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An important date is the % of teams per tactic used.
At the end, there are 3 defensive options never used = useless
I'd like to see a big change in patient, this is unrealistic and has been used in 90% of the matches with PG in C position

With the S56 News.... I think we'll need entire new sets of data to gather when the improvements to seldom used tactics became available for us to use... we might see some new METAs with those... can wait to try out some 4-Out 1-Inside style on Offense with a combination of Outside Box and 1.

Thanks for this, this helps a lot.

Could you maybe give a list for b3 games? maybe over the past 2-3 seasons for a bit more data?

I would refrain from making any conclusions based on those tactics percentages. A lot of the times managers would use less popular tactics in games they know they won't win, just to throw a little mix in their offense/defence.

Additionally, managers would often not know how best to use each tactic, thus not adapting to particular requirements.

An easy way to fix this, is give more information to the public on how to make each tactic work. Making unknown updates to an unknown tactic will only make that tactic name a little more popular, but not the tactic itself.

No improvement for fcp?

You need machine learning to help you figure out what's really happening

Are you going to help me get started on that?

Instead of going overboard, maybe a relatively simple* approach could already improve this analysis.

For example using a logistic regression with win vs lose as a binary outcome variable. Then as predictors you can use a few "baseline" predictors like win% in past 5 games, past 10 games, whether it's home vs away, % salary difference between teams, opponent teams win % in past 5 and 10 games. "After" this you can add chosen tactic as a predictor and see what remaining predictive power exists for each tactic and what their influence on win likelihood is.

90% of the work is getting the dataset together correctly. Runnnig the regression is a few lines of code.

Context: What this analysis does is try to take away the other factors that also relate to winning likelihood and try to isolate what effect remains of purely the chosen tactic.



* DS/ML folks please keep the simple part in mind before commenting what all wrong about this

Yes! This is what needs to be done. It would show more accurately which tactics are dominate at the moment as some tactics may have inflated winning percentages from being used by the better team in a match up more often.

However it wouldn't tell per say what tactics are most viable in the game engine as tactics could perform poorly due to us managers not knowing how to form the right team to run them. This would probably need to be done through simulations of different team types to see what offenses just don't work under any conditions.

Just throwing something more into the conversation to either help the analysis or make it more complicated...

I might be an oddball and do things differently than everybody else, so my way of doing things should not be taken as an assumption that others do the same... but I sometimes lean towards some tactics based if I think my team is considerably better or worse than the other team. If other managers do the same, then the win % of some tactics may be a little impacted by the typical relative strengths of teams using them.

For example, if I think I've got away better team and want to run up the score and help my PD for tiebreakers in the standings, I am more likely to use R&G or PTB on offense. Similarly, if I think I'm likely to lose badly, I might put my best versatile score a little out of position to try to get an advantage and run Patient offense and hope for a hot scorer taking a ton of shots while slowing down the pace of the game and hope for a lower combined game score and lower PD. Therefore, my team is likely to have a higher win % when playing PTB and also R&G, but will have a lower win % with Patient since I'm usually the heavy underdog even before tip off.

I also suspect that BO will have a lower win % since that may be the "de facto offense" for a significant percentage of non-bot managers who are not putting a lot of time into managing their team.

There are probably a lot of other examples of data bias that may or may not be finding its way into the data analysis. Data bias is real, how to account for it is tricky.

Thank you to the development team and all the people continuing to improve the game, you are the awesome! I love this game!!!

Last edited by chihorn at 1/18/2022 2:51:43 PM

You shouldn't read too much from that data. Some tactics are used for specific purposes. Push the ball is used to get more points difference in easy games. That is why it has the best win% in div.1. Look inside and Motion are the basic tactics. And the rest of the tactics are used to avoid predictions or try to surprise the opponent or in desperate mode, which explains in part worse results.

The way to go for you is testing with hand-made teams.
All tactics are supposed to do it well when used by the right teams. If that is true and you find those teams, give us some clues so we can find those lineups also. This would be more interesting than just empower tactics so we can use all of them with the current standard lineups. If you do just this, predictions with be not longer usable and your effort will be counterproductive.