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Kingsley is doing a good job of dreaming out, including about virtual reality – which he used while with the Tampa Bay Buccaneers, and machine learning. He does want to keep output from all of this data simple per Lovie’s desires for simplicity.
Data Available
This seems silly to put but... football games are split into plays. Teams alternate offense and defense, and have different players for each side. There are several players for each position who will rotate in and out of the game between plays based on the situation (i.e. your big slow guy might come in if you only need 1 yard to score, but a smaller faster guy if you need a long run). So only 11 people on the field per team at once, but ~52 players on a team roster in total.
You have 4 downs (plays) to gain 10-yard increments and if you can't advance 10 yards in those 4 plays, you turn the ball over to the other team's offense and they try to go the other direction. If you do advance, you get "first down" and 4 more chances to get 10 MORE yards... repeat until you either score (reach the endzone at one of the of the field) or you turn the ball over.
The defensive data that was shared can be split into a few groups:
- Single Game Participation and Positioning (x4 in sample)
- Data on one game between two teams.
- Each row describes one player, during one play
- For each player
- on_offense - on offense or defense? will be the same for everyone on the same team for a given play
- defined in CFB_Participation_Decode below:
- Pass Protection ID - how was this player involved in pass protection, if at all?
- Hand on Ground ID - did defender begin play in a stance w/ hands on ground, or upright?
- Gap Rushed ID - between which 2 players did defender attempt to reach the QB?
- Off Positioning ID - offense position codes
- Def Positioning ID - defense position codes
--- - Press Jam ID (unlabeled header, missing data?) - for man-to-man pass coverage, what type of defense was used (press/jam)?
- Pass Route ID (unlabeled header, missing data?) - what kind of pass route offensive player ran
- Pass Route Yards (unlabeled header, missing data?) - how many yards did offensive player gain
- Man Covered ID (missing data?) - presumably player ID of receiver the defender was covering
- Single Team Season Aggregate (x1 in sample)
- Data on one team for an entire season.
- Each row describes one play in one game.
- For each play
- Period - quarter of game (1-4)
- Is TwoMin? - inside two minute warning (close to end of half)
- Score Time
- Yd From Goal
- Lookup code tables (x9 in sample)
- PLAYERS$2016, TEAMS$2016 - simple lookups for player IDs ad team IDs to name, uniform number, etc.
- GAMES$2016 - lookup for game IDs to specific game including some environmental parameters. note that these are per-game not per-play, so who knows if things like temp are min/max/mean/etc. over the 3 hour game. Params include:
- Game Type - Regular season game vs. Post Season game (i.e. playoffs)
- Home/Away Team - who was home team and who wasn.t
- Surface - field surface material, e.g. grass/artificial.
- indoors? - indoor stadium or outside?
- Temperature - 18-101 in sample
- Humidity - empty in sample
- Clouds - 0-8 in sample
- Wind Direction - 1-19 in sample
- Wind Velocity - 0-29 in sample
- Attendance - crowd size
- Time of Game - assume PM if time is 3:15
- DECODE - lots of general lookups here for role/route/play type names.
- Participation - codes for how defensive linemen line up before the play, where their opposing offensive lineman is, their stance and role in a play
- HandoffType - what kind of offensive play the other team ran on the play
- Personnel - what offensive players were in the backfield (running backs and tight ends)?
- QB_At_Pass - what was QB doing when he threw the ball? moving/stationary
- WR_Bunch - when wide receivers line up near edge of field, how many and in what formation?
Defense Analysis Suggestions
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