Research any topic before you write.
Find related topics. | Discover entities. | See connections. | Build a topical map.
In baseball, wOBA (or weighted on-base average) is a statistic, based on linear weights, designed to measure a player's overall offensive contributions per plate appearance. It is calculated by taking the observed run values of various offensive events, dividing by a player's plate appearances, and scaling the result to be on the same scale as on-base…
The analysis highlights History, Historical versions of the formula and Usage as prominent areas in the source structure around WOBA. 1 topic appears in more than one source area, which can help identify connections that are less obvious in a linear reading.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around WOBA shows recurring relationship patterns in the source. For example, WOBA → Events, FanGraphs, In, It, Major League Baseball, OPS, RC, Sites, The, The Book, The Hardball Times, WAR Another extracted example is WOBA → Below, FanGraphs, HBP, HR, NIBB, One, RBOE, The, The Book's, This. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
run value baseball runs event average on-base base based player's fangraphs weights events offensive weighted coefficients hr linear values like
TTTA extracted 46 structured relationships around WOBA. Examples in this analysis include WOBA → is a → good estimator of team runs scored and The Hardball Times have studied wOBA → instance of → Sites. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| WOBA | is a | good estimator of team runs scored | 0.90 | text |
| The Hardball Times have studied wOBA | instance of | Sites | 0.80 | text |
| found it to perform comparably to or better than other similar tools | instance of | Sites | 0.80 | text |
| .320 or .360 can be read in the same way as OBP | instance of | since values | 0.80 | text |
| making a relatively complicated statistic seem familiar. xwOBA | instance of | since values | 0.80 | text |
| WOBA | related to 2023 | Per Fangraphs | 0.60 | section |
| WOBA | related to 2023 | NIBB | 0.60 | section |
| WOBA | related to 2023 | HBP | 0.60 | section |
| WOBA | related to 2023 | HR | 0.60 | section |
| WOBA | related to 2023 | AB | 0.60 | section |
| WOBA | related to 2023 | BB-IBB | 0.60 | section |
| WOBA | related to 2023 | SF | 0.60 | section |
The concept neighborhoods around WOBA bring nearby vocabulary together. In this analysis, examples include Base, Balls and Estimate. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For WOBA, one of the stronger structural bridges in this analysis connects WOBA with Historical versions of the formula. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around WOBA to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Historical versions of the formula & Usage, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — WOBA · EN edition · Analysis: TopicsToTalkAbout