Update README.md

README.md

@@ -11,23 +11,25 @@ source_datasets: https://github.com/linouk23/NBA-Player-Movements
 ---
 # 2015-2016 Raw Tracking Data from SportVU
 
-The modern era of basketball is characterized by the use of data to analyze performance and make decisions both on and off the court. 
+The modern era of basketball is characterized by the use of data to analyze performance and make decisions both on and off the court. Using tracking data combined with traditional play-by-play can allow for expert analysis of games.
 
 ## Dataset Details
 
 ### Dataset Descriptions
 
-Tracking data is the finest level of basketball data, whereas play-by-play and box score data are also used. This dataset gives raw SportVU tracking data from each game of the 2015-2016 NBA season. This was the last season with publically available tracking data. This data has the coordinates of all players at all moments of the game, for each game in the season. There are also identifiers for player id and team id, so that further analysis can be performed.
-- **Collected By:** SportVU
-- **Shared By:** Kostya Linou, Dzmitryi Linou, Martijn De Boer
+Tracking data is the finest level of basketball data, whereas play-by-play and box score data are also used. This dataset gives raw SportVU tracking data from each game of the 2015-2016 NBA season merged with play-by-play data. 2015-16 was the last season with publically available tracking data. This data has the coordinates of all players at all moments of the game, for each game in the season. There is also more information such as descriptors for players on the team (and their unique IDs) and the teams playing (and their unique IDs). Further, descriptors of the play that occured at each event is present, and the team in possession during the event, along with more necessary features.
+- **Collected By:** SportVU, Basketball Referece
+- **Shared By:** Kostya Linou, Dzmitryi Linou, Martijn De Boer, Sumitro Datta
 
 ### Dataset Source
 
-- **Repository:** https://github.com/linouk23/NBA-Player-Movements
+- **Repositories:**
+  - https://github.com/linouk23/NBA-Player-Movements
+  - https://github.com/sumitrodatta/nba-alt-awards/tree/main
 
 ## Uses
 
-This dataset has many potential uses. Primarily, visualization of plays, as illustrated in the initial repository is possible, creating a comprehensive view for analyzing actions on court. Beyond that, models could be trained to recognize certain play types or actions, as illustrated in previous papers (see Stephanos et al., 2022). Even further, video data could be connected to each moment of collection to create a model where video frames are mapped to tracked coordinates, increasing the accessibility of tracking data as only publically available video footage is necessary. 
+This dataset has many potential uses. Primarily, visualization of plays, as illustrated in the initial repository is possible, creating a comprehensive view for analyzing actions on court. Beyond that, models could be trained to recognize certain play types or actions, as illustrated in previous papers (see Stephanos et al., 2022). Analysis of defensive control could be performed by examining the data spatially. Even further, a broadcast tracking model could be creater if video data could be obtained and connected to each moment of collection. This would create a model where video frames are mapped to tracked coordinates, increasing the accessibility of tracking data as only publically available video footage is necessary. 
 
 - Stephanos et al.: https://www.sloansportsconference.com/research-papers/using-hex-maps-to-classify-cluster-dribble-hand-off-variants-in-the-nba
 
@@ -37,7 +39,17 @@ The data is in the format of a dictionary:
 
 - 'gameid': str
 - 'gamedate': str
-- 'eventid': str
+- 'event_info':
+  - 'eventid': str
+  - 'type': int
+  - 'possession_team_id': float
+  - 'desc_home': str
+  - 'desc_away': str
+- 'primary_info':
+  - 'team': str
+  - 'player_id': float
+  - 'team_id': float
+- 'secondary_info': same format as primary info
 - 'visitor':
   - 'name': str
   - 'teamid': int,
@@ -75,6 +87,8 @@ To load the data, you must run
 
 The reason for uploading this data to huggingface, is that in its current .7z form, the data is less accessible, and requires unzipping many files and then combining to access. Also, more sources for easily accessible tracking data, even if also available elsewhere, increase the chances of long-term preservation and accessibility for future NBA fans.
 
+On top of that, tracking data combined with play-by-play data is ideal format of sports data, as there is little confusion and allows for better labeling of events.
+
 ### Source Data
 
 From creator StatsPerform, "the SportVU camera system is installed in basketball arenas to track the real-time positions of players and the ball at 25 times per second." These methods were used to capture the data in this dataset.