Which AI Model to Use in Sport Betting?

Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries. The general patterns in Figure 1, that home underdogs perform better relative to the point spread than home favorites and that outcomes tend to be less extreme than predicted by extreme over/unders, are well-documented in the literature. The literature in this area is quite vast (search “NFL betting” in Google Scholar to see what I mean). I do not find the incremental contribution of this paper to be compelling enough for PLOS-ONE. It is likely that the resulting error is negligible, however, due to the likelihood of the payout discrepancy being fairly balanced across the home and visiting teams. Conventional ordinary least-squares (OLS) regression yields estimates of the mean of a random variable, conditioned on the predictors.

Sports Predictions based on Statistical Models

The dataset utilized in this work was the in-game roobet india statistics published on the NHL’s website and textual data from pre-game reports on For instance, Ötting (2021) used hidden Markov models (HMMs) to predict NFL play calls. Using a comprehensive play-by-play dataset from Kaggle, which includes 289,191 observations from regular season matches between 2009 and 2017, the study focused on predicting plays for the 2018 season. HMMs were chosen due to their ability to account for the time series structure of play call data, improving predictive power by modeling the team’s propensity to pass or run. Key covariates included game location (home/away), yards to go, number of down, formation (shotgun / no huddle), score difference, and field position.

• Arbitrage betting algorithms, also known as “sure bet” or “scalping” algorithms, are designed to identify opportunities to place bets on all possible outcomes of a sports event to guarantee a profit regardless of the outcome.
• The study utilized data from the 2011 Cricket World Cup, specifically , to create a player ranking index derived from batting and bowling statistics.
• In conjunction with this, Jogeeah et al. (2015) utilized a fuzzy logic model to predict race outcomes.

The use of predictive analytics not only enhances the chances of making successful predictions but also provides a significant advantage in the highly competitive sports betting market. In tennis, the development of models by Knottenbelt etal. (2012) highlights the importance of considering player-specific statistics and match conditions. The hierarchical Markov model and Bayesian approaches employed in these studies demonstrate the nuanced understanding required to predict the outcomes of tennis matches accurately. The emphasis on integrating comprehensive datasets and the high return on investment achieved by these models underscore the economic viability of machine learning in tennis betting.

Author response to Decision Letter 1

Rugby prediction models measure performance using metrics like MANOVA, AUC, classification accuracy, mean decrease accuracy, temporal persistence, standardized effect, prediction errors, adjusted R², and average absolute error. Studies by Fontana et al. (2017), Welch et al. (2018), and Xu and Yang (2022) used these metrics. In baseball, features used include pitch types, locations, game attributes, spectators, sentiment analysis, starting lineups, OPS, runs, pitching data, and batting data. Studies by Lee (2022), Park et al. (2018), and Kim and Lee (2023) incorporated these features into their models.

They are designed to mimic the structure and function of the human brain, allowing them to recognize complex patterns and relationships between different data points. According to experts, fusing neural networks with machine learning algorithms will equip them to analyze more complex data and solve more problems faster. Instead of just analyzing existing data and coming up with predictions, machine learning allows algorithms to learn and adapt from experience over time the way a human would, increasing their performance, accuracy, and efficiency. Also, ML helps betting algorithms identify more accurate and predictive factors that would otherwise be hard to perceive. ZCode uses a combination of statistical analysis, machine learning, and artificial intelligence to make predictions about upcoming sports events.