjoyment of their favorite sports—and maybe even gain the outside edge to winning bets. This second edition features new data, new players and teams, and new chapters on soccer, e-sports, golf, volleyball, gambling Calcuttas, analysis of camera data, Bayesian inference, ridge regression, and other statistical techniques. After reading Mathletics, you will understand why baseball teams should almost never bunt; why football overtime systems are unfair; why points, rebounds, and assists aren’t enough to determine who’s the NBA’s best player; and more. Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 29. Jun 2022) Sports Mathematics. COMPUTERS / Database Management / Data Mining. bisacsh Asian studies. Binary number. Buddhism. Catholicism. Chinese culture. Computation. Confucianism. Confucius. Integer. Major religious groups. New Confucianism. Philosophy. Positional notation. Protestantism. Real number. Requirement. Sinology. Sociology. Taoism. Thought. Nestler, Scott, author. aut http://id.loc.gov/vocabulary/relators/aut Pelechrinis, Konstantinos, author. aut http://id.loc.gov/vocabulary/relators/aut Title is part of eBook package: De Gruyter Princeton University Press Complete eBook-Package 2019 9783110663365 https://doi.org/10.1515/9780691189291?locatt=mode:legacy https://www.degruyter.com/isbn/9780691189291 Cover https://www.degruyter.com/document/cover/isbn/9780691189291/original |
language |
English |
format |
eBook |
author |
Winston, Wayne L., Winston, Wayne L., Nestler, Scott, Pelechrinis, Konstantinos, |
spellingShingle |
Winston, Wayne L., Winston, Wayne L., Nestler, Scott, Pelechrinis, Konstantinos, Mathletics : How Gamblers, Managers, and Fans Use Mathematics in Sports, Second Edition / Frontmatter -- Contents -- Preface -- Acknowledgments -- Abbreviations -- Part I. Baseball -- 1. Baseball’s Pythagorean Theorem -- 2. Who Had a Better Year: Mike Trout or Kris Bryant? -- 3. Evaluating Hitters by Linear Weights -- 4. Evaluating Hitters by Monte Carlo Simulation -- 5. Evaluating Baseball Pitchers, Forecasting Future Pitcher Performance, and an Introduction to Statcast -- 6. Baseball Decision Making -- 7. Evaluating Fielders -- 8. Win Probability Added (WPA) -- 9. Wins Above Replacement (WAR) and Player Salaries -- 10. Park Factors -- 11. Streakiness in Sports -- 12. The Platoon Effect -- 13. Was Tony Perez a Great Clutch Hitter? -- 14. Pitch Count, Pitcher Effectiveness, and PITCHf/x Data -- 15. Would Ted Williams Hit .406 today? -- 16. Was Joe DiMaggio’s 56-Game Hitting Streak the Greatest Sports Record of All Time? -- 17. Projecting Major League Performance -- Part II. Football -- 18. What Makes NFL Teams Win? -- 19. Who’s Better: Brady or Rodgers? -- 20. Football States and Values -- 21. Football Decision Making 101 -- 22. If Passing Is Better than Running, Why Don’t Teams Always Pass? -- 23. Should We Go for a One-Point or a Two-Point Conversion? -- 24. To Give Up the Ball Is Better than to Receive: The Case of College Football Overtime -- 25. Has the NFL Finally Gotten the OT Rules Right? -- 26. How Valuable Are NFL Draft Picks? -- 27. Player Tracking Data in the NFL -- Part III. Basketball -- 28. Basketball Statistics 101: The Four Factor Model -- 29. Linear Weights for Evaluating NBA Players -- 30. Adjusted +/− Player Ratings -- 31. ESPN RPM and FiveThirtyEight RAPTOR Ratings -- 32. NBA Lineup Analysis -- 33. Analyzing Team and Individual Matchups -- 34. NBA Salaries and the Value of a Draft Pick -- 35. Are NBA Officials Prejudiced? -- 36. Pick-n- Rolling to Win, the Death of Post Ups and Isos -- 37. SportVU, Second Spectrum, and the Spatial Basketball Data Revolution -- 38. In-Game Basketball Decision Making -- Part IV. Other Sports -- 39. Soccer Analytics -- 40. Hockey Analytics -- 41. Volleyball Analytics -- 42. Golf Analytics -- 43. Analytics and Cyber Athletes: The Era of e-Sports -- Part V. Sports Gambling -- 44. Sports Gambling 101 -- 45. Freakonomics Meets the Bookmaker -- 46. Rating Sports Teams -- 47. From Point Ratings to Probabilities -- 48. The NCAA Evaluation Tool (NET) -- 49. Optimal Money Management: The Kelley Growth Criterion -- 50. Calcuttas -- Part VI. Methods and Miscellaneous -- 51. How to Work with Data Sources: Collecting and Visualizing Data -- 52. Assessing Players with Limited Data: The Bayesian Approach -- 53. Finding Latent Patterns through Matrix Factorization -- 54. Network Analysis in Sports -- 55. Elo Ratings -- 56. Comparing Players from Different Eras -- 57. Does Fatigue Make Cowards of Us All? The Case of NBA Back-to- Back Games and NFL Bye Weeks -- 58. The College Football Playoff -- 59. Quantifying Sports Collapses -- 60. Daily Fantasy Sports -- Bibliography -- Index |
author_facet |
Winston, Wayne L., Winston, Wayne L., Nestler, Scott, Pelechrinis, Konstantinos, Nestler, Scott, Nestler, Scott, Pelechrinis, Konstantinos, Pelechrinis, Konstantinos, |
