Summary of Xgboost Learning Of Dynamic Wager Placement For In-play Betting on An Agent-based Model Of a Sports Betting Exchange, by Chawin Terawong and Dave Cliff
XGBoost Learning of Dynamic Wager Placement for In-Play Betting on an Agent-Based Model of a Sports Betting Exchange
by Chawin Terawong, Dave Cliff
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Multiagent Systems (cs.MA)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents the first results from using XGBoost, a powerful machine learning method, within the Bristol Betting Exchange (BBE), an open-source agent-based model designed to simulate a contemporary sports-betting exchange. The BBE generates synthetic data by feeding it into the XGBoost system, which aims to discover profitable betting strategies by learning from more profitable bets made by the bettor-agents. After training XGBoost, which results in one or more decision trees, a bettor-agent with a strategy determined by the learned decision tree(s) is added to the BBE and makes bets on a sequence of races under various conditions. The paper shows that XGBoost can learn profitable betting strategies and generalize to outperform each training data strategy. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses a special computer program called XGBoost to help make smart decisions about where to bet money on horse racing. They use a pretend game where computers play the role of people who place bets, and they teach the XGBoost program how to make good choices by looking at what works well in this fake game. The program then uses what it learned to make even better choices when betting on real horse races. The researchers found that the XGBoost program can really help you win money by making smart decisions. |
Keywords
* Artificial intelligence * Decision tree * Machine learning * Synthetic data * Xgboost