Using predictive statistics and MLB Draft slot values to forecast signing bonuses based on college performance metrics.
Texas Baseball faces a competitive recruiting landscape where top high school talent weighs the immediate financial opportunity of signing professionally against the developmental benefits of college baseball. To help recruits make informed decisions, the program needs a data-backed way to quantify the potential financial upside of choosing the college route.
Our mission is to build a predictive model that can answer a recruit's most pressing question: "If I come to Texas and perform at a certain level, what signing bonus can I expect when I enter the MLB Draft?" This tool transforms abstract potential into concrete projections, giving coaches a powerful recruiting asset that demonstrates exactly what a player could earn based on specific performance benchmarks like hits, stolen bases, and other statistical achievements.
We're developing a statistical model that connects college performance metrics to MLB Draft signing bonuses by analyzing historical data patterns. By examining the relationship between collegiate statistics and draft slot values, we can create projections that help recruits understand their potential earning trajectory.
The model integrates two critical data streams: player performance statistics from college baseball and corresponding signing bonus amounts based on MLB Draft slot positions. This allows us to build predictive algorithms that can generate personalized financial projections for recruits based on their anticipated performance levels at Texas.
This model gives Texas Baseball a tangible recruiting advantage by providing data-driven financial projections that demonstrate the economic value of developing as a collegiate athlete.
We're leveraging industry-standard analytical tools and comprehensive baseball databases to ensure our model is built on reliable data:
An R-based statistical model that generates signing bonus projections based on specific college performance metrics, enabling personalized financial forecasts for recruits.
A practical application that coaches can use during recruiting conversations to show prospects concrete earning potential based on performance scenarios.
Pre-calculated projections for various performance levels that demonstrate the relationship between statistical achievement and draft signing bonuses.
We're currently in the data collection stage, systematically gathering historical information from Baseball Reference. This foundational phase involves compiling comprehensive datasets that link college performance statistics to subsequent MLB Draft outcomes and signing bonuses.
Once our dataset is complete, we'll move into model development using R, where we'll build and test predictive algorithms that can accurately forecast signing bonuses based on performance inputs. The goal is to create a reliable tool that Texas Baseball can deploy immediately in recruiting situations to give prospects a clear, data-driven picture of their earning potential.