Muutke küpsiste eelistusi

E-raamat: Statistical Thinking in Sports [Taylor & Francis e-raamat]

Edited by (Emeritus Professor at Bowling Green State Uni.), Edited by
  • Formaat: 312 pages
  • Ilmumisaeg: 19-Jun-2019
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9780429147906
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 115,40 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 164,86 €
  • Säästad 30%
Statistical Thinking in SportsSuurem pilt
  • Formaat: 312 pages
  • Ilmumisaeg: 19-Jun-2019
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9780429147906
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9780429147906
Since the first athletic events found a fan base, sports and statistics have always maintained a tight and at times mythical relationship. As a way to relay the telling of a game's drama and attest to the prodigious powers of the heroes involved, those reporting on the games tallied up the numbers that they believe best described the action and best defined the winning edge. However, they may not have always counted the right numbers. Many of our hallowed beliefs about sports statistics have long been fraught with misnomers. Whether it concerns Scottish football or American baseball, the most revered statistics often have little to do with any winning edge.

Covering an international collection of sports, Statistical Thinking in Sports provides an accessible survey of current research in statistics and sports, written by experts from a variety of arenas. Rather than rely on casual observation, they apply the rigorous tools of statistics to re-examine many of those concepts that have gone from belief to fact, based mostly on the repetition of their claims. Leaving assumption behind, these researchers take on a host of tough questions-
  • Is a tennis player only as good as his or her first serve?
  • Is there such a thing as home field advantage?
  • Do concerns over a decline in soccer's competitive balance have any merit?
  • What of momentum-is its staying power any greater than yesterday's win?
  • And what of pressure performers? Are there such creatures or ultimately, does every performer fall back to his or her established normative?

    Investigating a wide range of international team and individual sports, the book considers the ability to make predictions, define trends, and measure any number of influences. It is full of interesting and useful examples for those teaching introductory statistics. Although the articles are aimed at general readers, the serious researcher in sports statistics will also find t
    • 1 Introduction
    		1(6)
    Jim Albert
    Ruud H. Koning
    1.1 Introduction
    		1(4)
    1.1.1 Patterns of world records in sports (two chapters)
    		2(1)
    1.1.2 Competition, rankings, and betting in soccer (three chapters)
    		2(1)
    1.1.3 An investigation into some popular baseball myths (three chapters)
    		3(1)
    1.1.4 Uncertainty of attendance at sports events (two chapters)
    		4(1)
    1.1.5 Home advantage, myths in tennis, drafting in hockey pools, American football
    		4(1)
    1.2 Website
    		5(2)
    Reference
    		5(2)
    2 Modelling the development of world records in running
    		7(26)
    Gerard H. Kuper
    Elmer Sterken
    2.1 Introduction
    		7(2)
    2.2 Modelling world records
    		9(3)
    2.2.1 Cross-sectional approach
    		10(1)
    2.2.2 Fitting the individual curves
    		11(1)
    2.3 Selection of the functional form
    		12(11)
    2.3.1 Candidate functions
    		12(5)
    2.3.2 Theoretical selection of curves
    		17(1)
    2.3.3 Fitting the models
    		18(1)
    2.3.4 The Gompertz curve in more detail
    		18(5)
    2.4 Running data
    		23(1)
    2.5 Results of fitting the Gompertz curves
    		23(3)
    2.6 Limit values of time and distance
    		26(2)
    2.7 Summary and conclusions
    		28(5)
    References
    		29(4)
    3 The physics and evolution of Olympic winning performances
    		33(30)
    Ray Stefani
    3.1 Introduction
    		33(1)
    3.2 Running events
    		34(6)
    3.2.1 The physics of running
    		34(3)
    3.2.2 Measuring the rate of improvement in running
    		37(1)
    3.2.3 Periods of summer Olympic history
    		38(2)
    3.2.4 The future of running
    		40(1)
    3.3 Jumping events
    		40(6)
    3.3.1 The physics of jumping
    		40(3)
    3.3.2 Measuring the rate of improvement in jumping
    		43(1)
    3.3.3 The future of jumping
    		44(2)
    3.4 Swimming events
    		46(3)
    3.4.1 The physics of swimming
    		46(1)
    3.4.2 Measuring the rate of improvement in swimming
    		47(2)
    3.4.3 The future of swimming
    		49(1)
    3.5 Rowing
    		49(4)
    3.5.1 The physics of rowing
    		49(1)
    3.5.2 Measuring the rate of improvement in rowing
    		50(2)
    3.5.3 The future of rowing
    		52(1)
    3.6 Speed skating
    		53(4)
    3.6.1 The physics of speed skating
    		53(1)
    3.6.2 Measuring the rate of improvement in speed skating
    		54(1)
    3.6.3 Periods of winter Olympic history
    		55(2)
    3.6.4 The future of speed skating
    		57(1)
    3.7 A summary of what we have learned
    		57(6)
    References
    		59(4)
    4 Competitive balance in national European soccer competitions
    		63(14)
    Marco Haan
    Ruud H. Koning
    Arjen van Witteloostuijn
    4.1 Introduction
    		63(1)
    4.2 Measurement of competitive balance
    		64(3)
    4.3 Empirical results
    		67(5)