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Nestler, Scott, Nestler, Scott, Pelechrinis, Konstantinos, Pelechrinis, Konstantinos, |
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Winston, Wayne L., |
title |
Mathletics : How Gamblers, Managers, and Fans Use Mathematics in Sports, Second Edition / |
title_sub |
How Gamblers, Managers, and Fans Use Mathematics in Sports, Second Edition / |
title_full |
Mathletics : How Gamblers, Managers, and Fans Use Mathematics in Sports, Second Edition / Scott Nestler, Konstantinos Pelechrinis, Wayne L. Winston. |
title_fullStr |
Mathletics : How Gamblers, Managers, and Fans Use Mathematics in Sports, Second Edition / Scott Nestler, Konstantinos Pelechrinis, Wayne L. Winston. |
title_full_unstemmed |
Mathletics : How Gamblers, Managers, and Fans Use Mathematics in Sports, Second Edition / Scott Nestler, Konstantinos Pelechrinis, Wayne L. Winston. |
title_auth |
Mathletics : How Gamblers, Managers, and Fans Use Mathematics in Sports, Second Edition / |
title_alt |
Frontmatter -- Contents -- Preface -- Acknowledgments -- Abbreviations -- Part I. Baseball -- 1. Baseball’s Pythagorean Theorem -- 2. Who Had a Better Year: Mike Trout or Kris Bryant? -- 3. Evaluating Hitters by Linear Weights -- 4. Evaluating Hitters by Monte Carlo Simulation -- 5. Evaluating Baseball Pitchers, Forecasting Future Pitcher Performance, and an Introduction to Statcast -- 6. Baseball Decision Making -- 7. Evaluating Fielders -- 8. Win Probability Added (WPA) -- 9. Wins Above Replacement (WAR) and Player Salaries -- 10. Park Factors -- 11. Streakiness in Sports -- 12. The Platoon Effect -- 13. Was Tony Perez a Great Clutch Hitter? -- 14. Pitch Count, Pitcher Effectiveness, and PITCHf/x Data -- 15. Would Ted Williams Hit .406 today? -- 16. Was Joe DiMaggio’s 56-Game Hitting Streak the Greatest Sports Record of All Time? -- 17. Projecting Major League Performance -- Part II. Football -- 18. What Makes NFL Teams Win? -- 19. Who’s Better: Brady or Rodgers? -- 20. Football States and Values -- 21. Football Decision Making 101 -- 22. If Passing Is Better than Running, Why Don’t Teams Always Pass? -- 23. Should We Go for a One-Point or a Two-Point Conversion? -- 24. To Give Up the Ball Is Better than to Receive: The Case of College Football Overtime -- 25. Has the NFL Finally Gotten the OT Rules Right? -- 26. How Valuable Are NFL Draft Picks? -- 27. Player Tracking Data in the NFL -- Part III. Basketball -- 28. Basketball Statistics 101: The Four Factor Model -- 29. Linear Weights for Evaluating NBA Players -- 30. Adjusted +/− Player Ratings -- 31. ESPN RPM and FiveThirtyEight RAPTOR Ratings -- 32. NBA Lineup Analysis -- 33. Analyzing Team and Individual Matchups -- 34. NBA Salaries and the Value of a Draft Pick -- 35. Are NBA Officials Prejudiced? -- 36. Pick-n- Rolling to Win, the Death of Post Ups and Isos -- 37. SportVU, Second Spectrum, and the Spatial Basketball Data Revolution -- 38. In-Game Basketball Decision Making -- Part IV. Other Sports -- 39. Soccer Analytics -- 40. Hockey Analytics -- 41. Volleyball Analytics -- 42. Golf Analytics -- 43. Analytics and Cyber Athletes: The Era of e-Sports -- Part V. Sports Gambling -- 44. Sports Gambling 101 -- 45. Freakonomics Meets the Bookmaker -- 46. Rating Sports Teams -- 47. From Point Ratings to Probabilities -- 48. The NCAA Evaluation Tool (NET) -- 49. Optimal Money Management: The Kelley Growth Criterion -- 50. Calcuttas -- Part VI. Methods and Miscellaneous -- 51. How to Work with Data Sources: Collecting and Visualizing Data -- 52. Assessing Players with Limited Data: The Bayesian Approach -- 53. Finding Latent Patterns through Matrix Factorization -- 54. Network Analysis in Sports -- 55. Elo Ratings -- 56. Comparing Players from Different Eras -- 57. Does Fatigue Make Cowards of Us All? The Case of NBA Back-to- Back Games and NFL Bye Weeks -- 58. The College Football Playoff -- 59. Quantifying Sports Collapses -- 60. Daily Fantasy Sports -- Bibliography -- Index |
title_new |
Mathletics : |
title_sort |
mathletics : how gamblers, managers, and fans use mathematics in sports, second edition / |
publisher |
Princeton University Press, |
publishDate |
2022 |
physical |
1 online resource (608 p.) : 197 line illus. 55 tables. |
contents |
