Here the data broken down by offensive formation and passing play distance:
https://drive.google.com/file/d/1fSoQuDWfs93H_ZTFQKG_0ommesFfMIj_/view?usp=sharingDataset Data has been agg'ed from six different leagues that IoT and I are active in. These are defensive plays that we have run against our opponents.
The Clean% column is the number of pass attempts against that defensive play minus the number of sacks plus the number of pressures divided by the number of pass attempts.
The position columns are the number of sacks and pressures by a player at that position for the defensive play divided by the total number of sacks and pressures against that defensive play.
Other NotesThe position columns will not always add up to 100% because I'm not agg'ing all positions, and sometimes add up to >100% because I'm parsing the play-by-play text to get the player. So when the play-by-play text has multiple pressures listed (it happens), you get more than one player agg'ed.
Here's a sample of that logic:
CASE
WHEN LDE1 = ''
THEN 0
WHEN Text LIKE '%sacked%'||rtrim(ltrim(LDE1, '#1234567890 '), ' - WRRBTEFBCBFSSSLBLGRGRTLTCDEDTSLBWLBMLBCBSSFS')||')%'
THEN 1
WHEN Text LIKE '%Pressure by %'||rtrim(ltrim(LDE1, '#1234567890 '), ' - WRRBTEFBCBFSSSLBLGRGRTLTCDEDTSLBWLBMLBCBSSFS')||'.%'
THEN 1
ELSE 0
END LDE1
UPDATE: Oh, right, one thing to keep in mind is that the data for the 46 Heavy is skewed badly by one season where I put an 89 SP/AC LB at the LDE spot and hammered off 70 sacks against a highly competitive league.
Last edited at 9/20/2020 11:02 am