    4.4 Can national competitive balance measures be condensed?
    		72(2)
    4.5 Conclusion
    		74(3)
    References
    		74(3)
    5 Statistical analysis of the effectiveness of the FIFA World Rankings
    		77(14)
    Ian McHale
    Stephen Davies
    5.1 Introduction
    		77(1)
    5.2 FIFA's ranking procedure
    		78(1)
    5.3 Implications of the FIFA World Rankings
    		79(1)
    5.4 The data
    		80(1)
    5.5 Preliminary analysis
    		80(4)
    5.5.1 Team win percentage, in and out of own confederation
    		80(2)
    5.5.2 International soccer versus domestic soccer
    		82(2)
    5.6 Forecasting soccer matches
    		84(1)
    5.7 Using the FIFA World Rankings to forecast match results
    		84(5)
    5.7.1 Reaction to new information
    		85(1)
    5.7.2 A forecasting model for match result using past results
    		86(3)
    5.8 Conclusion
    		89(2)
    References
    		89(2)
    6 Forecasting scores and results and testing the efficiency of the fixed-odds betting market in Scottish league football
    		91(20)
    Stephen Dobson
    John Goddard
    6.1 Introduction
    		91(1)
    6.2 Literature review
    		92(3)
    6.3 Regression models for goal scoring and match results
    		95(2)
    6.4 Data and estimation results
    		97(5)
    6.5 The efficiency of the market for fixed-odds betting on Scottish league football
    		102(5)
    6.6 Conclusion
    		107(4)
    References
    		107(4)
    7 Hitting in the pinch
    		111(24)
    Jim Albert
    7.1 Introduction
    		111(1)
    7.2 A breakdown of a plate appearance: four hitting rates
    		112(2)
    7.3 Predicting runs scored by the four rates
    		114(1)
    7.4 Separating luck from ability
    		114(3)
    7.5 Situational biases
    		117(7)
    7.6 A model for clutch hitting
    		124(1)
    7.7 Clutch stars?
    		125(2)
    7.8 Related work and concluding comments
    		127(8)
    References
    		133(2)
    8 Does momentum exist in a baseball game?
    		135(18)
    Rebecca J. Sela
    Jeffrey S. Simonoff
    8.1 Introduction
    		135(1)
    8.2 Models for baseball play
    		136(2)
    8.3 Situational and momentum effects
    		138(2)
    8.4 Does momentum exist?
    		140(9)
    8.4.1 Modeling transition probabilities
    		140(4)
    8.4.2 Modeling runs scored
    		144(5)
    8.5 Rally starters and rally killers
    		149(1)
    8.6 Conclusions
    		150(3)
    References
    		151(2)
    9 Inference about batter-pitcher matchups in baseball from small samples
    		153(14)
    Hal S. Stern
    Adam Sugano
    9.1 Introduction
    		153(1)
    9.2 The batter-pitcher matchup: a binomial view
    		154(1)
    9.3 A hierarchical model for batter-pitcher matchup data
    		155(5)
    9.3.1 Data for a single player
    		155(1)
    9.3.2 A probability model for batter-pitcher matchups
    		156(2)
    9.3.3 Results - Derek Jeter
    		158(2)
    9.3.4 Results - multiple players
    		160(1)
    9.4 Batter-pitcher data from the pitcher's perspective
    		160(3)
    9.4.1 Results - a single pitcher
    		161(2)
    9.4.2 Results - multiple players
    		163(1)
    9.5 Towards a more realistic model
    		163(1)
    9.6 Discussion
    		164(3)
    References
    		165(2)
