Accumulator Bet Selection Through Stochastic Diffusion Search

• URL: http://arxiv.org/abs/2004.08607v1
• Date: Sat, 18 Apr 2020 12:42:23 GMT
• Title: Accumulator Bet Selection Through Stochastic Diffusion Search
• Authors: Nassim Dehouche
• Abstract summary: An accumulator is a bet that combines multiple bets into a wager that can generate a total payout given by the multiplication of the individual odds of its parts. The complexity of selecting a set of matches to place an accumulator bet has dramatically increased with the easier access to online and offline bookmakers. We propose a binary optimization model for the problem of selecting the most promising combinations of matches, in terms of their potential payout and probability of a win.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An accumulator is a bet that presents a rather unique payout structure, in that it combines multiple bets into a wager that can generate a total payout given by the multiplication of the individual odds of its parts. These potentially important returns come however at an increased risk of a loss. Indeed, the presence of a single incorrect bet in this selection would make the whole accumulator lose. The complexity of selecting a set of matches to place an accumulator bet on, as well as the number of opportunities to identify winning combinations have both dramatically increased with the easier access to online and offline bookmakers that bettors have nowadays. We address this relatively under-studied combinatorial aspect of sports betting, and propose a binary optimization model for the problem of selecting the most promising combinations of matches, in terms of their total potential payout and probability of a win, to form an accumulator bet. The results of an ongoing computational experiment, in which our model is applied to real data pertaining to the four main football leagues in the world over a complete season, are presented and compared to those of single bet selection methods.

