Investigating the efficiency of the Asian handicap football betting market with ratings and Bayesian networks

Despite the massive popularity of the Asian Handicap (AH) football (soccer) betting market, its efficiency has not been adequately studied by the relevant literature. This paper combines rating systems with Bayesian networks and presents the first published model specifically developed for prediction and assessment of the efficiency of the AH betting market. The results are based on 13 English Premier League seasons and are compared to the traditional market, where the bets are for win, lose or draw. Different betting situations have been examined including a) both average and maximum (best available) market odds, b) all possible betting decision thresholds between predicted and published odds, c) optimisations for both return-on-investment and profit, and d) simple stake adjustments to investigate how the variance of returns changes when targeting equivalent profit in both traditional and AH markets. While the AH market is found to share the inefficiencies of the traditional market, the findings reveal both interesting differences as well as similarities between the two.

Cricket mix optimization using heuristic framework after ensuring Markovian equilibrium

International Cricket Council, in consultation with its member boards, prepares the Future Tours and Programme (FTP) which is an eight year long itinerary covering world championships in three formats of cricket, bilateral series and other tournaments. However, the FTP (2015–2023) had been criticized for its asymmetric itinerary and the point system for World Test championship and the FTP (2023–2031) is being criticized for including eight championships in limited formats and enhanced workload for players. Cricket mix standardization like marketing and product mix can work in homogeneous markets. This study derives three homogeneous markets of four teams each using hierarchical cluster analysis. For each market, it finds out the Markovian equilibrium analyzing cricket mix transition over past years. While the same can be used to derive the number of games per format per country, the study proposes a heuristic approach for fine tuning the same taking care of major stakeholders’ (e.g. Administrators, Players and Spectators) aspirations. Despite scores of criticisms and articles on the issue, there is hardly any scholastic contribution on game scheduling in the extant literature. This study thus is a pioneering effort in helping the policy makers to create a balance between cricket formats within each homogeneous market.

Modeling T20I cricket bowling effectiveness: A quantile regression approach with Bayesian extension

Bowling effectiveness is a key factor in winning cricket matches. The team captain should decide when to use the right bowler at the right moment so that the team can optimize the outcome of the game. In this study, we investigate the effectiveness of different types of bowlers at different stages of the game, based on the conceded percentage of runs from the innings total, for each over. Bowlers are generally categorized into three types: fast bowlers, medium-fast bowlers, and spinners. In this article, the authors divided the twenty over spell of a T20I match into four stages; namely, Stage 1: overs 1-6 (PowerPlay), Stage 2: overs 7-10, Stage 3: overs 11-15, and Stage 4: overs 16-20. To understand the broad spectrum of the behavior of game variables, a Quantile Regression methodology is used for statistical analysis. Following that, a Bayesian approach to Quantile Regression is undertaken, and it confirms the initial results.

“Choose your opponent”: A new knockout design for hybrid tournaments †

We present a new, simple knockout format for sports tournaments, that we call “Choose Your Opponent”, where the teams that have performed best during a preliminary group stage can choose their opponents during the subsequent knockout stage. The main benefit of this format is that it essentially solves a recently identified incentive compatibility problem when more than one teams from a group advance to the knockout stage, by effectively canceling the risk of tanking. This new design also makes the group stage more exciting, by giving teams a strong incentive to perform at their best level, and more fair, by limiting the risk of collusion and making sure that the best group winners are fairly rewarded in the knockout round. The choosing procedure would add a new, exciting strategic component to the competition. Advancing teams would choose their opponent during new, much anticipated TV shows which would attract a lot of media attention. We illustrate how this new format would work for the round of 16 of the UEFA Champions League, the most popular soccer club competition in the world.

Forecasting serve performance in professional tennis matches

Many research papers on tennis match prediction use a hierarchical Markov Model. To predict match outcomes, this model requires input parameters for each player’s serving ability. While these parameters are often computed directly from each player’s historical percentages of points won on serve and return, doing so fails to address bias due to limited sample size and differences in strength of schedule. In this paper, we explore a handful of novel approaches to forecasting serve performance that specifically address these limitations. By applying an Efron-Morris estimator, we provide a means to robustly forecast outcomes when players have limited match data over the past year. Next, through tracking expected serve and return performance in past matches, we account for strength of schedule across all points in a player’s match history. Finally, we demonstrate a new way to synthesize historical serve data with the predictive power of Elo ratings. When forecasting serve performance across 7,622 ATP tour-level matches from 2014-2016, all three of these proposed methods outperformed Barnett and Clarke’s standard approach.

