Splitting Goals Saved Above Average (GSAA) into 7 distinct shot-type bins, each probing a different goalkeeper skill. Standard GSAA lumps all shots together, masking a goalkeeper's specific strengths and weaknesses. By decomposing into headers, close-range 1v1s, long-range 1v1s, long-range foot shots to corners, close-range central foot shots, angled central foot shots, and shots after crosses, analysts can identify exactly which shot-stopping sub-skill a goalkeeper excels or struggles at. Four shot categories are excluded from the model: own goals (accidental), penalties (separate skill, insufficient sample), deflections (too noisy even for post-shot xG models), and forced-out-of-position shots (should penalize cross-claiming, not shot-stopping).
(1) Exclude: own goals, penalties, deflections (model can't accurately value them — velocity/direction change not captured), forced-out-of-position shots (fast cutbacks/rebounds where GK physically can't be positioned). (2) Bin remaining shots into 7 types:
(3) Compute GSAA per bin using post-shot xG. (4) Build a 7-axis radar dedicated entirely to shot-stopping.
GSAA (Goals Saved Above Average) is confounded by the team's shot concession profile. A GK who is elite at saving 1v1s but average at everything else will show different GSAA depending on whether their team concedes 20% or 50% of xG from 1v1s. By decomposing GSAA into shot-type components, you can predict how a GK's GSAA would change under a different defensive system — and the swing can be 2+ goals per season, which is often the difference between relegation and safety.