Issues in sports forecasting
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- Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
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- Christoph Schlembach & Sascha L. Schmidt & Dominik Schreyer & Linus Wunderlich, 2020. "Forecasting the Olympic medal distribution during a pandemic: a socio-economic machine learning model," Papers 2012.04378, arXiv.org, revised Jun 2021.
- Li, Yongjun & Wang, Lizheng & Li, Feng, 2021. "A data-driven prediction approach for sports team performance and its application to National Basketball Association," Omega, Elsevier, vol. 98(C).
- Manner Hans, 2016. "Modeling and forecasting the outcomes of NBA basketball games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(1), pages 31-41, March.
- Singleton, Carl & Reade, J. James & Brown, Alasdair, 2020.
"Going with your gut: The (In)accuracy of forecast revisions in a football score prediction game,"
Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 89(C).
- Carl Singleton & J. James Reade & Alsdair Brown, 2018. "Going with your Gut: The (In)accuracy of Forecast Revisions in a Football Score Prediction Game," Working Papers 2018-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
- Carl Singleton & J. James Reade & Alasdair Brown, 2019. "Going with your gut: the (in)accuracy of forecast revisions in a football score prediction game," Economics Discussion Papers em-dp2019-05, Department of Economics, University of Reading, revised 01 Nov 2019.
- Alexis Direr, 2013.
"Are betting markets efficient? Evidence from European Football Championships,"
Applied Economics, Taylor & Francis Journals, vol. 45(3), pages 343-356, January.
- Alexis Direr, 2013. "Are betting markets efficient? Evidence from European Football Championships," Applied Economics, Taylor & Francis Journals, vol. 45(3), pages 343-356, January.
- Alexis Direr, 2011. "Are Betting Markets Efficient ? Evidence from European Football Championships," Post-Print hal-00734531, HAL.
- Vincenzo Candila & Antonio Scognamillo, 2019. "On the Longshot Bias in Tennis Betting Markets: The Casco Normalization," Working Papers 3_236, Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno.
- Erik Å trumbelj, 2016. "A Comment on the Bias of Probabilities Derived From Betting Odds and Their Use in Measuring Outcome Uncertainty," Journal of Sports Economics, , vol. 17(1), pages 12-26, January.
- Štrumbelj, Erik & Vračar, Petar, 2012. "Simulating a basketball match with a homogeneous Markov model and forecasting the outcome," International Journal of Forecasting, Elsevier, vol. 28(2), pages 532-542.
- Peeters, Thomas, 2018. "Testing the Wisdom of Crowds in the field: Transfermarkt valuations and international soccer results," International Journal of Forecasting, Elsevier, vol. 34(1), pages 17-29.
- Schlembach, Christoph & Schmidt, Sascha L. & Schreyer, Dominik & Wunderlich, Linus, 2022. "Forecasting the Olympic medal distribution – A socioeconomic machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
- Baker, Rose D. & McHale, Ian G., 2013. "Forecasting exact scores in National Football League games," International Journal of Forecasting, Elsevier, vol. 29(1), pages 122-130.
- Marc Garnica-Caparrós & Daniel Memmert & Fabian Wunderlich, 2022. "Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports," Information Systems and e-Business Management, Springer, vol. 20(3), pages 551-580, September.
- Jeon, Gyuhyeon & Park, Juyong, 2021. "Characterizing patterns of scoring and ties in competitive sports," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
- Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
- Coussement, Kristof & De Bock, Koen W., 2013.
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- K. Coussement & K.W. de Bock, 2013. "Customer Churn Prediction in the Online Gambling Industry: The Beneficial Effect of Ensemble Learning," Post-Print hal-00788063, HAL.
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- Vittorio Maniezzo & Fabian Andres Aspee Encina, 2022. "Predictive Analytics for Real-time Auction Bidding Support: a Case on Fantasy Football," SN Operations Research Forum, Springer, vol. 3(3), pages 1-23, September.
- Ruud H. Koning & Renske Zijm, 2023. "Betting market efficiency and prediction in binary choice models," Annals of Operations Research, Springer, vol. 325(1), pages 135-148, June.
- June Buchanan & Yun Shen, 2021. "Gambling and marketing: a systematic literature review using HistCite," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(2), pages 2837-2851, June.
- Vaughan Williams Leighton & Liu Chunping & Dixon Lerato & Gerrard Hannah, 2021. "How well do Elo-based ratings predict professional tennis matches?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 91-105, June.
- Kovalchik, Stephanie & Reid, Machar, 2019. "A calibration method with dynamic updates for within-match forecasting of wins in tennis," International Journal of Forecasting, Elsevier, vol. 35(2), pages 756-766.
- Hubáček, Ondřej & Šourek, Gustav & Železný, Filip, 2019. "Exploiting sports-betting market using machine learning," International Journal of Forecasting, Elsevier, vol. 35(2), pages 783-796.
- Hubáček, Ondřej & Šír, Gustav, 2023. "Beating the market with a bad predictive model," International Journal of Forecasting, Elsevier, vol. 39(2), pages 691-719.
- Song, Kai & Shi, Jian, 2020. "A gamma process based in-play prediction model for National Basketball Association games," European Journal of Operational Research, Elsevier, vol. 283(2), pages 706-713.
- B. Jay Coleman, 2014. "Minimum violations and predictive meta‐rankings for college football," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(1), pages 17-33, February.
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Keywords
Sports forecasting Betting markets Efficiency Bias Sports models;Statistics
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