Frontmatter -- Contents -- Preface -- Acknowledgments -- Abbreviations -- Part I. Baseball -- 1. Baseball’s Pythagorean Theorem -- 2. Who Had a Better Year: Mike Trout or Kris Bryant? -- 3. Evaluating Hitters by Linear Weights -- 4. Evaluating Hitters by Monte Carlo Simulation -- 5. Evaluating Baseball Pitchers, Forecasting Future Pitcher Performance, and an Introduction to Statcast -- 6. Baseball Decision Making -- 7. Evaluating Fielders -- 8. Win Probability Added (WPA) -- 9. Wins Above Replacement (WAR) and Player Salaries -- 10. Park Factors -- 11. Streakiness in Sports -- 12. The Platoon Effect -- 13. Was Tony Perez a Great Clutch Hitter? -- 14. Pitch Count, Pitcher Effectiveness, and PITCHf/x Data -- 15. Would Ted Williams Hit .406 today? -- 16. Was Joe DiMaggio’s 56-Game Hitting Streak the Greatest Sports Record of All Time? -- 17. Projecting Major League Performance -- Part II. Football -- 18. What Makes NFL Teams Win? -- 19. Who’s Better: Brady or Rodgers? -- 20. Football States and Values -- 21. Football Decision Making 101 -- 22. If Passing Is Better than Running, Why Don’t Teams Always Pass? -- 23. Should We Go for a One-Point or a Two-Point Conversion? -- 24. To Give Up the Ball Is Better than to Receive: The Case of College Football Overtime -- 25. Has the NFL Finally Gotten the OT Rules Right? -- 26. How Valuable Are NFL Draft Picks? -- 27. Player Tracking Data in the NFL -- Part III. Basketball -- 28. Basketball Statistics 101: The Four Factor Model -- 29. Linear Weights for Evaluating NBA Players -- 30. Adjusted +/− Player Ratings -- 31. ESPN RPM and FiveThirtyEight RAPTOR Ratings -- 32. NBA Lineup Analysis -- 33. Analyzing Team and Individual Matchups -- 34. NBA Salaries and the Value of a Draft Pick -- 35. Are NBA Officials Prejudiced? -- 36. Pick-n- Rolling to Win, the Death of Post Ups and Isos -- 37. SportVU, Second Spectrum, and the Spatial Basketball Data Revolution -- 38. In-Game Basketball Decision Making -- Part IV. Other Sports -- 39. Soccer Analytics -- 40. Hockey Analytics -- 41. Volleyball Analytics -- 42. Golf Analytics -- 43. Analytics and Cyber Athletes: The Era of e-Sports -- Part V. Sports Gambling -- 44. Sports Gambling 101 -- 45. Freakonomics Meets the Bookmaker -- 46. Rating Sports Teams -- 47. From Point Ratings to Probabilities -- 48. The NCAA Evaluation Tool (NET) -- 49. Optimal Money Management: The Kelley Growth Criterion -- 50. Calcuttas -- Part VI. Methods and Miscellaneous -- 51. How to Work with Data Sources: Collecting and Visualizing Data -- 52. Assessing Players with Limited Data: The Bayesian Approach -- 53. Finding Latent Patterns through Matrix Factorization -- 54. Network Analysis in Sports -- 55. Elo Ratings -- 56. Comparing Players from Different Eras -- 57. Does Fatigue Make Cowards of Us All? The Case of NBA Back-to- Back Games and NFL Bye Weeks -- 58. The College Football Playoff -- 59. Quantifying Sports Collapses -- 60. Daily Fantasy Sports -- Bibliography -- Index |
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dewey-hundreds |
700 - Arts & recreation |
dewey-tens |
790 - Sports, games & entertainment |
dewey-ones |
796 - Athletic & outdoor sports & games |
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SportVU, Second Spectrum, and the Spatial Basketball Data Revolution -- </subfield><subfield code="t">38. In-Game Basketball Decision Making -- </subfield><subfield code="t">Part IV. Other Sports -- </subfield><subfield code="t">39. Soccer Analytics -- </subfield><subfield code="t">40. Hockey Analytics -- </subfield><subfield code="t">41. Volleyball Analytics -- </subfield><subfield code="t">42. Golf Analytics -- </subfield><subfield code="t">43. Analytics and Cyber Athletes: The Era of e-Sports -- </subfield><subfield code="t">Part V. Sports Gambling -- </subfield><subfield code="t">44. Sports Gambling 101 -- </subfield><subfield code="t">45. Freakonomics Meets the Bookmaker -- </subfield><subfield code="t">46. Rating Sports Teams -- </subfield><subfield code="t">47. From Point Ratings to Probabilities -- </subfield><subfield code="t">48. The NCAA Evaluation Tool (NET) -- </subfield><subfield code="t">49. Optimal Money Management: The Kelley Growth Criterion -- </subfield><subfield code="t">50. Calcuttas -- </subfield><subfield code="t">Part VI. Methods and Miscellaneous -- </subfield><subfield code="t">51. How to Work with Data Sources: Collecting and Visualizing Data -- </subfield><subfield code="t">52. Assessing Players with Limited Data: The Bayesian Approach -- </subfield><subfield code="t">53. Finding Latent Patterns through Matrix Factorization -- </subfield><subfield code="t">54. Network Analysis in Sports -- </subfield><subfield code="t">55. Elo Ratings -- </subfield><subfield code="t">56. Comparing Players from Different Eras -- </subfield><subfield code="t">57. Does Fatigue Make Cowards of Us All? The Case of NBA Back-to- Back Games and NFL Bye Weeks -- </subfield><subfield code="t">58. The College Football Playoff -- </subfield><subfield code="t">59. Quantifying Sports Collapses -- </subfield><subfield code="t">60. Daily Fantasy Sports -- </subfield><subfield code="t">Bibliography -- </subfield><subfield code="t">Index</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="a">restricted access</subfield><subfield code="u">http://purl.org/coar/access_right/c_16ec</subfield><subfield code="f">online access with authorization</subfield><subfield