Personalized Contest Recommendation in Fantasy Sports

• URL: http://arxiv.org/abs/2508.14065v1
• Date: Mon, 11 Aug 2025 07:22:33 GMT
• Title: Personalized Contest Recommendation in Fantasy Sports
• Authors: Madiraju Srilakshmi, Kartavya Kothari, Kamlesh Marathe, Vedavyas Chigurupati, Hitesh Kapoor,
• Abstract summary: This paper presents a scalable contest recommendation system, powered by a Wide and Deep Interaction Ranker (WiDIR) at its core.<n>Online experiments show a marked improvement over other candidate models in terms of recall and other critical business metrics.
• Score: 0.3984581084039074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In daily fantasy sports, players enter into "contests" where they compete against each other by building teams of athletes that score fantasy points based on what actually occurs in a real-life sports match. For any given sports match, there are a multitude of contests available to players, with substantial variation across 3 main dimensions: entry fee, number of spots, and the prize pool distribution. As player preferences are also quite heterogeneous, contest personalization is an important tool to match players with contests. This paper presents a scalable contest recommendation system, powered by a Wide and Deep Interaction Ranker (WiDIR) at its core. We productionized this system at our company, one of the large fantasy sports platforms with millions of daily contests and millions of players, where online experiments show a marked improvement over other candidate models in terms of recall and other critical business metrics.

Related papers

• 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)
• Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning [0.2399911126932527]
  We develop a model that can adaptively select players to maximize the team's potential performance.<n>Our approach leverages historical player data to train RL algorithms, which then predict future performance and optimize team composition.<n>Our results show that RL-based strategies provide valuable insights into player selection in fantasy sports.
  arXiv  Detail & Related papers  (2024-12-26T13:36:18Z)
• 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)
• Luck, skill, and depth of competition in games and social hierarchies [0.6345523830122168]
  We find that social competition tends to be "deep," meaning it has a pronounced hierarchy with many distinct levels. In most cases there is little evidence of upset wins, beyond those already implied by the shallowness of the hierarchy.
  arXiv  Detail & Related papers  (2023-12-07T21:42:44Z)
• All by Myself: Learning Individualized Competitive Behaviour with a Contrastive Reinforcement Learning optimization [57.615269148301515]
  In a competitive game scenario, a set of agents have to learn decisions that maximize their goals and minimize their adversaries' goals at the same time. We propose a novel model composed of three neural layers that learn a representation of a competitive game, learn how to map the strategy of specific opponents, and how to disrupt them. Our experiments demonstrate that our model achieves better performance when playing against offline, online, and competitive-specific models, in particular when playing against the same opponent multiple times.
  arXiv  Detail & Related papers  (2023-10-02T08:11:07Z)
• DraftRec: Personalized Draft Recommendation for Winning in Multi-Player Online Battle Arena Games [25.615312782084455]
  This paper presents a personalized character recommendation system for Multiplayer Online Battle Arena (MOBA) games. We propose DraftRec, a novel hierarchical model which recommends characters by considering each player's champion preferences and the interaction between the players. We train and evaluate our model from a manually collected 280,000 matches of League of Legends and a publicly available 50,000 matches of Dota2.
  arXiv  Detail & Related papers  (2022-04-27T07:46:17Z)
• Retrospective on the 2021 BASALT Competition on Learning from Human Feedback [92.37243979045817]
  The goal of the competition was to promote research towards agents that use learning from human feedback (LfHF) techniques to solve open-world tasks. Rather than mandating the use of LfHF techniques, we described four tasks in natural language to be accomplished in the video game Minecraft. Teams developed a diverse range of LfHF algorithms across a variety of possible human feedback types.
  arXiv  Detail & Related papers  (2022-04-14T17:24:54Z)
• The Evaluation of Rating Systems in Team-based Battle Royale Games [4.168733556014873]
  This paper explores the utility of several metrics for evaluating three popular rating systems on a real-world dataset of over 25,000 team battle royale matches. normalized discounted cumulative gain (NDCG) demonstrated more reliable performance and more flexibility.
  arXiv  Detail & Related papers  (2021-05-28T19:22:07Z)
• An Elo-like System for Massive Multiplayer Competitions [1.8782750537161612]
  We present a novel Bayesian rating system for contests with many participants. It is widely applicable to competition formats with discrete ranked matches. We show that the system aligns incentives: that is, a player who seeks to maximize their rating will never want to underperform.
  arXiv  Detail & Related papers  (2021-01-02T08:14:31Z)
• 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)
• Competing Bandits: The Perils of Exploration Under Competition [99.68537519404727]
  We study the interplay between exploration and competition on online platforms. We find that stark competition induces firms to commit to a "greedy" bandit algorithm that leads to low welfare. We investigate two channels for weakening the competition: relaxing the rationality of users and giving one firm a first-mover advantage.
  arXiv  Detail & Related papers  (2020-07-20T14:19:08Z)

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.