Data Is the Foundation, Not the Algorithm

In the AI prediction world, there is a saying: garbage in, garbage out. It is a cliche because it is true. The most sophisticated neural network in the world, trained on incomplete or biased data, will produce predictions that are incomplete and biased. Conversely, a relatively simple model trained on clean, comprehensive, well-structured data can outperform a complex model trained on a mess.

For MLB prediction specifically, the data landscape is unusually rich. Baseball has been quantified more thoroughly and for longer than any other major sport. Pitch-level data, batted ball data, defensive positioning data, and biomechanical data are all available at granularities that would have seemed like science fiction twenty years ago. The challenge is not data scarcity. It is data curation: knowing what to include, what to exclude, and how to structure it for maximum predictive signal.

Core Data Sources

AI MLB models typically draw from several data categories. Pitch-level data captures every pitch thrown in a game: velocity, movement, location, result. This data allows models to evaluate pitchers at the most granular level possible, assessing not just outcomes (strikeouts, walks) but the underlying pitch quality that drives those outcomes.

Batted ball data tracks what happens when contact is made: exit velocity, launch angle, spray direction, estimated distance. These metrics are better predictors of future offensive performance than traditional stats like batting average because they measure the quality of contact rather than the results, which are heavily influenced by defense and luck.

Contextual data includes everything outside the at-bat itself: weather conditions, park dimensions and altitude, rest days, travel distances, day versus night games, and bullpen usage over preceding games. These factors introduce measurable variance in game outcomes and represent informational signals that a purely stats-based model would miss.

Roster and transaction data tracks who is actually available for each game: injury reports, minor league call-ups, trade deadline acquisitions, and lineup construction. A model that does not know a team's best reliever was traded yesterday is operating on stale information, and stale information produces stale predictions.

The Problem of Historical Bias

Training a model on historical data implicitly assumes that the past is representative of the future. In baseball, this assumption is frequently wrong. The game changes structurally over time. The introduction of the pitch clock altered pitcher-batter dynamics. Rule changes affecting shift restrictions changed the value of certain batted ball profiles. Evolving approaches to bullpen usage changed the relevance of starter workload metrics.

A model trained on data from a decade ago has learned patterns from a version of baseball that no longer exists. Stolen base rates, strikeout rates, home run rates, and defensive alignment strategies have all shifted meaningfully. The model does not know this. It treats historical patterns as eternal truths, and if the data window extends too far back, those obsolete patterns contaminate the model's understanding of the current game.

The practical solution is windowed training: limiting the training data to a recent, representative period while retaining enough volume to learn robust patterns. The optimal window length is itself a tunable parameter. Too short, and the model lacks sufficient data to learn complex interactions. Too long, and irrelevant historical patterns dilute the signal from the current era.

Data Leakage: The Silent Killer

Data leakage occurs when information from the future accidentally contaminates the training data. In a baseball context, this can happen in subtle ways. If a model's training features include end-of-season statistics for games played mid-season, the model has access to information that was not available at prediction time. It will appear to perform brilliantly in backtesting and then collapse when deployed on live data.

Another common leakage vector is using game-level outcomes as inputs. If a feature accidentally encodes whether a team won the game being predicted (even indirectly, through a correlated variable), the model learns a shortcut that works perfectly on training data and is completely useless on new data.

Preventing leakage requires strict temporal discipline: at every point in the training pipeline, verify that no feature uses information from after the prediction timestamp. This sounds simple but becomes complex when features involve rolling averages, season-to-date statistics, or standings-based variables. Each one must be computed using only data available at the moment the prediction would have been made.

Seasonality and Non-Stationarity

Baseball performance is not constant across the season. Pitchers accumulate fatigue. Hitters adjust to pitcher repertoires over multiple exposures. Weather patterns shift as summer heat arrives and departed. Roster composition changes through call-ups, injuries, and trade deadline acquisitions. A model that treats all games as drawn from the same statistical distribution misses these dynamics.

Sophisticated models account for seasonality through time-varying features: rolling windows that weight recent performance more heavily, fatigue indicators that track workload accumulation, and roster-strength variables that update as transactions occur. These features allow the model to adapt its predictions to the current state of the season rather than relying on static, season-long averages.

Sample Size and the Small-Sample Trap

Baseball's large sample sizes (162 games per team, thousands of plate appearances per season) create an illusion of statistical stability. But many of the most interesting predictive signals live in small subsamples: a pitcher's performance against a specific lineup, a hitter's splits on zero rest with runners in scoring position, a team's record in one-run games at altitude. These splits might contain real signal, but they might also be pure noise masquerading as patterns.

Models must balance specificity against reliability. A feature with high specificity (pitcher A versus lineup B) might capture a genuine matchup advantage, but if it is based on twelve historical at-bats, the confidence interval around that feature's value is enormous. Regularization techniques help by shrinking extreme estimates toward population means, effectively saying "this specific matchup data is interesting, but I will not bet the farm on twelve at-bats."

Data Quality Is a Competitive Advantage

In a field where many models use similar algorithms, data quality becomes the primary differentiator. The model with cleaner data, better feature engineering, more disciplined leakage prevention, and smarter handling of edge cases will outperform over time, even if its algorithmic architecture is simpler. Data quality is not glamorous work, but it is the foundation on which every prediction stands or falls.