code="2">star</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">How to use math to improve performance and predict outcomes in professional sportsMathletics reveals the mathematical methods top coaches and managers use to evaluate players and improve team performance, and gives math enthusiasts the practical skills they need to enhance their understanding and enjoyment of their favorite sports—and maybe even gain the outside edge to winning bets. This second edition features new data, new players and teams, and new chapters on soccer, e-sports, golf, volleyball, gambling Calcuttas, analysis of camera data, Bayesian inference, ridge regression, and other statistical techniques. After reading Mathletics, you will understand why baseball teams should almost never bunt; why football overtime systems are unfair; why points, rebounds, and assists aren’t enough to determine who’s the NBA’s best player; and more.</subfield></datafield><datafield tag="538" ind1=" " ind2=" "><subfield code="a">Mode of access: Internet via World Wide Web.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">In English.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Description based on online resource; title from PDF title page (publisher's Web site, viewed 29. Jun 2022)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Sports</subfield><subfield code="x">Mathematics.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Database Management / Data Mining.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Asian studies.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Binary number.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Buddhism.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Catholicism.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Chinese culture.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Computation.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Confucianism.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Confucius.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Integer.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Major religious groups.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">New Confucianism.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Philosophy.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Positional notation.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Protestantism.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Real number.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Requirement.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Sinology.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Sociology.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Taoism.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Thought.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nestler, Scott, </subfield><subfield code="e">author.</subfield><subfield code="4">aut</subfield><subfield code="4">http://id.loc.gov/vocabulary/relators/aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pelechrinis, Konstantinos, </subfield><subfield code="e">author.</subfield><subfield code="4">aut</subfield><subfield code="4">http://id.loc.gov/vocabulary/relators/aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Title is part of eBook package:</subfield><subfield code="d">De Gruyter</subfield><subfield code="t">Princeton University Press Complete eBook-Package 2019</subfield><subfield code="z">9783110663365</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1515/9780691189291?locatt=mode:legacy</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.degruyter.com/isbn/9780691189291</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="3">Cover</subfield><subfield code="u">https://www.degruyter.com/document/cover/isbn/9780691189291/original</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">978-3-11-066336-5 Princeton University Press Complete eBook-Package 2019</subfield><subfield code="b">2019</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_BACKALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_CL_CHCOMSGSEN</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_EBACKALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_EBKALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_ECL_CHCOMSGSEN</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_EEBKALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_ESTMALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_PPALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_STMALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV-deGruyter-alles</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">PDA12STME</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">PDA13ENGE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">PDA18STMEE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">PDA5EBK</subfield></datafield></record></collection> |