Forecasting outcomes in tennis matches using within-match betting markets

Easton, Stephen; Uylangco, Katherine

doi:10.1016/j.ijforecast.2009.10.004

Cited by 21 publications

(10 citation statements)

References 8 publications

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“…Models perform best in some cases such as football (Constantinou, Fenton, and Martin, ) and UFC (Wise et al., ), whereas bookmakers’ predictions perform best in other scenarios (Boulier and Stekler, ; Scheibehenne and Broeder, ). In some cases, there is no statistically significant difference (Easton and Uylangco, ). In our setting, the two prediction methods should have a similar predictive power to guarantee that long‐ and short‐term predictions are equally important.…”

Section: Validation: Congruence Among Expectationsmentioning

confidence: 98%

Meeting Expectations at the 2016 Rio Olympic Games: Country Potential and Competitiveness

Otamendi

Doncel

Martín‐Gutiérrez

2020

Social Science Quarterly

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Objective Develop a framework to measure the extent to which countries meet their performance expectations at major sporting events using economists, experts, and fan knowledge. Method Long‐term expectations based on socioeconomic potential are calculated using sport‐agnostic econometric modeling. Short‐term expectations based on performance and competitiveness are calculated using betting odds, which incorporate both expert knowledge and “wisdom of the crowd.” Robust statistics based on the chi‐squared distribution are used to measure the extent to which countries’ expectations are met. Our method is tested using real data from the Rio 2016 Summer Olympic Games. Results Twelve countries over‐ or underperformed in terms of meeting either long‐ or short‐term expectations. Conclusions Uzbekistan was the only country that exceeded both long‐ and short‐term expectations.

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Section: Validation: Congruence Among Expectationsmentioning

confidence: 98%

Meeting Expectations at the 2016 Rio Olympic Games: Country Potential and Competitiveness

Otamendi

Doncel

Martín‐Gutiérrez

2020

Social Science Quarterly

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“…Online betting markets have facilitated the development of in-play betting, whereby bets can be placed on the result of an event (e.g., a tennis match) whilst it is taking place (Sauer, 2005;Huang et al, 2011;Dumitrescu et al, 2013;Viney, 2015;Dias, 2016). The general consensus of studies comparing the forecasting ability of in-play odds with the predictions of mathematical models based on information which arrives during the course of an event, is that in-play odds react quickly and appropriately to the arrival of new information, such as a goal being scored in soccer or a point won in tennis (e.g., Newton & Aslam, 2009;Easton & Uylangco, 2010;Sauer et al, 2010;McHale & Morton, 2011;Spanias & Knottenbelt, 2012;Dumitrescu et. al., 2013;Croxson & Reade 2014;Viney, 2015).…”

Section: Odds React Quickly To New Informationmentioning

confidence: 99%

Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements

Sung

McDonald

Johnson

et al. 2019

European Journal of Operational Research

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We examine the impact of price trends on the accuracy of forecasts from prediction markets. In particular, we study an electronic betting exchange market and construct independent variables from market price (odds) time series from 6,058 individual markets (a dataset consisting of over 8.4 million price points). Using a conditional logit model, we find that a systematic relationship exists between trends in odds and the accuracy of odds-implied event probabilities; the relationship is consistent with participants overreacting to price movements. In particular, in different time segments of the market, increasing and decreasing odds lead, respectively, to under-and over-estimation of odds-implied probabilities. We develop a methodology to detect and correct the erroneous forecasts associated with these trends in odds in order to considerably improve the quality of forecasts generated in prediction markets.

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“…There have been various attempts to predict the outcome of various sporting events using machine learning -dog racing (greyhound) [4] [5], tennis [6] [7], soccer [8] [9] [10], cricket [11] and horse racing [12] [13] [14].…”

Section: Related Workmentioning

confidence: 99%

“…Time-stamped price data 6 were collected for around 3000 markets from 1st of January 2016 to 17th of May 2016 using custom built C# application. Data recording for each market begins 3 hours before the scheduled start time.…”

Section: Data Extraction and Processingmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Price Movements in Betting Exchanges Using Cartesian Genetic Programming and ANN

Dzalbs

Kalganova

2018

Big Data Research

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Since the introduction of betting exchanges in 2000, there has been increased interest of ways to monetize on the new technology. Betting exchange markets are fairly similar to the financial markets in terms of their operation. Due to the lower market share and newer technology, there are very few tools available for automated trading for betting exchanges. The in-depth analysis of features available in commercial software demonstrates that there is no commercial software that natively supports machine learned strategy development. Furthermore, previously published academic software products are not publicly obtainable. Hence, this work concentrates on developing a full-stack solution from data capture, back-testing to automated Strategy Agent development for betting exchanges. Moreover, work also explores ways to forecast price movements within betting exchange using new machine learned trading strategies based on Artificial Neuron Networks (ANN) and Cartesian Genetic Programming (CGP). Automatically generated strategies can then be deployed on a server and require no human interaction. Data explored in this work were captured from 1st of January 2016 to 17th of May 2016 for all GB WIN Horse Racing markets (total of 204GB of data processing). Best found Strategy agent shows promising 83% Return on Investment (ROI) during simulated historical validation period of one month (15th of April 2016 to 16th of May 2016).

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Forecasting outcomes in tennis matches using within-match betting markets

Cited by 21 publications

References 8 publications

Meeting Expectations at the 2016 Rio Olympic Games: Country Potential and Competitiveness

Meeting Expectations at the 2016 Rio Olympic Games: Country Potential and Competitiveness

Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements

Forecasting Price Movements in Betting Exchanges Using Cartesian Genetic Programming and ANN

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