If they want to increase profit by forcing games to end faster, shortening the blind levels is not only much easier but much more effective than any “dynamic hand strength” formula or something. Not to mention easier to backtest and prove to this CEO.
I never want to say stuff like this is completely impossible. But realistically I think whatever the additional potential profit that a company has to gain by “rigging” the games at a user-to-user level is exponentially smaller and exponentially more complex to implement than people may think.
as a fellow data professional, I agree. There either needs to be an uprising in that sub or an entirely new one needs to be made, because the posts on there are, objectively, straight-up ass.
what years did you go? there's a good chance that it wasn't wind but just the collective sighing from watching our on-field product go to work
Almost all of the data you listed is available for free from the nflverse
EDIT: included the link to where to download play-by-play data for all seasons dating back to 1999:
https://github.com/nflverse/nflverse-data/releases/tag/pbp
The main differences being the level of detail in some of the data, specifically:
gaps/sides:
- it has: the direction (left/right/center) and location (a/b/c gap for rushes, short/mid/deep for passes)
- it does not have: the hash location for the snap, so it's unknown if a specific play was towards the field side or the boundary side
player alignments:
- it has: player participation (whether player x is on the field)
- it does not have: specific alignments, so it's not known if a WR is on the left or right or slot, if a DL is a 3t or a 2i, if a LB is the mike or will, etc.
formations:
- it has: basic personnel packages (11 vs 12 etc. for offenses, 3-4 vs 4-3 etc. for defenses) and formation info (shotgun vs I vs single back etc. for offenses, cover-1 vs cover-2 etc. for defenses)
- it does not have: specific formation within that package, so it's not known if the 3 WRs out of 11 personnel are 2x1 or bunch right, or if the defense is in over/under, etc.
Most of the data is in the main play-by-play dataset, which of course includes the fundamental datapoints (e.g. down, distance, yardline, seconds remaining, quarter, play type, yards, air yards, ...), but it has almost 400 columns in total. I'd be shocked if it didn't have the pieces you were looking for.
It's unwieldy to share an example of the 400-column dataset, but here's a glimpse of one of their other datasets (participation) which has the formation info in it:
| nflverse_game_id | play_id | possession_team | offense_formation | offense_personnel | defenders_in_box | defense_personnel | number_of_pass_rushers | n_offense | n_defense | ngs_air_yards | time_to_throw | was_pressure | route | defense_man_zone_type | defense_coverage_type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2023_01_ARI_WAS | 2036 | WAS | EMPTY | 1 RB, 1 TE, 3 WR | 4 | 2 DL, 4 LB, 5 DB | 4 | 11 | 11 | 5.46 | 2.035 | FALSE | SLANT | ZONE_COVERAGE | COVER_4 |
| 2023_01_ARI_WAS | 2208 | WAS | SHOTGUN | 1 RB, 1 TE, 3 WR | 4 | 0 DL, 5 LB, 6 DB | 3 | 11 | 11 | 12.08 | 2.903 | TRUE | IN | MAN_COVERAGE | COVER_1 |
| 2023_01_ARI_WAS | 2268 | WAS | SHOTGUN | 1 RB, 1 TE, 3 WR | 4 | 0 DL, 5 LB, 6 DB | 2 | 11 | 11 | 3.804 | TRUE | ZONE_COVERAGE | COVER_4 |
so it should fit right in with /r/dataisbeautiful
The NFL’s version of a walk.
The ‘nflverse’ group of packages (‘nflreadr’ and ‘nflfastr’) has all of those data points on their GitHub.
Most will be in their play-by-play datasets, while some (formations, personnel, etc) will be in the FTN charting datasets. These are also free/public but don’t cover as many historical seasons.
For international tournaments, I typically like to gravitate towards player props since these players - and coaches - obviously don’t play together regularly. It can be difficult to predict exactly what their team tactics will be, but player tendencies often remain the same. If it is a team with lots of experience together for both managers and players, then you can translate that to specific players’ props too.
But if we know, for examples: * a CB has the most fouls per 90 in their club matches: yellow card prop * a veteran midfielder is the captain on a possession based team: O/U passes attempted * a GK on a team with weak defense: O/U for saves
On a similar note in soccer: taking the underdog team to score first + the favorite team to take the most corners or to cover the corner spread.
A weaker team unexpectedly going up a goal is highly correlated with the other team having higher possession and shots, due to the weaker team getting overly defensive to protect their lead.
In my experience, I’ve seen positive correlations between underdogs winning and the favorite being the team to take the most corners.
Certain teams will turtle up if they manage to score first, which is a recipe for the other team to rack up corners as they try to break through their 10-man back line.
If you find a game where you like the ML odds of the underdog, then it’s generally not the worst idea to play it with the favorite as the team to take the most corners / corner spread.
Interested in your thoughts on this as I thought Mats was having a pretty standout year? They haven’t conceded many goals at all during this UCL campaign (IIRC they might be the fewest goals conceded and/or the most clean sheets).
Exactly. This preference is another studied area as well, where the frequency (aka variance) has a big impact on decisions. I’d take the cash out because it’s very rare to end up in the same situation again.
I personally agree and would take the cash out. But none of my comments are me arguing one way or the other. Just pointing out a phenomenon that explains why/how books can take such large discounts on their cash out offer.
computer guy opens to 2.2BB
“What is this, a raise for ants??”
Consider the choice between a prospect that offers an 85% chance to win $1000 (with a 15% chance to win nothing) and the alternative of receiving $800 for sure. A large majority of people prefer the sure thing over the gamble, although the gamble has higher (mathematical) expected value (also known as expectation). The expected value of a monetary gamble is a weighted average, in which each possible outcome is weighted by its probability of occurrence. The expected value of the gamble in this example is .85 X $1000 + .15 X $0 = $850, which exceeds the expected value of $800 associated with the sure thing.
To clarify, that was with respect to my last remark, not the entire comment. There are significant amounts of research to my first bullet on risk aversion, and the second bullet is an objective statement based on maths.
Add in the fact that 1. humans are overwhelmingly risk-averse in nature, and 2. any single bettor doesn’t have the volume of cash-out opportunities to outrun short-term variance which allow books to take even more off the top from what they offer. Even if the bettor knew the objective maths behind it and knew it’s not in their favor in the long run, most people would take the guaranteed cash anyway.
I don’t have any support/evidence for it, but I would also guess that this is even more true for larger sums of money. As in people would be willing to take cash outs that are higher dollar amounts ($30k), and more willing to let their bets ride for smaller ones ($1 bet). So they might shave an additional amount from the larger paying bets due to assuming there’s less elasticity there.
Leave it to the OSRS sub to turn the nostalgia dial up even higher. Brb going to watch Yeah Right.
Good reaction to that deflection
Koen Casteels save against France 49'
soccer