DSL Hitter Objective Analysis

The Headline: I built a per-hitter objective-setting and grading framework for returning DSL hitters. Each goal was scored 1 to 10 for difficulty against the full pool of qualifying DSL returners, and progress was graded A through F across three dimensions. Goals averaged 6.9/10 in difficulty, roughly two in three hitters improved year over year, and Chase rate was the most commonly assigned objective.

Project Overview

A note on the numbers: this write-up presents the framework and aggregate results only. Player names, raw tracking values, the individual goals, and the per-hitter evaluation notes are withheld. Chase, contact, and exit-velocity inputs are league tracking data and appear here only as the structure of the method, not as values.

How the Framework Works

Two custom scores do the work, and they are designed to be read together.

Objective Difficulty Rating (1 to 10). Every goal is scored by how rare that level of improvement actually was across the league. Targets reached by more than half of qualifying hitters score low (too easy, 1 to 3), targets in the fair range score in the middle (4 to 7), and targets met by fewer than 15 percent of hitters score high (very difficult, 8 to 10). The rating keeps an ambitious goal from looking like a failure just because it was hard.

Outcome Grade (A to F). A means the hitter met or exceeded the goal, B nearly met it, C improved at least halfway, D made some progress, and F is no progress or regression.

Read together, a high difficulty with a strong grade is an exceptional result against a hard goal, while a low difficulty with a poor grade flags underperformance on a target that should have been reachable. That combination is what the chart above plots.

Aggregate Results

• Average objective difficulty: 6.9/10. The goals set for returning hitters were appropriately challenging given their prior-season profiles, not soft and not unrealistic.
• Year-over-year progress: roughly two in three hitters improved on their objective metric versus the prior season.
• In-season development: close to half improved from the first half of the season to the second.
• Most common objective: chase rate, assigned more than any other metric, with most of those hitters trending in the right direction.
• Target-setting quality: about a third of all goals were flagged after the fact as either too difficult or too easy for the player’s profile, a finding that directly sharpens how the next round of goals gets set.

How a Team Uses This

The framework turns a stack of player numbers into an accountability tool the development staff can actually act on. By scoring difficulty against the full league pool, it separates a hitter who cleared an easy bar from one who made rare, hard-won progress, so a coach is not fooled by a goal that looked good on paper. The same scores flag where targets were mis-calibrated, which feeds directly back into setting fairer, more useful goals the following year.

I built it to be repeatable. The same structure can be rerun for any future season, extended to additional hitters, or adapted to pitchers, and it deliberately reports difficulty and grade rather than raw values so it can be shared without exposing player-level data.