Complete transparency into how MLBPrediction.com generates predictions. What data we use, how we process it, how we validate results, and how we hold ourselves accountable.
In the sports betting space, trust is earned through transparency. Anyone can claim a winning record. Anyone can show cherry-picked results. This page exists because we believe you deserve to know exactly how our analysis works, where our data comes from, and how we measure whether we are actually good at this.
Our model ingests data from publicly available sources. We do not use proprietary data feeds or insider information. Everything we use, you could verify independently if you wanted to.
We process raw data through our analytical pipeline to generate the metrics and probabilities that power our predictions. The methodology is described in detail across our Betting Model documentation.
Our framework is built on a core principle: use metrics that predict future performance, not metrics that describe past results. ERA describes what happened. xFIP predicts what will happen. Batting average describes past results. wOBA and barrel rate predict future production.
Starting pitchers are assessed through xFIP (expected fielding-independent pitching), SIERA (skill-interactive ERA), K-BB% (strikeout minus walk rate), and CSW% (called strikes plus whiffs). These metrics have been demonstrated through decades of sabermetric research to be the most stable and predictive measures of pitching quality. Our xFIP guide explains the methodology in depth.
Offense is measured through wOBA (weighted on-base average), barrel rate, hard-hit rate, chase rate, and platoon-adjusted expected statistics. We specifically avoid traditional metrics like RBI, batting average, and clutch stats that have low year-over-year correlation. Our wOBA guide and BABIP regression analysis explain the statistical reasoning.
Every game is adjusted for park factors, weather conditions, travel patterns, and rest days. A fly ball that is a home run at Coors Field is a routine out at Oracle Park. These adjustments matter and are frequently overlooked by the market. Our weather effects guide and environmental modeling documentation cover this module.
Individual game predictions are generated through Monte Carlo simulation, running thousands of game scenarios to produce probability distributions rather than point estimates. This is critical because a team with a 55% win probability should still lose 45% of the time. Understanding distributions, not just point predictions, is what separates rigorous analysis from guessing. See our simulation models documentation.
We track model performance through multiple metrics, not just win-loss records:
We publish all results, including losses. No disappearing picks. No retroactive edits. Every prediction is timestamped before games begin and graded publicly afterward.
We are not a tout service selling guaranteed picks. We are not a daily fantasy optimizer. We are not promising you will get rich.
We are an analytics platform that translates advanced baseball statistics into betting context. We believe in process over results, in data over narratives, and in transparency over hype.
Our analysis is provided for educational and informational purposes. We document our methodology openly because we believe the sports betting industry needs more accountability and less marketing.
For detailed technical documentation of each model component: