How to Beat the NFL Betting Market: Key Lessons for New Bettors

Using data from the 2010 racing season, the model predicted race winners with a 58% accuracy rate, outperforming professional tipsters who averaged a 44% success rate. The metrics used included the number of predicted winners and the profit per race, the dataset comprising 240 races from 2010. The prediction system, designed in Java, allowed for automatic weight adjustments based on new race data, enhancing future prediction accuracy, and demonstrating significant profit potential for bettors. Furthermore, JoashFernandes et al. (2020) developed a machine learning model to predict NFL plays (pass vs. rush) using data from the 2013–2017 NFL regular seasons. They compared several models, finding that a neural network achieved the highest accuracy of 75.3% with a false negative rate of 10.6%. To balance accuracy and interpretability, they created a decision tree model that retained 86% of the neural network’s accuracy (65.3%) and was practical for in-game use.

Betting Strategy and Risks

The metrics used for the evaluation included typical neural network loss functions and error terms adjusted during the training process to optimize weights for the most accurate data analysis possible. Ayub et al. (2023) introduced the Context-Aware Metric of player Performance (CAMP) for quantifying cricket players’ contributions to matches, specifically limited over matches between 2001 and 2019. The CAMP model incorporated various contextual factors such as opponent strength, game situations, and player quality, using data mining techniques to provide a comprehensive performance metric. The empirical evaluation demonstrated that CAMP’s ratings aligned with Man-of-the-Match decisions in 83% of the 961 matches analyzed, outperforming the traditional Duckworth-Lewis-Stern (DLS) method.

Similarly, Nimmagadda et al. (2018) developed a predictive model for T20 cricket matches, particularly focusing on the Indian Premier League (IPL). They utilized Multiple Linear Regression to predict the first innings score by considering variables like the current run rate, the number of wickets fallen, and the venue of the match. For the second innings, Logistic Regression was used to predict 1xbet login outcomes and a random forest algorithm was applied to predict the winner of the match.

• The model preprocesses historical data from the Korean Baseball Organization (KBO) by creating pairs of pre-game and post-game records, allowing the LSTM to learn dependencies between these events.
• Studies by Sipko and Knottenbelt (2015), and Cornman et al. (2017) used these metrics for performance evaluation.
• As the United States Supreme Court has recently repealed the federal ban on sports betting, research on sports betting markets is increasingly relevant for the growing sports betting industry.
• BettorEdge, for example, provides features designed to identify and capitalize on market inefficiencies while managing risks effectively.
• The final model, named EP2, achieved an accuracy of 62.4%, significantly higher than the 8.2% accuracy of naive prediction.

Understanding Market Inefficiencies in Niche Sports

They demonstrated that formal investment strategies, when applied with risk control modifications, significantly enhance profitability. Their adaptive fractional Kelly method was especially effective in different sports, highlighting the practical importance of mitigating the unrealistic assumptions inherent in pure mathematical strategies. Testing in horse racing, basketball, and soccer confirmed the necessity of these risk control methods to achieve optimal results. The primary objective of this systematic review is to explore the current challenges and advances in applying machine learning techniques to sports betting.

Beating the House: Identifying Inefficiencies in Sports Betting Markets

As a result, models must be designed to incorporate a wide range of variables and remain flexible to adapt to new information, which can be a complex task. In Soccer, features used in predictive models include dimensionality reduction, classifier combinations, historical patterns, team rankings, player attributes, spatio-temporal trajectory frames, event stream data, and player profiles. Studies by Tax and Joustra (2015), Hervert-Escobar et al. (2018b), and Wang et al. (2024) utilized these features to improve the accuracy and predictive performance of the model. Tennis prediction models have utilized datasets such as Wimbledon, OnCourt System, Jeff Sackmann’s dataset, Tennis-Data.co.uk, and the Match Charting Project. Specific studies like Sipko and Knottenbelt (2015) and Cornman et al. (2017) incorporated these datasets to predict match outcomes.

The model demonstrated improved accuracy over traditional methods by incorporating additional predictive factors beyond just run rate, such as player performance statistics and match venue. The results showed that these combined methods provided a more reliable prediction of match outcomes, with the model performance validated through various statistical metrics such as accuracy and error rates. Their Random Forest algorithm achieved a prediction accuracy of 65.15%, with logistic regression performing better in the final quartile of the season with an accuracy of 68.75%. Yeh et al. (2022) developed tools to measure the quality of continuously updated probabilistic forecasts, using Monte Carlo simulations on ESPN’s real-time probabilistic forecasts of NBA games. They found ESPN’s forecasts were generally well-calibrated, with a Brier score of 0.075, outperforming several naive models.

Machine learning has significantly impacted the sports betting landscape by improving both the accuracy of predictions and the efficiency of betting strategies. For bookmakers, ML models enable dynamic odds setting and sophisticated risk management, adjusting for new information as events unfold Thabtah et al. (2019). For bettors, ML provides the tools to develop data-driven strategies that improve the chances of success by identifying value bets and exploiting market inefficiencies Horvat and Job (2020); Haruna et al. (2021). As a result, the sports betting industry increasingly resembles a financial sector, with both bettors and bookmakers leveraging advanced predictive analytics to maximize returns.

Future work was planned to compare the performance of the k-NN algorithm with other machine learning methods such as SVM and decision trees. Furthermore, Chun et al. (2021) proposed an interdependent LSTM to predict baseball game outcomes using only information from the starting lineup, addressing the issue of incomplete data in pre-game predictions. The model preprocesses historical data from the Korean Baseball Organization (KBO) by creating pairs of pre-game and post-game records, allowing the LSTM to learn dependencies between these events. This approach was contrasted with traditional methods that suffer from accuracy loss due to unknown substitutions.

Robbins’ (2023) research highlights that while there are minor inefficiencies in the sports betting market, they are not significant enough to allow for consistent long-term profits. This highlights the importance of managing expectations and understanding that luck is an inherent part of betting on sports. While these players can significantly impact the outcome of a game, focusing solely on them ignores other critical factors like team dynamics, injuries, and recent trends. In Soccer, performance metrics include prediction accuracy, McNemar’s test, RPS, top-3 accuracy, F1 score, human expert assessments, AP, precision, recall, AUC, BIC, RMSE, and cross-entropy.