Traditional tennis statistics, such as aces, double faults, winners, and unforced errors, provide intuitive box-score summaries of player performance. However, these primitive metrics evaluate isolated aspects of performance rather than summarizing a player's overall performance in a single measure. As a result, comparing individual players often requires tables or other visualizations that combine multiple separate metrics. This makes interpretation less direct.
Composite metrics address this limitation by combining multiple aspects of play into a single numerical measure. For example, metrics such as Dominance Ratio (DR) and hold-plus-break percentage summarize performance more directly by combining serve and return outcomes. However, these metrics are deterministic: they are calculated from observed outcomes and do not value point-level events (e.g., aces, winners, and unforced errors) according to their score-dependent impact on winning the current game or match. Therefore, they cannot distingish a winner at 40–0 and a winner on break point, even though they often have very different effects on the game.
Unlike these deterministic metrics, this project studies a probabilistic metric called Event-Driven Gain in Expectancy (EDGE). Using tour-wide point-by-point match data, EDGE empirically quantifies the score-dependent value of point-level events, including positive events such as aces, winners, and forced errors drawn, as well as negative events such as double faults and unforced errors. Each event is valued according to its change in game-win expectancy (i.e., how much it changes the probability of winning the current game). These event values are then aggregated across a player's matches to produce a context-aware composite measure of player performance, relative to the tour average. A positive EDGE indicates that a player’s point-level events generate greater gains in game-win expectancy than the tour average, while a negative EDGE indicates that they generate less. An EDGE of zero represents tour-average performance. Thus, higher EDGE reflects more valuable plays in high-leverage score situations.
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