000			02028cam a2200241 i 4500
003			MIUC
005			20190925124005.0
008			190925s2016 njua b 001 0 eng d
020			_a9780133886436
020			_a0133886433
040			_aYDXCP _beng _erda _cYDXCP _dBTCTA _dBDX _dSA$ _dXFF _dOCLCF _dUZ0 _dHTC _dDLC _dMIUC
082	0	4	_a796.021
100	1		_aMiller, Thomas W., _d1946- _92205
245	1	0	_aSports analytics and data science : _bwinning the game with methods and models / _cThomas W. Miller.
260			_aNew Jersey : _bPearson, _cc2016.
300			_axiii, 337 p. : _bill. b&w ; _c25 cm.
504			_aIncludes bibliographical references (pages 299-328) and index.
505	0		_a1. Understanding sports markets -- 2. Assessing players -- 3. Ranking teams -- 4. Predicting scores -- 5. Making game-day decisions -- 6. Crafting a message -- 7. Promoting brands and products -- 8. Growing revenues -- 9. Managing finances -- 10. Playing what-if games -- 11. Working with sports data -- 12. Competing on analytics -- A. Data Science Methods -- B. Professional Leagues and Teams
520			_aThis is a complete, practical guide to sports data science and modeling, with examples from sports industry economics, marketing, management, performance measurement, and competitive analysis. Thomas W. Miller, faculty director of Northwestern University’s pioneering Predictive Analytics program, shows how to use advanced measures of individual and team performance to judge the competitive position of both individual athletes and teams, and to make more accurate predictions about their future performance. Miller’s modeling techniques draw on methods from economics, accounting, finance, classical and Bayesian statistics, machine learning, simulation, and mathematical programming. Miller illustrates them through realistic case studies, with fully worked examples in both Python and R.
650		0	_aSports _xStatistical methods _9599
650		0	_aSports _xStatistics _9599
942			_2ddc _cBK