The three Eras of the NBA regular seasons: Historical trend and success factors

The NBA (National Basketball Association) is going through a transition process in its way of practice, planning, and comprehension of the game. With the exponential growth of the data that has been collected, detailed statistical analyses have been conducted for each part of the game. This has been overwhelming exploited in a way never seen before, especially when dealing with the three-point shot. In this paper, we are interested in characterizing NBA’s gameplay over time to identify trends and success factors. In particular, this study aims: (i) to identify which factors were crucial for teams’ regular season success in the past and understand the factors that are more relevant to succeed in the present day; and (ii) to group seasons and regular season winning teams into clusters of common characteristics and gameplay behavior. Historical events and trends help us to understand how teams were successful in past regular seasons, how they played, and how their style of play has changed. Leading to a better comprehension of the game. The game-related statistics of the NBA’s regular seasons, from 1979-80 to 2018-19, were analyzed using principal component analysis, cluster analysis, and LASSO regression. It is possible to identify three main Eras that we define as the Classic Era of the NBA (1980–1994), the Transitional Era of the NBA (1995–2013), and the Modern Era of the NBA (since 2013). As the results of this study make a historic analysis of the NBA, indicating the three eras of NBA regular seasons since the introduction of the three-point line, their playing styles, and their respective factors for success, this present research may be the base study that will help researchers better investigate the NBA, its past, present, and future.

Random forest model identifies serve strength as a key predictor of tennis match outcome

Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of particular interest to bettors and betting companies alike is potential use of sports records to predict tennis match outcomes prior to match start. We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods. Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real-time predictions. We were able to predict match outcomes with upwards of 80%accuracy, much greater than predictions using betting odds alone, and identify serve strength as a key predictor of match outcome. By combining prediction accuracies from three models, we were able to nearly recreate a probability distribution based on average betting odds from betting companies, which indicates that betting companies are using similar information to assign odds to matches. These results demonstrate the capability of relatively simple machine learning models to quite accurately predict tennis match outcomes.

A deep learning approach to injury forecasting in NBA basketball

Predicting athlete injury risk has been a holy grail in sports medicine with little progress to date due to a variety of factors such as small sample sizes, significantly imbalanced data, and inadequate statistical approaches. Modeling approaches which are not able to account for the multiple interactions across factors can be misleading. We address the small sample size by collecting longitudinal data of NBA player injuries using publicly available data sources and develop a state of the art deep learning model, METIC, to predict future injuries based on past injuries, game activity, and player statistics. We evaluate model performance using metrics appropriate for imbalanced data and find that METIC performs significantly better than other traditional machine learning approaches. METIC uses feature learning to create interactive features which become meaningful in combination with each other. METIC can be used by practitioners and front offices to improve athlete management and reduce injury incidence, potentially saving sports teams millions in revenue due to reduced athlete injuries.

The impact of new judging criteria on 10-8 scores in MMA

This paper investigates the impact of changes in judging criteria on 10-8 scores in Zuffa-owned mixed martial arts (MMA) promotions. Utilizing a differences-in-differences framework, the 2017 liberalization of 10-8 scoring criteria in the Unified Rules of MMA is examined across various judge groups. Findings suggest that traveling judges and Nevada judges – those most likely to be at the forefront of the regulatory evolution of the sport – had already liberalized their 10-8 scoring one year prior to the effective date of the new criteria. Other judges appear to have effectively implemented the new criteria since January 2017 with 10-8 probabilities on par with traveling and Nevada judges. The effect of an earlier change in judging criteria is also examined in Nevada. Results suggest the numerous and distributed regulatory agencies involved in the sport of MMA were effective in the implementation of new policies for scoring rounds.

Overtaking in Formula 1 during the Pirelli era: A driver-level analysis

The introduction of DRS and rapidly-degrading tires in 2011 boosted on-track overtaking levels in Formula 1 to unprecedented highs. Since then, overtaking has steadily decreased again, culminating in a 60-percent reduction in 2017. In this paper, using a Poisson model on individual-level overtaking data from 2011 to 2018, it was found that about half the decrease can be attributed to the cars, 20 to 30 percent to the reduction in field size and about 20 percent to more uniform race strategies.