Embedded Files
DIGS Database
DIGS Database
Data imported via Fangraphs, Baseball-Reference, D1 Baseball, or NJCAA.
Data imported via Fangraphs, Baseball-Reference, D1 Baseball, or NJCAA.
MLB Leaderboards
MLB Leaderboards
Data from Fangraphs & Savant. Leaderboards via Sheets & Looker Data Studio.
MiLB Leaderboards
MiLB Leaderboards
Data from Fangraphs and MLB Savant.
Leaderboards created with Sheets & published to Looker Data Studio.
2025: NorCal MOBILE | USA Regional
2024: USA Regional Leaderboards
Leaderboards created with Looker Studio. Periodic updates.
DIGS Explained
DIGS Explained
DIGS is a game score metric measuring IP, SO, BB, HBP, HR, & H. It is designed for multi-inning pitchers, is adjusted for park, league, & year for MLB; age, level, park, & league for MiLB; and age, conference, & park (when applicable) for NCAA & JuCo. While DIGS marries a results-based model (including raw H & HR) with a batted ball regression model (where batted ball types & quality of contact are used at the MLB level & league average results are used for MiLB & college). The formula is designed so a player's reported DIGS score will lean heavily to the regression model early on, then gradually slide more to the results model as his BF total rises during the season. DIGS scoring (where 50 is average) is used to estimate a player's ERA, then converted to DIGS+ (where 100 is average).
DIGS is a game score metric measuring IP, SO, BB, HBP, HR, & H. It is designed for multi-inning pitchers, is adjusted for park, league, & year for MLB; age, level, park, & league for MiLB; and age, conference, & park (when applicable) for NCAA & JuCo. While DIGS marries a results-based model (including raw H & HR) with a batted ball regression model (where batted ball types & quality of contact are used at the MLB level & league average results are used for MiLB & college). The formula is designed so a player's reported DIGS score will lean heavily to the regression model early on, then gradually slide more to the results model as his BF total rises during the season. DIGS scoring (where 50 is average) is used to estimate a player's ERA, then converted to DIGS+ (where 100 is average).
DIGS+ Scale
DIGS+ Scale
160 | Elite (+++)
140 | Excellent (++)
120 | Very Good (+)
100 | League Average
80 | Below Average
60 | Replacement
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Created and managed by Kyle Goings. Follow me on on Twitter, & look for select leaderboards at Scout the Statline!
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