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Comparing Market Efficiency with Traditional and Non-Traditional Ratings Systems in ATP Tennis

Explore market efficiency in ATP tennis through traditional and non-traditional ratings systems. Compare Elo and SPARKS models over the past 8 years.

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Comparing Market Efficiency with Traditional and Non-Traditional Ratings Systems in ATP Tennis

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  1. 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 SciencesRMIT UniversityMelbourne, Australia www.rmit.edu.au/sportstats

  2. 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. RMIT Sports Statistics

  3. Background to ATP Tennis • ATP: Association of Tennis Professionals; • Consists of 65 individual tournaments each year for men playing at the highest level; • Additional: • 178 tournaments played in the Challenger Tour; • 534 tournaments played in Futures tennis. RMIT Sports Statistics

  4. ATP Tennis Rankings • “Used to determine qualification for entry and seeding in all tournaments for both singles and doubles”; • The rankings period is always the past 52 weeks prior to the current week. RMIT Sports Statistics

  5. ATP Tennis Rankings – Sept 12th, 2011 RMIT Sports Statistics

  6. How Predictive are Tennis Rankings? Case Study RMIT Sports Statistics

  7. How Predictive are Tennis Rankings? Case Study RMIT Sports Statistics

  8. Challenges Associated with Predicting Outcomes in ATP Tennis • 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. RMIT Sports Statistics

  9. 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). RMIT Sports Statistics

  10. 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. RMIT Sports Statistics

  11. Elo Ratings and the SPARKS Model www.rmit.edu.au/sportstats

  12. Introduction to Ratings Systems • 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. RMIT Sports Statistics

  13. Introduction to SPARKS • Initially developed by Bedford and Clarke (2000) to provide an alternative to traditional ratings systems; • Differ from Elo-type ratings systems as SPARKS considers the margin of the result; • Has been recently utilised to evaluate other characteristics such as the travel effect in tennis (Bedford et al., 2011). RMIT Sports Statistics

  14. Introduction to SPARKS • where • where RMIT Sports Statistics

  15. Introduction to SPARKS RMIT Sports Statistics

  16. SPARKS: Case Study RMIT Sports Statistics

  17. Longitudinal Examination of SPARKS RMIT Sports Statistics

  18. Limitations of SPARKS: Case Study + RMIT Sports Statistics

  19. Limitations of SPARKS: Case Study Player 2 competitive in all three sets. + RMIT Sports Statistics

  20. Limitations of SPARKS: Case Study Player 2 competitive in all three sets. Player 2 competitive in 1 out of 4 sets. RMIT Sports Statistics

  21. Historical Results of the SPARKS Model • The following table displays historical results of the raw SPARKS model over the past 8 years. + RMIT Sports Statistics

  22. Historical Results of the SPARKS Model • The following table displays historical results of the raw SPARKS model over the past 8 years. RMIT Sports Statistics

  23. Banding of Probabilities • 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. RMIT Sports Statistics

  24. Banding and the SPARKS Model + RMIT Sports Statistics

  25. Banding and the SPARKS Model Represent the underdog. Represent the favourite. RMIT Sports Statistics

  26. Banding and the SPARKS Model RMIT Sports Statistics

  27. Banding and the SPARKS Model (2003-2010) + RMIT Sports Statistics

  28. Banding and the SPARKS Model (2003-2010) Over-estimates the probability of the favorite winning. Under-estimates the probability of the under-dog winning. RMIT Sports Statistics

  29. Elo Ratings www.rmit.edu.au/sportstats

  30. Introduction to Elo Ratings • Elo ratings system developed by ÁrpádÉlő to calculate relative skill levels of chess players where: RN = New rating RO = Old rating O = Observed Score E = Expected Score W = Multiplier (16for masters, 32 for lesser players) RMIT Sports Statistics

  31. Probability Bands: Elo Ratings + RMIT Sports Statistics

  32. Probability Bands: Elo Ratings RMIT Sports Statistics

  33. Probability Bands: Elo Ratings (2003-2010) RMIT Sports Statistics

  34. Probability Bands: Elo Ratings (2003-2006) + RMIT Sports Statistics

  35. Probability Bands: Elo Ratings (2003-2006) High variability in the majority of probability bands during the burn-in period. RMIT Sports Statistics

  36. Probability Bands: Elo Ratings (2007-2010) + RMIT Sports Statistics

  37. Probability Bands: Elo Ratings (2007-2010) RMIT Sports Statistics

  38. Advantages and Shortcomings of SPARKS and Elo Ratings • SPARKS considers the margin of the result, often a difficult task in the context of tennis; • Elo is only concerned with whether the player wins or loses, not the margin of victory in terms of the number of games or sets won; • Elo provides a more efficient model in terms of probability banding, suggesting that evaluating the margin of matches may be misleading at times. RMIT Sports Statistics

  39. Market Efficiency of ATP Tennis in Recent Years www.rmit.edu.au/sportstats

  40. ATP Betting Markets Used in the Current Analysis RMIT Sports Statistics

  41. Overall Efficiency of Each Market between 2003 and 2010 + RMIT Sports Statistics

  42. Overall Efficiency of Each Market between 2003 and 2010 RMIT Sports Statistics

  43. Overall Efficiency of Each Market between 2003 and 2010 + RMIT Sports Statistics

  44. Overall Efficiency of Each Market between 2003 and 2010 Heightened stability and efficiency across markets and seasons since 2008. RMIT Sports Statistics

  45. Converting Market Odds into a Probability 2011 US Open Final RMIT Sports Statistics

  46. Accounting for the Over-Round • The sum of the probability-odds in any given sporting contest typically exceeds 1, to allow for the bookmaker to make a profit; • The amount that this probability exceeds 1 is referred to as the over-round; • For example, if the sum of probabilities for a given match is equal to 1.084, the over-round is equal to .084 or 8.4% RMIT Sports Statistics

  47. Accounting for the Over-Round 2011 US Open Final 6 – 2 6 – 4 6 – 7 6 – 1 RMIT Sports Statistics

  48. Comparison of Over-Round Across Markets + RMIT Sports Statistics

  49. Comparison of Over-Round Across Markets Kruskal-Wallis test with follow-up Mann-Whitney U tests: Significant difference between all betting markets aside from Pinnacle Sports and Stan James. RMIT Sports Statistics

  50. Over-Round for Bet 365 (2003-2010) RMIT Sports Statistics

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