NBA basketball has one of the most analytical fan bases of all professional sports, yet they are reliant on the packaging and distribution of advanced stats by professional statisticians. Players, fans, coaches, and the media can access spreadsheets of data post-game, but with little context given. It is only after they peruse through that data with a keen eye that they can discover important insights. Their only alternative is to wait for someone to present the most important stats from any given game, but that would mean that they are relying on someone else's interpretation of what is important and meaningful.
The new GameMap visual serves as a solution to this statistical blockade by providing contextual data with very little effort needed by the viewer. It uses a color-temporal approach to simultaneously show lineups and statistical values. It is interactive and simple to use.
- Click plays for a description.
- Click playing blocks for relevant stats.
- Click player names to view on/off court data and totals.
- Highlight sections of the game for a custom analysis.
- Switch between views (TEAM, PACE, AST%, REB%, +/-, etc.)
STATS DISCOVERED IN LESS THAN 1 MIN.
- Kevin Durant had a 79.8% usage rate in the last five minutes of the fourth qtr and scored 8 points in the last two minutes to force overtime | BKN vs. MIL - Game 7
- Sixers ORtg/Eff. for the 28min Joel Embiid was on the court was 150.1 vs. 89.1 for the 20min he was off the court | PHI vs. WAS - Game 3
- The receivers of Trae Young’s 10 assists were Clint Capela(6), John Collins(3), and Lou Williams(1) | PHI vs. ATL - Game 7
- Chris paul assisted on 62.5% percent of his team’s 2nd qtr field goals and he played the whole period | PHX vs. LAC - Game 3
These features allow viewers to see when each play occurred and who was on the court. They can analyze player performance by time block or any time range they highlight. They can select the last five minutes of crunch time, the entire first half, or the fourth quarter minutes when both point guards were in the game. They are given context that is not found in traditional box scores or play-by-play data.
The viewer can see whether a rookie was playing during garbage time or crunch time, whether they were scoring with All-NBA defenders or with rotational players on the court. They can see if usage rates changed during crunch time and which players were being featured. They may even discover that the ball was in the hands of someone other than the team’s leading scorer. They can see if the pace changed after a point guard substitution was made. They can see AST% rates to determine who the primary playmakers are and who they tend to target.
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