Comparing Market Efficiency with Traditional and Non- Traditional Ratings Systems in ATP Tennis Dr Adrian Schembri Dr Anthony Bedford Bradley O’Bree Natalie Bressanutti RMIT Sports Statistics Research Group School of Mathematical and Geospatial Sciences RMIT University Melbourne, Australia www.rmit.edu.au/sportstats
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Comparing Market Efficiency with Traditional and Non-Traditional Ratings Systems in ATP Tennis
Comparing Market Efficiency with Traditional and Non-Traditional Ratings Systems in ATP Tennis. Dr Adrian Schembri Dr Anthony Bedford Bradley O’Bree Natalie Bressanutti RMIT Sports Statistics Research Group School of Mathematical and - PowerPoint PPT Presentation
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Comparing Market Efficiency with Traditional and Non-Traditional Ratings Systems in ATP Tennis
Dr Adrian Schembri Dr Anthony Bedford Bradley O’BreeNatalie Bressanutti
RMIT Sports Statistics Research GroupSchool of Mathematical andGeospatial SciencesRMIT UniversityMelbourne, Australia
www.rmit.edu.au/sportstats
Aims of the Presentation
Structure of ATP tennis, rankings, and tournaments;
Challenges associated with predicting outcomes of tennis matches;
Utilising the SPARKS and Elo ratings to predict ATP tennis;
Evaluate changes in market efficiency in tennis over the past eight years.
Individual sport and therefore natural variation due to individual differences prior to and during a match;
Constant variations in the quality of different players: Players climbing the rankings; Players dropping in the rankings; Players ranking remaining stagnant.
The importance of different tournaments varies for each individual players.
Recent Papers on Predicting ATP Tennis and Evaluating Market Efficiency
Forrest and McHale (2007) reviewed the potential for long-shot bias in men’s tennis;
Klaassen and Magnus (2003) developed a probability-based model to evaluate the likelihood of a player winning a match, whilst Easton and Uylangco (2010) extended this to a point-by-point model;
A range of probability-based models are available online, however these are typically volatile and reactive to events such as breaks in serve and each set result (e.g., www.strategicgames.com.au).
Aims of the Current Paper
Evaluate the efficiency of various tennis betting markets over the past eight years;
Compare the efficiency of these markets with traditional ratings systems such as Elo and a non-traditional ratings system such as SPARKS;
Identify where inefficiencies in the market lie and the degree to which this has varied over time.
Typically used to: Monitor the relative ranking of players with other players
in the same league; Identify the probability of each team or player winning
their next match.
Have been developed in the context of individual (chess, tennis) or group based sports (e.g., AFL football, NBA);
The initial ratings suggest which player is likely to win, with the difference between their old ratings being used to calculate a new rating after the match is played.
Probability banding is used primarily to determine whether a models predicted probability of a given result is accurate;
Enables an assessment of whether the probability attributed to a given result is appropriate based on reviewing all results within the band;
For example, if 200 matches within a given tennis season are within the .20 to .25 probability band, then between 20% and 25% (or approx 45 matches) of these matches should be won by the players in question.
Form of an individual player will affect the context and potential outcome of the entire match, as opposed to a team-based sport where individual players have less impact or can be substituted off if out of form.
Micro-events within a match, at times, have an impact on the outcome of the match. Examples: Rain delays Injury Time outs Code violations
A set multiplier of ‘6’ was used for the SPARKS model based on the original SPARKS model published in 2000;
Only a limited number of betting markets were incorporated, and therefore it was not possible to utilise Betfair data into the analysis;
Differences in market efficiency and inefficiency were not evaluated at the surface level. This would be particularly interesting if evaluated for clay, given the volatility of player performance on clay when compared with other surfaces.
Develop a model that combines SPARKS and Elo ratings;
Extend the current findings to incorporate women’s tennis given that evidence has shown greater volatility in the women’s game.
Incorporate data on other potential predictors of tennis outcomes. Examples include: The set sequence of the match Surface Importance of the tournament (e.g.,
Whilst considerable variability was evident during the 2003 – 2007 seasons, an increase in consistency across markets since 2008.
Following a lengthy burn-in period of four years, the Elo model outperformed SPARKS and most betting markets across the majority of probability bands;
Whilst not efficient in terms of probability banding, the SPARKS model was able to predict an equivalent proportion of winners to the betting markets, and outperformed some markets in recent years;
A model that combines both Elo and SPARKS may yield the most efficient model.