Predicting the outcome of shots based on the events and tracking data available for the 2015/16 season.
The repo combines events data and tracking data from the 2015/16 season.
- Events data: NBA events and shots api
- Tracking data: https://github.com/linouk23/NBA-Player-Movements
| SHOT_TYPE | SHOT_ZONE_BASIC | SHOT_ZONE_AREA | SHOT_ZONE_RANGE | ACTION_TYPE_SIMPLIFIED |
|---|---|---|---|---|
| 2PT | Mid-Range | Right Side Center(RC) | 16-24 ft. | Layup |
| 3 PT | Above the Break 3 | Right Side(R) | 24+ ft. | Dunk |
| Restricted Area | Center(C) | 8-16 ft. | Hook Shot | |
| In The Paint (Non-RA) | Left Side(L) | Less Than 8 ft. | Jump Shot | |
| Left Corner 3 | Left Side Center(LC) | |||
| Right Corner 3 |
| AVERAGE_DEFENDER_DISTANCE | SHOOTER_DEFENDER_DISTANCE | OFFENSE_SPACING | DEFENSE_SPACING | SHOT_CLOCK |
|---|
I used convex hull technique to calculate the OFFENSE_SPACING and DEFENSE_SPACING. This is explained much better here: http://projects.rajivshah.com/sportvu/Chull_NBA_SportVu.html
AVERAGE_DEFENDER_DISTANCE and SHOOTER_DEFENDER_DISTANCE are calculated using the distance formula.
From the charts below we can infer that, although that is not the case always, we do see that among the teams with shooting percentage higher than the league average, the spacing also is better and vice versa
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