How MLB Games Are Predicted

What Does Prediction Mean in Baseball

Predicting a baseball game does not mean knowing what will happen. It means assigning probabilities to possible outcomes based on available information. When someone says a team has a 60% chance to win, they are not claiming certainty. They are expressing that, given everything known before the game, that team wins more often than not in similar situations.

This distinction matters because baseball outcomes are inherently uncertain. A starting pitcher might leave early with an injury. A routine fly ball might carry over the fence on a windy day. A closer might blow a three-run lead. These events happen regularly, and no amount of analysis can eliminate them from the equation.

The goal of prediction is not to be right about every game. The goal is to be accurate over time, which means that when you assign a 60% probability, the event should occur roughly 60% of the time across many similar situations. This is called calibration, and it is the foundation of meaningful prediction.

Probability Versus Certainty

One of the most common misconceptions about baseball prediction is that it should produce certainties. People often expect predictions to tell them who will win, not how likely each team is to win. But baseball does not work that way, and any system that claims certainty is either misleading or misunderstood.

Consider a matchup where a dominant starting pitcher faces a struggling offense. The starting pitcher might be assigned a 70% win probability. That sounds strong, but it also means the struggling offense wins 30% of the time. In a 162-game season, a 30% outcome happens constantly. It is not an upset when it occurs. It is an expected part of the distribution.

Prediction systems that embrace probability are being honest about what they can and cannot know. Those that claim certainty are setting unrealistic expectations. The best approach is to think in terms of edge and value, not guarantees.

Models Versus Human Analysis

There are two broad approaches to predicting baseball games: statistical models and human analysis. Each has strengths and weaknesses, and most serious approaches combine elements of both.

Statistical Models

Statistical models use historical data to estimate probabilities. They might factor in team run differentials, pitcher quality metrics, home field advantage, rest days, and dozens of other variables. Modern approaches include machine learning methods such as XGBoost, Random Forest, and neural networks, with research showing these can achieve accuracy levels in the 57-65% range depending on methodology and feature selection. The advantage of models is consistency. They do not get tired, emotional, or distracted. They process information the same way every time.

The weakness of models is that they can only work with quantifiable data. If a team has internal chemistry issues, a model cannot account for that unless it shows up in performance metrics. Models also struggle with small sample sizes, like early-season predictions when teams have only played a handful of games.

Human Analysis

Human analysis incorporates context that models cannot capture. An experienced analyst might notice that a pitcher's velocity has been declining in recent starts, or that a team tends to struggle in day games after night games. These observations can be valuable, but they are also prone to bias and inconsistency.

The best analysts are often those who use models as a starting point and then adjust based on context that the model cannot see. Pure intuition without data is unreliable. Pure data without context can miss important factors. The intersection is where the most accurate predictions often live.

Core Inputs Used Before a Game

Regardless of the approach, certain inputs are fundamental to predicting baseball games. Understanding what goes into a prediction helps evaluate how much confidence to place in any given estimate.

Starting Pitching

The starting pitcher is the single most important variable in most games. Metrics like ERA, FIP, xFIP, SIERA, and strikeout-to-walk ratio help estimate how a pitcher is likely to perform. But raw numbers are not enough. Matchup context matters. Some pitchers struggle against left-handed lineups. Others fade late in games. Evaluating a starter requires looking beyond surface statistics.

For a deeper look at pitcher evaluation, see How Starting Pitchers Are Evaluated Before a Game.

Bullpen Context

Modern baseball games are often decided in the late innings, which means bullpen quality and availability are critical. A dominant closer means nothing if he pitched three of the last four days. Research has shown that the strength of a team's bullpen is more indicative of its ability to win than the quality of its offense. Understanding bullpen usage patterns, fatigue levels, and matchup advantages adds significant value to any prediction.

For more on this topic, see How Bullpens Impact MLB Predictions.

Offensive Strength

Team offense is typically measured through run production, but that number can be misleading. A team might score five runs per game on average, but do so unevenly, scoring ten runs one night and getting shut out the next. Metrics like wRC+, wOBA, and on-base percentage help estimate offensive quality more reliably than raw runs scored.

Understanding which metrics stabilize quickly and which require larger samples is covered in What Metrics Matter Most in MLB Predictions.

Park and Weather

Where a game is played affects how it unfolds. Coors Field inflates offense. Petco Park suppresses it. Wind direction at Wrigley can turn warning track flies into home runs. Temperature affects ball carry. Humidity affects grip and break. These factors are often overlooked but can meaningfully shift expected outcomes.

For a detailed breakdown, see How Weather Affects MLB Outcomes.

Rest and Travel

Teams that just finished a long road trip may underperform due to fatigue. Teams on extended home stands may have an advantage beyond typical home field. Pitchers on extra rest may perform differently than those on standard rest. These situational factors are part of any complete prediction model.

Why Variance and Randomness Always Exist

Even with perfect information about every relevant variable, baseball outcomes would still be uncertain. This is because the game itself contains irreducible randomness. A ground ball might find a hole or get snagged by a diving infielder. A pitch at the edge of the zone might be called a strike or a ball. A fly ball might carry or die on the warning track.

This randomness is not a flaw in prediction. It is a feature of the sport. Baseball has more variance than almost any other major team sport, which is why underdogs win more often and why long-term thinking is essential.

Understanding this variance is what separates thoughtful prediction from wishful thinking. A prediction that claims to eliminate uncertainty is not more sophisticated. It is less honest. The goal is to be right more often than chance, over time, while acknowledging that individual results will always include surprises.

What This Means for Evaluating Predictions

When evaluating any prediction system, including this one, the focus should be on process rather than short-term results. A prediction that loses was not necessarily wrong. A prediction that wins was not necessarily right. What matters is whether the probabilities assigned were accurate over a meaningful sample.

This requires patience and proper record-keeping. It means not overreacting to hot or cold streaks. It means asking whether a system was well-calibrated, not whether it picked the right side last night. For more on this topic, see Can You Actually Predict Baseball Games Accurately.