Phil Gosselin 2017 MLB Splits
Updated
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3B
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MLB
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13,938 views
Should you bet Phil's strikeout props?
K%, xFIP, and hard-hit rate vs LHB and RHB — the splits that decide over/under Ks.
Upgrade to FTA+Splits by Batter Handedness Detailed pitching stat breakdown for Phil Gosselin by the handedness of batters faced, including K%, BB%, wOBA, and xFIP.
K%, BB%, wOBA, xFIP, and more vs LHB / vs RHB / Overall
Rolling Splits Phil Gosselin's performance across L7, L14, L30, and full-season windows to track hot and cold streaks.
wOBA, ISO, K%, Hard Hit% across L7, L14, L30, and Season
Phil Gosselin Splits Breakdown
Splits data reveals how Phil Gosselin performs across different time windows and matchup situations. FTA+ tracks rolling performance over the last 7, 14, and 30 days alongside full-season numbers so you can identify hot streaks, cold spells, and platoon advantages before they're priced into DFS salaries.
Pitcher Handedness Splits
See how Phil performs against left-handed vs right-handed batters. Strikeout rate, walk rate, wOBA allowed, and xFIP splits help identify platoon advantages that create value in DFS and prop markets.
Pitch Mix & Approach
Different pitch mixes against lefties and righties can dramatically change a pitcher's effectiveness. Understanding these tendencies helps predict strikeout upside and blowup risk in specific matchups.
Rolling Windows (L7, L14, L30)
Track wOBA, ISO, hard hit rate, and barrel rate across different time periods. A player trending up in L7 and L14 but below their season line may be breaking out — or a hot streak may be cooling off.
Park Factors & Context
MLB performance is heavily influenced by ballpark dimensions, altitude, and weather. Splits data paired with park factor context helps you evaluate whether Phil's recent numbers are environment-driven or skill-driven.
Phil Gosselin Splits 2017
View Phil Gosselin's batting splits including wOBA, ISO, wRC+, OBP, K%, BB%, and hard hit rate against left-handed pitchers (LHP) and right-handed pitchers (RHP). Rolling window performance across the last 7, 14, and 30 days reveals hot streaks and cold spells before they're reflected in season-long averages. Splits data is essential for evaluating matchup-dependent value in DFS and identifying platoon advantages in player props.