Drew Romo -- Performance Analysis

Drew Romo, catcher for the Chicago White Sox, has accumulated 16 at-bats through 7 games in the 2026 season. Despite a low .188 batting average with 3 hits, his power metrics are notably strong. He has driven in 4 runs with 3 home runs, scored 4 runs, and drawn 8 walks against only 3 strikeouts, producing an on-base percentage of .480 and slugging percentage of .750 for a 1.230 OPS. His weighted on-base average (wOBA) of 0.500 and isolated slugging (ISO) of 0.563 suggest significant power production despite the low hit total.

Batting splits reveal a stark platoon split: against right-handed pitchers, Romo is hitting .250 with a .900 SLG, while against left-handed pitchers he is .125 with a .500 SLG. He has drawn 15% walks in both splits and strikes out at 7.1% against RHP compared to 18.2% against LHP. Overall, Romo maintains a 60% groundball rate and has stolen no bases.

Defensively, Romo has appeared in 7 games with a perfect 1.000 fielding percentage. He has recorded 56 putouts and 3 assists with no errors across 59 total chances. However, he has committed 3 passed balls and faced no stolen base attempts. His current WAR stands at 0.5 with 0.1 defensive WAR contribution.

Drew Romo is a catcher for the Chicago White Sox entering the 2025 MLB season. The provided statistics show early-season numbers of a .000 batting average with no home runs, RBIs, or stolen bases, indicating limited playing time or very early season data. The platform offers comprehensive advanced analytics beyond traditional box score statistics, including Statcast data from Baseball Savant and sabermetric measurements. Key metrics available include exit velocity and launch angle data, barrel rate calculations, sweet spot percentage, and expected batting average (xBA), slugging percentage (xSLG), and weighted on-base average (xwOBA). These expected statistics are designed to measure quality of contact independent of defensive positioning and luck factors. Additional proprietary tools include boost factors for projection algorithms and opposing pitcher/lineup matchup analysis. The advanced statistics framework helps distinguish between sustainable production and performance due for regression by moving beyond surface-level statistics.