    10 Outcome uncertainty measures: how closely do they predict a close game?
    		167(12)
    Babatunde Buraimo
    David Forrest
    Robert Simmons
    10.1 Introduction
    		167(2)
    10.2 Measures of outcome uncertainty
    		169(2)
    10.3 Data
    		171(1)
    10.4 Preliminary analysis of the betting market
    		172(1)
    10.5 Model
    		173(2)
    10.6 Out-of-sample testing
    		175(1)
    10.7 Concluding remarks
    		176(3)
    References
    		177(2)
    11 The impact of post-season play-off systems on the attendance at regular season games
    		179(24)
    Chris Bojke
    11.1 Introduction
    		179(2)
    11.2 Theoretical model of the demand for attendance and the impact of play-off design
    		181(2)
    11.3 Measuring the probability of end-of-season outcomes and game significance
    		183(2)
    11.4 The data: the 2000/01 English Football League second tier
    		185(5)
    11.5 Statistical issues in the measurement of the determinants of attendance
    		190(4)
    11.5.1 Skewed, non-negative heteroscedastic data
    		190(2)
    11.5.2 Clustering of attendance within teams and unobserved heterogeneity
    		192(1)
    11.5.3 Multicollinearity
    		192(1)
    11.5.4 Final statistical model
    		193(1)
    11.6 Model estimation
    		194(3)
    11.6.1 Choice of explanatory variables
    		194(1)
    11.6.2 Regression results
    		195(2)
    11.7 The impact of the play-off system on regular league attendances
    		197(2)
    11.8 Conclusions
    		199(4)
    References
    		201(2)
    12 Measurement and interpretation of home advantage
    		203(14)
    Ray Stefani
    12.1 Introduction
    		203(1)
    12.2 Measuring home advantage
    		204(3)
    12.3 Rugby union, soccer, NBA
    		207(4)
    12.4 Australian rules football, NFL, and college football
    		211(1)
    12.5 NHL hockey and MLB baseball
    		212(2)
    12.6 Can home advantage become unfair?
    		214(1)
    12.7 Summary
    		214(3)
    References
    		215(2)
    13 Myths in Tennis
    		217(24)
    Jan Magnus
    Franc Klaassen
    13.1 Introduction
    		217(1)
    13.2 The data and two selection problems
    		218(3)
    13.3 Service myths
    		221(8)
    13.3.1 A player is as good as his or her second service
    		223(1)
    13.3.2 Serving first
    		224(2)
    13.3.3 New balls
    		226(3)
    13.4 Winning mood
    		229(5)
    13.4.1 At the beginning of a final set, both players have the same chance of winning the match
    		230(1)
    13.4.2 In the final set the player who has won the previous set has the advantage
    		231(1)
    13.4.3 After breaking your opponent's service there is an increased chance that you will lose your own service
    		232(1)
    13.4.4 After missing break points in the previous game there is an increased chance that you will lose your own service
    		233(1)
    13.5 Big points
    		234(4)
    13.5.1 The seventh game
    		234(1)
    13.5.2 Do big points exist?
    		235(2)
    13.5.3 Real champions
    		237(1)
    13.6 Conclusion
    		238(3)
    References
    		239(2)
    14 Back to back evaluations on the gridiron
    		241(22)
    David J. Bern
    14.1 Why do professional team sports track player statistics?
    		241(1)
    14.2 The NFL's quarterback rating measure
    		242(1)
    14.3 The Scully approach
    		243(1)
    14.4 Modeling team offense and defense
    		244(8)
    14.5 Net Points, QB Score, and RB Score
    		252(1)
    14.6 Who is the best?
    		253(1)
    14.7 Forecasting performance in the NFL
    		254(5)
    14.8 Do different metrics tell a different story?
    		259(1)
    14.9 Do we have marginal physical product in the NFL?
    		260(3)
    References
    		261(2)
    15 Optimal drafting in hockey pools
    		263(14)
    Amy E. Summers
    Tim B. Swartz
    Richard A. Lockhart
    15.1 Introduction
    		263(1)
    15.2 Statistical modelling
    		264(4)
    15.2.1 Distribution of points
    		264(2)
    15.2.2 Distribution of games
    		266(2)
    15.3 An optimality criterion
    		268(1)
    15.4 A simulation study
    		269(4)
    15.5 An actual Stanley Cup playoff pool
    		273(3)
    15.6 Discussion
    		276(1)
    References
    		276(1)
    References 277(14)
    List of authors 291(4)
    Index 295
    Jim Albert, Ruud H. Koning
    Püsilink: https://www.kriso.ee/db/9780429147906_pe.html