arxiv_ml 75% Match Research Paper Sports bettors,Sports analysts,Data scientists in sports,Betting platform developers 19 hours ago

Assessing win strength in MLB win prediction models

reinforcement-learning › game-playing

📄 Abstract

Abstract: In Major League Baseball, strategy and planning are major factors in determining the outcome of a game. Previous studies have aided this by building machine learning models for predicting the winning team of any given game. We extend this work by training a comprehensive set of machine learning models using a common dataset. In addition, we relate the win probabilities produced by these models to win strength as measured by score differential. In doing so we show that the most common machine learning models do indeed demonstrate a relationship between predicted win probability and the strength of the win. Finally, we analyze the results of using predicted win probabilities as a decision making mechanism on run-line betting. We demonstrate positive returns when utilizing appropriate betting strategies, and show that naive use of machine learning models for betting lead to significant loses.

Key Contributions

This paper trains a comprehensive set of machine learning models for MLB game prediction and relates their predicted win probabilities to win strength (measured by score differential). It demonstrates that these models show a relationship between predicted probability and win strength, and analyzes the results of using these probabilities for run-line betting, showing positive returns with appropriate strategies while highlighting losses from naive usage.

Business Value

Provides insights for sports bettors and betting platforms on how to leverage predictive models for more profitable betting strategies. It can also inform sports analysts and teams about factors influencing game outcomes.

Paper Metadata

Innovation Type

Analysis and Application

Deployment Feasibility

Highly feasible, as it involves applying existing ML models to sports data and analyzing betting outcomes. The insights can be directly used by betting platforms and analysts.

Limitations Addressed

The naive use of machine learning models for betting, which can lead to significant losses. It shows how to use predicted win probabilities more effectively for betting.

Performance Gains

positive returns when utilizing appropriate betting strategies

Technical Tags

MLB predictionwin probabilitymachine learning modelsscore differentialbetting strategyrun-line bettingdecision makingmodel comparisonstatistical analysissports analytics

Research Topics

Sports AnalyticsMachine LearningPredictive ModelingStatistical AnalysisBehavioral Economics

Methods & Architectures

training machine learning modelsstatistical analysismodel comparison

Applications & Tasks

Sports Major League Baseball (MLB) Betting Game Outcome PredictionWin Strength AssessmentBetting Strategy Optimization Predicting winning teamEstimating win probabilityInforming betting decisions

Related Fields

Sports BettingData ScienceStatistical ModelingBehavioral Finance

Keywords

MLBbaseballpredictionwin probabilitymachine learningbettingsports analyticsscore differentialrun-linestrategydecision makingstatistical analysisgame outcomewin strengthgambling

Academic Context

#Sports Analytics#Machine Learning#Predictive Modeling#Statistical Analysis#Behavioral Economics

Commercial Potential

Potential Products

Sports betting advisory servicePredictive analytics tool for sportsAutomated betting strategy software

Target Industries

Sports BettingFantasy SportsSports Media

Use Case Examples

Developing betting strategies for MLB gamesAssessing the value of a team's predicted win probabilityAnalyzing historical game data to improve prediction models

Competitive Edge

Extends existing MLB prediction models by explicitly linking predicted win probability to win strength and analyzing its application in betting, offering a more nuanced approach than simple win prediction.

Market Opportunity

Large global sports betting market.

Revenue Models

Subscription fees for insightsrevenue sharing with betting platforms.

Resource Requirements

Compute Needs

Minimal compute for training standard ML models on tabular sports data.

Data Requirements

Historical MLB game data including scores, team statistics, and potentially player information.

Deployment Constraints

Accuracy of predictions is subject to inherent randomness in sports and external factors not captured by the model.

Scalability

Models can be retrained annually with new data; inference is generally fast.

Regulatory Considerations

Betting regulations vary by jurisdiction.

Production Readiness

Maturity Level

Applied Research

Time to Market

3-6 months for developing a betting strategy tool.

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