Related papers

• Optimal Online Bookmaking for Any Number of Outcomes [6.0158981171030685]
  We study the Online Bookmaking problem, where a bookmaker dynamically updates betting odds on the possible outcomes of an event.<n>We show that for any event and any number of betting rounds, the bookmaker's optimal loss is the largest root of a simple.<n>Our solution shows that bookmakers can be as fair as desired while avoiding financial risk, and the explicit characterization reveals an intriguing relation between the bookmaker's regret and Hermites.
  arXiv  Detail & Related papers  (2025-06-19T12:11:58Z)
• SPARTA ALIGNMENT: Collectively Aligning Multiple Language Models through Combat [73.529925653031]
  We propose SPARTA ALIGNMENT, an algorithm to collectively align multiple LLMs through competition and combat.<n>For each iteration, one instruction and two models are selected for a duel, the other models evaluate the two responses, and their evaluation scores are aggregated through a adapted elo-ranking based reputation system.<n>The peer-evaluated combat results then become preference pairs where the winning response is preferred over the losing one, and all models learn from these preferences at the end of each iteration.
  arXiv  Detail & Related papers  (2025-06-05T07:51:23Z)
• Optimal Online Bookmaking for Binary Games [19.70394327203169]
  We study the problem of bookmaking with the goal of maximizing the return in the worst-case. We formalize this problem as the emph Optimal Online Bookmaking game, and provide the exact solution for the binary case.
  arXiv  Detail & Related papers  (2025-01-12T20:23:46Z)
• A Best-of-Both Approach to Improve Match Predictions and Reciprocal Recommendations for Job Search [15.585641615174623]
  This paper introduces and demonstrates a novel and practical solution to improve reciprocal recommendations in production by leveraging pseudo-match scores. Specifically, our approach generates dense and more directly relevant pseudo-match scores by combining the true match labels with relatively inaccurate but dense match predictions. Our method can be seen as a best-of-both (BoB) approach, as it combines the high-level ideas of both direct match prediction and the two separate models approach.
  arXiv  Detail & Related papers  (2024-09-17T08:51:02Z)
• Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities [69.34646544774161]
  We formulate a new variant of multi-player multi-armed bandit (MAB) model, which captures arrival of requests to each arm and the policy of allocating requests to players. The challenge is how to design a distributed learning algorithm such that players select arms according to the optimal arm pulling profile. We design an iterative distributed algorithm, which guarantees that players can arrive at a consensus on the optimal arm pulling profile in only M rounds.
  arXiv  Detail & Related papers  (2024-08-20T13:57:00Z)
• Not All Preference Pairs Are Created Equal: A Recipe for Annotation-Efficient Iterative Preference Learning [81.69044784288005]
  Iterative preference learning requires online annotated preference labels. We study strategies to select worth-annotating response pairs for cost-efficient annotation.
  arXiv  Detail & Related papers  (2024-06-25T06:49:16Z)
• Probabilistic Contrastive Learning for Long-Tailed Visual Recognition [78.70453964041718]
  Longtailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Recent investigations have revealed that supervised contrastive learning exhibits promising potential in alleviating the data imbalance. We propose a novel probabilistic contrastive (ProCo) learning algorithm that estimates the data distribution of the samples from each class in the feature space.
  arXiv  Detail & Related papers  (2024-03-11T13:44:49Z)
• Machine learning for sports betting: should model selection be based on accuracy or calibration? [0.0]
  We train models on NBA data over several seasons and run betting experiments on a single season. We show that using calibration, rather than accuracy, as the basis for model selection leads to greater returns.
  arXiv  Detail & Related papers  (2023-03-10T16:22:38Z)
• Predicting Football Match Outcomes with eXplainable Machine Learning and the Kelly Index [0.0]
  A machine learning approach is developed for predicting the outcomes of football matches. The dataset originated from the Premier League match data covering the 2019-2021 seasons. The paper also devised an investment strategy in order to evaluate its effectiveness by benchmarking against bookmaker odds.
  arXiv  Detail & Related papers  (2022-11-28T19:32:58Z)
• Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games [0.0]
  In freemium games, the revenue from a player comes from the in-app purchases made and the advertisement to which that player is exposed. Within this scenario, it is extremely important to be able to detect promptly when a player is about to quit playing. We investigate how to improve the current state-of-the-art in churn prediction by combining sequential and aggregate data.
  arXiv  Detail & Related papers  (2022-09-06T14:49:18Z)
• Online Search With Best-Price and Query-Based Predictions [2.3204178451683264]
  We study learning-augmented algorithms in which there is a potentially erroneous prediction concerning the input. We provide experimental results on data obtained from stock exchange markets.
  arXiv  Detail & Related papers  (2021-12-02T20:18:37Z)
• Consensus-Guided Correspondence Denoising [67.35345850146393]
  We propose to denoise correspondences with a local-to-global consensus learning framework to robustly identify correspondence. A novel "pruning" block is introduced to distill reliable candidates from initial matches according to their consensus scores estimated by dynamic graphs from local to global regions. Our method outperforms state-of-the-arts on robust line fitting, wide-baseline image matching and image localization benchmarks by noticeable margins.
  arXiv  Detail & Related papers  (2021-01-03T09:10:00Z)
• Faster Algorithms for Optimal Ex-Ante Coordinated Collusive Strategies in Extensive-Form Zero-Sum Games [123.76716667704625]
  We focus on the problem of finding an optimal strategy for a team of two players that faces an opponent in an imperfect-information zero-sum extensive-form game. In that setting, it is known that the best the team can do is sample a profile of potentially randomized strategies (one per player) from a joint (a.k.a. correlated) probability distribution at the beginning of the game. We provide an algorithm that computes such an optimal distribution by only using profiles where only one of the team members gets to randomize in each profile.
  arXiv  Detail & Related papers  (2020-09-21T17:51:57Z)
• Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport [51.20042288437171]
  We propose a Two-Stage Spatial-Temporal Network (TSSTN) that can provide accurate real-time win predictions. Experiment results and applications in real-world live streaming scenarios showed that the proposed TSSTN model is effective both in prediction accuracy and interpretability.
  arXiv  Detail & Related papers  (2020-08-14T12:00:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.